rafmacalaba/datause-encoder-data huggingface dataset:
decisions. Hence, we now look at how important public infrastructure quality is for Brazil’s firms and whether the degree of spatial heterogeneity in quality shown in Figures 2 and 3 is a relevant factor for them. For this, we rely on the World Bank’s Enterprise Survey for Brazil in 2007, which is the closest to our benchmark scenario of 2010.

Table 2 shows descriptive statistics for selected variables in the Enterprise Survey for Brazil by firm size. Overall, 45.6% of firms experienced power outages over the fiscal year 2007, and small, medium, and large firms experienced them in a similar proportion. When asked how much electricity is an obstacle, 45.9% of firms identify it as a major or very severe one. For transport, this number equals 27.5%, but still, 31.8% find it a moderate obstacle. The survey also asks, if the establishment were to relocate, how much would roads and energy reliability be part of the decision? 70.8% and 76.0% of the firms find roads and energy reliability to be at least important for this decision, respectively. Finally, we see no significant differences across firm sizes when asked about the number and duration of power interruptions. These numbers suggest that (i) the quality of infrastructure matters for the firm’s location decision and (ii) firm size alone is insufficient to avoid power outages or obstacles derived from transport.

# **3 A Spatial Equilibrium Model with Endogenous Complementary Infrastructure Networks**

## **3.1 Model Outline**

This section outlines a spatial multisector equilibrium model with endogenous electric power and road transportation infrastructure networks. The model builds on the quantitative spatial modeling literature (Redding and Rossi-Hansberg, 2017) and is closely related to frameworks of Fajgelbaum and Schaal (2020) and Santamaria (2020), where the government chooses the quality of infrastructure to maximize social welfare. Our model extends these earlier studies by incorporating an additional infrastructure type (electricity grid), which generates locationspecific productivity spillover effects. It also allows for different degrees of efficiency and coordination in infrastructure investment across government branches.

We consider a country with a fixed set of _N_ microregions (or locations). Consumers (or households) can move freely within the country. Each consumer supplies inelastically one unit of labor in a given location. The household consumes products from the agricultural, manufacturing, and services sectors. The electricity supply in each location is taken as given and can be consumed as a service by households or used as a production input by firms. The production and consumption activities take place in the center of the microregion. Throughout the paper, we use the index _i_ for production locations and _j_ for consumption locations.

9
two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths. We use conflict types 3 and 4, which correspond to civil wars.

Since we do not observe the exact characteristics of institutions that are related with conflict, we need to identify the institutional variables that best capture the lack of contract enforcement and insecure property rights. We start the analysis by using the average protection against expropriation risk. This variable captures the risk of expropriation of private foreign investment by government, and goes from 0 to 10, where a higher score means less risk. This index is the average between 1985 and 1995. This variable, which comes from Acemoglu et al (2001), was originally used by Knack and Keefer (1995). Table 1 lists the ranking of the 15 countries with the lowest and the highest protection against expropriation risk. The civil war variable indicates whether the country suffered any civil war during the period 1960-2005. All of the fifteen countries with the lowest scores suffered a civil war, while only seven of the 15 countries with the strongest protection suffered a civil war.

Our second proxy for economic institutions is a measure of law and order. It measures the strength and impartiality of the legal system, and the popular observance of the law. The source for this variable is the International Country Risk Guide (ICRG). The law and order variable can take values from 0 to 6. Higher numbers indicate a stronger legal system.<sup>4</sup> Table 2 lists the ranking of the 15 countries with the weakest law and order system, and the 15 countries with the strongest legal system. The law and order index is the average of the law and order indicator from 1984 to 1999. All of the fifteen countries with the lowest scores suffered a civil war, while only four of the fifteen countries with the strongest legal system suffered a civil war.

> 4 Other variables from the ICRG are the quality of bureaucracy and corruption. The results are robust to the use of these variables.

5
the urban and rural subsamples, respectively. Program mean impacts on student exam scores differ between the urban and rural subsamples in the first year but the differences in later years are not significant.

