rafmacalaba/datause-encoder-data huggingface dataset:
-9-

their policies of exchange rate auctions. Unless changes<sup>appear to be</sup> sustainable, the credibility of actions and the supply response<sup>to them</sup> are likely to be limited. The sustainability of reform measures, therefore, ought to be a goal of liberalization attempts (see Rodrik 1988).

Based on the sample of twenty-four countries<sup>with implementation</sup> data, background studies, and interviews with World<sup>Bank economists, four</sup> factors were identified as constraints to more thorough<sup>implementation</sup> and sustainability. Weak uacroticonomic perfonnance and Instability is a first impediment. Economic instability and external imbalances are serious constraints to liberalization, while export growth makes liberalization easier. Recession, inability to address inflation, and real appreciation of the currency have inhibited trade reforms to varying degrees in Costa Rica, Jamaica, Mexico, and the Philippines.<sup>Balance of</sup> payments problems resulting from a fall in copper prices and faulty exchange rate management contributed to the reversal<sup>of reform policy in</sup> Zambia. Export performance and foreign exchange<sup>availability</sup> also offset the sustainability of reform. Strong and rapid supply response improves the sustainability of reforms by reducing the transition costs of<sup>reforms</sup> associated with the release of resources from<sup>previously highly protected</sup> sectors. Slow export expansion hurt Kenya's<sup>liberalization</sup> attempts. Export diversification is just beginning in Costa Rica and Cote d'Ivoire, which made more rapid progress in commercial policy reform<sup>but were also</sup> vulnerable to declining terms of trade. Even in Chile,<sup>the rapid growth</sup> of exports and the availability of foreign exchange have been important in preventing policy reversals. In Jamaica, the availability of
# **Tables**

Table 1: Individual and household characteristics by past migration status

||B|angladesh|||Nepal|||Pakistan||
|---|---|---|---|---|---|---|---|---|---|
||Return<br>migrants|Non-<br>migrants|p-value|Return<br>migrants|Non-<br>migrants|p-value|Return<br>migrants|Non-<br>migrants|p-value|
|**_Demographics_**||||||||||
|Male (in the full samle)|0.75|0.47|0.00|0.96|0.42|0.00|0.87|0.48|0.00|
|Age|37.0|36.5|0.22|32.7|37.4|0.00|39.6|35.4|0.00|
|Age: 20-29|0.25|0.31|0.00|0.41|0.32|0.00|0.23|0.36|0.00|
|Age: 30-39|0.34|0.30|0.04|0.40|0.25|0.00|0.28|0.27|0.53|
|Age: 40-49|0.25|0.23|0.13|0.17|0.24|0.00|0.25|0.22|0.03|
|Age: 50-59|0.15|0.16|0.75|0.03|0.19|0.00|0.25|0.15|0.00|
|Years of education|6.6|5.5|0.00|7.8|7.1|0.00|6.4|6.5|0.72|
|Illiterate|0.19|0.32|0.00|0.07|0.22|0.00|0.30|0.34|0.00|
|Primary|0.22|0.25|0.05|0.20|0.19|0.59|0.18|0.15|0.09|
|Lower secondary|0.33|0.21|0.00|0.28|0.15|0.00|0.19|0.14|0.00|
|Higher secondary|0.20|0.15|0.00|0.41|0.35|0.00|0.27|0.26|0.60|
|Tertiary|0.06|0.07|0.13|0.04|0.10|0.00|0.07|0.11|0.00|
|**_Household characters_**||||||||||
|Married|0.83|0.84|0.73|0.86|0.82|0.00|0.89|0.75|0.00|
|Head of household|0.68|0.74|0.00|0.55|0.60|0.00|0.65|0.60|0.00|
|Household size|4.7|4.6|0.01|5.1|5.4|0.00|8.7|7.1|0.00|
|Number of dependents|1.7|1.5|0.00|1.7|1.7|0.27|3.7|2.8|0.00|
|Current migrants in the HH.|0.21|0.05|0.00|0.19|0.18|0.56|-|-|-|
|Rural|0.93|0.91|0.21|0.40|0.35|0.01|0.77|0.59|0.00|

