# Voters’ Information, Corruption, and the Efficiency of Local Public Services

^{1}

^{2}

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^{4}

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## Abstract

**:**

## 1. Introduction

## 2. The Model

#### 2.1. Setting

_{t}the value that the manager provides to the company in time t. In particular, θ measures how much the manager is able to reduce the cost, with respect to a benchmark. In what follows, with a slight abuse of definition, we will designate θ as managerial productivity. θ

_{t}depends both on managerial talent η

_{t}, and on how much effort he puts into managing the company (which we designate as productive effort), denoted ${a}_{t}^{p}$, according to the following relation:

^{p}, the manager also can exert effort in an activity that, while potentially generating a private benefit for the manager, has no direct impact, either positive or negative, on the firm’s performance. This effort, which we designate as unproductive (having in mind the perspective of the company), is denoted a

^{u}. Effort in unproductive activity generates a marginal return τ

_{a}to the manager (while generating a null return for the company). τ

_{a}thus measures how rewarding distorting effort away from the productive activity is; when τ

_{a}= 0, the effort distortion is not rewarding at all. Hence, we regard τ

_{a}as a measure of the level of corruption in the institutional environment in which the firm operates, in a way that is similar to [9]. To keep things simple, we assume that all the managers, irrespective of their talent, have the same return from the unproductive activity. Managers keep devoting their effort to unproductive activity, and benefiting from it, even once they are ousted from the firm. This reflects the notion that, in a corrupt environment, managerial positions in politically-related companies allow for the development of long-term links and networks that can be exploited, even after the manager loses his job [17]. However, we assume that the returns from a

^{u}when the manager is ousted from his job, denoted τ

_{β}, are potentially lower than when the manager is in charge, so that τ

_{β}≤ τ

_{a}. A manager that has lost his job, and thus has a

^{p}= 0, R = 0, but a

^{u}≥ 0.

_{t}evolves over time according to the following relation:

_{t}= ρ

_{t − 1}+ ρ

_{t}

_{t − 1}and ρ

_{t}(which we will refer to as period-specific skills) are i.i.d. random shocks, and $\rho ~N(\overline{\rho},{\sigma}_{\rho}^{2})$.

_{t − 1}for the incumbent manager becomes common knowledge. However, before exerting efforts ${a}_{t}^{p}$ and ${a}_{t}^{u}$, the manager does not fully know his talent. In particular, he is unaware of the period t-specific skill, ρ

_{t}. There is no asymmetric information in this model; in period t, both the manager and the voter know ρ

_{t − 1}, but neither the manager nor the voter know ρ

_{t}. At stage two, the voter observes the same noisy signal of managerial productivity:

_{t}is an i.i.d. shock N (0; ${\sigma}_{\epsilon}^{2}$), uncorrelated with talent. The voter observes the same signal ${\widehat{\theta}}_{t}$. The variance of the noise ${\sigma}_{\epsilon}^{2}$ reflects the extent of imprecision in the observability of managerial behavior. A high ${\sigma}_{\epsilon}^{2}$ thus indicates less voter’s information on managerial behavior. The voter uses ${\widehat{\theta}}_{t}$ to make her own inference on the level of the time-specific skill ${\widehat{\rho}}_{t}$. In period t + 1, the managerial competence is ρ

_{t}+ ρ

_{t + 1}. Thus, the voter’s expectation on the level of managerial competence at time t + 1, in case the incumbent manager is reappointed, is ${\widehat{\rho}}_{t}+\overline{\rho}$, where the unconditional expectation $\overline{\rho}$ is the best predictor of ρ

_{t + 1}. If, instead, a new manager is appointed at t + 1, both ρ

_{t}and ρ

_{t + 1}are randomly drawn; in this case, the best predictor at time t of a new manager’s competence at t + 1 is 2$\overline{\rho}$. At stage three, elections are held, pitting the incumbent politician to a randomly drawn challenger. The voter recognizes that the fate of the manager is tied to that of the politician. She thus re-elects the incumbent politician if the manager he is associated to is, in expectation, more skilled than the manager linked to the challenger, which occurs if ${\widehat{\rho}}_{t}>\overline{\rho}$.