<mark>Given that the program increases exam participation rates, we test whether the absence of significant positive mean impacts on student exam scores is due to negative selection of students into examtaking. We do not find evidence supporting this hypothesis. Aside from this test, we are left to speculate on factors t</mark> hat could have blocked impacts. Some studies argue or show that implementation failures can explain the lack of program impacts.<sup>23</sup> <mark>We d</mark> iscount that the absence of positive mean impacts on student exam scores is due to this problem. Third-party validations indicate that the program has been implemented satisfactorily, and markedly better than many other programs administered by the provincial education department. Program registration and orientation, the distribution of school performance score cards, and the transfer of bonus awards to teacher bank accounts were carried fully and in a timely and efficient way. The PEC exam and ASC survey activities were also carried out as scheduled. Credibility in the program was reportedly low in the first year of implementation: program school teachers conveyed that they were initially skeptical that they would receive bonuses (consistent with program rules). This skepticism appears to have faded away after program school teachers experienced how the government performed in running the program and they received their bonus awards.

We also discount that the absence of positive mean impacts on student exam scores is due to bonus awards being too low, the bonus formula being too complex for teachers to understand, or the group-based nature of the bonuses (in the case of the two treatment variants that offer bonuses to all teachers) inducing significant freeriding. The mean bonus payout was 16 percent of the yearly basic salary for a teacher at the baseline mean pay grade and with baseline mean years of government service.<sup>24</sup> All definitions and data sources for bonus calculations were communicated by program administrators to program school teachers during school visits and at

> 23 For example, a new program may experience teething problems (see Bouguen, Filmer, Macours, and Naudeau 2013). In such cases, positive impacts may only materialize well after program inception, when problems are effectively resolved. Separately, implementation performance may be sensitive to program implementer characteristics such as the implementer’s interest, commitment, capability, and effort (Allcott 2015), and governments may be more likely or have larger shortfalls in these characteristics relative to, for example, (certain types of) NGOs (Bold et al 2013).

> 24 This relative size is several percentage points higher than, for example, the bonuses in teacher-incentive field experiments in India (Muralidharan and Sundararaman 2011) and Kenya (Glewwe et al 2010); both experimental evaluations find positive mean program impacts.

22
**Table 3:** Model and Data Moments

|Moments|Data|Model|
|---|---|---|
|Mean market share of top 5% (within sector)|65 %|66%|
|Market share of top 10%|69%|54%|
|Market share of top 25%|84%|85%|
|Market share of top 50%|94%|93%|
|Market share of ICT frms|7.3%|7.7%|
|Fraction of ICT frms|4.1%|4.0%|
|Aggregate markup|10%|11%|
|Median number of frms|1077|1080|

41
# **Figure 3.  Investment growth after global downturns**

_After an unusually strong rebound in 2010, investment growth in EMDE commodity exporters in 2014‐15 slowed well below the average growth rates after previous global downturns. In EMDE commodity importers, investment growth has been consistently more anemic than after previous global downturns._

## **A. Advanced economies**

<!-- Start of picture text --> Percent Average 2009<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-2 -1 0 1 2 3 4 5 6<br>Year<br><!-- End of picture text -->

## **B. EMDE commodity exporters**

<!-- Start of picture text --> Percent<br>Average 2009<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2 -1 0 1 2 3 4 5 6<br>Year<br><!-- End of picture text -->

## **C. EMDE commodity importers**

## **D. World**

<!-- Start of picture text --> Percent Percent Average 2009<br>16 Average 2009 16<br>12<br>12<br>8 8<br>4 4<br>0 0<br>-4 -4<br>-8 -8<br>-2 -1 0 1 2 3 4 5 6 -2 -1 0 1 2 3 4 5 6<br>Year Year<br><!-- End of picture text --> Sources: Haver Analytics, International Monetary Fund, Oxford Economics, World Bank. Notes: Unweighted average investment growth. The horizontal axis denotes years. Zero refers to the year of the start of global downturns, which include global recessions and slowdowns. Average refers to unweighted average investment growth during global recessions and slowdowns of 1975, 1982, 1991, 1998, and 2001.

