rafmacalaba/data-use-mentions-extended huggingface dataset:
area) identifiers.

In addition to the survey questionnaires, a set of **geospatial variables** are derived by the data producers at the household and plot levels and provided to data users as part of the raw data files. These typically include measures of distance, climatology, soil and terrain. A selected set of these geospatial variables are included in the HP’s plot and household-level datasets. Household GPS locations are also provided as part of the public data releases on which the HP data is based. Note that the household geolocations are slightly offset from the true locations, in order to preserve the anonymity of households and survey villages. An offset range of 0-2 km is used for urban areas, while a range of 0-5 km is used in rural areas, where communities are more dispersed, and risk of disclosure may be higher. An additional 0-10 km offset for 1% of rural clusters (10% in Mali) effectively increases the known range for all rural points to 10 km while introducing only a small amount of noise (Central Statistical Agency and Living Standards Measurement Study (LSMS), World Bank, 2021; Ministry of Finance and National Institute of Statistics, 2016; Ministry of Rural Development, 2019; National Bureau of Statistics, 2021a, 2021b; National Statistical Office, 2020). The publicly available raw data is typically structured according to questionnaire modules (e.g. agriculture questionnaire, module 1).

Harmonized Panel datasets and variable creation 8
Policy Research Working Paper 10203

# **Abstract**

The paper assembles and harmonizes sectoral data from several sources to study the industrial trends in six Central American economies. The industrial employment share contracted by 2.5 percentage points on average over the past two decades. This deindustrialization was not tradedriven in which economies substitute domestic production of industrial goods via cheaper imports. Instead, an increase in barriers restricting the efficient flow of labor across sectors drives this decline. Adopting policies that target such barriers can potentially deliver considerable industrial expansion. But the economic impact of this policy is likely to be marginal, with aggregate output increasing by 3 percent or less if barriers are eliminated. At the same time, this approach also carries several risks, and rather than reining in inefficiency might introduce new distortions making the economy more inefficient. Perhaps a more prudent growth strategy will be to concentrate on boosting productivity, which, although challenging, has a direct effect on output.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at rishabhsinha@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
prices in response to the increase in labor costs (Lemos, 2004b). It was easier for them to raise prices during periods of inflation like the years used for this study (1982‐2000).

Although in developing countries like Brazil the minimum wage policy applies to both the public and the private sector, there is evidence that the government does not respond to minimum wage increases to the same extent as the private sector. A study by Lemos (2007) based on monthly data from household surveys covering the period 1982‐2000 shows that in the short term the minimum wage in Brazil did not have an adverse effect on employment overall or on the public or the private sector considered separately. However, jobs were lost in the private sector and gained in the public sector over the long term, suggesting an inelastic demand for labor. The increase in the public sector would largely offset the contraction in the private sector, with a net result that the minimum wage had very little effect on the overall economy.

The empirical literature on the developing countries has also looked at the adjustments that businesses have made depending on their size and cost structure. For example, Gindling and Terrell (2009) examined the effect of changes in the minimum wage on employment in Honduras based on panel data for the years 1990‐2004. These authors estimated −0.46 percent elasticity for large companies, while with small companies the minimum wage had a positive impact on employment, clearly reflecting the movement of workers who lost their jobs in the large companies to the smaller ones. As in Costa Rica, variations in the minimum wage had a greater effect on employees with less education who were working in smaller companies. An earlier study by El‐Hamidi and Terrell (2001), based on employment information in Costa Rica for 1980‐ 1992, showed that the minimum wage policy actually helped to create jobs in the country, mainly because of an increase in qualified employment. According to these authors, the industries that hired a highly qualified
</sup>|−0.007|
||(0.305)|(0.321)|(0.409)|(0.305)|(0.354)|
|Fixed-Efects|i and j|ig and j|i and j|i and j|i and j|
|N|1,628|1,628|213|1,512|2,362|
|L|17|17|16|17|17|
|B|175|175|45|154|181|
|Estimator|OLS|OLS|OLS|OLS|OLS|

Standard errors are clustered at the borrowing country level. N , L and B denote number of observations, number of lending countries and number of borrowing countries. All columns, except for column (2), include country fixed effects for countries i and j. Column (2) includes fixed effects for lending country j and for ig, where ig implies that country i is the borrowing country and the lending country belongs to group g. We distinguish three groups of lending countries indexed by g: Anglo-Saxon countries, continental Western European countries and other. Data on remittances is taken from Docquier, Rapoport and Salomone (2012). Polity is the Polity 2 indicator of the Polity IV dataset.

