rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8. Correlation between Bribery and Agency Attributes in Boliviaa**

<!-- Start of picture text --> Bribery and Transparency<br>100<br>high<br>80<br>;60<br>r 0 42<br>20 I-<br>0 20 40 60 80 1h0<br>low Transparency within agencies hig<br>Bribery and Citizen Voice/Feedback<br>90<br>high r a- 0 37<br>75 -<br>60-<br>m *. .,<br>30<br>1<br>Iow 5<br>low0 20 40 60 80 h 9<br>Citizen Voice/Feedback mechanisma<br><!-- End of picture text --> Bribery and Pay Satisfaction <!-- Start of picture text --> 80-<br>h.gh * r= -0.08<br>60 -<br>40 -~~~~~<br>20 - * ** .<br>0<br>low 0 20 40 60 80 100<br>low Wage level saUsfacfon high<br><!-- End of picture text --> Source. Kaufmann, Mehrez and Gurgur (2002).

a. The graph reports the correlation across public agencies between the indicated performance measure (on the vertical axis) and the indicated agency attribute (on the horizontal axis), based on a survey of public officials in Bolivia. Transparency refers to the average of six questions relating to the openness of decision making procedures in each agency Citizen voice or feedback refers to the average of four questions regarding the presence of formal feedback mechanisms available to users of the institution. Wage level satisfaction refers to the fraction of respondents who rated their compensation as satisfactory. Each dot depicts a public agency, averaged across the public officials respondents for each institution.

48
# **Figure 1C: Moderating effect of congestion by foreign ownership status**

<!-- Start of picture text -->
-.2 -.15 -.1 -.05 0 .05 .1 .15<br>Congestion (Mean=0)<br>Domestically owned Foreign-owned<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Effect of Urban Density (ln)<br>-.6<br>-.8<br><!-- End of picture text -->

Note: The data for these graphs come from the results in Table 4, Panel C (Congestion) and the x-axis is based on the full-range of the variable. Please note that we look here at the differences between slopes and not the difference in point estimates. The interaction term (presented in Table 4) tells us whether or not the marginal effect of urban density depends on the value of lack of mobility. Since this coefficient is significant, we would conclude that the interaction effect of urban density and lack of mobility depends on the sector/export/ownership status. The p-values of the point estimates answer a completely different question, namely whether the groups at a specific point in the distribution differ from each other.

# **5. Concluding remarks**

We use firm-level, geocoded data on 51,000 establishments in 649 cities in 98 developing countries, from the World Bank Enterprise Surveys and a new global database of city-level mobility and congestion indexes from Akbar et al. (2021) to unpack the relationship between urban density and firm productivity. We estimate the “pure” firm productivity gains of urban density in the developing world, net of negative externalities associated with congestion, crime, and pollution. Our results show that while on average the agglomeration economies in the developing countries are comparable in size to those in advanced economies, not all firms profit from their location in dense urban areas. The firms that reap the highest returns to urban density tend to be in the tradables sector; these are manufacturing firms, exporters, and foreign-owned firms, and larger and more experienced firms. In addition, we show that part of the ‘pure’ agglomeration benefits in developing economies are lost through poor mobility in large metropolitan areas due both to limited uncongested mobility and traffic congestion. The latter has a stronger negative effect on 24
In the first stage, we regress the level of banking sector development— proxied by the number of commercial bank branches per 100,000 adults— in country _i_ and year _t_ against three variants of banking sector development in other (neighboring) countries _(_ Branches−it−iiii∗ ) _:_ (i) the unweighted average of the number of bank branches in all countries with common borders to country _i_ , (ii) the geographic distance-discounted average of the number of bank branches in all countries in the same region excluding country _i_ , and (iii) the geographic distance-discounted average of the number of bank branches in all countries excluding country _i_ .<sup>10</sup> The three variants of our instrumental variable both capture foreign financial development as well as the fact that the link between domestic and foreign financial sector development is stronger for neighboring countries. Both the regional and global distance-discounted averages are defined in this equation: The proxy for geographic proximity (i.e., geodesic distance) is obtained from the CEPII database. Common regions are defined as in World Bank (2021). In both the first and second-stage regressions, we control for the same set of control variables as in equation (1), including the level of per capita income (in 2010 U.S. dollars) and the three institutional quality measures taken from WGI (i.e., control of corruption, regulatory quality, and rule of law). All regressions include year fixed effects (yt) and country fixed effects (ci).