29
9 levels of the ecological model (individual, relationship-level, community and society). Socialization and learning of violent behaviors, for example, occurs at the individual level if a child witnesses intimate partner violence between adults in his or her household, or if a child is a victim of abuse (Ellsberg, Peña et al. 1999; Kishor and Johnson 2004). At the relationship level, association with peers who themselves commit intimate partner violence or who promote norms of male dominance over women increases the likelihood of intimate partner violence (Heise 1998). At the community level, one study found that neighborhood crime rates affect the probability of intimate partner violence, presumably through community norms about violence and criminality (O'Campo, Gielen et al. 1995). At the societal level, cultural norms that support violence as an accepted way to resolve conflicts reinforce household norms to this effect.

Other types of risk factors operate at multiple levels. Power relations and patriarchal gender norms operate at the individual level by influencing the degree to which women can attain education and financial autonomy, themselves risk or protective factors for intimate partner violence. At the relationship level, power relations manifest themselves through male control of wealth and household decision-making and through marital conflict. Finally, at the community and societal levels, power and patriarchy play out in norms that support male dominance of women, and in limited public roles for women.

The fact that risk factors operate at multiple levels has important implications for the design of interventions to address GBV. Interventions, to be effective, will generally need to address risk factors at these different levels of aggregation.<sup>12</sup> Thus, we use the ecological model as a way to organize the presentation of the good practice interventions in the following sections. (See Figure 1.2 for a graphical representation of the ecological model.)

**Figure 1.2   Ecological Model**

<!-- Start of picture text --> Societal Community Relationship Individual<br>Source: WHO, 2002<br><!-- End of picture text -->

> 12 This does _not_ mean that each and every intervention must intervene at multiple levels, but rather that each level should be addressed by some intervention.
|Work is harmful or<br>dangerous||0.490***||0.190*||-0.0636|||
|---|---|---|---|---|---|---|---|---|
|||(0.156)||(0.106)||(0.125)|||
|Concern about job loss|-0.174*|-0.232**|0.130*|0.160**|0.102|0.129|||
||(0.103)|(0.110)|(0.0694)|(0.0734)|(0.0788)|(0.0836)|||
|Any part of wages|||||||||
|transferred off the record?|0.162|0.201|0.152|0.231*|0.352***|0.371***|||
||(0.149)|(0.168)|(0.106)|(0.124)|(0.114)|(0.132)|||
|Constant|1.464*|0.850|7.529***|7.941***|1.937***|2.202***|-3.893***|-4.567***|
||(0.813)|(0.884)|(0.848)|(0.934)|(0.727)|(0.820)|(0.672)|(0.592)|
|Observations|4624|4104|4616|4098|4425|3930<br>|4902|4389|

Source: Authors’ estimation using RLMS 2016.

Note: Odd number columns include all observations, even number columns exclude type-2 informals (i.e., who workers employed but not at an enterprise in their primary job). This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their subjective work characteristics and the incidence of envelope wages, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). All dependent variables are defined as binary. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

50 Informal Employment and Worker’s Wellbeing in Russia / June 2019
14 poverty reduction based on data of the late 1980s and the early 1990s. With better comparable household data now available (including emerging panel data), and with another decade of economic reform in many countries<sup>8</sup> , we are in a good position to revisit this issue.

Following Bouton et al (1994) we calculate a macroeconomic policy index or score, based on changes in three key elements of sound macroeconomic policy: fiscal, monetary, and exchange rate policy. The overall macro-policy score is a weighted average of these components, the weights being derived from international cross section growth regressions. These scores are computed for the three-year period prior to each survey, and changes in the index are then compared. The index is so computed that increases in the score (either lower negative values or higher positive values) indicate an _improvement_ in economic policy (Table 9).