_Source:_ Bangladesh HIES 2016-17; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. _Note:_ Sample is restricted to males aged 20-59. Sample weights are applied to summary statistics.
The relationship between the market value of dwellings on the one hand and their specific characteristics and those of their localities on the other is known as a hedonic price function. In the proposed approach, this function is specified as: where captures the physical characteristics of each dwelling, those of the locality it sits in, and the average value of dwellings in the areas. The last term, , is a stochastic disturbance XX<sup>ii</sup> LL<sup>ii</sup> with zero mean that is independently distributed across households but whose variance may be different across locations. VV<sup>ii</sup> εε<sup>hii</sup> The approach proposed in this paper to assess the cost of disasters is to use recent microeconomic data on , and to estimate equation (8). Then, data from a field survey or satellite imagery are used to assess how the characteristics of each individual dwelling might have been affected. VV<sup>h</sup> XX<sup>h</sup> LL<sup>ii</sup> With this assessment at hand, it is possible to use the estimated equation (8) to predict the postXX<sup>h</sup> disaster values of and, by extension, of . This information then allows computing the decomposition in equation (6) for all areas affected by the disaster. VV<sup>h</sup> VV<sup>ii</sup>

# **4. The Gorkha earthquakes**

Triggered by a slip in the Main Frontal Thrust, the Gorkha earthquakes were Nepal’s worst natural disaster since 1934. The first wave struck on the 25<sup>th</sup> of April 2015, with a Richter magnitude of 7.5. Its epicenter was in Barpak, in Gorkha District. It was followed by a major aftershock on the 12<sup>th</sup> of May 2015, with a 7.4 magnitude. Its epicenter was at the border of the Dolakha and Sindhupalchowk districts. These high-intensity quakes triggered a large human toll: 8,961 deaths and 22,302 injuries (Government of Nepal 2015).

The epicenter of the first Gorkha earthquake is located 80km away from the capital city of Kathmandu, and that of the aftershocks is 60km away. The affected area encompasses the Western, Central and Eastern Hills (figure 1.a). These regions are relatively well-off by Nepalese standards (figure 1.b). However, Nepalese households display a high degree of vulnerability even under normal circumstances (Tiwari et al. 2016). Besides, the districts affected are populous, with housing of poor quality, so that very significant impacts could be expected.

A PDNA was conducted by the Government of Nepal, in collaboration with international partners, in the immediate aftermath of the disaster. The assessment covered 31 districts affected by the earthquakes and aimed to assess impacts across sectors of activity and social groups. Field visits were conducted to evaluate the destruction of housing, infrastructure and social amenities, and to estimate the impact on production of goods and delivery of services. Specific sector teams assessed damages, losses and recovery needs in the 14 worst-affected districts.

8
similar to that using IHDS data, but more precise given the larger samples in DLHS-3 data.

The main analysis focuses on how religion differentially affects households of that religion, but an important caveat is that religion may also influence average sanitation practices within locations. For example, Hindu religious customs could influence sanitation practices of nearby Muslims and Hindus, such that influence of religion becomes absorbed into location fixed effects. While the study focuses on whether Hindu households have different sanitation practices than nearby Muslim households, it also explores how religion influences average sanitation practices in locations (i.e., the location fixed effects). The analysis estimates using LASSO that average sanitation practices in locations are more predicted by locations’ rural status and female literacy than by locations’ religion shares.<sup>18</sup>

# **IV.B Analysis of Migrant Households**

The prior analysis found that religion-specific gaps in latrine ownership were smaller when controlling for characteristics of household locations or including fixed effects by household location. To further disentangle religion from the local economic environment, migrant households are now examined.

The analysis focuses on Hindu and Muslim migrants, who have moved from rural areas and converged in similar urban locations that allow the analysis to hold constant the available sanitation infrastructure and other features of an urban area. Another advantage to analyzing rural-to-urban migrants is that they have similar generational exposure to urban areas, which is important giving the long history of Muslim urbanization in India (see Figure 1). An important caveat to this analysis is that migrant households may be less religious, on average, than non-migrant households. Thus, differences between Hindu migrants and Mus-

> 18In practice, location fixed effects are estimated by regressing latrine ownership on household religion and PSU fixed effects. LASSO is then used to select which variables most predict variation in the fixed effects. Following the procedure suggested by Belloni, Chernozhukov and Hansen (2014), the analysis includes the household characteristics and location characteristics (from Table 2) along with their pair-wise interactions (including their squares). Out of this set of variables, 21 variable interactions are selected. The most commonly selected variables are distance from fuel source (selected 14 times), which proxies for the remoteness of rural areas, and female literacy (selected 7 times). A location’s Muslim population share is chosen once, in an interaction with female literacy.