Table 4 shows the results of the first-stage regression for an unconstrained sample for 2004-2018 (some of these observations will be dropped for the full estimation due to the lack of data on informal activity). Banking sector development in country _i_ is significantly positively correlated with banking sector development in countries that are geographically close or in the same region. This supports our hypothesis that a well-developed banking system in one country “spills over” into a better developed banking system— of the type that would be accessed by informal firms and workers— in geographically close countries. Hence, we take this as an indication that banking sector development in other (neighboring) countries is a suitable instrumental variable for domestic banking sector development.

# **4. Empirical results**

As expected, the results of this exercise point to a causal impact of banking sector development on informal activity. The following subsection summarizes the main results. These are subjected to a battery of robustness tests in the subsequent subsection.

> 10 Similar distance-discounted instruments have been used in studies such as De Haan et al. (2021) and Pleninger and Sturm (2020).

10
Table 5: Number of employees by EPAs

|Region|Mean|p25|p50|p75|
|---|---|---|---|---|
|Asia|238|40|82|323|
|ECA|91|31|53|164|
|LAC|128|31|66|229|
|MENA|77|39|78|102|
|OECD|886|320|587|1,400|
|SSA|81|25|42|75|
|Total|269|35|86|300|

Note: The table provides the simple average of the ranking across crountries, where 1 is the most important objective, and 8 is the least important. ECA = Eastern Europe, LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; OECD = Organisation for Economic Co-operation and Development; SSA = Sub-Saharan African countries.

growth in the period 2005-2010 (33 percent), with Eastern European and Sub-Saharan African countries presenting the highest growth rates (47 percent and 34 percent respectively). The average EPAs’ budget is around 0.18 percent of the value of exports of goods and services in these countries, with a standard deviation of 0.64 and a median of 0.04 percent. The regions with the largest average expenditure as a share of their exports are Sub-Saharan Africa (SSA) and Latin America and the Caribbean (LAC), respectively 0.34 and 0.21 percent. They are followed by countries in Eastern Europe and Asia (0.18 percent), Middle East and North Africa (MENA), Asia and the OECD with average budgets of around 0.09 to 0.12 percent of exports.

Table 6: Average annual budget in USD 2010 (thousands)

|Region|Asia|ECA|LAC|MENA|OECD|SSA|Total|
|---|---|---|---|---|---|---|---|
|mean|39,900|34,000|21,300|9,828|139,000|6,744|42,700|
|p75|39,600|13,000|14,200|16,000|202,000|4,200|49,100|
|p50|3,468|2,955|5,130|5,214|93,200|1,704|5,572|
|p25|850|1,291|2,242|3,700|63,600|1,014|1,700|

When analyzing the budget per capita reported by EPAs in 2010 we observe a positive correlation with per capita GDP and exports per capita (figure 1).<sup>9</sup> Richer economies spend proportionally more on EPAs and those countries with larger EPAs’ budget also export more. We also observe a positive correlation between the size of EPAs, in terms of number of employees and per capita gross domestic product (GDP). In addition, there is a positive association between EPAs budget and country size, in terms of total population. Yet, despite this positive correlation the graphs show a large dispersion between these variables.

These charts summarize some of the key challenges of empirical studies analyzing the im-

> 9Table 10 in the appendix shows a positive and significant correlation between EPAs’ per capita budget and per capita GDP (0.61) and a higher correlation between EPAs’ per capita budget and per capita exports (0.71). EPAs size is also positively correlated with per capita GDP (0.56), and with total EPAs Budget (0.83). Total EPAs’ budget is positively correlated with total population (0.2).

7
**Figure 11: Index of relative monthly water consumption by quintile**

<!-- Start of picture text --> 300<br>280<br>260<br>Managua<br>240 Merida<br>220 Sonsonato<br>Santa Ana<br>200<br>San Miguel<br>180<br>Panama City<br>160 Colombia<br>140 Uruguay<br>120 Paraguay<br>100<br>80<br>1 2 3 4 5<br>Income quintiles<br>first quintile consumption<br>Water consumption as percentage<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data There are a number of plausible explanations as to why the relationship between income and water consumption might be relatively flat. One is that family sizes tend to be inversely related to income. In Uruguay, for example, bottom quintile households have low per capita water consumption (2.6 cubic meters per month) but a large number of family members (4.6), whereas top quintile households have high per capita water consumption (5.9 cubic meters per month) and a small number of family members (2.3). These two trends more or less offset each other leaving household water consumption virtually constant across the income distribution in Uruguay. In addition, due to cramped living conditions, poor households are more likely to share a single connection between several households.