**Table 9: Changes in macro-economic policy scores, selected countries**

||_Change_<br>_during._|_Fiscal_<br>_policy_|_Monetary_<br>_policy_|_Exchangerate_<br>_policy_|_Averag_<br>_Unweighted_|_eScore_<br>**_Weighted_**|
|---|---|---|---|---|---|---|
|C6ted'Ivoire|1985-88|-2|1|-2|-1.0|**-1.7**|
|Ethiopia|1989-95|-1|_-0.5_|2.5|0.7|1.0|
||1994-97|2|1.5|2.5|2.0|2.2|
|Ghana|1988-92|-I|_1.5_|2|0.8|0.8|
||1992-98|0|_-0.5_|_0.5_|0.0|0.2|
|Madagascar|1993-97|0.0|_-0.5_|0.0|-0.2|-0.1|
||1997-99|1.0|1.0|0.0|0.7|0.5|
|Mauritania|1987-95|3|_0.5_|2.5|2.0|2.4|
|Nigeria|1985-92|1|-1|3|1.0|1.8|
||1992-96|1|-I|-2.5|-0.8|-1.0|
|Uganda|1992-97|2|_1.5_|_-0.5_|1.0|**0.7**|
||1997-00|0|_0.5_|0.5|0.3|0.3|
|Zambia|1993-96|2|2|0.5|1.5|**1.2**|
||1996-98|1|1|-1|0.3|0.0|
|Zimbabwe|1991-96|-1|_-0.5_|_1.5_|0.0|0.3|

Sources: Demery and Squire (1997); authors' computations from World Bank data.

Given weaknesses in the underlying survey data, we prefer not to retain two countries included in the original Demery and Squire piece (Tanzania and Kenya). For Ethiopia, Ghana and Nigeria, we update the estimates by introducing trends in the 1990s. Finally, we add the cases of Madagascar, Mauritania, Uganda, Zambia and Zimbabwe, giving altogether a coverage of fifteen episodes of change in nine countries. Most countries experienced improvements in their macroeconomic policy indicators-those for the second period (i.e. the three-year period prior to the second survey) being generally better than those of the earlier period (the three years prior to

> 8 The data used in many previous assessments were often of doubtful quality and given the lags involved in implementing the reforms, the 1 990s might be a more appropriate decade to examnine the growth path induced by economnic policy reforms in Africa (Collier and Gunning, 1997).
- 28 -

||||Table|3. Corre|lation||||
|---|---|---|---|---|---|---|---|---|
||_C_|_Y_|_WE_|_WL_|_WK_|_Z P1_|_Z P2_|_S L_|
|_Y_|0.998||||||||
|_WL_|0.123|0.120|||||||
|_WE_|-0.014|-0.014|-0.002||||||
|_WK_|0.023|0.024|0.018|0.880|||||
|_Z P1_|0.017|0.025|-0.033|-0.006|-0.009||||
|_Z P2_|-0.001|0.002|-0.013|-0.024|-0.008|0.183|||
|_S L_|-0.099|-0.099|0.171|-0.046|-0.102|0.052|-0.025||
|_S E_|-0.091|-0.092|-0.064|0.156|-0.072|0.000|-0.016|0.101|

# **V. MAIN ESTIMATION RESULTS**

Both SUR and SFA models are estimated with data from seven SEE countries; the results are shown in Table 4.<sup>28</sup> The coefficients are broadly consistent with economic theory; recall that the current paper relies on a simple firm cost minimization model and assumes that a certain group of firms would share the same cost function. The coefficient of output _Y_ is positive and significant, and the operating cost increases with unit labor costs (wages) as well as energy and fuel expenditures. The coefficients are not dramatically different between the two models, though the statistical significance may change for some of the coefficients.

# **_Price elasticities of factor demand_**

Given the estimated cost parameters, the own price elasticities of demand for production factors are evaluated at the sample means by the delta method (Equation (6)). All the own price elasticities are found significantly negative and consistent with economic theory (Table 5). The price elasticity of industrial energy demand in question is estimated at –0.403 by the SUR regression and –0.366 by the SFA model. Therefore, on average, one can expect that a 10-percent increase in energy prices would reduce the industrial demand for energy by about 4 percent. These estimates are within the conventional range supported by the existing literature (e.g., Taylor, 1975; Bernstein and Griffin, 2005), and seem to be relatively inelastic, though not extraordinarily.