12
# **LASSO Specification Tables**

Table A20: Impact on primary outcomes—with double-LASSO selected controls

|||(1)<br>SRH<br>behavior<br>(15yo+)|(2)<br>Economic<br>outcomes|
|---|---|---|---|
|T1|(safe spaces)|0.216***|0.119|
|||(0.080)|(0.104)|
|T1|+ Leaders|0.162*|0.062|
|||(0.091)|(0.137)|
|T2|(safe spaces + IGA)|0.017|0.155*|
|||(0.077)|(0.087)|
|T2|+ Leaders|-0.012|0.276***|
|||(0.113)|(0.088)|
|T3|(safe spaces + husbands’ clubs)|0.203**|0.333***|
|||(0.095)|(0.083)|
|T3|+ Leaders|0.117|0.251**|
|||(0.076)|(0.126)|
|T4|(control spillover)|0.092|0.058|
|||(0.071)|(0.076)|
|T1|= T1 + L|.582|.701|
|T2|= T2 + L|.806|.201|
|T3|= T3 + L|.384|.523|
|T1|= T2|.0206|.741|
|T2|= T3|.0649|.0518|
|T1|= T3|.894|.0466|
|T1|+ L = T2 + L|.17|.124|
|T2|+ L = T3 + L|.272|.847|
|T1|+ L = T3 + L|.64|.26|
|N||2,855|3,003|
|Co|ntrol mean|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; SRH = sexual and reproductive health; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for stratification fixed effects and the double-LASSOselected variables within a set of baseline values that include the outcome variable at baseline, the adolescent individual characteristics and her household’s. When the baseline value is missing, it is imputed and if it is selected by the double-LASSO procedure a dummy indicating whether the variable is imputed or not is added as control.

52
Small firms constitute the most dynamic segment of that labor market virtually everywhere and Moldova is not an exception in this respect. What matters for job creation is the size of the small firm sector. Here again Moldova is lagging behind, as its small firm sector is small by standards of successfull transition economies. According to the survey of employers (ASA), only 17 percent of workers are employed in small (up to 50 employees) firms. In contrast, in Lithuania almost one-third of all workers are employed in small firms.<sup>18</sup> Hence, the small enterprise sector is underdeveloped in Moldova. This is another reason for the unsatisfactory job creation record of the Moldavian economy, which again points to the unfriendly business environment.

# **_Business vs. non-business sector firms_**

A surprising feature of the Moldavian labor market is that job turnover in the nonbusiness sector is substantially higher than in the business sector. Usually, the nonbusiness sector, consisting of public and social services, is the source of stable jobs. In contrast, the business sector, exposed to fluctuations in product demand, offers less stable jobs and is thus characterized by higher job turnover. This apparently is not the case in Moldova. Job turnover in the non-business sector is very high, with both the job creation and job destruction rates approaching 12 percent. This implies considerable movements of worker from downsizing institutions and organizations toward expanding ones.

In contrast, job turnover in the business sector is at a modest level, with the rate of job creation some 30 percent lower than in the non-business sector and the rate of job destruction 20% lower. Accordingly, contrary to what one would have expected, fewer workers change jobs in the business than in the non-business sector.

The distinctive feature of the agricultural sector is low job creation (less than four percent), and high job destruction (close to 14 percent), and thus a substantial fall in the overall number of jobs in the formal sector agricultural enterprises. However, this fall

> 18 These figure are likely to be biased downward as they relate only to firms which are legal persons (firms owned by natural persons are as a rule small).  However, the point that is being made is that the size of the small enterprise sector in Moldova is much smaller than in Lithuania and other successful transition economies.

22
### **Beneficiary selection**

Across the 207 localities, individuals interested in the program were invited to enroll before the assignment to treatment or control groups. Enrollment data was collected by the implementing NGO. It was then used to check eligibility and screen applicants by level of vulnerability. Out of 14,880 interested individuals, 12,692 met the eligibility criteria as they were either 18-40 years old, a single mother above 15 years old, a widow or disabled individual (up to 60 years old) and were not benefiting from another program.