5
# **6. Did remittances cause the increase in real unit labor costs?**

Can the rise in domestic currency real unit labor costs that characterized many of the high remittance recipient economies since the early 2000s be attributed to the remittances themselves? Although there are theoretical reasons for suspecting that high remittance receipts were a causal factor, as discussed in section 2, there are also other possible causes; for example, a fall in the capital intensity of production. To evaluate the influence of remittance receipts on the evolution of domestic currency real unit labor costs, we estimate panel data regressions using data from the countries in table 2. The regression equation that we estimate is as follows. log (RULC(LC))i = β1RMTi + β2KYi + β3ODAi i (6) The dependent variable is the log of local currency real unit labor costs for each country in each + εε year while the regressors are remittances as a share of GDP, the capital output ratio and net receipts of official development assistance (ODA) as a share of GDP. The subscripts i and t denote the country and year, respectively. The expected sign of the coefficient on remittances is positive and that on the capital output ratio is negative, for the reasons discussed in sections 2 and 3. The theoretical impact of aid is most likely to be positive. Aid inflows could have positive effects on aggregate demand and therefore potentially also on labor demand, although that depends upon what sectors benefit the most from the increased aggregate demand. It is difficult to envisage other variables which would have a systematic effect on RULC, as this would require that the variable has a systematic effect on real wages which is different to that of its effect on labor productivity. There are many variables which would affect labor productivity, such as improvements in human capital, but these variables would also tend to have similar effects on real wages leaving RULC unchanged.

The data are annual observations. For most of the 28 countries in the sample, the data cover the
, Atlas method<br>(current US$)|140|140|1985|172|188|2010|
|Agriculture, value added (% of<br>GDP)|5|113|1985|10|176|2010|
|Life expectancy at birth, total<br>(years)|165|193|1985|135|200|2010|
|Mortality rate, infant (per 1,000<br>live births)|17|186|1985|63|192|2010|
|Improved sanitation facilities (% of<br>populationwithaccess)|159|162|1990|157|194|2010|
|Primary completion rate, both<br>sexes (%)|63|84|1988|24|124|2012|
|Youth Literacy (15- 24 Years Old)|18|18|1981|44|51|2011|
|AdultLiteracy|18|18|1981|46|50|2011|

Source: World Development Indicators (World Bank 2016b) Contrary to the prediction, Nepal made significant progress in reducing poverty in a span of a decade and a half. While the poverty headcount rate had remained virtually unchanged at approximately 40 percent until 1995 (World Bank 1999), it declined to 31 percent by 2003 (World

> 4 According to the 1984/85 Multi-Purpose Household Budget Survey, only 1 percent and 6 percent of the poor had access to a latrine in the rural terai and hills, respectively. In urban hills and terai, approximately 50 percent and 90 percent of the poor had no access to a latrine (World Bank 1991). The earliest available national estimate of the access to improved sanitation facilities was 4 percent as of 1990 (World Bank 2016b).

> 5 The four countries with a higher share of agriculture in GDP were Somalia, Burundi, Ethiopia and Uganda.

3
effects of land titling concentrate on either rural or urban households, this study builds on data collected in a peri-urban setting. That is, in the study location there is considerable competition among alternative land uses: agricultural, commercial and residential. The increasing rate of urbanization of our study location over the duration of the evaluation period – the study site is located on the outskirts of Accra, Ghana’s rapidly growing capital and commercial center – presents its inhabitants with changes to their local economy that challenge existing structures and institutions but also provides new opportunities in terms of income generation. Finally, the data set on which this study builds has genderdisaggregated information on plot ownership and thus identifies which plots are controlled by men or women in the study households. This allows us to examine the gender-disaggregated impacts of land titling on investment and credit.

The remainder of this paper is organized into five sections. Section 2 presents background information on land tenure in the Ghanaian context and on the implementation arrangements of the MiDA Land Titling Project. In Section 3, we present the theoretical framework that guides our empirical analysis. The program, our data and the regression model we use are explained in more detail in Section 4. Section 5 discusses our estimates of the impact of the land titling project on tenure security, 4
Table 4.2 reports results of this regression. Irrigation is not included because the variable for percent of total land under irrigation was never statistically significant. The coefficient for percent of maize area planted to hybrids is positive and significant in all specifications. The trend variable is positive and significant and lowers the coefficient for hybrids. Fertilizer is not significant in the regressions except when the trend variable is not included; over the period, national average fertilizer use and hence calculated use on maize grew slowly.

Figure 4.6 shows actual yield of maize and projected yield using estimated coefficients from specification two and actual values for fertilizer use, rainfall, and trend, but with zeros for postreform hybrids. The gap between actual and projected yields was greatest in 1990 at almost 2 t/ha. Impact of post-reform private hybrids on maize yields in Turkey parallels a similar impact of imported hybrids on maize yields in Greece during the 1970s (see Figure 4.7). Virtually all maize in Greece is irrigated, which largely explains why national average yields for Greece exceed those for Turkey. Figure 4.7 also shows lower maize yields in Syria, which limits variety introduction, and higher yields in EU countries, which allow free intra-EU movement of varieties.

