rafmacalaba/datause-encoder-data huggingface dataset:
resource prices, the degree of persistence,  pR, is set at 0.93 and the standard deviation of the nonsystematic shock <sup>pR</sup> is set at 0.25, as in Maliszewski (2009).<sup>12</sup> For the nonresource sector, elasticities of production with respect to labor,  N and  T, are set equal to 0.7 and 0.6, respectively, to capture the fact that production in the nontradable sector is relatively more labor intensive than production in the nonresource tradable sector (  N >  T). The elasticity of output with respect to public capital,  is set at 0.17, which corresponds to the long-run value estimated through meta-regression analysis by Bom and Ligthart (2014, Table 4) for core public capital. Thus, public infrastructure is equally productive in the production of tradables and nontradables.

Regarding the government, the fraction of resource revenues saved in the sovereign fund,  , is set initially equal to 0. The initial stock of assets in the sovereign fund, F, is set at 1 percent of GDP (assumed to correspond to an initial lump-sum transfer from the government), whereas the coefficient <sup>F</sup> , which measures the management fee paid to non-residents, is set as in Agénor (2016) at 0.25 percent of the stock of assets held in the fund.<sup>13</sup> The interest rate on assets held in the sovereign wealth fund, r<sup>F</sup> , is set at 4 percent initially, and sensitivity analysis is conducted later on.

According to IMF data, total revenue and grants amounted to 20.1 percent of GDP in 2012 (corresponding to T/Y in the model), whereas total expenditure amounted to 19.6 of GDP in the same year. To abstract from debt accumulation, it is assumed that the overall balance, 0.5 percent of GDP, corresponds to the share of transfers to households in GDP. The

> 12Maliszewski’s study focuses on oil prices; we use the same numbers for resource prices in general, given that these prices tend to exhibit strong co-movements.

> 13Because the model is log-linearized near an initial steady state, an initially positive value of F is needed. The results are not much affected in a value smaller than 1 percent of output is used.

22
<!-- Start of picture text --> 100%<br>90%<br>30%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>o%°<br>1995 2004<br>& Not Known @ Pvt. Unaided m@ Pvt. Aided<br>@ Local Body @ Government<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90% O@ Not<br>80% Known<br>70% m@ Pvt.<br>60% nade<br>50% m Pvt.<br>40% Aided<br>30% B Local<br>Body<br>20%<br>10% = Govern<br>ment<br>0%<br>1 2 3 4 5<br><!-- End of picture text -->
# **Table A6.1 The Effect of PMT on Height-for-Age of Children 6-12 Months: Using Binary Indicator for Program Exposure**

||Poole|d OLS|Comm<br>Fixed-|unity<br>Effects|
|---|---|---|---|---|
|Year (2000=1)|-0.255|-0.345|-0.418|-0.524|
||[0.392]|[0.394]|[0.702]|[0.705]|
|Program since ‘98 (Yes=1)*Year|0.264|0.384|0.507|0.617|
||[0.383]|[0.386]|[0.712]|[0.722]|
|Gender (boy=1)|0.105|0.115|0.004|0.005|
||[0.124]|[0.124]|[0.197]|[0.206]|
|Age|-0.126***|-0.126***|-0.170***|-0.170***|
||[0.036]|[0.035]|[0.057]|[0.058]|
|Mother education (years)|0.003|0.004|-0.023|-0.023|
||[0.015]|[0.015]|[0.023]|[0.024]|
|Father education (years)|0.008|0.010|0.014|0.012|
||[0.015]|[0.015]|[0.022]|[0.022]|
|Mother height (cm)|0.063***|0.061***|0.059***|0.057***|
||[0.012]|[0.012]|[0.018]|[0.019]|
|Father height (cm)|0.047***|0.049***|0.070***|0.069***|
||[0.012]|[0.012]|[0.019]|[0.019]|
|Head of HH (male=1)|-0.011|-0.027|0.275|0.275|
||[0.221]|[0.221]|[0.443]|[0.462]|
|Number of children 5-14 yo in HH|0.016|0.012|0.016|0.027|
||[0.061]|[0.061]|[0.088]|[0.090]|
|HH owns private toilet (Yes=1)|0.137|0.101|0.178|0.190|
||[0.135]|[0.135]|[0.221]|[0.227]|
|HH has sanitation (Yes=1)|0.055|-0.008|0.255|0.313|
||[0.170]|[0.193]|[0.404]|[0.463]|
|Type of community (urban=1), C|-|0.337**||0.071|
|||[0.169]||[0.465]|
|Distance to district capital (km), C|-|0.000||-0.002|
|||[0.003]||[0.006]|
|Distance to bus station (km), C|-|0.030***||0.008|
|||[0.011]||[0.021]|
|Land w/ technical irrigation (%), C|-|-0.009||0.258|
|||[0.333]||[0.830]|
|Comm. has asphalt road (Yes=1), C|-|-0.242||0.008|
|||[0.162]||[0.392]|
|Comm. has sewerage system (Yes=1), C|-|0.071||-0.299|
|||[0.140]||[0.340]|
|Comm. has piped water (Yes=1), C|-|-0.006||-0.132|
|||[0.152]||[0.369]|
|Constant|-17.118***|-17.314***|-20.068***|-19.380***|
||[2.456]|[2.458]|[3.833]|[3.944]|
|R-squared|0.14|0.16|0.57|0.58|