#### 2.2. Equilibrium Effort and Selection

^{2}. Finally, we solve for the expected managerial productivity $E\left({\theta}_{t}\right)=E\left({\eta}_{t}\right)+{a}_{t}^{p}$. The full details of the model are available in the working paper version of this paper [20]. The results are summarized in the following:

**Proposition.**

_{a}and τ

_{β}increase). Also, it declines when the voter has less precise information (i.e., high ${\sigma}_{\epsilon}^{2}$).

## 3. Empirical Analysis

#### 3.1. The Econometric Model

_{it}is the total cost that is incurred by municipality i at time t, y

_{it}is a vector of outputs, p

_{it}is a vector of input prices, β is a vector of parameters to be estimated, v

_{it}is a standard error term measuring random noise, and u

_{it}is a non-negative error term, to be interpreted as cost inefficiency. The latter follows a truncated normal distribution whose pre-truncation mean is parameterized on a set of exogenous factors z

_{it}—such as our key variables of interest, voters’ information, and corruption—and a vector of parameters δ to be estimated. The two sets of parameters (β and δ) are estimated simultaneously. This is what is referred to as a one-step procedure, as opposed to a two-step approach, which consists of estimating cost inefficiency without including exogenous factors, and subsequently fitting a model in which a set of variables is used to explain the estimated inefficiency. Some authors [29,30] suggest the adoption of a linear specification of the mean value of the inefficiency term:

_{it}, thus allowing for an increase/decrease of the estimated cost inefficiency, in line with our theoretical model. In principle, other possibilities would be feasible to analyze the impact of social environment characteristics on the level of costs. An alternative would be, for instance, the inclusion of a set of environmental features z

_{it}directly in $c\left({y}_{it},{p}_{it},{z}_{it};\beta \right)$, thus allowing for a modification of its shape. This option is, however, not appropriate, given our purposes, since it assumes that the social characteristics of the operating environment do not impact directly on the efforts of the municipalities, or on their negotiation capabilities.

_{it}term, which is not observable. Jondrow et al. [31], therefore, suggest to estimate u

_{it}as its conditional expectation ${\stackrel{\u2322}{u}}_{it}$, given the fitted value of ${\epsilon}_{it}$ $={u}_{it}+{v}_{it}$, i.e., ${\stackrel{\u2322}{u}}_{it}=E\left({u}_{it}|{\epsilon}_{it}\right)$. The latter can then be transformed into a measure of distance from the optimal frontier, following Battese and Coelli [32], who define the cost inefficiency measure, CI

_{it}, as:

#### 3.2. Data and Variables

- -
- the total cost (TC), which is the sum of labor, capital, and fuel costs that are incurred to provide the MSW service;
- -
- the tons of MSW disposed (y
_{D}); - -
- the tons of MSW sent for recycling (y
_{R}); - -
- the price of labor (p
_{L}), given by the ratio of the total salary expenses to the number of full-time equivalent employees; - -
- the price of diesel fuel (p
_{F}); - -
- the price of capital (p
_{k}), obtained by dividing the depreciation costs by the capital stock.

^{2}and 500,000 inhabitants). Moreover, in our dataset, there is a total of 101 provinces (out of 110); thus, a suitable degree of cross-section variability is ensured.

#### 3.3. The Cost Frontier Specification

_{it}) term, which follows a truncated normal distribution with mean ${\mu}_{it}$, and a symmetric random noise (v

_{it}). We further assume that v

_{it}and u

_{it}are homoskedastic and independent of each other, and uncorrelated with the output and input price vectors, y

_{r}and p

_{s}.

_{r}are represented by the volume of MSW disposed (r = D), and the volume of MSW recycled (r = R). On the side of productive factors, prices refers to labor (s = L), capital (s = K), and fuel (s = F).