Among the applicants, 5,116 were selected for the program. The selection was based on an individual proxy score computed using the enrollment data.<sup>21</sup> Selected individuals are those above the vulnerability cut-off, i.e. those considered the most vulnerable. The cut-off is identical for all localities assigned to the same intervention, but varies across the three interventions based on the budget and target number of beneficiaries determined during the program preparation process. As further detailed below, the identification of treatment and spillover effects uses “common support” cut-offs across treatment modalities.

# **3 Estimation strategy and data**

## **3.1 Estimation strategy**

We estimate intent-to-treat treatment effects by taking differences in outcomes between treatment and control groups at endline. For an outcome _Y_ , we estimate the following ordinary least squares (OLS) regression by using the main sample to estimate direct effects, and the sample of non-selected individuals to estimate spillover effects: where i indexes the individual and j the locality. _β_ is the pooled Intention-To-Treat (ITT) estimate of the program’s overall impact. _Tj_ captures treatment assignment in locality _j_ and _Sj_ the randomization strata, i.e. a dummy for rural villages or urban districts within

> 21Table A3 details the composition of the score. The score weighs criteria including disability, marital status, education, employment, economic status, assets and economic responsibility in the household. Weights were chosen to maximize the dispersion of the score.

10
# **Table 2.  Simulations of Trade Reform**

|_Case (all inequality_<br>_measures are population_<br>_weighted)_<br>|# of obs.|_Gini_|_Theil_<br>_Entropy _|_Mean_<br> _Log Dev_|_Atkinson_<br>_e=0.5_<br>_e=2_<br>_e=5_|
|---|---|---|---|---|---|
|Base (with existing<br>protection of merchandise<br>trade by high-income<br>countries)|159|0.5426|0.5408|0.5396|0.2418 0.6190 0.8266|
|With high-income<br>countries merchandise<br>trade reform|159|0.5425|0.5402|0.5394|0.2416 0.6189 0.8263|

Source: Authors’ estimates based on GTAP results for changes in household income due to full merchandise trade reform by high-income countries in 2001 (de Mensbrugghe 2005). The “Base” inequality measures here differ from Table 1, since the baseline year in the case of trade is 2001 (the year used by de Mensbrugghe 2005), while in the case of aid it is 2002.

The figure below presents the impact of high-income countries’ trade liberalization in goods across deciles of world population. Efficiency gains imply that all deciles lose because of protection. However, what matters for inequality is who loses more or less than the world average, represented by the dotted line in Figure 2. As anticipated on the basis of inequality measures, we can see in Figure 3 that low- and middle-income deciles, especially deciles one (Sub-Saharan Africa), eight (China, Malaysia and Philippines, many Latin American and Caribbean countries, Turkey) and nine (Middle East and North Africa, Thailand, Brazil, and Russia) as well as the second-highest vintile (some EU-25 countries, Hong Kong and Singapore, Australia and New Zealand, Japan, Canada) benefit the most from merchandise trade reform in high-income countries, while the top vintile (USA and part of Canada) loses with respect to the average gain in the world economy but not in absolute terms (indeed, it still gains 0.02%). Other relative losers include deciles 3, 5 and 6. In other words, trade liberalization in developed countries would benefit less those groups of people than average.

17
|TableXVFractionof Workers|ActivelySearchingforaJob,by UnemploymentInsurance(UT)<br>Recipiency<br>Activesearch|
|---|---|
|Registeredandpresent Ul|40.61l|
|RegisteredandpastUl|65.07*|
|Registeredandnever UI|32.18*|
|Notregistered|13.48"|

**Source:** Labor Force Survey, 1996.1-1996.4, 1999.1-1999.4, and 2000.1-2000.4.

( ) Significantly different from closest value (from above/below) in the same column at 5 percent (10 percent) level.

56
_Hypothesis 7. Given the complementarity of corruption in the society with corruption in the utility industry, we expect utility bribes to be lower in countries with lower level of rents (as measured by a higher level of imports, and lower export shares of fuel, mineral, and metal export), in countries that are more democratic or have longer exposures to democracy; and in countries that are growing more rapidly._

# III. EMPiRICAL **IMPLEMENTATION**

## **111.1** Data

The main source of data used in this paper is the World Business Environment Survey (WBES), a cross-sectional survey of industrial and service enterprises conducted in mid-1999 by the World Bank and several other agencies.'<sup>8</sup> The main purpose of the WBES is to identify perceived constraints on enterprise performance and growth in developing and transition economies. The survey, therefore, has a large number of questions on how taxation, regulation, the performance of the financial sector, the institutional environment and corruption affect business operations. In contrast, the survey includes little information on enterprise characteristics or performance. In particular, although some information on assets, sales, broad sector of operations, ownership, employees, and enterprise growth was collected, detailed balance sheet information and profit and loss statements were not collected from participating enterprises.