Notes: n is 525. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

37
# **Table 4. Poisson Regression: Financial Policy Response Activity Index in EMDEs (September 1**<sup>**st**</sup> **, 2020)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|Finan|cial PolicyRes|ponse Activity|Index|
|Log GDP per capita (USD)|0.450***<br>(0.062)|0.533***<br>(0.072)|0.467***<br>(0.064)|0.560***<br>(0.077)|
|Log population size|0.318***|0.353***|0.308***|0.347***|
||(0.044)|(0.046)|(0.049)|(0.052)|
|Days to record 100 Covid-19 cases|0.000|0.001|0.001|0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Fiscal balance (% of GDP)||-0.013||-0.013|
|||(0.020)||(0.020)|
|Current account (% of GDP)||-0.011||-0.015*|
|||(0.007)||(0.008)|
|Government debt (% GDP)||-0.002||-0.003|
|||(0.003)||(0.003)|
|Private debt (% GDP)||-0.002||-0.003|
|||(0.002)||(0.002)|
|Oxford fiscal support (% of GDP)|||-0.003|0.806|
||||(0.603)|(0.696)|
|Oxford government response stringency index|||0.246<br>(0.528)|0.169<br>(0.540)|
|Constant|-6.626***|-7.877***|-6.759***|-8.104***|
||(1.059)|(1.219)|(1.144)|(1.352)|
|Observations (countries)|113|105|102|94|
|Pseudo R<sup>2</sup>|0.446|0.474|0.435|0.465|

Note: The sample consists of EMDEs countries only (i.e., upper-middle, lower-middle and low-income countries). The dependent variable is the number of financial sector policy measures taken (FPRAI). Robust standard errors. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively.

In ancillary regressions, we explore the role of additional banking sector characteristics described in Section 3.2. As noted, the sample size decreases substantially, so the analysis should be interpreted with care. The results, reported in Appendix C, Table C2, show that economic development and population remain positively and strongly correlated with the total number of financial policies implemented. Of the banking characteristics, private bank credit (as % of GDP) and operating on Basel III capital requirements are associated with fewer financial policy measures.

