rafmacalaba/datause-encoder-data huggingface dataset:
Table A24: Treatment and conformism, cross section

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Dependent Variable:_|_HIV knowl-_|_HIV_<br>_atti-_|_HIV testing_|_Attitudes_<br>_to-_|_Risky sexual be-_|
||_edge_|_tudes_||_wards_<br>_risky_<br>_sexual behavior_|_havior (for sex-_<br>_ually active)_|
|**Panel A: Conformity**||||||
|Treated|0.890***|0.346**|0.355**|0.147|0.126|
||(0.244)|(0.138)|(0.148)|(0.090)|(0.131)|
||[0.001]|[0.025]|[0.025]|[0.208]|[0.341]|
|Treated*Conformity|0.186|-0.147|-0.017|-0.057|-0.069|
||(0.149)|(0.110)|(0.099)|(0.081)|(0.087)|
||[0.216]|[0.463]|[0.876]|[0.669]|[0.669]|
|Conformity|-0.001|0.188**|-0.045|0.177***|0.121*|
||(0.128)|(0.089)|(0.070)|(0.061)|(0.071)|
|R-squared|0.086|0.052|0.092|0.024|0.091|
|P-Value of joint signif.|0.000|0.014|0.018|0.113|0.348|
|**Panel B: Tradition**||||||
|Treated|0.900***|0.344**|0.354**|0.144|0.118|
||(0.244)|(0.136)|(0.147)|(0.090)|(0.131)|
||[0.000]|[0.024]|[0.024]|[0.216]|[0.370]|
|Treated*Tradition|-0.031|-0.127*|0.017|0.007|-0.139|
||(0.151)|(0.068)|(0.088)|(0.090)|(0.106)|
||[0.975]|[0.174]|[0.982]|[0.926]|[0.321]|
|Tradition Index|-0.012|0.145***|-0.055|0.137*|0.215**|
||(0.114)|(0.054)|(0.060)|(0.078)|(0.094)|
|R-squared|0.085|0.051|0.092|0.024|0.092|
|P-Value of joint signif.|0.000|0.014|0.018|0.114|0.386|
|**Panel C: Self Direction**||||||
|Treated|0.865***|0.334**|0.349**|0.144|0.125|
||(0.239)|(0.136)|(0.149)|(0.093)|(0.127)|
||[0.000]|[0.028]|[0.028]|[0.225]|[0.326]|
|Treated*Self-direction|0.134|0.005|0.125*|0.038|0.137*|
||(0.121)|(0.071)|(0.068)|(0.081)|(0.078)|
||[0.288]|[0.937]|[0.206]|[0.640]|[0.164]|
|Self-direction|-0.538***|-0.149***|-0.093**|-0.077|-0.154**|
||(0.100)|(0.052)|(0.046)|(0.068)|(0.059)|
|R-squared|0.095|0.054|0.092|0.021|0.091|
|P-Value of joint signif.|0.000|0.016|0.021|0.123|0.324|
|Controls <sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|
|Observations|4,986|4,986|4,986|4,986|3,618|
|Mean Dep. Var. (Control)|0.04|0.044|-0.139|0.00186|-0.063|
|Std Dev Dep. Var. (Control)|0.000|0.0133|0.0186|0.113|0.383|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. FWER adjusted p-values in square brackets, based on 10,000 replications. (a) Controls in each regression include: female, age, currently attending school, years of education, english spoken at home, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