During 1990-92, farm-level annual gross financial value of additional production due to postreform hybrids was about $ 130 million ($ 255/ha over 515,000 ha; see Table 4.3). CIMMYT reports 1992 farm-level maize and nitrogen prices of $ 178, which we use to calculate financial benefits. We did not find cost of production surveys showing cost differences between local and hybrid maize; we estimate higher costs for hybrid seed and for harvesting and drying a larger crop (see Table 4.3); we assume similar fertilizer use on local and hybrid maize. With these assumptions about costs, we estimate annual net financial benefits in 1990-92 of $ 97 million (see Table 4.3). This is the sum of net increases in farm incomes realized by all
effect that Rodrik (2016b) defines as “premature deindustrialization.” In traditional structural transformation, the growth of industry, especially manufacturing, played a big role. Several developing countries did not follow the development pattern of already industrialized countries and basically skipped the transition from agriculture to manufacturing, going straight to service sector development. The modern service sector in the developing world, however, is highly polarized, with few very well-paid jobs and a vast majority of low-paid jobs in retail and construction (Molini and Paci 2015; World bank 2019).

# 4 Our Proposal to Reduce the Inequality Underestimation

## 4.1 Introduction

The previously provided empirical estimations clearly demonstrate that the choice of the consumption or income variable is far from neutral. We propose a solution to overcoming the emerging potential mismesaurement. In the past decades several methods have been suggested to include the welfare of hard-to-survey populations, in particular, the extremely rich (e.g. Hlasny and Verme 2021; and references therein). The most famous method is to compare top incomes in household surveys with tax records (see, for example, Atkinson et al. 2011). In developing countries, where generally it is difficult to obtain this type of information from tax authorities, analysis on top incomes started later than in developed countries but has recently gained momentum (see, for example, Leigh and Van der Eng 2009; Alvaredo 2010; and Sanhueza and Mayer 2011).

More recently, Clementi et al. (2020) depart from the top-income literature and present recalibration exercises in order to involve a much bigger portion of the distribution. The principal novelty of this approach consists in the fact that, different than in other works, the reestimation of the top incomes tail is based on parameters coming from the same sample. In what follows, we first outline the methodology proposed by Clementi et al. (2020) for the purposes of analyzing inequality in SSA and then present the results of its applications to the sample of African countries considered in this study.
more banks are slated for privatization, but with no definite date set. The privatization and liquidation of state banks under PROES has contributed significantly to the decrease in government ownership in the Brazilian banking system. By end-1999 it has fallen to 43% of total assets. This decline has been accompanied by a significant increase in foreign participation in the Brazilian banking system (Table 1).

# **3. PROES and the determinants of the different options**

While there is a large literature analyzing the politics of privatization of non-financial companies, there are few papers discussing the political decision process to privatize government-owned banks.<sup>24</sup> We therefore build on the economics of state ownership of banks for our hypothesis testing on states’ different choices under PROES.<sup>25</sup> To explore the factors behind the different methods of transformation of state banks, we construct a variable capturing the different options. _Transformation_ equals one if the state bank was liquidated, two if the federal government took it over, three if the state government privatized it, and four if the bank was restructured and stayed in government ownership. This variable is structured in a way that higher values signify more decision power retained by the state government.<sup>26</sup> However, the decision power of state government varied, since five state banks were already taken over by the Central Bank under the RAET before the PROES was

> 24 On the politics of privatization in general, see Perotti (1995) and Biais and Perotti (2002). Boehmer et al. (2005) use a panel of 101 countries over the period 1982-2000 to analyze the determinants of bank privatization decisions, while Clarke and Cull (2002) assess the factors behind the timing of provincial bank privatization in Argentina.

> 25 Compare La Porta et al. (2002).

> 26 We also tried a simple dummy variable that takes value one if a bank is restructured and zero otherwise.  The use of dummy variables as
# Toilet Construction and Use

ANCOVA estimates for outcomes related to the construction and use of toilets were generally smaller in magnitude than the DID estimates. For the full sample, unlike DID estimates, no significant impact was observed on the coverage of basic facilities across treatment arms or access to toilets in arm 1. In arms 2 and 3, access improved significantly relative to the control group, by around 2 percentage points, compared with 4 percentage points estimated under DID. Estimated effects on the coverage of safely managed facilities, toilets under construction, and open defecation also had significance levels similar to DID estimates but smaller in magnitude. As per ANCOVA estimates, open defecation declined in the treatment arms relative to the control group by around 7 to 8 percentage points, and households without initial access but reporting construction of toilets increased by 5.9 percentage points in arm 1, 7 percentage points in arm 2, and 12 percentage points in arm 3.