25
Table 3: Differences in response rates by mode of data collection

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||_O_|_utcome: Sur_|_vey complet_|_ed_|
|Phone survey|0.422***|0.425***|0.427***|0.417***|
||(0.037)|(0.037)|(0.038)|(0.038)|
|Mean of Dep.Var (WhatsApp)|0.298|0.299|0.299|0.299|
|R-squared|0.178|0.182|0.212|0.242|
|Observations|599|597|597|597|
|Controls (Age, Sex)|No|Yes|Yes|Yes|
|Controls (Education)|No|No|No|Yes|
|Municipality FE|No|No|Yes|Yes|

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on completion rate. “Phone survey” is a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variable in columns (1)-(4) is a dummy equal to one if the survey was completed (See more details in Table A8). The control variables in columns (2)-(3) include age in years and sex of the respondent. We also add the educational level of the respondent for primary, high-school or tertiary education -the omitted category- in column (4). We impute zero for the missing values in control variables in column (4). To account for potential time-invariant characteristics within municipalities, we estimate a model with municipality fixed effects and present results in columns (3) and (4). Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

31
As the monetary regime of a country is arguably correlated with other characteristics of the economic, institutional and financial environment of a country we do not attempt to estimate the level effect of the monetary regime on household income and consumption using our limited crosssectional data. Instead, we examine whether households which experienced an _Income shock_ are less likely to have to cut back on consumption if they are located in a country which can use monetary policy to combat the crisis. Thus we employ our cross-sectional data to assess the differential effect of monetary flexibility on how households have to respond to the crisis if they are affected an income shock.<sup>18</sup> However, we cannot rule out that the observed relations between the monetary regime and household reaction to income shocks may be driven by a wide range of other differences in the macroeconomic and institutional environment across countries.

Table 6 presents separate estimates of the impact of the monetary regime for high-income and middle-income crisis countries. In all regressions we include household location, household income and country fixed effects as covariates. In columns (1,3) we examine changes in staple foods consumption, while in columns (2,4) we examine changes in non-essential consumption. The key explanatory variable in all specifications is the interaction term _Floating currency * Income shock_ which captures the differential effect of the monetary regime on households which experienced an income shock (versus those that did not).

The estimates displayed in columns (1-2) of Table 6 suggest that in the high-income crisis countries the monetary regime may have affected the transmission of the banking crisis to households. In the two countries with independent monetary policy (UK, Sweden) households that were hit by an income shock were less likely to cut back on staple food consumption than households hit by an income shock in the euro-zone (France, Germany, Italy). That said, households in the UK and Sweden were more likely to react to income shocks by cutting back on non-essential consumption.

The estimates displayed in columns (3-4) of Table 6 provides some evidence that in the middleincome crisis countries, monetary flexibility also affected the transmission of the banking crisis to households. The column (3) estimates show no differential reaction of staple food consumption by households hit by income shocks in countries with floating exchange rates (Hungary, Ukraine) compared to the currency-union / peg countries (Slovenia, Latvia, Russia, Kazakhstan). However, the column (4) estimates suggest that in Hungary and Ukraine households that were hit by an income shock had to cut back less on non-essential consumption than households hit by an income shock in the four other countries.

> 18 This approach obviously ignores the potential mitigating effect that a flexible monetary regime may have on the incidence of income shocks.

22
difference in the number of extreme heat days across country income groups, ranging from 6 in HCs to 32 in LMCs.

The shapes of the response functions for firm revenues to differences in days with extreme heat mirror those in Figure 1 for average temperature (see Figure A3). Specifically, a 1 standard deviation increase in the number of extreme heat days within a fiscal year relative to the locations’ long-term mean— corresponding to around 10 additional hot days—reduces corporate revenues by about 6-12% in LMCs.<sup>5</sup> Smaller firms in UMCs also tend to be negatively affected by more extreme heat with 10 additional heat days reducing corporate revenues by about 3%. Firms in HCs are not adversely affected by additional extreme heat days in our sample which points to a higher resilience of firms to rising temperatures possibly supported by the small average number of such days in in HCs.

# **6.2 Impact channels**

What drives the effect of high temperatures on firm revenues? We examine the channels through which rising temperatures impact corporate revenues by estimating the effects of temperature differences on firms’ production factors (Table 2). We find that temperature shocks reduce the labor productivity in firms of all sizes in LMCs. Consistent with the impact on revenues, the adverse effect of higher-than-expected temperatures on labor productivity in LMCs is about 50% larger for small and medium-size firms than for large firms. In line with the impact on corporate revenues, years with positive temperature deviations raise labor productivity in firms of all sizes in HCs, which are relatively cold in our sample.

