rafmacalaba/datause-encoder-data huggingface dataset:
- 31 -

||(0.0015)|(0.0016)|(0.0013)|(0.0016)|
|---|---|---|---|---|
|ln_Water Boiler_|0.0001|0.0001|0.0020 **|0.0010|
||(0.0009)|(0.0009)|(0.0009)|(0.0011)|
|ln_Computer_|0.0041 ***<br>(0.0014)|0.0044 ***<br>(0.0014)|0.0042 ***<br>(0.0012)|0.0042 ***<br>(0.0015)|
|ln_PowerOutages_|0.0007|0.0003|0.0016 *|0.0001|
||(0.0009)|(0.0009)|(0.0008)|(0.0009)|
|D(_Urban_)|0.141 ***<br>(0.015)|0.128 ***<br>(0.015)|0.167 ***<br>(0.015)|0.167 ***<br>(0.022)|
|Constant|4.223 ***<br>(0.251)|3.414 ***<br>(0.229)|6.990 ***<br>(0.487)|6.767 ***<br>(0.365)|
|Obs.|4762|4762|4494|4678|
|Adj R-squared|0.552|0.549||0.495|
|_F_-statistics|96.78|96.33||62.31|
|Wald chi2|||1780.39||
|λ|||0.180 ***<br>(0.053)||
|Number of district dummies|35|35|34|35|

1/ In the Hewlett estimation model, ln _MP_ and ln _D_ are replaced with the predicted values in the equation that expands the Nordin's difference variable.

2/ One of the district fixed-effects is omitted because it predicts success perfectly. Note: The dependent variable is the log of observed electricity consumption (kWh). The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

The Nelson-Olsen estimation shows that the metering effect is significant; the coefficient is estimated at –1.477. Since the metering variable is not in logarithm, the elasticity of electricity consumption with respect to metering can be calculated as about –0.26. This confirms that the normative consumption amounts are systematically downward biased. Unmetered customers would be likely overconsuming electricity. It is consistent with theory.

The price elasticity is now also considered unbiased and found negative, though statistically insignificant.<sup>19</sup> Note that in this specification, the marginal prices and Nordin’s difference variable are replaced with their predicted values over the instrumental variables and other exogenous covariates. This aims to focus on the issues of partial unobservability and selfselection of metering and avoid further complications in the estimation. Not surprisingly,

> 19 An interactive term between the meter installation and the marginal price can be introduced to examine the price effect for metered customers. This is because the price effect may be limited when customers do not have meters. However, the coefficient of the interactive term has been found insignificant and positive. And the coefficient of ln _MP_ remained negative and insignificant.
Our first aim was extending the analysis to as many countries as possible, subject to the feasibility of imputation. Hence, we ended up analyzing 15 countries of the region. In cases where imputation was not feasible for certain periods, we used data from at least two survey rounds in those countries. Table 1 presents the corresponding countries, the rounds used in each country, and levels of aggregate data imputation.

**Table 1.** National household surveys used and level of imputation.

|**Country**|**Survey**|**Years (rounds)**|**Community-level**<br>**Income Equation**<br>**(Z, C)**|**Community-**<br>**level Risk**<br>**Equation (N)**|
|---|---|---|---|---|
|Argentina|EPHC-S2|2003, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Bolivia (Plurinational<br>State of)|EH|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Brazil|PNAD, PNADC-E5|2008, 2011, 2017,<br>2021|ADM 1|ADM 1|
|Brazil (cities)|PNAD, PNADC-E5|2017, 2021|ADM 1|ADM 2|
|Chile|CASEN|2009, 2011, 2017,<br>2022|ADM 1|ADM 2|
|Colombia|GEIH|2011, 2021|ADM 1|ADM 1|
|Dominican Republic|ENFT, ECNFT-Q03|2002, 2008, 2011,<br>2017, 2021|ADM 1|ADM 2|
|Ecuador|ENEMDU|2008, 2011, 2017,<br>2022|ADM 1|ADM 2|
|El Salvador|EHPM|2002, 2008, 2011,<br>2017,2022|ADM 1|ADM 2|
|Guatemala|ENCOVI|2011, 2014|ADM 1|ADM 1|
|Honduras|EPHPM|2008, 2011, 2017,<br>2019|ADM 1|ADM 2|
|Mexico|ENIGH, ENIGHNS|2002, 2008, 2010,<br>2016, 2022|ADM 1|ADM 2|
|Panama|EH|2002, 2008, 2011,<br>2017, 2021|ADM 1|ADM 1|
|Paraguay|EPH|2002, 2008, 2011,<br>2017, 2022|ADM 1|ADM 1|
|Peru|ENAHO|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 3|
|Uruguay|ECH, ECH-S2|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|

Note: Province or state level imputation is indicated with ADM 1, district imputation is indicated with ADM 2, and lower-unit levels are indicated with ADM 3.

