# Long- and Short-Run Forest Dynamics: An Empirical Assessment of Forest Transition, Environmental Kuznets Curve and Ecologically Unequal Exchange Theories

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## Abstract

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## 1. Introduction

## 2. Causal Framework and Hypotheses Tested

#### 2.1. Environmental Kuznets Curve and the “Economic Development” Pathway to Forest Transition

- Hypothesis 1: There is an inverted U-shaped relationship between deforestation and GDP per capita at an aggregate level. We test this hypothesis using the quadratic form of GDP per capita as an aggregate measure of economic growth.
- Hypothesis 2: A reduction in agricultural employment results in lower deforestation rates.
- Hypothesis 3: Increases in agricultural intensification, measured by either total factor productivity (TFP) and agricultural yields, lead to higher levels of forest area. In addition, we introduced an interaction term between agricultural intensification and GDP per capita on our model, hypothesizing that there may be a synergistic relation between the effects of these two dynamics on forest cover.

#### 2.2. “Forest Scarcity” and “State Forest Policy” Forest Transition Pathways

- Hypothesis 4: Countries with higher government ability to draft and enforce stricter environmental and forest policies have a stronger effect of past decreasing forest area on present forest area. We operationalize this through an interaction between a proxy of environmental governance capacity and lagged forest cover. As a proxy for the government ability to draft and implement environmental legislation, we use an aggregate measure of the following indices: control of corruption, government effectiveness, regulatory quality, rule of law, voice and accountability (see variable “government quality” on Table A1 on Appendix A).

- Hypothesis 5: There is a national feedback response from forest scarcity or forest abundance to the forest cover under the long equilibrium relationship. We test this hypothesis using the error correction term (ECT) variable: a significant ECT indicates that there is a feedback from forest abundance or scarcity on further forest cover levels, and thus the forest scarcity mechanism appears to hold, with larger values of ECT indicating stronger response.

#### 2.3. “Globalization” Pathway of Forest Transition, and Ecologically Unequal Exchange Theory

- Hypothesis 6a: National net exports of agricultural products have a negative effect on the national forest cover of the exporter country.
- Hypothesis 6b: National net exports of roundwood have an ambigous effect on national forest cover of the exporter country. On the one hand, roundwood exports may contribute to forest exploitation. On the other hand, these may incentivize tree plantation and improved management of forest resources [29].

- Hypothesis 7: Exports of agricultural products to high-income countries have a negative effect on the national forest cover of the exporter country. We tested this hypothesis for low-, middle-, and high-income country groups, the latter being for consistency purposes as the theory is not supposed to apply to trade between high-income countries.

## 3. Materials and Methods

#### 3.1. Data and Variables

#### 3.2. Time Series Properties of the Data

#### 3.3. Specification of the Dynamic Error Correction Model

_{i,}), namely ECT. The ECT indicates the degree to which the equilibrium behavior drives short-run dynamics. The last period’s deviation from a long-run equilibrium influences its short-run dynamics. The ECT’s coefficient (${\lambda}_{15}$ in Equation (2)) should be statistically significant and comprised between -1 and 0 to adjust the variables towards the equilibrium keeping the long-run relationship intact, with a larger value indicating a more rapid adjustment [48]. This concept could be formalized through the idea that a proportion of the disequilibrium from one period is corrected in the next period [40]. The ECM also allows to obtain unbiased estimates of the short-run explanatory variables’ effects.

^{2}, agricultural area, and rural population density on the long-run equation. We also estimated our long-run model with the inclusion of variables on: i) the net agricultural exports, and ii) the net forest product exports. However, these variables had a very small impact on forest dynamics (see Appendix C, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8, Table A9). Therefore, we decided to keep the long-run model without these additional variables. In order to characterize indirect impacts, we specified two long-run equations, with and without the agricultural area (Equations (1) and (3). Changes in agricultural area are expected to be a key mediator through which other underlying causes such as GDP and rural population density affect forest cover (Figure 1). Thus, using models with and without agricultural area allowed us to identify the net effects of these underlying variables as well as to assess how much of this effect is mediated by agricultural area changes. The presence of cointegration is sufficient to prove the existence of at least one non-spurious long-run causal relationship between the variables. Yet, the estimated coefficients represent the net effect of a system of relationships and statistical infer-ence is not straightforward (see Appendix D for further explanation). Thus, we use the long run estimators only for our sample.

^{2}from our short-run model. For both variables, their absence did not influence much the results; however, we opted for maintaining them in the presented results as they constitute important control variables. We also tested the interaction terms of GDP with TFP and GDP with agricultural yield in the short run. However, we did not include those interaction terms in the final results as they were not significant, and their inclusion would have reduced the degree of freedom or our models.

#### 3.3.1. Long-Run Model Specifications (with and without Agricultural Cover Area)

#### 3.3.2. Short-Run Model Specifications (with and without Agricultural Cover Area)

#### 3.4. Assessment of Indirect Effects in the Short Run

- Step 1. Short-run regression: direct effects.

- Step 2. Orthogonalization of independent variables: indirect effects.

- Step 3. Netting out indirect and direct effects: total effect.

- Step 4. Total vs. direct effect.

## 4. Results

#### 4.1. Panel Unit Root Test and Cointegration Test Results

^{2}, agricultural area, and rural population density for income and forest transition clusters, respectively (Table 2 and Table 3), showed that the variables are non-stationary of order 1, I(1), for GDP, GDP

^{2}, and agricultural area. Concerning rural population density, the results suggest an I(0) variable but the visual inspection of the data suggests that it can be considered as a non-stationary variable (Appendix F, Figure A12, Figure A13, Figure A14). Furthermore, other panel data studies which used population as control variable in their analysis conclude I(1) as a result [52,53]. For the sake of simplicity, only one test (inverse Chi-squared) was reported (additional results are available upon request). Assuming that all our variables are I(1), we tested the presence for panel cointegration through the Pedroni test [45]. The results indicated the presence of cointegration among the considered variables for income and for forest transition clusters, respectively (Appendix G).

#### 4.2. Results by Income Groups

^{2}, agricultural area, and rural population density (Table 4 and Table 5). This is corroborated by the results on the variable ‘residual’, which corresponds to the ECT, and which is always significant and between 0 and −1. This result indicates that the variables from the long-run equations evolve together along a dynamic path that has a long-run equilibrium. We observed differences between income groups on the speed of adjustment towards the level of forest at the long-run equilibrium relationship. The ECTs are −0.326 and −0.369 for low-income countries; −0.119 and −0.120 for middle-income countries; −0.084 and −0.109 for high-income countries with and without agricultural area for each income group, respectively (Table 5). This indicates that for low-income countries, the speed of adjustment towards the level of forest that is in a long-run equilibrium with any level of GDP per capita, agricultural area, and rural population density is much faster than for middle- and high-income countries.

^{2}on forest for high-income countries. Additionally, a negative effect of agricultural area on forest cover for all income groups, with important differences on the coefficients’ size: −1.357 for low-income countries, −0.258 for middle-income countries and −0.515 for high-income countries. The graphical display of these results for the variables forest and GDP (Figure 2a) shows a somewhat flat parabola between forest cover and GDP per capita for high-income countries, and almost a flat line for low- and middle-income countries. When not including the agricultural cover area in the long-run model (columns (2), (4), (6) of Table 4, and Figure 2b), results changed slightly. Over the long run, for low-income countries, the GDP had a negative effect on forest, while GDP

^{2}a positive effect. Instead, for high-income countries, GDP showed a positive effect on forest and GDP

^{2}had a negative one. For middle-income countries, rural population density became positive. In addition, in the absence of agricultural area, the coefficients’ sizes of the other variables are affected, with larger coefficients of all variables for low-income countries and slightly smaller coefficients of GDP and GDP

^{2}variables for middle- and high-income countries.