xxxi
<!-- Start of picture text --> Fines & non‐monetary sanctions for Environmental impacts of<br>non‐compliance with environ. laws & transporting products & members of<br>regulations (G4‐EN29) workforce (G4‐EN30)<br>SME 39.3% SME 29.7%<br>Large 56.8% Large 35.7%<br>MNE 62.1% MNE 38.3%<br>All 56.5% All 35.8%<br>Total environmental protection % of new suppliers that were<br>expenditures & investments (G4‐ screened using environmental criteria<br>EN31) (G4‐EN32)<br>SME 26.5% SME 25.1%<br>Large 38.6% Large 39.3%<br>MNE 41.1% MNE 46.8%<br>All 38.0% All 40.0%<br>Significant actual & potential negative<br>environmental impacts in the supply<br>chain and actions taken (G4‐EN33)<br>SME 15.1%<br>Large 27.8%<br>MNE 34.4%<br>All 28.4%<br><!-- End of picture text --> By contrast, a lower percentage of firms discloses information related to biodiversity, habitats, and hazardous waste – with MNEs reporting less than large firms. MNEs’ disclosure share on the description of significant impacts of activities, products, and services on biodiversity in protected areas and areas of high biodiversity value outside protected areas (EN12) and habitats protected or restored (EN13) were only 21.3% and 19.3%, respectively. Similarly, MNEs’ disclosure coverage on the weight of transported, imported, exported, or treated waste deemed hazardous, and the percentage of transported waste shipped internationally (EN25) was only 21%. In those cases, the share of large firms disclosing information was substantially higher.

28
# **4.1 The** **_base_ scenario**

When setting up the model base, we made use of historical data on GDP growth, FDI, remittances government spending. From 2013 onwards, the growth rates of major macro variables are set to values that are close to their medium-term averages; i.e., the simulations are not geared toward capturing year-to-year volatility but medium- and long-run trends. However, in some areas, we diverge from this general rule. Regarding world market prices, we assume constant export prices from 2013 onwards given that knowledge about how these prices may evolve is extremely limited. During the whole simulation period, we keep import prices unchanged. Until 2012, changes in world market export prices are part of a set of variables used to calibrate the model to available statistics. Regarding FDI, their volatile development is caught in the statistics with its recent downs and ups until 2011, but from year 2012 and onwards, FDI growth, after recovering from the slump of earlier years, declines towards an annual rate of 5 percent at the end of the simulation period, with an average rate of 12.5 percent for the period 2012-2020. Similarly, after the rebound in 2011, annual remittance growth declines gradually to 3 percent by 2020; for remittances, the resulting average rate is 3.1 percent per year for 2012-2020. And as for factor payments from abroad, we assume that they grow at an annual rate of 5 percent 2013-2016, but then at rates that gradually decline to 3 percent by 2020 (see Figure 8). The lowering growth of these variables reflects an expected decline in the domestic population in migration-prone age.

**Figure 8. Base assumptions for selected balance-of-payments flows (mn 2008 lei)**

<!-- Start of picture text --> 25,000 3,000<br>2,500<br>20,000<br>2,000<br>15,000<br>1,500<br>10,000<br>1,000<br>Private transfers from RoW (left axis)<br>Factor income from RoW (left axis)<br>5,000<br>500<br>Official transfers from RoW (right axis)<br>FDI (right axis)<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> The government of Moldova is assumed to keep its budget (including borrowing on the receipts side) balanced through uniform scaling of its domestic tax rates (direct and indirect). Government transfers to households are set to grow by 5 percent per annum from 2013 onwards. For years 20092012, we make use of available statistics and IMF projections for transfer growth (Qehaja 2012). Tax 17
- 60 -

introducing e-procurement, which can increase the efficiency in distributing the tenderrelated information and collecting the bid-related information. As the result, procurers can extend the bid preparation period without compromising anything.

In Nepal, some of the government owned projects have extremely short bid preparation periods. While an average of 31 days are allowed for the bid preparation in World Bank projects, 21 days are granted in the government owned projects (Table 15). As an indicative target, 30 days may have to be given for bid preparation. About 70 percent of World Bank contracts satisfy this target, but the majority of the government financed projects are below the threshold. By increasing the bid preparation period by 10 days in the government projects, 1.5 bidders would additionally enter the market according to our estimation result.<sup>26</sup> This would then reduce the equilibrium bid by about 8 percent, which would materialize a cost savings of NRs14.9 million or $0.2 million in the sample government projects in total.