To construct the aggregate household characteristics, we use the SEDLAC household survey data at province/state level. We generate the following household aggregate characteristics: the proportion (ZZ), of households engaged in agriculture in the state, the state's employment rate, the state's unemployment rate, the share of self-employed and entrepreneurs in the state, the share of the 11
population that is categorized as moderately or severely food insecure using the FIES depending on the dependent variable; and the estimate of _β_ 1 represents how much higher the share is for the poor population. Based on the simple model above, if there was a sufficiently large penalty to undernourishment, we would expect both the prevalence and the severity of undernourishment and undernutrition to be worse for poor Nigerians than for non-poor, which would correspond to _β_ 1 _>_ 0.

Given that the severity of undernourishment and undernutrition are exacerbated along the welfare distribution and not just discontinuously at the poverty line, and given that the income at which households begin to tackle macro- and micro-nutrient deficiencies is an empirical issue, we also estimate how the FIES varies across all expenditure deciles. Specifically, we estimate the following specification: where _Expend Decile_<sup>_j_</sup> _i_<sup>denotes the expenditure decile of household</sup><sup>_i_based on the expen-</sup> diture used to identify the household’s poverty status, with higher deciles corresponding to higher levels of expenditure; and all other variables are the same as above. The es— timate of _β_ 0 represents the share of the population in the first expenditure decile — the poorest that is categorized as either at least moderately food insecure or severely food insecure according to the FIES depending on the dependent variable; and each _βj_ represents how much larger the share is in decile _j_ than in the poorest decile.

Based on the simple model above, if there was a sufficiently large penalty to undernourishment, we would expect both the prevalence and the severity of undernourishment and undernutrition to be worst for the Nigerians in the poorest expenditure decile and for food access to improve for higher deciles, which corresponds to _βj <_ 0 for all _j_ and for _βj_ to decrease in _j_ . Furthermore, the model also predicts this improvement in certain

> poor food access and monetary poverty. Rather, we simply want to characterize how households divide consumption among food goods and between food and non-food goods given a fixed amount of income, regardless of educational attainment, household size, etc. Adding control variables would obscure this relationship.

17
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aggregate mortality data and find evidence of excess mortality among adult women in Sub-Saharan Africa. In contrast, this paper uses individual-level data and analyzes how son preference affects women’s fertility preferences and choices in a context where sex ratios at birth are in the normal range and where little is known about parental gender preferences. In addition, it explores how parental gender preferences can affect the development of traditional social institutions common in the West African context, namely child fostering and polygyny, as well as other marital outcomes and arrangements, including divorce and female headship. These different sets of outcomes are analyzed separately and, taken together, can shed some light on the complex mechanisms through which the desire for own biological sons can be satisfied in Nigeria.

More specifically, the paper puts forward two channels through which adult women might be missing in Sub-Saharan Africa and in other contexts where fertility choices and family structure are found to be strongly affected by son preference. The first is son-preferring fertility behavior (or son-preferring stopping rules) in which women with girls among earlier-born children are more likely to continue having children and to have shorter birth intervals. This behavior is medically known to adversely affect women’s health (as well as children’s health) and increase the risk of maternal mortality and morbidity due to maternal depletion. Maternal mortality is in fact among the highest in the world in Nigeria (second only to India in absolute number of deaths), which alone accounts for 14 percent of all maternal deaths worldwide (WHO, 2010). Women of lower socioeconomic status are more likely to be affected due to their poorer access to maternal care and more vulnerable health. The second channel refers to the association between parental gender preferences and specific marital outcomes and living arrangements in which women are often found to be particularly vulnerable and disadvantaged (i.e., polygyny and female headship).

I use the 2008 Nigeria Demographic and Health Survey (NDHS) and find evidence of son preference in several dimensions. A comparison of women’s characteristics by the sex of the firstborn reveals that while younger women (younger than 30) are very similar in terms of observables, significant differences appear in the subsample of older women, which is suggestive of selective mortality.<sup>4</sup> I check for the exogeneity of the sex of the first-born child and discuss other potential sources of bias in the data. The empirical strategy relies on the identifying assumption that the sex of the first-born is uncorrelated with the error term after conditioning for a set of observable characteristics. Results are as follows. First, compared to women who had a first-born boy, women with a first-born girl exhibit a 2 percent increase in the number of children ever born. While the

> 4Selective mortality is also supported by nonparametric evidence of a lower fraction of women with a first-born girl among women in the older age group.