These results indicate that the hypothesized link between income and water consumption does not always hold, and is in general much weaker than is commonly assumed. The implication is that any IBT structure, however well designed, would not be able to redistribute income between rich and poor. Few studies of the distributional performance of IBT structures for water are available for the Latin America region. However, Robles (2002) shows that the IBT structure based on a 15 cubic meter threshold for Paraguay delivers only 20% of subsidy resources to the poor, but that this percentage would rise to 60% if the subsistence threshold was reduced to 5 cubic meters. A recent study of IBT structures in South Asia have found that only 20-30% of subsidies delivered through IBT structures are captured by the poor, and that this share would barely improve even if the design of the IBT structure were to improve, for example by reducing the subsistence threshold (Ragupathi and Foster, 2002).

26
_“Short-run” estimates: The static model_ . Table 3 presents estimates of agricultural per capita GDP using annual data using RE and FE with robust standard errors. We tried various lag structures for the explanatory variables. The results are robust to the method of estimation and to other changes in specification. The sign pattern of the coefficients does not change, though their sizes and standard errors are mildly affected by the actual lags used. We present in Table 3 the estimates using one-year lags for the explanatory variables.

The fact that now we have many more observations allows us to use annual time dummy variables in the regressions. The time dummies control for unobserved common-to-the-countries shocks that vary over time.<sup>25</sup> The pattern of significance of the estimated coefficients and their signs are highly consistent with the “long-run” estimates reported earlier. Consistent with the short-run/long-run interpretation, in general the elasticity estimates using annual data have lower absolute values than the coefficients estimated using the five-year averages.

In particular, the effects of total government expenditures in rural areas are still positive and statistically significant, but their values are about 50% lower than the values estimated using five-year averages. Similarly, the share of government subsidies in total expenditures is now negative and significant, as before, but the estimates are smaller in absolute value. Still, the message is the same: total government expenditures are important in promoting agricultural income, but the structure of public expenditures is even more important. Switching resources from subsidies to public goods can be an extraordinarily effective instrument to achieve faster agricultural growth.

The reduction of the size of the effect of the non-agriculture income is even more pronounced than that of the public expenditures variables. In fact, the effect of non-agriculture GDP falls by about 66% when annual data is used compared to five-year averages, though the coefficient is still significant. This drastic reduction in the size of this coefficient may reflect not only the short-run/long-run dichotomy, but also a possible reduction of the size of the omitted variable biases caused by the fact that we now partially control for effects of omitted variables that do change over time. In any case, an elasticity of about 0.13 still reflects a sizable positive impact of the rest of the economy upon agriculture.

> 25 Both the two-way FE and RE estimates passed the White’s test of heteroscedasticity at the 5% level of statistical significance.

20
registration fi nes, are fi nes related with the fi rm not registering the worker for a work permit, work load fi nes are fi nes related with the fi rm not complying with the official work load, wage fi nes are fi nes related with the fi rm not paying the minimum established by the law, hours of work fi nes are fi nes related with the fi rm not complying with the number of hours of work and the mandatory pauses, FGTS fi nes are fi nes related with the fi rm not making the mandatory discounts to the FGTS, transport subsidy fi nes are fi nes related with fi rm not paying the mandatory transport subsidy, and other fi nes are fi nes related with other mandatory obligations of the fi rm to their workers. We construct the total labor fi nes, as the sum of all these different fi nes. This data was aggregated from city to subregion level using information provided by the Ministry of Labor on all the cities that belong to each subdelegacia.

Finally, we also use information from two Brazilian statistical and research institutes (IPEA and IBGE). Data for population and for the total number of plants in 2002 is collected by the National Statistics Institute (IBGE). City level data for GDP (1985 and 2000), population (1991 and 2000), share of females (2000), geographical area (2000), transportation costs to the nearest capital city (1995), number of train stations in the city (1995) and homicide rate per 100000 inhabitants (2002) is collected by the Instituto de Pesquisa Economica Aplicada (IPEA). These variables are all observed at the city level and are aggregate to the level of the subregion.