_{F}) to ensure a homogeneity of degree one in input prices, while β

_{sr}= β

_{rs}and β

_{sm}= β

_{ms}impose symmetry. Other non-imposed properties, in particular, concerning the concavity of the cost function in input prices, are checked ex post.

#### 3.4. Results

_{D}and β

_{R}indicate that a 1% increase in MSW disposed or MSW sent to recycling results, ceteris paribus, in a 0.721 to 0.750% or 0.198 to 0.221% increase in costs, respectively. Scale economies at the sample mean can be computed as the inverse of the sum of output elasticities. In this case, the adopted two-output cost frontier specification yields values at around unity in all of the models, thus suggesting that the average municipality exhibits constant returns to scale. The estimates of labor and capital price elasticities are given by the parameters β

_{L}and β

_{K}. According to Shephard’s lemma, they equal the optimal labor and capital cost shares at the local approximation point. The share of the factor (i.e., fuel) that is used as numeraire can then be obtained residually. All of the three models estimate a labor cost share (between 39% and 43%) that is higher than the capital cost share (between 16% and 23%), and about the same as the fuel cost share (between 35% and 45%). This seems reasonable and in line with the typical cost structure in this service. Second-order parameters give flexibility to the functional form, allowing for a pointwise estimate of the output and input price elasticities. In particular, the parameter β

_{DR}is negative and significant, suggesting cost complementarities in the joint provision of disposal and recycling services. The specification of the cost function is simple, but it contains all the relevant information to fit the precisely observed costs. For more details concerning the technological features of MSW services see [43], which focuses on the impact of different recycling shares on refusal collection costs, and provides a complete analysis of scale and scope economies.

_{VOT}in Model 1 is negative and highly statistically significant. A greater propensity to participation by citizens—and therefore less opacity in the relationship between citizens and decision-makers—can substantially reduce the cost inefficiency. This is in line with [4], as well as with a large amount of anecdotal evidence pointing at the notion that a greater pressure by public opinion is able to route managers and policy-makers towards more efficient decisions. As expected, δ

_{CORR}is instead positive, suggesting that more widespread corruption negatively affects the efficiency performance of MSW services. On the whole, this lends support to our theoretical section.

_{VOT}measures the impact of the degree of voters’ information in the base case in which waste is collected directly by individual municipalities or through inter-municipal consortia, while the parameter of the interacted term δ

_{VOT_CORP}should be interpreted as the incremental effect due to the presence of limited liability companies. By itself, the corporatization of waste collection generally reduces cost inefficiency (δ

_{CORP}= −0.135, p < 0.05). This result is in line with empirical evidence about the positive effects of corporatization on the performance of local public services provision [44]. The marginal impact of voters’ information in the case of service supply through distinct business organizations is significant (δ

_{VOT_CORP}= −0.396), while δ

_{VOT}is not statistically significant. This means that voters’ information reduces the cost inefficiency, only if the service is managed through the establishment of independent companies, while the presence of associations of municipalities or of direct in-house management blur the potential benefits of a higher transparency.

_{CORR_LW}represents the incremental cost inefficiency due to corruption under left-wing political guidance. In Model 2, δ

_{CORR}still remains positive and highly statistically significant, while the interaction term δ

_{CORR_LW}is inefficiency-reducing. The resulting effect of corruption in municipalities led by left-wing local councils is equal to 0.071, and it is significant. This implies that in municipalities ruled by right-wing parties and by independent parties (“civic lists”), waste collection services suffer more from cost inefficiency due to corruption. The behavior of left-wing municipal councils is, however, more spending-oriented (δ