3
Table 14. Program impact on infrastructure at the health facility.

|Dependent variable|New units|Toilets|Water<br>source|Electricity|
|---|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|(4)|
|Program impact|-0.09|0.09|0.05|0.05|
||(0.12)|(0.11)|(0.10)|(0.10)|
|Controls|Yes|Yes|Yes|Yes|
|District ...xed e¤ects|Yes|Yes|Yes|Yes|
|Observations<br>|50|50|50|50|
|R<sup>2</sup>|0.50|0.34|0.29|0.42|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level.

b. Dependent variable is a dummy variable indicating whether any constructions or renovations of infrastructure have been done at the health facility during the last year.

c. Robust standard errors in parenthesis.

d. Control variables: see note (c) in Table 6.

59
Although reduced form econometrics provides useful insights on the distributional effects of trade shocks, structural estimation is the only known method to address certain important issues, such as adjustment dynamics, general equilibrium effects, and counterfactual policy simulations, despite the challenges we mentioned earlier.

In this paper, we study the impact of trade liberalization along the life cycle of workers from different skill and experience groups without imposing any strong restrictions on workers’ expectations in the estimation stage. Therefore our estimation strategy, which is described fully in Artuc (2012), is applicable to environments that are subject to aggregate uncertainties. The econometrician does not need to know the distribution of aggregate shocks due to changes in trade policies, labor market policies, financial crises, technological progress, etc. We investigate how age interacted with education and experience affects the mobility of workers, and report the increase in mobility costs as workers get older. Then, we show that workers’ mobility determines loss and gain from trade shocks, and provide a general picture of welfare changes across different worker subgroups.

To illustrate the connection between mobility and diffusion of gains from trade, imagine that all workers were perfectly mobile across sectors. Then, all workers would be unanimously better off or worse off after a policy shock thanks to factor price equalization. If workers were immobile and attached to their original sectors, then there would be distinct winners and losers from free trade. In that case, workers’ sectors would determine their gain and loss. In reality, mobility costs probably lie between these two extremes and vary across groups. A major source of variation in mobility has to do with the age of affected workers, causing differences in their position towards free trade. For example, the Pew Global Attitudes survey, conducted in 2002, shows that young people are more enthusiastic about free trade compared to older people.

After the empirical exercise we conduct using the Current Population Survey and the 1979 cohort of the National Longitudinal Survey of Youth (henceforth CPS and NLSY respectively), we calibrate production, input demand and consumption demand functions to 4
The rest of the paper is organized as follows. Section 2 describes the data used in the analysis. Section 3 discusses the empirical strategy we follow. The results are summarized in section 4. Finally, section 5 concludes.

# **2. Data**

We use data from three main sources, covering January 2013 to June 2016. The first data set, which we refer to as the loan-level data, consists of the universe of commercial loans in Mexico, which we obtained from regulatory reports sent monthly by every commercial bank to the bank regulator. The reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing commercial loans. All loans, regardless of their size, are reported. Each loan has an identifier of the issuing bank, as well as the borrower’s identifier, location, sector, and number of employees. The data set includes information on the interest rate, outstanding amount, type of financing (i.e., whether the loan is for working capital or investment purposes), and start and end dates (maturity) of each loan. Given that some borrowers have more than one loan issued by the same bank at a given point in time, we adopt a similar approach as La Porta et al. (2003) and aggregate individual loans at the _firm-bank-month_ level. We then report loan characteristics, such as the interest rate, fraction of the loan covered by collateral, and maturity at origination, using a weighted average by loan value. This approach puts greater weight on larger loans, ensuring that our results are economically meaningful.

Our second data source is Orbis, a _firm-year_ -level data set compiled by Bureau van Dijk, which contains information on the balance sheets and income statements of a large set of Mexican firms. The data set reports information on assets and revenues of firms as well as their total and bank-specific liabilities by type of financing. As shown by Morais et al. (2019), this sample of firms is representative of the universe of sectors and locations in Mexico, albeit somewhat skewed toward larger firms. We complement this data set with a measure of GDP for Mexico’s 32 states, normalized to 2004, which was obtained from the National Statistics Institute. In addition to the total GDP, we also use information on the GDP contributed by the primary sector of each state, consisting of mining and agriculture; the GDP contributed by the secondary sector, covering manufacturing and construction; and the GDP contributed by the tertiary sector, defined as services.

7
<!-- Start of picture text --> Policy Research<br><!-- End of picture text --> WPS 921

- This paper a joint product of the Infrastructure Operations Division, Western Africa Department, and - the Transport Division, Infrastructure and Urban Developmcnt Department is part of a larger effort to define the macroeconomic linkages and impact ofinfrastructure. Copies of the paperare available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Marie Laygo, room H3-151, extension 31261 (June 1992, 35 pages).