^{2}became significant for low-income countries, and rural population density became significant and negative for middle-income countries.

#### 4.3. Results by Forest Transition Groups

^{2}a positive one for the pre-transition countries of our sample. For the cases of late- and post-transition countries, GDP showed a positive effect and GDP

^{2}a negative one. Rural population density showed a positive effect on forest cover for late- and post-forest transition countries, and a negative effect for pre-transition countries. Agricultural area showed a negative effect on forest cover for all forest transition clusters.

#### 4.4. Results for All Countries Together

#### 4.5. Assessment of Indirect Effects on the Short Run

^{2}became non-significant when indirect effects were included. For the rest of the results, there were only small changes in the coefficient’s size between the direct and total effects, with slightly larger significant coefficients for the total effects than for the direct ones (such in the case of the governance quality and its interaction term).

## 5. Discussion

^{2}, agricultural area, and rural population density, for all the different country groups. Thus, they move together in a dynamic equilibrium relationship in the long run (Table 4, Table 6 and Table 8). This is an important result since the long-run processes of forest are a key element for a good understanding of forest dynamics [54]. Short-run shocks propagate to these variables, but they tend to converge to these long-term equilibrium dynamic relations, as reflected by the error correction terms that correct a proportion of the deviation from the equilibrium each year. Low-income countries and those at pre-forest transition stages have faster adjustments of their forest cover area to the long-term dynamics. This suggests than land dynamics in these two groups may be quicker and would thus require a more adaptive design of policies and a more cautious monitoring of them.

- Hypothesis 1, the EKC, was validated when agricultural cover land is present for high-income countries and for pre-transition countries in the long run (Table 4 and Table 6), and for the post-transition countries in the short run (Table 7). This suggests that current low- and middle-income countries are experiencing different trajectories of relationships between economic development and forest cover compared to high-income countries.
- Hypothesis 2, on the negative effect of agricultural employment on forest, was only validated for late forest transition countries in the short run (Table 7).
- Hypothesis 3, on the positive effect of agricultural intensification on forest, was only validated when we take into account the indirect effects in our analysis, and only for high-income countries (see “yield” variable on Table 11). Thus, for high-income countries, agricultural yield had a positive impact on forest when we take into account the indirect effects of other variables on forest through agricultural yields. We also tested for the joint effect of economic growth and agricultural intensification on forest through an interaction term of the two, but we did not find any evidence of those synergies.

- •
- Hypothesis 4, on the positive effect of government quality of forest when forest cover in previous period is low, was validated for middle-income, pre-transition countries and also for all countries together (as shown by the negative effect of the interaction term on Table 5, Table 7, and Table 9). In those countries the feedback mechanism of governance compensates for previous periods of deforestation. This suggests that the quality of governance plays a crucial role when countries face scarcity of forest. This goes along with other works that have also highlighted the role of governance on deforestation [17,56,57]. On the same line, Hypothesis 5, about the existence of a feedback response from forest scarcity or forest abundance that adjusts forest over the long run to the dynamic equilibrium relationship with the other variables, was validated for all country’s groups (see “ECT” on Table 5, Table 7, and Table 9). Several main mechanisms have been suggested to operate this feedback, including perception of scarcity of forest services and prices of forest products [25,58].

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A. Variables’ Description

Variable’s Name | Definition | Units | Source |
---|---|---|---|

Forest (%) | Coefficient between: Forest cover (ha): the sum of tree-covered areas which includes any geographical area dominated by natural tree plants with a cover of 10 per cent or more. Areas planted with trees for afforestation purposes and forest plantations are included in this class and mangroves. And land area (ha): country area excluding area under inland waters and coastal waters. | % | Land Cover CCI Product User Guide Version 2.0 (2017). Reference: [14] (accessed on 15 January 2019) |

Agr land (%) | Coefficient between: (i) agricultural cover area defined as the sum of three CCI categories: herbaceous crops, wood crops, and grassland and (ii) land area. | % | Land Cover CCI Product User Guide Version 2.0 (2017). Reference: [14] (accessed on 15 January 2019) |

Rural pop density | Population living in rural areas over the land area of the country | Persons/ ha | The World Bank, Reference: [35] (accessed on 15 November 2018) |

GDP cap andGDP2 cap | Gross domestic product divided by midyear population. | Constant U.S. dollars (converted from domestic currencies using 2010 official exchange rates). | The World Bank, Reference: [35] (accessed on 15 November 2018) |

TFP | The ratio of an output index (total amount of crop and livestock output) to an index of land and non-land inputs (all land, labor, capital and material resources employed in farm production). To reduce potential index number bias in TFP growth estimates, cost shares are varied by decade whenever such information is available. For outputs, base year prices are fixed (the base period for output prices is 2004-06). Source: https://www.ers.usda.gov/data-products/international-agricultural-productivity/documentation-and-methods/ | Unitless (ratio of outputs and inputs expressed in monetary terms). The change rate uses 1961 as the baseline year. | United States Department of Agriculture, Reference: [68] (accessed on 9 May 2019) |

Yield | Aggregate of all crops’ harvested production/harvested area for all crops. | Tonnes/ha. | FAOSTAT (FAO, 2018) Reference: [11] (accessed 15 June 2018) |

Agr (PIN) | Producer price index (2004–2016 = 100). It measures the average annual change over time in the selling prices received by farmers (prices at the farm gate or at the first point of sale). | FAOSTAT (FAO, 2018). Reference: [11] (accessed 15 June 2018) | |

Agri empl (%) | Agricultural employment: Employment in agriculture (% of total employment) (modeled ILO estimate). | % | The World Bank, Reference: [35] (accessed on 15 November 2018) |

Agr prod net ex | Area of land embodied on exports of agricultural products (ha) minus imports agricultural products (ha). | ha | Own calculations using the data from [27]. |

For prod net ex | Exports of roundwood (m3) minus imports of roundwood (m3) | m3 | FAOSTAT (FAO, 2018) Reference: [11] (accessed 15 June 2018) |

Government quality | An average of the following four index: control of corruption, government effectiveness, regulatory quality, rule of law, voice and accountability. | Unitless | Worldwide Governance Indicators, www.govindicators.org, Reference: [69] (accessed on 15 June 2017) |

Trade high | Exports of agricultural products send to high-income countries divided by the total exports of agricultural products. | % | Own calculations using data from [27]. |

Avg Temperature | Average temperature per year (computed from monthly data) | mm | The World Bank, Reference: [35] (accessed on 2 September 2019) |

Avg Rain | Average temperature per year (computed from monthly data) | Degrees Celsius | World Bank, Climate knowledge portal. Reference: [35] (accessed on 2 September 2019) |

## Appendix B. Countries’ Classification

- Low-income economies: Afghanistan, Benin, Burkina Faso, Central African Republic, Congo, Dem.Rep., Ethiopia, Guinea, Guinea Bissau, Kyrgyzstan, Liberia, Madagascar, Malawi, Mozambique, Nepal, Niger, Sierra Leone, Tanzania, Togo, Uganda, and Zimbabwe.
- Middle-income economies: Angola, Argentina, Azerbaijan, Bangladesh, Belarus, Belize, Bhutan, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Cambodia, Cameroon, China, Colombia, Congo, Rep., Costa Rica, Cote d’Ivoire, Cuba, Dominican Republic, Ecuador, El Salvador, Equatorial Guinea, Fiji, Gabon, Georgia, Ghana, Guatemala, Guyana, Honduras, India, Indonesia, Iran, Islamic Rep., Kazakhstan, Kenya, Lao PDR, Malaysia, Mexico, Morocco, Myanmar, Namibia, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Senegal, South Africa, Sri Lanka, Sudan, Suriname, Thailand, Ukraine, Venezuela, RB, Vietnam, and Zambia.
- High-income economies: Australia, Austria, Canada, Chile, Croatia, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Japan, Korea, Rep., Latvia, Lithuania, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, and Uruguay.