_{LW}= 0.171, p < 0.01). In Model 3, all of the additional variables that are included in the inefficiency model are significant, with the exception of GDP. The geographical dummies confirm the well-known north–south division, suggesting higher (lower) refuse collection costs for southern (northern) municipalities, while the time trend is negative and significant at the 1% level across all the models, indicating cost-reducing technological progress. Interesting enough, the coefficient for LONGIT is negative, suggesting that, after having checked for the three macro regions (north, center and south), eastern municipalities are associated with lower costs. This implies, for example, that municipalities localized in the northeastern Veneto region (or Lazio and Apulia, for center and south, respectively) are more efficient than municipalities that are localized in Piedmont (Sardinia and Sicily, respectively). More importantly, the effects of corruption and voters’ information are confirmed, even if the magnitude of coefficients reduces with respect to Model 2. The last rows in Table 2 show the statistics for the λ coefficient, which is defined as the ratio between the standard deviation of the inefficiency term σ

_{u}and the standard deviation of random noise. The values are statistically significant at the 1% level, indicating that the inefficiency term has a significant contribution on the total variation of the composed error. Then, the likelihood ratio tests of the unrestricted Model 3 (U) against the restricted (R) Models 1 and 2 indicate that including a large set of explanatory variables of expected inefficiency would be preferable.

#### 3.5. Impact of Voters’ Information and Corruption on Costs

#### 3.6. Robustness Checks

_{year}and FORDOM

_{s}. FORDOM

_{year}accounts for the number of years during which each province has been ruled (the maximum value is for the provinces controlled by the Papal State, who ruled for 700 years). FORDOM

_{s}accounts for the number of different dominators that governed a specific province at different periods of time in the seven centuries taken into consideration. In particular, it is constructed as a Krugman’s specialization index: FORDOM

_{s}= ${\sum}_{i}|{b}_{i}-\stackrel{-}{b}|,$ where i identifies the nine possible dominations (the Normans, the Swabians, the Anjou, the Aragonese, the Bourbons, the Papal State, the Savoy, the Austrians, and the Republic of Venice), b

_{i}is the percentage of years that a specific dominator ruled a province (i.e., b

_{i}= total number of years/700), and $\overline{b}$ is the average of b for all provinces. A high value of FORDOM

_{s}means that the province has been ruled by the same regime for a long period of time, while a low value occurs if there have been different dominations over the centuries.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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Variable | Description | Mean | Std. dev. | Min | Max |
---|---|---|---|---|---|

TC | Total cost (000 €) | 5436 | 23,965 | 46 | 48,065 |

y_{D} | Waste disposed (t) | 17,125 | 71,195 | 118.44 | 1,462,128 |

y_{R} | Waste recycled (t) | 3770 | 13,044 | 8.86 | 210,211 |

p_{L} | Price of labor (€/employee) | 36,394 | 5744 | 21,000 | 62,613 |

p_{K} | Price of capital (depreciation rate) | 0.087 | 0.013 | 0.049 | 0.124 |

p_{F} | Price of diesel fuel (€/liter) | 1.023 | 0.122 | 0.780 | 1.370 |

DEN | Population density (inhabitants per square km) | 903 | 1241 | 22 | 9441 |

TOUR | Beds in tourist accommodation per 100,000 inhab. | 1939 | 7127 | 1 | 127,983 |

CORP | Limited responsibility company (dummy) | 0.819 | 0.386 | 0 | 1 |

HOUSE | In-house provision (dummy) | 0.100 | 0.300 | 0 | 1 |

INTMUN | Inter-municipal partnership (dummy) | 0.081 | 0.273 | 0 | 1 |

LWPOL | Left-wing political orientation (dummy) | 0.287 | 0.453 | 0 | 1 |

RWPOL | Right-wing political orientation (dummy) | 0.178 | 0.383 | 0 | 1 |

CIVIC | Civic or municipal lists (dummy) | 0.534 | 0.499 | 0 | 1 |

VOTINFO | Newspaper readers (per 1000 inhabitants) | 351 | 105 | 148 | 599 |

CORRUPT | Crimes against public faith (per 100,000 inhab.) | 5.492 | 1.819 | 1.703 | 15.113 |