The lower productivity of workers among firms in LMCs also translate into lower wages. A 1 standard deviation increase in temperatures relative to historical trends in firms’ locations and fiscal years reduces wages, measured by average labor compensation per worker, by approximately 9% (Table 2, third row). The reduction in labor productivity is consistent with previous firm-level studies for individual countries such as India (see Somanathan et al.

> 5The results for the point estimates for extreme heat days are not reported but available from the authors upon requests.

18
Policy Research Working Paper 5764

# **Abstract**

Thousands of Moldovans emigrated for work abroad over the last few years following nearly a decade of economic stagnation in their home country. At about 30 percent of the labor force, Moldova’s emigrant population is in relative terms among the largest in the world. This study uses a unique household survey to examine the impact of emigration on wages in Moldova. The authors find a positive and significant impact of emigration on wages and the result is robust to the use of alternative samples and specifications. The size of the emigration coefficient varies depending on the sample and model specification, but the baseline result suggests that, on average, a 10 percent increase in the emigration rate is associated with 3.2 percent increase in wages. At the same time, there is evidence of significant differences across economic sectors in the estimated effect of emigration on wages. The authors speculate and provide some evidence that offsetting changes in labor demand, as revealed by information on employment growth by sector, may help explain some of the heterogeneity.

This paper is a product of thePoverty Reduction and Economic Management Unit, Europe and Central Asia Region. 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://econ.worldbank. org. The author may be contacted at LBouton@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
Table S4: Proxy: By-Country Data Quality Grade, No Country Time Trend.

||ln(GDP)<br>(1)|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|
|---|---|---|---|---|
|Grades|All|A, B|C|D, E|
|ln(NO2)|0.307***|0.230***|0.172**|0.230|
||(0.060)|(0.079)|(0.073)|(0.143)|
|ln(Lights)|0.100***|0.055|0.084**|0.134**|
||(0.027)|(0.037)|(0.042)|(0.059)|
|Observations|2,752|464|1,681|562|
|Countries|178|29|109|37|
|Year|05-20|05-20|05-20|05-20|
|R-squared|0.105|0.105|0.056|0.105|

_Notes._ All specifications include country and year fixed effects. When using nights lights, fixed effects for either OLS sensor (average over DMSP satellites; pre-2014) or VIIRS (2014-2020) are included. A country-specific time trend is included where indicated. Robust standard errors, clustered by country are reported in parentheses. R-Squared is adjusted and within all fixed effects. Three countries, Cura ̧cao, Palestine, and Sint Maarten, are ungraded.

# **B.3 Alternative Country Grades**

Henderson et al. (2012) note that it is difficult to disentangle which countries actually have poor quality national accounts from the Penn World Tables letter grades used in the main text. They instead turn to World Bank grades for a subsample of countries. Here, we show how our primary results regarding objective data quality rankings in Table 7 would change using this alternative grading scheme for the subset of 114 graded countries.

Table S7 presents the results in two scatter plots, where marker size is proportional to the number of countries. In Panel A, we show that while the Penn World Tables and World Bank grades have a strong positive correlation, there are important differences between the two rankings. In Panel B, we show how our objective measure compares with the alternative World Bank ranking. While also positively correlated, our ranking still represents a significant reordering of countries. Thus, our qualitative results discussed with respect to our objective rankings are the same if we were to conduct the discussion versus this alternative grading scheme.

# **B.4 2020 Misstatement**

Table 8 in the text list an abbreviated set of countries with understatement or overstatement in 2020. Tables S6 and S7 expand Table 8 Panels A and B, respectively, to show understatement and overstatement across all countries for which 2020 GDP data was available at the time of S13
year observation the sum of undistributed yearly profits in the corporations where they are shareholders, in proportion of their shares.