> 28 The results have been found indifferent about whether or not to include country-specific fixed effects. In addition, it has also been found robust against the clustering of errors by country and sector. Therefore, the following discussion will mainly present the unclustered estimation results without the country fixed effects.
53 Table A-1 Peru: Macroeconomic Indicators

||Growtha|Inflationa|<br>Terms of<br>Tradeb|External<br>DebtC|Resource<br>Transferd|Real<br>Wages|
|---|---|---|---|---|---|---|
|1970|7.3|5|100|3.68|4.1||
|1971|5.1|7|85|3.69|0.8||
|1972|5.8|7|80|3.83|1.0||
|1973|6.2|10|101|4.13|-0.6||
|1974|6.9|17|103|5.24|-5.4||
|1975|2.4|24|88|6.26|-9.8||
|1976|3.3|34|84|7.38|-5.6||
|1977|-0.3|38|79|8.57|-3.4||
|1978|-1.7|58|75|9.32|3.3||
|1979|4.3|67|100|9.33|12.5||
|1980|2.9|60|111|9.59|3.8|100|
|1981|3.0|73|104|9.63|-4.4|98|
|1982|0.9|73|94|11.1|-3.7|101|
|1983|-12.0|125|104|12.4|0.2|84|
|1984|4.7|112|96|13.3|4.5|70|
|1985|2.3|158|82|13.8|6.9|60|
|1986|8.9|63|64|14.4|-2.0|76|
|1987|6.5|115|61|15.4|-3.8|80|
|1988*|-8.5|1720||16.2||61|

*estimates aPercent per year, b Terms of Trade Index, 1970-100, c Total external debt,Billion $; d Resource transfer abroad as a percent of GDP. Resource transfer i measured in the national accounts as net exports excepting factor payments.
algorithms based on village and town names. The population censuses describe village and town public goods, amenities and household characteristics, including the primary source of cooking fuel. Fuel use is reported as the share of households in a location using firewood (68% of households at baseline), imported fuels (chiefly propane, 8%) or local nonwood fuels (crop residue and dung, 22%) as a primary source of energy. Fuel use is reported at the subdistrict level in 2001 and at the village level in 2011.

The Economic Censuses are complete enumerations of all nonfarm establishments undertaken in 1990, 1998, 2005 and 2013, including informal and non-manufacturing firms. We matched these on village names to the three population censuses using a fuzzy matching algorithm. The Economic Census reports total employment and industry for all firms. We create variables describing total employment in (i) firms engaged in logging and (ii) firms whose primary input is raw lumber, which include sawmilling and planing of wood, manufacture of wooden products such as furniture and wooden containers, manufacture of cork, and manufacture of pulp and paper products. The industry categorization for the 2005 Economic Census places logging firms in the same industry category as firms engaged in the conservation of forest plantations, management of forest tree nurseries and other afforestation categories. We therefore exclude 2005 from analysis of employment in logging firms.

# **IV Impacts of Rural Feeder Roads on Forest Cover**

This section describes the impact of new feeder roads on local deforestation. The main challenge to causal identification of the impacts of rural roads is endogeneity. Because roads are costly to build, their placement is typically correlated with other factors that could also be predictors of deforestation. For example, roads could be targeted to places that are expected to grow or to places that are lagging economically. Road placement may also depend on geographic (e.g. slope, terrain, soil quality) or political factors. Any of these scenarios would bias OLS estimates of the effect of new roads on deforestation.<sup>9</sup> Causal

> 9Appendix Table A1 shows estimates from cross-sectional OLS regressions of village-level log forest cover in 2001 on an indicator variable that takes the value one if a village has a paved road in 2001. While the bivariate relationship is strongly negative and highly statistically significant, the estimate gets progressively

11