**<u>Table 15. Average bid preparation period (days)</u>**

||Obs|Mean|Std.|Dev.|Min|Max|
|---|---|---|---|---|---|---|
|World Bank project|82|31.1||6.6|15|52|
|Governmentproject|73|21.6||9.0|6|33|

# **_Local contractors’ capabilities need to be fostered to enhance competition_**

Our estimation results show that firm location does not matter to bid prices but influences the bidder entry strategy. If a project site is far from firms’ locations, they are less likely to apply for the public tender. Hence, the local contractors’ capacity will emerge as an important challenge for the country. Clearly, more local enterprises exist in some districts than others. Particularly, the share of local firms in the pool of potential contractors is low in the districts of Kailali, Palpa and Rautahat (Figure 39).<sup>27</sup> In addition, local firms do not appear

> 26 Ditto.

> 27 There is no significant difference even if the figure is calculated with the data of firms that actually participated in the competition, because there is no systematic difference in firms’ entry decisions between local and nonlocal firms. The probability of participation is slightly higher for local firms (0.31) than nonlocal enterprises (0.26).
Table 2. Price Elasticity of Tobacco Consumption, Medium-Bound Estimate, by Decile

|_Country_|_1_|_2_|_3_|_4_|_Dec_<br>_5_|_ile_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Bangladesh|-0.71|-0.52|-0.50|-0.42|-0.32|-0.30|-0.29|-0.22|-0.19|-0.17|
|Bosnia and Herzegovina|-1.08|-0.87|-0.69|-0.70|-0.60|-0.58|-0.53|-0.46|-0.46|-0.34|
|Chile|-0.64|-0.58|-0.52|-0.47|-0.41|-0.35|-0.29|-0.24|-0.18|-0.12|
|Indonesia|-0.64|-0.59|-0.55|-0.53|-0.52|-0.50|-0.49|-0.48|-0.47|-0.46|
|Moldova|-0.51|-0.39|-0.40|-0.34|-0.32|-0.32|-0.32|-0.25|-0.24|-0.26|
|Russian Federation|-0.68|-0.61|-0.58|-0.54|-0.52|-0.51|-0.49|-0.46|-0.45|-0.41|
|South Africa|-0.36|-0.26|-0.24|-0.31|-0.34|-0.17|-0.24|-0.21|-0.13|-0.22|
|Ukraine|-0.59|-0.51|-0.52|-0.46|-0.44|-0.43|-0.42|-0.41|-0.36|-0.33|

_Source:_ Estimates based on national socioeconomic surveys. _Note:_ In most cases, a multiple time cross-section model with time fixed effects is used. Demographic controls include the age, education, and gender of the household head, the share of individuals by age-group in each household, and urban status. Deciles have been created using per capita household expenditure.

Figure 2. Price Elasticity of Tobacco Consumption, Medium-Bound Estimate, by Decile <!-- Start of picture text --> 0.00<br>1 2 3 4 5 6 7 8 9 10<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>Chile Ukraine Moldova<br>-1.20 South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br><!-- End of picture text --> _Source:_ Estimates based on national socioeconomic surveys. _Note:_ In most cases, a multiple time cross-section model with time fixed effects is used. Deciles have been created using per capita household expenditure.

Page 11
or less ( _Primary or less_ ) and another equal to one if the respondent completed secondary education and some education beyond secondary education ( _Secondary_ ).

# **3.3. Country-level variables**

We consider the eight indicators provided by the World Bank’s WBL database that capture laws that may impact women’s economic opportunities from their entry into the labor market through to their retirement.<sup>6</sup> Thirty-five legislative issues are aggregated into the following eight indicators, composed of four or five binary questions in each: _Workplace_ explores specific barriers to women’s opportunities in the labor market, _Pay_ targets women’s pay equality, _Marriage_ assesses legal constraints related to marriage, _Parenthood_ focuses on the availability and equality of paid parental leave and the rights of pregnant women, _Entrepreneurship_ examines women-specific legal constraints to launching and running a business, _Assets_ considers gender differences in property ownership, control, and inheritance and, lastly, _Pension_ measures gender equality as it relates to retirement and pensions. Finally, we consider the aggregate _WBL index_ , which is an unweighted average of the aforementioned eight indicators. Each indicator ranges from 0 to 100, where a score of 100 implies that there are no legal inequalities between men and women in the areas covered by the index.