LATIT | Latitude coordinate | 42.524 | 2.661 | 35.503 | 46.610 |

LONGIT | Longitude coordinate | 12.413 | 2.789 | 7.333 | 18.377 |

GDP | Per-capita value added | 21,782 | 7,014 | 11,639 | 36,542 |

Variables | Parameters | Model 1 | Model 2 | Model 3 |
---|---|---|---|---|

lny_{D} | β_{D} | 0.750 *** | 0.740 *** | 0.721 *** |

(0.010) | (0.010) | (0.012) | ||

lny_{R} | β_{R} | 0.198 *** | 0.200 *** | 0.221 *** |

(0.008) | (0.008) | (0.010) | ||

lnp_{L} | β_{L} | 0.434 *** | 0.415 *** | 0.388 *** |

(0.048) | (0.048) | (0.054) | ||

lnp_{K} | β_{K} | 0.200 *** | 0.231 *** | 0.164 *** |

(0.044) | (0.045) | (0.048) | ||

(lny_{D})^{2} | β_{DD} | 0.164 *** | 0.164 *** | 0.157 *** |

(0.012) | (0.012) | (0.012) | ||

(lny_{R})^{2} | β_{RR} | 0.088 *** | 0.087 *** | 0.093 *** |

(0.007) | (0.007) | (0.007) | ||

(lnp_{L})^{2} | β_{LL} | −0.047 | −0.141 | 0.070 |

(0.371) | (0.368) | (0.374) | ||

(lnp_{K})^{2} | β_{KK} | −1.113 *** | −1.017 ** | −0.620 |

(0.400) | (0.399) | (0.441) | ||

(lny_{D})(lny_{R}) | β_{DR} | −0.111 *** | −0.110 *** | −0.109 *** |

(0.008) | (0.008) | (0.008) | ||

(lnp_{L})(lny_{D}) | β_{LD} | 0.047 | 0.050 | 0.028 |

(0.047) | (0.047) | (0.047) | ||

(lnp_{L})(lny_{R}) | β_{LR} | 0.028 | 0.043 | 0.042 |

(0.034) | (0.035) | (0.035) | ||

(lnp_{L})(lnp_{K}) | β_{LK} | −0.140 | −0.134 | −0.256 |

(0.308) | (0.307) | (0.336) | ||

(lnp_{K})(lny_{D}) | β_{KD} | 0.031 | 0.027 | −0.007 |

(0.050) | (0.050) | (0.051) | ||

(lnp_{K})(lny_{R}) | β_{KR} | −0.056 * | −0.069 ** | −0.033 |

(0.033) | (0.033) | (0.034) | ||

lnDEN | β_{DEN} | 0.077 *** | 0.079 *** | 0.071 *** |

(0.008) | (0.008) | (0.010) | ||

lnTOUR | β_{TUR} | 0.014 *** | 0.016 *** | 0.016 *** |

(0.004) | (0.004) | (0.004) | ||

Constant | β_{0} | −0.277 *** | −0.299 *** | −0.351 *** |

(0.030) | (0.037) | (0.040) | ||

Inefficiency model | ||||

lnVOTINFO | δ_{VOT} | −0.515 ** | −0.060 | 0.028 |

(0.206) | (0.101) | (0.087) | ||

CORP | δ_{CORP} | −0.135 ** | −0.040 | |

(0.064) | (0.033) | |||

lnVOTINFO × CORP | δ_{VOT_CORP} | −0.396 ** | −0.259 *** | |

(0.182) | (0.098) | |||

lnCORRUPT | δ_{CORR} | 0.352 ** | 0.389 *** | 0.275 *** |

(0.139) | (0.131) | (0.066) | ||

LWPOL | δ_{LW} | 0.171 *** | 0.115 *** | |

(0.060) | (0.030) | |||

lnCORRUPT × LWPOL | δ_{CORRLW} | −0.318 ** | −0.221 *** | |

(0.136) | (0.077) | |||

SOUTH | δ_{S} | 0.249 *** | ||

(0.064) | ||||

NORTH | δ_{N} | −0.218 *** | ||

(0.062) | ||||

LONGIT | δ_{LONG} | −0.366 *** | ||

(0.089) | ||||

LATIT | δ_{LAT} | 1.223 *** | ||

(0.393) | ||||

GDP | δ_{GDP} | 0.001 | ||

(0.108) | ||||

TIME | δ_{T} | −0.051 *** | ||

(0.017) | ||||