- Pre-transition: Angola, Belize, Bolivia, Cambodia, Cameroon, Congo, Dem. Rep., Congo, Rep., Equatorial Guinea, Guinea, Guinea-Bissau, Guyana, Honduras, Indonesia, Liberia, Madagascar, Malawi, Myanmar, Papua New Guinea, Paraguay, Peru, Senegal, Sri Lanka, Suriname, Tanzania, and Zambia.
- Late-transition countries: Afghanistan, Argentina, Australia, Benin, Brazil, Burkina Faso, Colombia, Ecuador, El Salvador, Ethiopia, Guatemala, Mozambique, Namibia, Nicaragua, Niger, Nigeria, Pakistan, Panama, Uganda, Venezuela, RB, and Zimbabwe.
- Post-transition countries: Austria, Azerbaijan, Bangladesh, Belarus, Bhutan, Bosnia and Herzegovina, Bulgaria, Canada, Central African Republic, Chile, China, Costa Rica, Cote d’Ivoire, Croatia, Cuba, Czech Republic, Dominican Republic, Estonia, Fiji, Finland, France, Gabon, Georgia, Germany, Ghana, Greece, Hungary, India, Iran, Islamic Rep., Italy, Japan, Kazakhstan, Kenya, Korea, Rep., Kyrgyzstan, Lao PDR, Latvia, Lithuania, Malaysia, Mexico, Morocco, Nepal, New Zealand, Norway, Philippines, Poland, Portugal, Romania, Russian Federation, Sierra Leone, Slovak Republic, Slovenia, South Africa, Spain, Sudan, Sweden, Switzerland, Thailand, Togo, Turkey, Ukraine, United Kingdom, United States, Uruguay, and Vietnam.

**Figure A1.**Forest transition countries’ classification. Source: Pendrill et al. [26] (p. 6).

## Appendix C. Model with Control Variables in the Long Run

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

GDP cap (log) | 0.199 | −0.383 | −0.061 | −0.018 | −0.56 | 0.023 |

GDP2 cap (log) | −0.014 | 0.03 | 0.004 | 0.001 | 0.033 | −0.002 |

Rural pop (log) | 0.009 | −0.138 | 0.019 | −0.035 | −0.085 | −0.066 |

Agr land (log) | −1.54 | −0.243 | −0.611 | |||

Agr prod net ex (ha) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Cons | −3.765 | −0.282 | −1.093 | −0.985 | 0.484 | −1.161 |

Observations | 390 | 390 | 1216 | 1216 | 583 | 583 |

R-squared | 0.562 | 0.053 | 0.18 | 0.028 | 0.411 | 0.107 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

LD.GDP cap (log) | 0.127 | 0.222 | −0.014 | 0.008 | 0.013 | 0.048 |

(0.16) | (0.136) | (0.041) | (0.04) | (0.113) | (0.105) | |

LD.GDP2 cap (log) | −0.011 | −0.018 | 0.001 | 0 | 0 | −0.001 |

(0.014) | (0.011) | (0.003) | (0.002) | (0.006) | (0.005) | |

LD.Rural pop (log) | 0.081 | 0.124 | −0.038 | −0.044 | 0.018 | 0.005 |

(0.199) | (0.167) | (0.031) | (0.03) | (0.03) | (0.027) | |

LD.Agr land (log) | −0.527 *** | −0.01 | −0.113 ** | |||

(0.113) | (0.013) | (0.049) | ||||

LD.Agr empl | 0 | −0.001 | 0 | 0 | 0 | 0 |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

D.Agr prod ex. (ln) | −0.006 | −0.001 | 0 | 0 | 0.006 | 0.007 |

(0.014) | (0.012) | (0.001) | (0.001) | (0.006) | (0.005) | |

LD.Agr prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.For prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.TFP | −0.719 | 3.189 | −0.23 | −0.603 | 0.488 | 0.31 |

(2.975) | (2.52) | (0.505) | (0.498) | (0.741) | (0.694) | |

LD.Agr (PIN) (log) | −0.018 | −0.004 | 0.007 ** | 0.007 ** | 0.002 | 0.001 |

(0.018) | (0.015) | (0.003) | (0.003) | (0.006) | (0.006) | |

LD.Yield (log) | −0.006 | −0.017 | 0 | 0 | 0.006 | 0.006 * |

(0.013) | (0.011) | (0.002) | (0.002) | (0.003) | (0.003) | |

LD.Trade high | 0 | −0.001 | 0 ** | 0 ** | 0 | 0 ** |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

LD.Government | −0.016 | −0.01 | −0.002 | −0.005 | −0.006 | −0.007 |

(0.021) | (0.017) | (0.003) | (0.003) | (0.008) | (0.008) | |

DL(Gov/L.For) | −0.006 | −0.003 | 0.001 * | 0 | −0.002 | −0.003 |

(0.01) | (0.008) | (0) | (0) | (0.005) | (0.005) | |

LD.Temperature | −0.003 | −0.003 | −0.001 * | −0.001 * | 0 | 0 |

(0.003) | (0.002) | (0) | (0) | (0) | (0) | |

LD.Rainfall | 0 ** | 0* | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

L | −0.324 *** | −0.373 *** | −0.128 *** | −0.125 *** | −0.087 *** | −0.123 *** |

(0.04) | (0.025) | (0.011) | (0.01) | (0.018) | (0.015) | |

Cons | 0.035 *** | 0.018 *** | −0.003 *** | −0.003 *** | −0.007 *** | −0.007 *** |

(0.006) | (0.004) | (0) | (0) | (0.001) | (0.001) | |

Observations | 226 | 226 | 771 | 771 | 364 | 364 |

R-squared | 0.389 | 0.563 | 0.191 | 0.211 | 0.131 | 0.216 |

**Table A4.**Long run, forest transition clusters, net agricultural exports as additional control variable.

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Early_&_Pre | Early_&_Pre | Late | Late | Post | Post | |

GDP cap (log) | −0.123 | −0.102 | 0.069 | 0.082 | 0.048 | 0.000 |

GDP2 cap (log) | 0.008 | 0.007 | −0.003 | −0.005 | −0.004 | −0.007 |

Rural pop (log) | −0.045 | −0.04 | 0.123 | 0.052 | 0.017 | −0.008 |

Agr land (log) | −0.243 | −0.481 | −0.502 | |||

Agr prod net ex (ha) | 0 | 0 | 0 | 0 | 0 | 0 |

Cons | −0.592 | −0.183 | −2.14 | −1.658 | −1.769 | −1.393 |

Observations | 499 | 499 | 410 | 410 | 1280 | 1280 |

R-squared | 0.277 | 0.177 | 0.424 | 0.096 | 0.178 | 0.027 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

**Table A5.**Short run, forest transition clusters, net agricultural exports as additional control variable.

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Early_&_Pre | Early_&_Pre | Late | Late | Post | Post | |

LD.GDP cap (log) | 0.064 | 0.078 | −0.084 | −0.046 | −0.132 *** | −0.144 *** |

(0.056) | (0.056) | (0.058) | (0.053) | (0.036) | (0.036) | |

LD.GDP2 cap (log) | −0.004 | −0.004 | 0.005 | 0.002 | 0.008 *** | 0.009 *** |

(0.004) | (0.004) | (0.004) | (0.003) | (0.002) | (0.002) | |

LD.Rural pop (log) | −0.158 | −0.14 | 0.027 | 0.033 | 0.012 | −0.007 |

(0.166) | (0.165) | (0.047) | (0.042) | (0.026) | (0.026) | |

LD.Agr land (log) | −0.06 * | −0.04 | −0.086 ** | |||

(0.032) | (0.037) | (0.043) | ||||

LD.Agr empl | 0 | 0 | −0.001 ** | −0.001 | 0 | 0 |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