For the subsamples of households without access to toilets at baseline, we observed a significant impact of the project on the coverage of basic and safely managed facilities. The estimated coefficients were very similar to those of DID estimates. Impacts on open defecation across treatment arms were also similar. Open defecation declined by 1.2 percentage points in arm 1, 3.8 percentage points in arm 2, and 6.5 percentage points in arm 3. However, unlike DID estimates, in which all the coefficients were non-significant, the impact on arm 3 was found to be significant at the 10 percent level. For the subsample of households with access to toilets at the baseline, we found no significant impact on basic coverage. The estimated impacts on safely managed facilities were also similar to DID, except that the impact on arm 1 was significant at the 10 percent level. We also found large reductions in open defecation across treatment arms for this subgroup, which is in line with the findings from the DID analysis.

Hygiene Awareness and Hygienic Behaviors

# Adults

Based on the ANCOVA estimates,
||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Child health**(children < 5years old)|||||
|Diarrhea prevalence 48 hours|0.04|0.014|0.013|0.01|
|||(0.014)|(0.013)|(0.013)|
|Diarrhea prevalence 7 days|0.06|0.019|0.017|0.011|
|||(0.019)|(0.018)|(0.018)|
|ALRI prevalence 48 hours|0.041|-0.035***<br>(0.013)|-0.035**<br>(0.014)|-0.039***<br>(0.014)|
|||[-96%]|[-96%]|[-96.5%]|
|ALRI prevalence 7 days|0.051|-0.042**<br>(0.017)|-0.041**<br>(0.016)|-0.047**<br>(0.018)|
|||[-82.3%]|[-80.3%]|[-89%]|
|Weight-for-age z-score|-0.69|0.0616|0.037|0.106|
|||(0.098)|(0.092)|(0.094)|
|Length/Height-for-age z-score|-1.453|-0.0328|-0.034|0.0371|
|||(0.106)|(0.091)|
identity) differences in wages and promotions.

To confirm that our treatment effects result from the implied gender of agents’ assigned names, we investigate whether customers are aware of the agents’ names. In 11% of chats, customers mention the agent’s assigned name—indicating names are a salient feature of the interaction.<sup>3</sup> Helpfully, we can also rule out a potential confound—customers responding to a “mismatch” between the gender implied by agents’ assigned name and the agents’ actual gender. It is unlikely that this confound explains our results because every agent in our experimental sample is female, and any mismatch comes from being assigned a male-sounding name.<sup>4</sup> Consequently, this “mismatch effect” would likely attenuate our estimated effect of discrimination against female agents. In sum, receiving a female name reduces productivity via consumers’ gender preferences.

These reductions in worker productivity may be explained by several mechanisms: general customer disinterest in working with female agents, differential bargaining, or overtly negative interactions.<sup>5</sup> Data from agent-customer chat interactions suggest that customer disinterest is the most likely channel. We find that consumers respond more slowly to female agents, only responding after receiving additional messages from the agent.<sup>6</sup> This result suggests some consumers are hesitant to engage with female agents unless the agents persist through additional messaging. We also find that consumers are less likely to express any non-neutral conversational tone, which we interpret as another measure of engagement with the agent.

The data do not support other possible mechanisms. We find no evidence that consumers differentially bargain when agents receive female-sounding names. This result is interesting as bargaining (e.g., asking for discounts) is common in these interactions (occurring in 15% of conversations), and differences in bargaining by gender feature prominently in studies of wage gaps and job-application behavior (e.g., Card et al. (
is the most important among the poor. The claim that consumers’ subsidies are a form of social protection schemes does not really hold if we observe data for LPG across countries.

Prices for electricity appear less diverse, but that can be explained by the way the prices are listed—in kilowatt hours (average across tariffs blocks). As a percentage of the free market price, electricity subsidies are the highest in Tunisia. The lowest subsidies are for Libya (30.6 percent) and Morocco (42.3 percent) but still high. To reach the market price, Libya would have to increase prices by 44 percent, an increase that would not go unnoticed by the population, and Tunisia would have to increase prices by 583 percent, a staggering figure.

Prices for gasoline and diesel are closer to the free market price for most countries except the Islamic Republic of Iran and Libya. The Islamic Republic of Iran and Libya in particular would have to raise prices of gasoline fivefold and more than sevenfold, respectively, to reach the free market price. For the Islamic Republic of Iran in 2014 this finding is remarkable given that this country went through a comprehensive reform of the subsidies system in 2010 that supposedly eliminated most subsidies and was costly in terms of cash transfers administered to the population in compensation of the removal of subsidies.

Table 2: Energy Unit Prices and Subsidies, in US$ at PPP (2014)

||Price|Subs.|Subs.<br>(%)|Increase<br>(%)|Price|Subs.|Subs.<br>(%)|Increase<br>(%)|
|---|---|---|---|---|---|---|---|---|
|||_LPG (1_|_3 kg) _|||_Electricity (_|_kWh, av.)_||
|Djibouti|28