Constant | δ_{0} | −0.174 | 0.103 | 0.271 *** |

(0.264) | (0.131) | (0.079) | ||

Std. Dev. one-sided error term | σ_{U} | 0.288 *** | 0.226 *** | 0.173 *** |

(0.064) | (0.046) | (0.039) | ||

Std. Dev. two-sided error term | σ_{V} | 0.234 *** | 0.232 *** | 0.231 *** |

(0.009) | (0.010) | (0.014) | ||

Lambda | λ | 1.229 *** | 0.973 *** | 0.749 *** |

(0.065) | (0.050) | (0.051) | ||

Log-Likelihood Function | −213.537 | −195.575 | −161.686 | |

Likelihood Ratio test | 103.70 *** | 67.78 *** | ||

Number of observations | 1587 | 1587 | 1587 |

Mean | Std. Dev. | Min | Max | |
---|---|---|---|---|

Voters’ information | 0.051 | 0.011 | 0.022 | 0.069 |

if CORP = 1 | ||||

Corruption | −0.042 | 0.022 | −0.074 | −0.006 |

if LWPOL = 1 | −0.011 | 0.002 | −0.014 | −0.006 |

if LWPOL = 0 | −0.054 | 0.012 | −0.074 | −0.023 |

If CORP = 1 | If LWPOL = 1 | If LWPOL = 0 | ||
---|---|---|---|---|

Average population | 45,662 | 54,152 | 35,828 | |

∆ corruption (to minimum value) | Cost change (% variation) | −0.016 | −0.074 | |

Cost change (million €) | −0.1 | −0.4 | ||

Cost change (€ per inhabit.) | −1.91 | −7.75 | ||

∆ corruption (to maximum value) | Cost change (% variation) | 0.016 | 0.075 | |

Cost change (million €) | 0.1 | 0.3 | ||

Cost change (€ per inhabit.) | 1.93 | 7.66 | ||

∆ voters’ information (to minimum value) | Cost change (% variation) | 0.056 | ||

Cost change (million €) | 0.4 | |||

Cost change (€ per inhabit.) | 6.380 | |||

∆ voters’ information (to maximum value) | Cost change (% variation) | −0.036 | ||

Cost change (million €) | −0.2 | |||

Cost change (€ per inhabit.) | −3.878 |

**Table 5.**Impact of voters’ information and corruption on costs for some large municipalities (Model 3).

Rome | Milan | Turin | Palermo | Florence | Bari | ||
---|---|---|---|---|---|---|---|

Average population | 2,711,491 | 1,297,244 | 910,437 | 662,046 | 366,074 | 321,747 | |

Geographical region | Center | North | North | South | Center | South | |

∆ corruption (to minimum value) | Cost change (% variation) | −0.073 | −0.074 | −0.036 | −0.110 | −0.059 | −0.011 |

Cost change (million €) | −31.3 | −19.2 | −5.0 | −11.3 | −4.0 | −0.5 | |

Cost change (€ per inhabit.) | −11.56 | −14.83 | −5.50 | −17.12 | −10.96 | −1.64 | |

∆ corruption (to maximum value) | Cost change (% variation) | 0.037 | 0.050 | 0.041 | 0.095 | 0.041 | 0.032 |

Cost change (million €) | 15.7 | 12.8 | 5.7 | 9.8 | 2.8 | 1.4 | |

Cost change (€ per inhabit.) | 5.77 | 9.85 | 6.29 | 14.81 | 7.66 | 4.45 | |

∆ voters’ inform. (to minimum value) | Cost change (% variation) | 0.074 | 0.062 | 0.062 | 0.028 | 0.080 | 0.041 |