D.Agr prod ex. (ln) | 0.005 | 0.007 | −0.001 | 0 | 0 | 0 |

(0.009) | (0.009) | (0.006) | (0.005) | (0.001) | (0.001) | |

LD.Agr prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.For prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.TFP | 1.667 | 0.068 | −0.202 | −0.059 | 0.034 | −0.11 |

(1.664) | (1.641) | (1.144) | (1.024) | (0.549) | (0.54) | |

LD.Agr (PIN) (log) | 0.013 | 0.012 | 0.001 | 0.003 | 0.005 | 0.006 |

(0.011) | (0.011) | (0.007) | (0.006) | (0.004) | (0.004) | |

LD.Yield (log) | −0.009 | −0.011 | −0.004 | −0.004 | 0.001 | 0 |

(0.01) | (0.01) | (0.005) | (0.004) | (0.002) | (0.002) | |

LD.Trade high | 0 | 0 | 0 | 0 | 0 *** | 0 *** |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.Government | −0.018 | −0.026 ** | 0.004 | 0.003 | −0.001 | −0.003 |

(0.011) | (0.011) | (0.009) | (0.008) | (0.004) | (0.004) | |

DL(Gov/L.For) | 0.002 * | −0.001 | 0.001 | 0.001 | 0 | −0.001 |

(0.001) | (0.001) | (0.006) | (0.005) | (0.002) | (0.002) | |

LD.Temperature | −0.002 | −0.001 | −0.002 * | −0.002 * | 0 | 0 |

(0.002) | (0.002) | (0.001) | (0.001) | (0) | (0) | |

LD.Rainfall | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

L | −0.314*** | −0.302 *** | −0.201 *** | −0.206 *** | −0.171 *** | −0.165 *** |

(0.029) | (0.027) | (0.024) | (0.017) | (0.01) | (0.009) | |

Cons | −0.006** | −0.011 *** | 0.021 *** | 0.023 *** | −0.006 *** | −0.007 *** |

(0.003) | (0.003) | (0.003) | (0.002) | (0) | (0) | |

Observations | 307 | 307 | 254 | 254 | 800 | 800 |

R-squared | 0.336 | 0.338 | 0.304 | 0.433 | 0.3 | 0.32 |

**Table A6.**Long run, income clusters, net agricultural exports and net exports of forests products as additional control variables.

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

GDP cap (log) | 0.171 | −0.422 | −0.063 | −0.02 | −0.495 | 0.061 |

GDP2 cap (log) | −0.012 | 0.034 | 0.005 | 0.001 | 0.028 | −0.006 |

Rural pop (log) | −0.009 | −0.136 | 0.019 | −0.035 | −0.068 | −0.048 |

Agr land (log) | −1.51 | −0.244 | −0.579 | |||

Agr prod ex. (ln) | 0.003 | 0.009 | 0 | 0 | 0.079 | 0.09 |

_cons | −3.698 | −0.293 | −1.084 | −0.973 | −0.813 | −2.528 |

Observations | 390 | 390 | 1216 | 1216 | 583 | 583 |

R-squared | 0.562 | 0.06 | 0.179 | 0.026 | 0.524 | 0.253 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

**Table A7.**Short run, income clusters, net agricultural exports and net exports of forests products as additional control variables.

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

LD.GDP cap (log) | 0.122 | 0.237 * | −0.014 | 0.007 | 0.023 | 0.05 |

(0.159) | (0.137) | (0.041) | (0.041) | (0.113) | (0.105) | |

LD.GDP2 cap (log) | −0.01 | −0.02 * | 0.001 | 0 | −0.001 | −0.002 |

(0.013) | (0.011) | (0.003) | (0.002) | (0.006) | (0.005) | |

LD.Rural pop (log) | 0.058 | 0.178 | −0.04 | −0.046 | 0.021 | 0.006 |

(0.199) | (0.168) | (0.031) | (0.03) | (0.03) | (0.027) | |

LD.Agr land (log) | −0.523 *** | −0.01 | −0.109 ** | |||

(0.113) | (0.013) | (0.049) | ||||

LD.Agr empl | 0 | −0.001 | 0 | 0 | 0 | 0 |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

D.Agr prod ex. (ln) | −0.005 | −0.004 | 0 | 0 | 0.008 | 0.01 * |

(0.014) | (0.012) | (0.001) | (0.001) | (0.006) | (0.005) | |

LD.Agr prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.For prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.TFP | −0.807 | 3.61 | −0.233 | −0.606 | 0.469 | 0.198 |

(2.962) | (2.54) | (0.505) | (0.499) | (0.737) | (0.689) | |

LD.Agr (PIN) (log) | −0.018 | −0.005 | 0.007 ** | 0.007 ** | 0 | −0.002 |

(0.018) | (0.015) | (0.003) | (0.003) | (0.006) | (0.006) | |

LD.Yield (log) | −0.006 | −0.015 | 0 | −0.001 | 0.006* | 0.006 ** |

(0.013) | (0.011) | (0.002) | (0.002) | (0.003) | (0.003) | |

LD.Trade high | 0 | −0.001 | 0 ** | 0 ** | 0 | 0 * |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

LD.Government | −0.015 | −0.012 | −0.002 | −0.005 | −0.007 | −0.01 |

(0.021) | (0.018) | (0.003) | (0.003) | (0.008) | (0.008) | |

DL(Gov/L.For) | −0.005 | −0.004 | 0.001 * | 0 | −0.003 | −0.005 |

(0.01) | (0.009) | (0) | (0) | (0.005) | (0.005) | |

LD.Temperature | −0.003 | −0.003 | −0.001 * | −0.001 * | 0 | 0 |

(0.003) | (0.002) | (0) | (0) | (0) | (0) | |

LD.Rainfall | 0** | 0* | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

L | −0.326 *** | −0.372 *** | −0.127 *** | −0.125 *** | −0.104 *** | −0.142 *** |

(0.039) | (.025) | (0.011) | (0.01) | (0.021) | (0.016) | |

_cons | 0.036 *** | 0.015 *** | −0.003 *** | −0.003 *** | −0.009 *** | −0.009 *** |

(0.006) | (0.004) | (0) | (0) | (0.002) | (0.001) | |

Observations | 226 | 226 | 771 | 771 | 364 | 364 |

R-squared | 0.395 | 0.558 | 0.19 | 0.209 | 0.137 | 0.228 |

**Table A8.**Long run, forest transition clusters, net agricultural exports and net exports of forests products as additional control variables.

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Early_&_Pre | Early_&_Pre | Late | Late | Post | Post | |

GDP cap (log) | 0.005 | 0.004 | 0.066 | 0.079 | 0.049 | 0.084 |

GDP2 cap (log) | 0 | 0 | −0.003 | −0.005 | −0.004 | −0.007 |

Rural pop (log) | −0.061 | −0.061 | 0.126 | 0.059 | 0.017 | −0.008 |

Agr land (log) | −0.279 | −0.489 | −0.502 | |||

Agr prod ex. (ln) | −0.078 | −0.049 | 0.003 | 0.005 | 0.001 | 0.001 |

_cons | −0.066 | 0.069 | −2.176 | −1.693 | −1.778 | −1.401 |

Observations | 499 | 499 | 410 | 410 | 1280 | 1280 |

R-squared | 0.319 | 0.193 | 0.423 | 0.073 | 0.178 | 0.027 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

**Table A9.**Short run, forest transition clusters, net agricultural exports and net exports of forests products as additional control variables.