# 4. Enforcement of Regulation, Informal Labor and Firm Performance

## 4.1. Main Results

In this section we examine the effect of enforcement of labor regulation on informal employment hired by the fi rm and on various fi rm outcomes. We estimate a set of relationships based on the following equation: 14
Policy Research Working Paper 9143

# **Abstract**

The fallout of nitrogen pollution is considered one of the largest global externalities facing the world, impacting air, water, soil, and human health. This paper combines data from the Demographic and Health Survey data set across India, Vietnam, and 33 African countries to analyze the causal links between pollution exposure experienced during the very earliest stages of life and later-life health. The results show that pollution exposure experienced in the critical years of development—from birth until age three—is associated with decreased height as an adult, a well-known indicator of overall health and productivity, and is robust to several statistical checks. Because adult height is related to education, labor productivity, and income, this also implies a loss of earning potential. The analysis begins within an assessment in India, where the data are more available, and is then extended to geographic settings including Vietnam and 33 countries in Africa. The results are consistent and show that early-life exposure to nitrogen pollution in water can lower height-for-age scores during childhood in Vietnam and during infancy in Africa. These findings add to the evidence on the enduring consequences of water pollution and identify a critical area for policy intervention.

This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ezaveri@worldbank.org, jruss@worldbank.org, rdamania@worldbank.org, sebdburo@gmail.com, and arodella@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Internships outside institution are mandatory|0.94|0.24|0.94|0.24|0.90|
|---|---|---|---|---|---|
|Mandatory internships during the program|0.54|0.50|0.36|0.48|**0.01**|
|Time assigned to practical training (%)|52.37|13.78|51.06|13.14|0.47|
|Remediation support||||||
|Remediation classes before starting the program|0.54|0.50|0.65|0.48|0.11|
|Remediation classes during the program|0.30|0.46|0.41|0.49|**0.08**|
|Non-class-based remediation|0.30|0.46|0.37|0.49|0.21|
|Years since last update to curriculum|2.26|2.09|2.08|2.16|0.53|
|_Costs_||||||
|Annual tuition (2019 PPP USD)|1,049.59|1,389.20|1,546.45|1,701.53|**0.01**|
|Funding options used by some students||||||
|Loans by banks|0.25|0.43|0.30|0.46|0.35|
|Loans by government program|0.36|0.48|0.38|0.49|0.80|
|Other loans|0.22|0.42|0.37|0.49|**0.01**|
|Government scholarships|0.45|0.50|0.38|0.49|0.26|
|HEI scholarships|0.64|0.48|0.75|0.44|**0.07**|
|_Link with productive sector_||||||
|Engagement with firms||||||
|Collect employment data for graduates more than once per year|0.44|0.50|0.52|0.50|0.22|
|Communicate with local firms about their needs more than once per year|0.38|0.49|0.39|0.49|0.79|
|Program has staff assigned to collect grads' employment data|0.71|0.46|0.76|0.43|0.33|
|Industry helps with student evaluation or curriculum design|0.68|0.47|0.61|0.49|0.24|
|Industry has agreements with HEI to hire program grads|0.30|0.46|0.45|0.50|**0.02**|
|Industry has agreements to train faculty|0.32|0.47|0.40|0.49|0.22|
|Industry lends or provides equipment to program for student training|0.54|0.50|0.50|0.50|0.53|
|Somebody in charge of industry relations|0.85|0.36|0.89|0.31|0.29|
|Job search assistance||||||
|HEI coordinates job interviews with firms|0.42|0.50|0.46|0.50|0.57|
|HEI trains students for job interviews|0.48|0.50|0.58|0.50|0.11|

78
Table 3 Productivity Correlations

||(Q/Wj,<br>(Q/K)j|(Q Mj,l(Q/M);|<br>(Q/K)j,<br>(Q/M)j|
|---|---|---|---|
|1980|0.645|0.031|-0.051|
|1985|0.720|-0.220|-0.130|
|1989|0.661|-0.149|-0.321|
|j=1....226||||

Table 4

|||Patterns of Convergence within Subsampl<br>(coefficients of variation)|es|
|---|---|---|---|
||Q/W|Q/K<br>Q/M|NTFP|
|A. Sales|100 perc|ent within plan (74):||
|1980|0.883|0.998<br>0.314|0.363|
|1985|0.832|0.904<br>0.307|0.332|
|1989|0.749|0.701<br>0.294|0.271|
|B. Sales|partially|within plan (105):||
|1980|0.800|1.044<br>0.206|0.265|
|1985|0.737|0.693<br>0.201|0.194|
|1989|0.662|0.574<br>0.217|0.153|
|C. No with|in<br>plan|sales<br>(44):||
|1980|0.834|0.800<br>0.2-2|0.221|
|1985|0.506|0.614<br>0.206|0.144|
|1989|0.452|0.490<br>0.220|0.123|

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