Queiroz and Gautam investigate the association between per capita income and the magnitude and quality of road infrastructure. They adopt an empirical approach, directly comparing or correlating a country's income with selected variables associated with existing road networks.

density of paved roads (km/million inhabitants) varies from 170 in low-income economies to 1,660 in middle-income and 10,110 in highincome economies. That is, the average density of paved roads in high-income economies is 59 times that in the low-income group.

Cross-section analysis of data fromi 98 countries, and time-series analysis of U.S. data since 1950, show consistent and significant associations between economic development (per capita GNP) and road infrastructure (per capita length of paved road network).

Road conditions also seem to be associated with economic development: The average density of paved roads in good condition varies from 40 km/million inhabitants in low-income economies to 470 in middle-income and 8,550 in high-income economies.

The data show that the per capita stock of road infrastructure in high-income economies is dramatically greater than in middle- and lowincome economies. For instance, the average The empirical information presented can be used as indicators of areas of weakness or strength in a country's stock of road infrastructure.

The Policy ResearchWorking Paper Series disseminates the findings of work under way in theBank. Anobjectiveof the series **_;s_** to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
**Figure 5. Performance and Poverty and Inequality Impacts of the MSA Scheme under Baseline Scenario (prereform)**

<!-- Start of picture text --> a. Coverage (% of households in each decile) b. Benefit incidence (% benefits allocated to each decile)<br><!-- End of picture text --> <!-- Start of picture text --> 11.5 1<br>2<br>3<br>4<br>5<br> 1 2 3 4 5 6 7 8 9 10 100.0<br>c. Generosity (benefits as % of MYPP) d. Inequality, poverty, and poverty gap impact<br>4.4<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.11<br>Marginal Contributions to<br>0.01<br>Poverty<br>0.00 0.05 0.10 0.15<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**Conversely, although effective for a limited period each year (five months), the heating allowance exhibits more extensive coverage than the Monthly Social Allowance (MSA).** The coverage of the heating allowance extends to households in the fifth decile, with more than half of the benefits going to the first decile and less than one-third being directed to those in the second decile (as indicated in Figure 6, Panels A and B). Notably, this assistance covers approximately one-third of the poor population, surpassing the coverage achieved by MSA, although it remains relatively modest. Despite its broader reach, the heating allowance's impact on reducing income inequality is barely perceptible. Additionally, its marginal effect on poverty reduction is minimal, with its primary influence observed in a relatively small reduction in the poverty gap (Figure 6, Panel D). While the absolute value of reducing the poverty gap is notable, it underscores the complexity of addressing poverty and inequality and the importance of considering various factors, including the duration and timing of assistance, when designing effective policies to improve the economic well-being of vulnerable populations.

20
operations financed by the World Bank focused on reducing average tariffs. For instance, the Industry and Trade Policy SAL for Madagascar in 1987 (P001511, p. 37f) had second tranche release conditions eliminating all import prohibitions, simplifying the system of import tariffs, and reducing their maximum to 80 percent. Release of the third tranche was conditional upon adoption of a four‐year tariff reduction program aimed at reducing average protection from its current level of 46 percent to 35 percent.

Similarly, in Kenya in 1990, by operation P001653 (paragraph 2.09), the average tariff was to fall from its current level of 20 percent by five percentage points. In Sierra Leone in 2005, the average tariff of 17.5 percent was to come down to 13.3 percent, supported by the Fourth Economic Rehabilitation and Recovery Grant in 2005 (P083477, paragraph 68).

Scope for further reductions in tariff levels soon fell, and with it the level of ambition of programs supported by policy‐based reform. In Mauritius, the weighted mean of tariffs on manufactured products was 27% in 1998; this had fallen to 14% by 2004<sup>31</sup> and to 6.5 percent by 2006.<sup>32</sup> The Bank supported Mauritius with the First Trade and Competitiveness DPL in 2006, in virtue of which the average tariff rate was to fall by two percentage points (P101570, paragraph 85).

> Rwanda 26% in 1993, Seychelles 25% in 2000, Tanzania 19% in 1988, Zimbabwe 39% in 1996.  See <u>https://data.worldbank.org/indicator/TM.TAX.MANF.WM.AR.ZS?contextual=region&end=2016&locatio ns=ZG‐ZA‐MW‐MG‐MU‐ZW‐TZ‐SZ&name_desc=true&start=1988&view=chart.</u>

> 31 Indicator TM.TAX.MANF.WM.AR.ZS in the World Bank’s open data source; derived from the World Integrated Trade Solution system, in turn based on data from United Nations Conference on Trade and Developments Trade Analysis and Information System database, and the World Trade Organization’s Integrated Data Base and Consolidated Tariff Schedules database.

> 32 The 2006 number is from the program document of P101570, paragraph 17.

39