Cost change (million €) | 32.5 | 15.9 | 9.1 | 2.9 | 5.3 | 1.9 | |

Cost change (€ per inhabit.) | 11.99 | 12.24 | 10.00 | 4.34 | 14.57 | 5.84 | |

∆ voters’ inform. (to maximum value) | Cost change (% variation) | −0.019 | −0.020 | −0.035 | −0.091 | −0.022 | −0.084 |

Cost change (million €) | −8.5 | −5.1 | −5.2 | −9.4 | −1.5 | −3.8 | |

Cost change (€ per inhabit.) | −3.13 | −3.91 | −5.66 | −14.18 | −4.10 | −11.92 |

Variables | Parameters | Model 4 | Model 5 |
---|---|---|---|

Instruments: Putnam civic-ness | Instruments: Dominations (1100–1800) | ||

Number of instruments: 8 | Number of instruments: 2 | ||

lnVOTINFO | δ_{ACC} | 0.025 | −0.014 |

(0.063) | (0.068) | ||

CORP | δ_{CORP} | −0.015 | −0.024 |

(0.022) | (0.023) | ||

lnVOTINFO × CORP | δ_{ACC_CORP} | −0.173 *** | −0.141 ** |

(0.064) | (0.067) | ||

lnCORRUPT | δ_{CORR} | 0.411 *** | 0.200 ** |

(0.053) | (0.081) | ||

LWPOL | δ_{LW} | 0.084 *** | 0.083 *** |

(0.016) | (0.017) | ||

lnCORRUPT × LWPOL | δ_{CORRLW} | −0.431 *** | −0.105 |

(0.080) | (0.106) | ||

SOUTH | δ_{S} | 0.199 *** | 0.174 *** |

(0.034) | (0.036) | ||

NORTH | δ_{N} | −0.115 *** | −0.103 *** |

(0.032) | (0.035) | ||

LONGIT | δ_{LONG} | −0.252 *** | −0.207 *** |

(0.056) | (0.063) | ||

LATIT | δ_{LAT} | 1.210 *** | 1.305 *** |

(0.272) | (0.276) | ||

GDP | δ_{GDP} | −0.004 | −0.008 |

(0.055) | (0.059) | ||

TIME | δ_{T} | −0.038 *** | −0.045 *** |

(0.011) | (0.011) | ||

Constant | δ_{0} | 0.193 *** | 0.835 *** |

(0.040) | (0.038) | ||

First stage F-statistic (instruments only) ^{(a)} | F (8, 1568) 71.32 | F (2, 1574) 77.82 | |

(p-value) | (0.000) | (0.000) | |

Number of observations | 1587 | 1587 |

^{(a)}The F-statistic tests the validity of instruments: the null hypothesis is that, respectively, Putnam civic-ness dummies and domination indexes are jointly not significantly different from 0 in the first-stage regression. The F-statistic must be at least larger than 10 to avoid the problem of weak instruments.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Abrate, G.; Boffa, F.; Erbetta, F.; Vannoni, D. Voters’ Information, Corruption, and the Efficiency of Local Public Services. *Sustainability* **2018**, *10*, 4775.
https://doi.org/10.3390/su10124775

**AMA Style**

Abrate G, Boffa F, Erbetta F, Vannoni D. Voters’ Information, Corruption, and the Efficiency of Local Public Services. *Sustainability*. 2018; 10(12):4775.
https://doi.org/10.3390/su10124775

**Chicago/Turabian Style**

Abrate, Graziano, Federico Boffa, Fabrizio Erbetta, and Davide Vannoni. 2018. "Voters’ Information, Corruption, and the Efficiency of Local Public Services" *Sustainability* 10, no. 12: 4775.
https://doi.org/10.3390/su10124775