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Early_&_Pre | Early_&_Pre | Late | Late | Post | Post | |

LD.GDP cap (log) | 0.026 | 0.056 | −0.081 | −0.044 | −0.132 *** | −0.144 *** |

(0.056) | (0.056) | (0.058) | (0.053) | (0.036) | (0.036) | |

LD.GDP2 cap (log) | −0.001 | −0.003 | 0.004 | 0.002 | 0.008 *** | 0.009 *** |

(0.004) | (0.004) | (0.004) | (0.003) | (0.002) | (0.002) | |

LD.Rural pop (log) | −0.013 | −0.044 | 0.016 | 0.013 | 0.012 | −0.007 |

(0.166) | (0.165) | (0.047) | (0.043) | (0.026) | (0.025) | |

LD.Agr land (log) | −0.061 * | −0.041 | −0.086 ** | |||

(0.032) | (0.037) | (0.043) | ||||

LD.Agr empl | 0 | 0 | −0.001 ** | 0 | 0 | 0 |

(0.001) | (0.001) | (0) | (0) | (0) | (0) | |

D.Agr prod ex. (ln) | −0.005 | 0.001 | 0 | 0.002 | 0 | 0 |

(0.009) | (0.009) | (0.006) | (0.005) | (0.001) | (0.001) | |

LD.Agr prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.For prod net ex | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.TFP | 2.084 | 0.232 | −0.179 | 0.016 | 0.037 | −0.106 |

(1.68) | (1.646) | (1.143) | (1.033) | (0.549) | (0.539) | |

LD.Agr (PIN) (log) | 0.019 * | 0.015 | 0.001 | 0.004 | 0.005 | 0.006 |

(0.012) | (0.011) | (0.007) | (0.006) | (0.004) | (0.004) | |

LD.Yield (log) | −0.014 | −0.013 | −0.004 | −0.005 | 0.001 | 0 |

(0.01) | (0.01) | (0.005) | (0.004) | (0.002) | (0.002) | |

LD.Trade high | 0 | 0 | 0 | 0 | 0*** | 0 *** |

(0) | (0) | (0) | (0) | (0) | (0) | |

LD.Government | −0.012 | −0.023 ** | 0.004 | 0.004 | −0.001 | −0.003 |

(0.011) | (0.011) | (0.009) | (0.008) | (0.004) | (0.004) | |

DL(Gov/L.For) | 0.002 * | −0.001 | 0.001 | 0.002 | 0 | −0.001 |

(0.001) | (0.001) | (0.006) | (0.005) | (0.002) | (0.002) | |

LD.Temperature | −0.002 | −0.001 | −0.002 ** | −0.002 * | 0 | 0 |

(0.002) | (0.002) | (0.001) | (0.001) | (0) | (0) | |

LD.Rainfall | 0 | 0 | 0 | 0 | 0 | 0 |

(0) | (0) | (0) | (0) | (0) | (0) | |

L | −0.318 *** | −0.304 *** | −0.2 *** | −0.195 *** | −0.171 *** | −0.165 *** |

(0.03) | (0.027) | (0.023) | (0.017) | (.01) | (0.009) | |

_cons | −0.006 ** | −0.011 *** | 0.021 *** | 0.022 *** | −0.006 *** | −0.007 *** |

(0.003) | (0.003) | (0.003) | (0.002) | (0) | (0) | |

Observations | 307 | 307 | 254 | 254 | 800 | 800 |

R-squared | 0.328 | 0.335 | 0.305 | 0.422 | 0.301 | 0.32 |

## Appendix D. Long-Run Causality Test

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

GDP cap (log) | 0.168 | −0.357 | (+)−0.071 | (+)−0.030 | −0.406 | 0.072 |

GDP2 cap (log) | −0.013 | 0.029 | 0.005 | 0.002 | 0.024 | −0.004 |

Rural pop (log) | −0.025 | −0.108 | 0.02 | −0.031 | (+)−0.073 | −0.072 |

Agr land (log) | −1.357 | −0.258 | (+)−0.515 | |||

Constant | −3.461 | −00.352 | −1.081 | −0.937 | −0.0014 | −1.474 |

(0.417) | (0.525) | (0.105) | (0.114) | (0.407) | (0.462) | |

Observations | 470 | 470 | 1455 | 1455 | 703 | 703 |

R-squared | 0.489 | 0.042 | 0.187 | 0.027 | 0.36 | 0.126 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Pre | Pre | Late | Late | Post | Post | |

GDP cap (log) | −0.136 | −0.087 | 0.073 | 0.095 | 0.047 | 0.089 |

GDP2 cap (log) | 0.009 | 0.006 | −0.003 | −0.006 | −0.003 | −0.006 |

Rural pop (log) | −0.019 | (+)−0.024 | 0.108 | 0.051 | 0.014 | −0.015 |

Agr land (log) | −0.244 | −0.475 | (+)−0.472 | |||

_cons | −0.492 | −0.207 | −2.182 | −1.728 | −1.76 | −1.459 |

(0.176) | (0.184) | (0.155) | (0.188) | (0.096) | (0.102) | |

Observations | 599 | 599 | 493 | 493 | 1536 | 1536 |

R-squared | 0.283 | 0.181 | 0.382 | 0.062 | 0.168 | 0.019 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

## Appendix E. Assessment of Indirect Effects in the Short Run

- (1)
- We specify our basic model (direct effects):$$Y={\lambda}_{1}{X}_{1}+{\lambda}_{2}{X}_{2}+\mu $$
- (2)
- We identify the indirect effects of the independent variables according to our causal diagram. In this example, we assume that ${X}_{2}$ has an indirect effect on Y through the variable ${X}_{1}$.$${X}_{2}={\gamma}_{1}{X}_{1}+residua{l}_{x2}$$
- (3)
- We substitute ${X}_{2}$ in $Y$:$$Y={\lambda}_{1}{X}_{1}+{\lambda}_{2}\left({\gamma}_{1}{X}_{1}+residua{l}_{x2}\right)+\mu \phantom{\rule{0ex}{0ex}}Y=\left({\lambda}_{1}+{\lambda}_{2}{\gamma}_{1}\right){X}_{1}+{\lambda}_{2}residua{l}_{x2}+\mu $$
- (4)
- We compare ${\lambda}_{1}$ with ${\lambda}_{1}+{\lambda}_{2}{\lambda}_{1}$.

## Appendix F. Temporal Evolution

## Appendix G. Unit Root Tests’ Results

Low-income economies | |||||||||
---|---|---|---|---|---|---|---|---|---|

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 42.3409 | 0.3703 | 103.0908 | 0 | 28.4854 | 0.9131 | 213.1503 | 0 | |

Constant and trend | 22.7278 | 0.9872 | 100.1469 | 0 | 92.284 | 0 | 174.3859 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 141.6243 | 0 | 370.5433 | 0 | 54.2312 | 0.066 | 136.8267 | 0 | |

Constant and trend | 347.5515 | 0 | 489.7693 | 0 | 67.2637 | 0.0045 | 106.2696 | 0 | |

Middle-income economies | |||||||||

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 127.8523 | 0.3404 | 309.0733 | 0 | 75.2122 | 0.9997 | 539.318 | 0 | |

Constant and trend | 59.723 | 1 | 363.1026 | 0 | 190.8576 | 0.0001 | 405.9193 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 174.823 | 0.0012 | 329.7181 | 0 | 172.5064 | 0.0018 | 518.1225 | 0 | |

Constant and trend | 415.1723 | 0 | 390.1991 | 0 | 138.4452 | 0.1466 | 422.5851 | 0 | |

High-income economies | |||||||||

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 56.0176 | 0.622 | 175.6909 | 0 | 89.148 | 0.0086 | 222.2363 | 0 | |

Constant and trend | 38.2678 | 0.9871 | 189.5342 | 0 | 59.0724 | 0.5096 | 192.3877 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 38.4231 | 0.9864 | 96.044 | 0.0022 | 46.4387 | 0.9004 | 252.7817 | 0 | |

Constant and trend | 89.2417 | 0.0085 | 98.3846 | 0.0013 | 96.1258 | 0.0021 | 200.7362 | 0 |