### **4.2.3 Some caveats**

When considering the pre-tax income aggregate used to measure inequality in our study, some caveats should be kept in mind. First, similarly to all studies that combine survey data and tax records, our income aggregates are not fully comparable between the two sources. Survey data does not capture all income sources (dividends, for example) nor captures the same income sources every year (interest income is only included in the survey instrument for some periods). On the other hand, tax records do not include pension income or imputed rent, and we do not try to impute those for tax record observations. Since capital income is very small at the bottom of the distribution and imputed rent very small at the top, we do not expect our results to be very sensitive to those choices. The extent to which pension income is relevant at the top of the distribution is less clear – it is certainly much less relevant than in high-income countries with stronger pension systems and an older population, but we are unable to precisely assess the relevance in Honduras in the absence of administrative data on pension distribution. In Appendix B, we also discuss how assigned undistributed measure. profits are computed from a _taxable profits_ Due to meaningful changes in CIT forms over the period we study, we opt to use a measure that is more comparable over the period instead of using a broader measure of total profits.

# **5 Methods & Diagnostics**

Once we obtained distributions of income in both the and tax administrative survey data, our next step is to combine these two sources to obtain a comprehensive distribution of income. In this section we describe the approach used to obtain this combined distribution and provide a series of diagnostics on the steps taken.

## **5.1 Merging survey and tax records**

Similar to other countries where labor informality is predominant and where the threshold to file PIT is high, tax records will only accurately capture the income of a small share of the total population. Our main goal is thus to complement the top of the income distribution observed in the survey with tax record information – where we often observe much higher incomes than those reported in the survey data.

In order to do so, we follow the method of replacing survey income at the top with income from tax records. Whereas some previous work has chosen to replace 10
of days it takes to comply with export regulations and procedures from Djankov, Freund and Pham (2009). _F_ & _Rc_ is the geography-determined trade to GDP ratio provided in Frankel and Romer (1999), which was estimated using a trade gravity framework where only geographic variables were used as explanatory variables of bilateral trade flows.<sup>9</sup> _DummiesR_ are regional dummies, and _ec_ is the standard white-noise error. When testing for what works and what doesn’t in EPA modalities we add to (2) the variables discussed in section 2.

To address reverse causality or any remaining unobserved heterogeneity that may lead to omitted variable bias, we instrument EPA budgets. First, we use the number of years to the next election. The idea is that close to election time governments may be willing to increase expenditures for political purposes. We will check for non-linearities in this relationship, because governments with distant elections may also lack incentives to balance the budget as they are not likely to be penalized in the immediate future. The second instrument is the number of years since the creation of the EPA. It is not clear how the longevity of an EPA affects the size of its budget. On the one hand, experienced EPAs could have larger budgets as they become rooted in the government’s institutional structure, and its staff become more knowledgeable and influential in budgetary decision making. On the other hand, one of the critiques of the previous generation of EPAs is that they were not adequately funded, and there may be some hysteresis in the corresponding budgets. Again, to capture any potential non-linearity in the relationship we also include as an instrument the square of the number of years since the creation of the EPA.

A concern with the use of the number of years since the EPA was created as an instrument is that it may be correlated with exports per capita. If an EPA is established when exports reach a sufficiently high level (reverse causality), and if this threshold has been increasing over time as world markets became more integrated, then the number of years since the EPA was created may reflect a lower level of exports. To address this issue, we also present results where we control for the level of exports over GDP at the time of the creation of the EPA. If

> 9That is, EPAs cannot influence the geographic components of trade, such as geographic distance and common borders between trading partners, but their budget may be correlated with the geography-determined trade to GDP ratio as countries with larger trade flows might provide better funding to their EPAs.

10
# **Infrastructure in Latin America**

## **César Calderón and Luis Servén***

The World Bank

**JEL Codes:** H54, O40, D31, O55

**Keywords:** Infrastructure development, growth, Latin America

**_______________________________________________________________________**

* This paper was commissioned as a chapter for the Oxford University Press _Handbook of Latin American Economies,_ edited by José Antonio Ocampo and Jaime Ros. We thank the editors for comments on an earlier draft, and Rei Odawara and Junko Sekine for able research assistance. The views expressed here are only ours and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.