Although the WBES was conducted in many countries throughout the world, and some effort was made to ensure cross-country comparability, the degree of detail varies greatly between regions. For example, although data was collected on actual sales, fixed assets, and debts in some regions, only categorical data on the same information was collected in other regions. For the purpose of this study, the most important difference between the surveys completed in various regions is that questions that allow us to calculate bribes paid to utilities

> 18 The survey of the transition economies was conducted in collaboration with the European Bank for Reconstruction and Development. Hellman _et al._ (2000) and European Bank for Reconstruction and Development (1999) provide more complete descriptions of the survey.

10
INFRASTRUCTURE AND GROWTH IN AFRICA

**Figure 8. Potential growth per capita benefits in Africa due to higher infrastructure development**

Scenario 1: Reaching the infrastructure level of the leader in Africa, using the [IK1] index of aggregate infrastructure and 2001–5 averages

# a. Africa

<!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>-1.0<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text -->

# b. North Africa

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Algeria Egypt Libya Morocco Tunisia<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text -->

# c. West Africa (ECOWAS)

<!-- Start of picture text --> 9.0<br>8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>Benin Burkina Cote Ghana Guinea Gambia, Guinea Mauritania Niger Nigeria Senegal Sierra Togo<br>Faso d'Ivoire The Bissau Leone<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text --> 38
Table 1 summarizes key context features that can vary not only across fuels, but also across locations. The Appendix provides examples of the application of (1) to actual situations.

**Table 1 Likely Magnitudes of Effects of Local Context Factors on Costs of Household Fuels**

|||**Contribution to Increased House**|**hold Costs**|
|---|---|---|---|
|**Fuel**|**Under-developed**<br>**Fuel Markets**|**Financial Market Distortions**|**Indoor Air Pollution**|
|Fuelwood|Limited|Low|High|
|Dung/Ag.<br>Residue|Limited|Low|High|
|Electricity|Governmentsupplied|Low|Low|
|LPG|May be high due to<br>transportcosts|May be high if first cost is<br>important|Low|
|Kerosene|Generallylimited|Limited|Limited|
|Biogas|Nontradable. No<br>markets.|Very important, because of high<br>first cost and need for large|Low|
|||animals||
|**Fuel**|**Poorly functioning**<br>**labor markets**|**Stronger Standards for Child**<br>**Schooling and Higher Status of**<br>**Women**|**Policies Restricting Open Access**<br>**to Biomass**|
|Fuelwood|Limited|High, because reduces “free”<br>household labor/increases<br>opportunity cost of time.|High in short run. Possibly<br>negative in long run due to<br>increased productivity of common<br>lands.|
|Dung/Ag.<br>Residue|Limited|High, because reduces “free”<br>household labor/increases<br>opportunity cost of time.|Likely high in short run, because<br>reduced open access reduces<br>animal raising subsidies. Possibly<br>negativein longrun.|
|Electricity|Limited, because of<br>subsidies|Limited|None|
|LPG|High|Limited|None|
|Kerosene|High|Limited|Notdirectlyrelevant|
|Biogas|May be high<br>depending on<br>subsidies|High, because opportunity cost of<br>time rises and biogas depends on<br>animal dung|Likely high in short run, because<br>reduced open access reduces<br>animal raising subsidies. Possibly<br>negativein longrun.|

# **5.  Ranges for Social Cost Evaluations**

A meta-analysis presented in Jeuland and Pattanayak (2012) provides ranges of possible values for many of the key parameters going into the social cost calculation in (1). Their parameter ranges can be used to demonstrate the wide range of potential social costs for different cooking methods. This in turn emphasizes further the importance of local context.

In Table 2 we present the ranges of values from Jeuland and Pattanayak (2012) for direct cost parameters for traditional and ICS. The parameters in Table 2 include the costs of the stove, fuel and cooking time, but leave out health and environmental effects.

**8 |** P a g e