Pre-Transition Economies | |||||||||
---|---|---|---|---|---|---|---|---|---|

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 25.4884 | 0.9939 | 151.3153 | 0 | 10.0256 | 1 | 160.2524 | 0 | |

Constant and trend | 25.8388 | 0.9929 | 121.4375 | 0 | 28.8431 | 0.9775 | 121.9925 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 145.1717 | 0 | 93.3585 | 0 | 91.9331 | 0.0001 | 174.0693 | 0 | |

Constant and trend | 179.279 | 0 | 102.9771 | 0 | 65.6635 | 0.0299 | 139.9722 | 0 | |

Late-Transition Economies | |||||||||

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 89.4357 | 0.0418 | 161.6554 | 0 | 51.541 | 0.9314 | 318.8514 | 0 | |

Constant and trend | 33.5529 | 0.9999 | 221.0974 | 0 | 118.776 | 0.0001 | 238.9642 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 89.7655 | 0.0398 | 399.3701 | 0 | 86.1799 | 0.0675 | 292.8106 | 0 | |

Constant and trend | 345.0809 | 0 | 439.3871 | 0 | 111.2495 | 0.0007 | 228.1819 | 0 | |

Post-Transition Economies | |||||||||

For (log) | ∆ For (log) | GDP cap (log) | ∆ GDP cap (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 111.2867 | 0.3949 | 274.8844 | 0 | 131.2791 | 0.0634 | 495.6008 | 0 | |

Constant and trend | 61.3268 | 0.9999 | 310.2488 | 0 | 194.5949 | 0 | 411.7363 | 0 | |

Rural pop (log) | ∆ Rural pop (log) | Agr (log) | ∆ Agr (log) | ||||||

Inverse chi-squared | p | Statistic | p-value | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Constant | 119.9333 | 0.2036 | 303.577 | 0 | 95.0633 | 0.8085 | 440.8511 | 0 | |

Constant and trend | 327.6055 | 0 | 435.9887 | 0 | 124.9217 | 0.1269 | 361.4367 | 0 |

Low Income | Middle Income | High Income | ||||
---|---|---|---|---|---|---|

Constant | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Modified Phillips–Perron t | 3.0233 | 0.0013 | 4.9632 | 0 | 5.1023 | 0 |

Constant and trend | ||||||

Modified Phillips–Perron t | 3.4613 | 0.0003 | 5.7663 | 0 | 5.4084 | 0 |

Without agricultural area | ||||||

Low income | Middle income | High income | ||||

Constant | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Modified Phillips–Perron t | 3.578 | 0.0002 | 5.3606 | 0 | 4.5923 | 0 |

Constant and trend | ||||||

Modified Phillips–Perron t | 4.2702 | 0 | 5.7043 | 0 | 5.2077 | 0 |

Pre-Transition | Late-Transition | Post-Transition | ||||
---|---|---|---|---|---|---|

Constant | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Modified Phillips–Perron t | 2.8929 | 0.0019 | 4.6014 | 0 | 5.8305 | 0 |

Constant and trend | ||||||

Modified Phillips–Perron t | 3.3326 | 0.0004 | 5.565 | 0 | 5.5252 | 0 |

Without agricultural area | ||||||

Pre-transition | Late-transition | Post-transition | ||||

Constant | Statistic | p-value | Statistic | p-value | Statistic | p-value |

Modified Phillips–Perron t | 2.4393 | 0.0074 | 4.5979 | 0 | 5.3066 | 0 |

Constant and trend | ||||||

Modified Phillips–Perron t | 3.6286 | 0.0001 | 5.099 | 0 | 6.8802 | 0 |

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**Figure 1.**Theoretical causal framework. Note: Green, red, and grey arrows identify a positive, negative, or ambiguous effect from one variable to another, respectively. Dotted arrows are double headed, meaning a bidirectional effect between variables. Forest cover area represents the dependent variable of the model. Variables with an orange shape are those implemented in our long-run model while both orange and blue shape variables have been implemented in the short-run model. The ${\lambda}_{1\dots 10}$ represent variables’ coefficients of our models.

**Figure 2.**Graphical representation of the long-run results (four our sample). Note: Figure (

**a**) shows the result for the long-run equation for income groups in Table 4 columns 1, 3, and 5. The turning points are the following: US$639.1 for low income, US$1211.9 for middle income, and US$4675.1 for high income. Figure (

**b**) shows the result for the long-run equation in Table 4 columns 2, 4, and 6 (model without agricultural area). The turning points are the following: US$432.7 for low income, US$1808 for middle income, and US$8103.1 for high income. Figure (

**c**) shows the results for the long-run equation for the forest transition phases classification, in Table 6, columns 1, 3, and 5. The turning points are the following: US$1900.7 for early and pre-transition, US$190,994.5 for late-transition, and US$2514.9 for post-transition. Figure (

**d**) shows the result for the long-run equation in Table 6, columns 2, 4, and 6 (model without agricultural area). The turning points are the following: US$1408.1 for early and pre-transition, US$2724.4 for late-transition, and US$1669 for post-transition.

Hypothesis | Dependent Variable | Independent Variable (Level or First Differences) | Expected Signs |
---|---|---|---|

Hypothesis 1 | Forest cover (% Land area) | GDP per capita | Negative |

GDP per capita^{2} | Positive | ||

ΔForest cover (% Land area) | ΔGDP per capita | Negative | |

ΔGDP per capita^{2} | Positive | ||

Hypothesis 2 | ΔForest cover (% Land area) | Δagricultural employment | Negative |

Hypothesis 3 | ΔForest cover (% Land area) | ΔTFP | Positive |

Δagricultural yields | Positive | ||

Hypothesis 4 | ΔForest cover (% Land area) | Δ(Government quality/Forest t-1) | Positive |

Hypothesis 5 | ΔForest cover (% Land) | Error correction term | Negative |

Hypothesis 6 | ΔForest cover (% Land area) | ΔNet Exports of roundwoodΔNet Exports of agricultural products | Positive/Negative |

Hypothesis 7 | ΔForest cover (% Land area) | ΔTrade to high-income countries | Negative |

Africa | America | East and South Asia and Pacific | Europe and Central Asia |
---|---|---|---|

Angola | Argentina | Afghanistan | Austria |

Benin | Belize | Australia | Azerbaijan |

Burkina Faso | Bolivia | Bangladesh | Belarus |

Cameroon | Brazil | Bhutan | Bosnia and Herzegovina |

Central African Republic | Canada | Cambodia | Bulgaria |

Congo, Dem. Rep. | Chile | China | Croatia |

Congo, Rep. | Colombia | Fiji | Czech Republic |

Cote d’Ivoire | Costa Rica | India | Estonia |

Equatorial Guinea | Cuba | Indonesia | Finland |

Ethiopia | Dominican Republic | Japan | France |

Gabon | Ecuador | Korea, Rep. | Georgia |

Ghana | El Salvador | Lao PDR | Germany |

Guinea | Guatemala | Malaysia | Greece |

Guinea-Bissau | Guyana | Myanmar | Hungary |

Kenya | Honduras | Nepal | Iran, Islamic Rep. |

Liberia | Mexico | New Zealand | Italy |

Madagascar | Nicaragua | Pakistan | Kazakhstan |

Malawi | Panama | Papua New Guinea | Kyrgyzstan |

Morocco | Paraguay | Philippines | Latvia |

Mozambique | Peru | Sri Lanka | Lithuania |

Namibia | Suriname | Thailand | Norway |

Niger | United States | Vietnam | Poland |

Nigeria | Uruguay | Portugal | |

Senegal | Venezuela, RB | Romania | |

Sierra Leone | Russian Federation | ||

South Africa | Slovak Republic | ||

Sudan | Slovenia | ||

Tanzania | Spain | ||

Togo | Sweden | ||

Uganda | Switzerland | ||

Zambia | Turkey | ||

Zimbabwe | Ukraine | ||

United Kingdom |

Variable’s Name | Definition | Source |
---|---|---|

Forest (%) | Coefficient between: (i) forest cover (ha) (the sum of tree-covered areas and mangroves categories) and (ii) land area (ha) (country area excluding area under inland waters and coastal waters). | Land Cover CCI Product User Guide Version 2.0 (2017) |