To provide robust estimations about the relationship between female financial inclusion and gender legal framework at the country level, we control for three additional country-level variables following Demirgüç-Kunt and Klapper’s (2013) recommendations. All these variables are provided by the World Bank’s World Development Indicators.<sup>7</sup> First, we introduce the natural logarithm of GDP per capita ( _lnGDPpercapita_ ) as economic development is strongly correlated with financial inclusion. Second, as high inflation levels may affect the willingness of the population to hold accounts in formal financial institutions, we consider the natural logarithm of the inflation rate ( _lninflation_ ). Finally, we capture the depth and breadth of the financial sector in an economy using the ratio of deposit money bank assets to GDP ( _Financialsystemdeposits_ ) which is a widely used financial development indicator (Creane et al., 2007).

> 6 The Women, Business and the Law (WBL) database is freely available at https://wbl.worldbank.org/en/wbl. 7 The World Development Indicators (WDI) data set is available at: https://databank.worldbank.org/source/worlddevelopment-indicators.

9
WPS3945 data is available for 2531 rural households in 143 villages in 16 districts across all 4 provinces. The survey contains detailed household and individual characteristics, including demographics, occupation, health, education, investments, assets, household expenditure, and the migration experience of all household members. For migrants, data were also collected on the year and duration of migration, migration destination, remittances, and social networks accessed prior to and post migration. Migrants were interviewed directly when possible. Otherwise, the individual designated as the male head of the household reported migration and other information for each migrant.

For purposes of the analysis we con fi ne attention to male migrants age 18 or older who migrated for economic reasons.<sup>7</sup> Using this de fi nition, 977 men (about 15% of all men in this age range) are classi fi ed as migrants Of these, 32% were back from a migration episode in the survey year, the rest were current migrants. Since migration is typically recurrent, a household is classi fi ed as a migrant household if it reported at least one male member with some migration experience current or past. At the household level, 699 households (26% of all households) had at least one male migrant.

The median age at fi rst migration in the sample is 22. The typical migrant is either a household head (38%) or an older son of the head (54%). One indicator of the extent to which migrants are attached to their families of origin in the villages is that over two-thirds are married and have their spouses and/or children living in the village. Almost two-thirds of migrants also reported sending some remittances to their families in the village and three-fourths of those who sent remittances did so on a regular basis.<sup>8</sup> The survey has a companion section on cash and kind transfers received and given by the household, and the identity of all who send or receive such transfers. The median reported amount remitted annually by migrant household members is about Rs. 24,000.<sup>9</sup> In contrast, transfers by non-household members are insigni fi cant.<sup>10</sup> left and had not set up a permanant home elsewhere. This enabled collection of all relevant data on current migrants.

> 7 There is virutally no migration among children under 18. The few who do not live at home move to join a family member or to attend school in a neighboring rural area. Women also typically migrate to join family members, most often a spouse.While 8% of reported migrants are women, over 82% report migrating to join a family member. Only 13 women (1% of the sample of migrants) report migrating for any economic reason.

> 8 Remittances from international migrants constitute the single largest source of foreign exchange earnings for the country. According to one estimate, US$2.4 billion (or 4% of the country’s GNP) is currently remitted annually by international migrants (see Gazdar (2003))..

> 9 About $500 annually at the prevailing exchange rate in 2001.

> 10 See Mansuri (2006) for a more extensive discussion of migration destination and remittance fl ows in

5
# **1 Introduction**

A growing literature documents that workers exposed to a better disease environment in early life earn higher wages as adults. Although this literature has focused on developed countries, economists have hypothesized that this effect of early-life disease externalities could be importantly larger in developing countries, where disease insults are worse and more varied (Currie and Vogl, 2013; Spears, 2012b). If early-life disease indeed importantly limits human capital in developing countries, wages could be a mechanism through which health has important effects on developing economies and, because of income and consumption tax revenue, on the government’s budget. It is therefore important to understand and quantify effects of the disease environment on subsequent wages in developing countries.