Agr land (%) | Coefficient between: (i) agricultural cover area (sum of herbaceous crops, wood crops, and grassland) and (ii) land area. | Land Cover CCI Product User Guide Version 2.0 (2017) |

Rural pop density | Population living in rural areas over the land area of the country | World Bank |

GDP cap; GDP2 cap | Gross domestic product divided by midyear population. | World Bank |

TFP | The ratio of an output index (total amount of crop and livestock output) to an index of land and non-land inputs (all land, labor, capital and material resources employed in farm production). To reduce potential index number bias in TFP growth estimates, cost shares are varied by decade whenever such information is available. For outputs, base year prices are fixed (the base period for output prices is 2004-06). Source: https://www.ers.usda.gov/data-products/international-agricultural-productivity/documentation-and-methods/ (accessed on 9 May 2019) | United States Department of Agriculture |

Yield | Aggregate of all crops’ harvested production/harvested area for all crops. | FAOSTAT (FAO, 2018) |

Agr (PIN) | Producer price index (2004–2016=100). It measures the average annual change over time in the selling prices received by farmers (prices at the farm gate or at the first point of sale). | FAOSTAT (FAO, 2018) |

Agri empl (%) | Agricultural employment: employment in agriculture (% of total employment) (modeled ILO estimate). | The World Bank |

Agr prod net ex | Area of land embodied on exports of agricultural products (ha) minus imports agricultural products (ha). | Own calculations using the data from [27] |

For prod net ex | Exports of roundwood (m3) minus imports of roundwood (m3) | FAOSTAT (FAO, 2018) |

Government quality | An average of the following five indices: control of corruption, government effectiveness, regulatory quality, rule of law, voice and accountability. | Worldwide Governance Indicators |

Trade high | Exports of agricultural products send to high-income countries divided by the total exports of agricultural products. | Own calculations using data from [27] |

Avg temperature | Average temperature per year (computed from monthly data) | World Bank |

Avg rain | Average temperature per year (computed from monthly data) | World Bank |

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

GDP cap (log) | 0.168 | −0.357 | −0.071 | −0.030 | −0.406 | 0.072 |

GDP2 cap (log) | −0.013 | 0.029 | 0.005 | 0.002 | 0.024 | −0.004 |

Rural pop (log) | −0.025 | −0.108 | 0.020 | −0.031 | −0.073 | −0.072 |

Agr land (log) | −1.357 | −0.258 | −0.515 | |||

Const. | −3.461 | −0.352 | −1.081 | −0.937 | −0.014 | −1.474 |

Obs. | 470 | 470 | 1455 | 1455 | 703 | 703 |

R-squared | 0.489 | 0.042 | 0.187 | 0.027 | 0.360 | 0.126 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Low | Low | Middle | Middle | High | High | |

LD.GDP cap (log) | 0.097 | 0.244 * | −0.009 | 0.013 | 0.004 | 0.043 |

(0.145) | (0.126) | (0.039) | (0.039) | (0.106) | (0.101) | |

LD.GDP2 cap (log) | −0.008 | −0.020 * | 0.001 | −0.001 | 0.000 | −0.001 |

(0.012) | (0.011) | (0.002) | (0.002) | (0.005) | (0.005) | |

LD.Rural pop (log) | 0.043 | 0.181 | −0.040 | −0.048 * | 0.007 | 0.001 |

(0.174) | (0.149) | (0.030) | (0.029) | (0.027) | (0.025) | |

LD.Agr land (log) | −0.453 *** | −0.013 | −0.111 ** | |||

(0.104) | (0.013) | (0.046) | ||||

LD.Agr empl | 0.000 | −0.001 | −0.000 | −0.000 | −0.000 | −0.000 |

(0.001) | (0.001) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.Agr prod netex | 0.000 | 0.000 | −0.000 | −0.000 | 0.000 | 0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.For prod netex | 0.000 | 0.000 | 0.000 | 0.000 | −0.000 | −0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.TFP | −0.170 | 3.296 | −0.103 | −0.494 | 0.143 | 0.032 |

(2.713) | (2.350) | (0.481) | (0.474) | (0.692) | (0.661) | |

LD.Agr (PIN) (log) | −0.014 | −0.004 | 0.006 * | 0.006 * | 0.002 | 0.001 |

(0.016) | (0.014) | (0.003) | (0.003) | (0.006) | (0.006) | |

LD.Yield (log) | −0.006 | −0.015 | −0.001 | −0.001 | 0.005 | 0.005 |

(0.012) | (0.011) | (0.002) | (0.002) | (0.003) | (0.003) | |

LD.Trade high | −0.000 | −0.001 | 0.000 * | 0.000 ** | −0.000 | −0.000 |

(0.001) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.Government | −0.018 | −0.013 | −0.002 | −0.005 | −0.005 | −0.008 |

(0.019) | (0.016) | (0.003) | (0.003) | (0.008) | (0.008) | |

LD.Temperature | −0.003 | −0.002 | −0.001 * | −0.001 * | −0.000 | −0.000 |

(0.002) | (0.002) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.Rainfall | 0.000 | 0.000 ** | 0.000 | 0.000 | −0.000 | −0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

ECT | −0.326 *** | −0.369 *** | −0.122 *** | −0.120 *** | −0.083 *** | −0.109 *** |

(0.036) | (0.024) | (0.010) | (0.009) | (0.016) | (0.013) | |

DL(Gov/L.For) | −0.006 | −0.004 | 0.001 * | −0.000 | −0.002 | −0.003 |

(0.009) | (0.008) | (0.000) | (0.000) | (0.005) | (0.005) | |

Const. | 0.034 *** | 0.013 *** | −0.003 *** | −0.003 *** | −0.006 *** | −0.007 *** |

(0.006) | (0.003) | (0.000) | (0.000) | (0.001) | (0.001) | |

Obs. | 244 | 244 | 828 | 828 | 390 | 390 |

R-squared | 0.406 | 0.558 | 0.186 | 0.209 | 0.141 | 0.203 |

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Pre | Pre | Late | Late | Post | Post | |

GDP cap (log) | −0.136 | −0.087 | 0.073 | 0.095 | 0.047 | 0.089 |

GDP2 cap (log) | 0.009 | 0.006 | −0.003 | −0.006 | −0.003 | −0.006 |

Rural pop (log) | −0.019 | −0.024 | 0.108 | 0.051 | 0.014 | −0.015 |

Agr land (log) | −0.244 | −0.475 | −0.472 | |||

Const. | −0.492 | −0.207 | −2.182 | −1.728 | −1.760 | −1.459 |

Obs. | 599 | 599 | 493 | 493 | 1536 | 1536 |

R-squared | 0.283 | 0.181 | 0.382 | 0.062 | 0.168 | 0.019 |

Year dummies | Yes | Yes | Yes | Yes | Yes | Yes |

Independent Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Pre | Pre | Late | Late | Post | Post | |

LD.GDP cap (log) | 0.061 | 0.074 | −0.070 | −0.039 | −0.120 *** | −0.13 1*** |

(0.054) | (0.054) | (0.053) | (0.049) | (0.034) | (0.034) | |

LD.GDP2 cap (log) | −0.003 | −0.004 | 0.004 | 0.002 | 0.007 *** | 0.008 *** |

(0.004) | (0.004) | (0.003) | (0.003) | (0.002) | (0.002) | |

LD.Rural pop (log) | −0.125 | −0.102 | 0.018 | 0.016 | 0.009 | −0.004 |

(0.154) | (0.154) | (0.045) | (0.041) | (0.024) | (0.024) | |

LD.Agr land (log) | −0.066 ** | −0.050 | −0.083 ** | |||

(0.030) | (0.035) | (0.041) | ||||

LD.Agr empl | 0.000 | −0.000 | −0.001 * | −0.000 | −0.000 | −0.000 |

(0.001) | (0.001) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.Agr prod net ex | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.For prod net ex | 0.000 | 0.000 | 0.000 | 0.000 | −0.000 | −0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.TFP | 1.686 | 0.304 | −0.225 | −0.101 | −0.055 | −0.165 |

(1.564) | (1.550) | (1.075) | (0.970) | (0.518) | (0.509) | |

D.Agr (PIN) (log) | 0.008 | 0.007 | 0.002 | 0.004 | 0.004 | 0.005 |

(0.011) | (0.011) | (0.006) | (0.006) | (0.004) | (0.004) | |

LD.Yield (log) | −0.009 | −0.010 | −0.004 | −0.004 | 0.001 | 0.000 |

(0.009) | (0.009) | (0.004) | (0.004) | (0.002) | (0.002) | |

LD.Trade high | 0.000 | 0.000 | 0.000 | 0.000 | −0.000 ** | −0.000 ** |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

LD.Government | −0.018 * | −0.025 ** | 0.003 | 0.003 | −0.002 | −0.004 |

(0.010) | (0.011) | (0.009) | (0.008) | (0.004) | (0.004) | |

LD.Temperature | −0.003 | −0.002 | −0.002 * | −0.002* | −0.000 | −0.000 |

(0.002) | (0.002) | (0.001) | (0.001) | (0.000) | (0.000) | |

LD.Rainfall | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |

ECT | −0.290 *** | −0.278 *** | −0.196 *** | −0.195 *** | −0.165 *** | −0.161 *** |

(0.027) | (0.025) | (0.021) | (0.015) | (0.009) | (0.009) | |

DL(Gov/L.For) | 0.002 * | −0.001 | 0.001 | 0.001 | 0.000 | −0.001 |

(0.001) | (0.001) | (0.006) | (0.005) | (0.001) | (0.001) | |

Const. | −0.007 *** | −0.010 *** | 0.020 *** | 0.022 *** | −0.006 *** | −0.007 *** |

(0.002) | (0.002) | (0.002) | (0.002) | (0.000) | (0.000) | |

Obs. | 330 | 330 | 274 | 274 | 858 | 858 |

R-squared | 0.321 | 0.318 | 0.310 | 0.429 | 0.302 | 0.323 |

Variables | (1) | (2) |
---|---|---|

All Countries | All Countries | |

GDP cap (log) | −0.007 | 0.02 |

GDP2 cap (log) | 0.001 | −0.001 |

Rural pop (log) | 0.011 | −0.029 |

Agr land (log) | −0.35 | |

Const. | −1.475 | −1.164 |

Observations | 2628 | 2628 |

R-squared | 0.189 | 0.017 |

Year dummies | Yes | Yes |

Independent Variables | (1) With Agr Land | (2) Without Agri Land |
---|---|---|

LD.GDP cap (log) | −0.006 | 0.004 |

(0.021) | (0.02) | |

LD.GDP2 cap (log) | 0.001 | 0 |

(0.001) | (0.001) | |

LD.Rural pop (log) | 0.007 | 0.006 |

(0.027) | (0.027) | |

LD.Agr land (log) | −0.064 *** | |

(0.016) | ||

LD.Agr empl | 0 | 0 |

(0) | (0) | |

LD.Agr prod net ex | 0 | 0 |

(0) | (0) | |

LD.For prod net ex | 0 | 0 |

(0) | (0) | |

LD.TFP | 0.309 | −0.333 |

(0.523) | (0.512) | |

LD.Agr (PIN) (log) | 0.004 | 0.004 |

(0.003) | (0.003) | |

LD.Yield (log) | −0.001 | −0.002 |

(0.002) | (0.002) | |

LD.Trade high | 0 | 0 |

(0) | (0) | |

LD.Government | −0.002 | −0.008 ** |

(.003) | (0.003) | |

LD.Temperature | −0.001 | −0.001 * |

(0) | (0) | |

LD.Rainfall | 0* | 0 |

(0) | (0) | |

ECT | −0.205 *** | −0.202 *** |

(0.009) | (0.008) | |

DL(Gov/L.For) | 0.002 *** | 0 |

(0.001) | (0.001) | |

Const. | −0.002 *** | −0.003 *** |

(0) | (0) | |

Observations | 1462 | 1462 |

R-squared | 0.285 | 0.31 |

Effects Variables | Low | Middle | High | |||
---|---|---|---|---|---|---|

Direct | Total | Direct | Total | Direct | Total | |

GDP cap (log) | Not sig | Not sig | Not sig | Not sig | Not sig | 0.000886 ** |

GDP2 cap (log) | Not sig | Not sig | Not sig | Not sig | (+)0.00747 *** | (+)0.00747 *** |

Government | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Rural pop (log) | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Government | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Government/L.Forest | Not sig | Not sig | 0.000856 * | 0.000973 ** | Not sig | Not sig |

Agr land (log) | −0.453 *** | −0.453 *** | Not sig | Not sig | −0.111 ** | −0.0971 ** |

Agr prod net ex | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

For prod net ex | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

TFP | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Yield (log) | Not sig | Not sig | Not sig | Not sig | Not sig | 0.00551 * |

Agr (PIN) (log) | Not sig | Not sig | 0.00593 * | 0.00603 * | Not sig | Not sig |

Trade high | Not sig | Not sig | 0.0000829 * | 0.0000827 * | Not sig | Not sig |

Effects Variables | Pre | Late | Post | |||
---|---|---|---|---|---|---|

Direct | Total | Direct | Total | Direct | Total | |

GDP cap (log) | Not sig | Not sig | Not sig | Not sig | −0.120 *** | −0.117 *** |

GDP2 cap (log) | Not sig | Not sig | Not sig | Not sig | 0.00725 *** | Not sig |

Rural pop (log) | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Government | −0.0178 * | −0.0241 ** | Not sig | Not sig | Not sig | Not sig |

Government/L.Forest | 0.00185 * | 0.00256 *** | Not sig | Not sig | Not sig | Not sig |

Agr land (log) | −0.0657 ** | −0.0634 ** | Not sig | Not sig | −0.0834 ** | −0.0858 ** |

Agr prod net ex | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

For prod net ex | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

TFP | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Yield (log) | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Agr (PIN) (log) | Not sig | Not sig | Not sig | Not sig | Not sig | Not sig |

Trade high | Not sig | Not sig | Not sig | Not sig | −0.0000686 ** | −0.0000686 ** |

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## Share and Cite

**MDPI and ACS Style**

Rodríguez García, V.; Caravaggio, N.; Gaspart, F.; Meyfroidt, P.
Long- and Short-Run Forest Dynamics: An Empirical Assessment of Forest Transition, Environmental Kuznets Curve and Ecologically Unequal Exchange Theories. *Forests* **2021**, *12*, 431.
https://doi.org/10.3390/f12040431

**AMA Style**

Rodríguez García V, Caravaggio N, Gaspart F, Meyfroidt P.
Long- and Short-Run Forest Dynamics: An Empirical Assessment of Forest Transition, Environmental Kuznets Curve and Ecologically Unequal Exchange Theories. *Forests*. 2021; 12(4):431.
https://doi.org/10.3390/f12040431

**Chicago/Turabian Style**

Rodríguez García, Virginia, Nicola Caravaggio, Frédéric Gaspart, and Patrick Meyfroidt.
2021. "Long- and Short-Run Forest Dynamics: An Empirical Assessment of Forest Transition, Environmental Kuznets Curve and Ecologically Unequal Exchange Theories" *Forests* 12, no. 4: 431.
https://doi.org/10.3390/f12040431