Our paper examines the impact of the early-life disease environment on adult wages in India. We match male workers in nationally representative survey data on wages in the 2000s to district-level census data from the 1970s and 1980s on two important measures of disease environment: infant mortality and santitation coverage. Our results suggest that being born in a district-year with widespread open defecation or a high infant mortality rate significantly reduces earnings decades later. Because infant mortality and open defecation remain significant threats to health in India and other developing countries, these estimates are of continuing economic and policy importance.

Poor sanitation, and infectious disease more generally, are well-known to have negative externalities on neighbors (Galiani, Gertler, and Schargrodsky, 2005; Geruso and Spears, 2014). We show that the disease environment also has _fiscal_ externalities: we estimate considerable effects on government tax revenues of reductions in income and consumption due to early-life disease. In particular, our baseline results indicate that reducing open defecation would increase tax revenue by enough to completely offset a cost of up to $462 per household that stops defecating in the open. We find that a fiscally neutral elimination of open defecation in India would increase the net present value of lifetime after-tax wages by more than $1800 for an average male worker born today. Therefore, the main contribution of this paper is to show that partial causal effects already understood to exist in the literature – the effect of the epidemiological environment on early-life health, of early-life health on 1
Policy ReseaRch WoRking PaPeR 4646

# **Abstract**

This paper presents new and official survey information on bank regulations in 142 countries and makes comparisons with two earlier surveys. The data do not suggest that countries have primarily reformed their bank regulations for the better over the last decade. Following Basel guidelines many countries strengthened capital regulations and official supervisory agencies, but existing evidence suggests that these reforms will not improve bank stability or efficiency. While some countries have empowered private monitoring of banks, consistent with the third pillar of Basel II, there are many exceptions and reversals along this dimension.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand bank supervision and regulation around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ademirguckunt@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
Production, Supply and Distribution Online database. The sample covers the 1960‐2014 period yielding 55 annual observations.

# **5. Estimation Procedure**

The nature of our model—a long run relationship applied to 55 annual observations for six commodities—can be represented best within a panel framework. However, the likely presence of common shocks and spatial dependence (common features of commodity prices) implies that panel estimation procedures assuming no cross‐sectional correlations are likely to be inconsistent. Therefore, we first use the Pesaran (2004) approach under both a standard fixed and random effects framework, as well as the Breusch‐Pagan test under a fixed effect model to test for cross‐sectional dependence. As can be seen in table 1, the null hypothesis of cross‐sectional independence is overwhelmingly rejected at 1 percent significance level.

A second concern is the stationarity properties of variables that vary by commodity (ToT, nominal prices, and stocks‐to‐use ratios). To address this concern, we use the panel unit root test of Pesaran (2003) which is based on the mean of individual augmented Dickey‐Fuller (ADF) t‐statistics of each panel.<sup>**3**</sup> Elimination of cross‐sectional dependence is achieved by augmenting the standard ADF regressions with the cross‐ sectional averages of lagged levels and first differences of the individual series. Results in table 2 confirm rejection of the null hypothesis of no unit root for ToT and nominal prices at most lags in all panels, both with and without a time trend. By contrast, the stocks‐to‐use ratio does not contain a unit root when no trend is considered in the estimation equation.

For the variables that do not vary by commodity, we test for the presence of unit roots by using the ADF and the Phillips‐Perron (PP) tests (Phillips and Perron 1988) with and without trend. Results reported in table 3 show that the unit root null hypothesis cannot be rejected for most of the income measures when a trend is included. In particular, the income is stationary when measured by GDP of high‐income countries, regardless of the test used or the indicator considered. Nevertheless, the overall picture is that the level of income tends to contain a unit root, in favor of first‐difference stationary. Similar conclusions apply to the exchange rate, interest rate, manufacturing prices, and oil prices.

We next test for the presence of a cointegration relationships between ToT and income. As was the case in the panel unit root tests, the first generation of panel cointegration tests relied on the assumptions of homogeneity and independence across

> **3** The first generation of panel unit root tests, such as those proposed by Levin et al. (2002) and Im et al. (2003) assumed cross‐sectional independence. See Gengenbach et al. (2010) for a recent review on panel unit root tests.

_8_