rafmacalaba/datause-encoder-data huggingface dataset:
|**Repayment**<br>ndent variable is the standard deviation of<br>ion would be zero; if there are many months<br>be higher. Observations are at the loan level.<br> group liability loan, 0 if it is an individual<br> `Behrampura` is a dummy for one of the MFI<br>and a group liability loan. Robust standard<br>e.|(3)<br>-813.105***<br>(280.590)<br>0.069***<br>(0.020)<br>-477.704**<br>(241.601)<br>Yes<br>0.503<br>689<br>1243.496|
|---|---|
|**Deviation of **<br>where the depe<br>tandard deviat<br>eviation would<br> if the loan is a<br>particular loan. <br>h an individual<br>h point estimat|(2)<br>-1002.052***<br>(186.520)<br>0.085***<br>(0.011)<br>732.502***<br>(121.145)<br>14.284<br>(148.592)<br>No<br>0.176<br>689<br>1243.496|
|**e: Standard **<br>ctional data, <br>nth, then the s<br>he standard d<br>ble equal to 1<br>ived for that <br>o received bot<br>s beneath eac|(1)<br>-288.666<br>(185.204)<br>1352.427***<br>(100.308)<br>No<br>0.005<br>689<br>1243.496|
|**e 5: Dependent Variabl**<br>S regressions with cross-se<br> amount is repaid every mo<br>the required amount, then t<br>`an Dummy` is a dummy varia<br>o the amount the client rece<br>includes loans of clients wh<br>vel, are given in parenthese|p Liability Loan Dummy<br> Amount<br>ampura<br>tant<br>idual FEs<br>uared<br>n of Dep Var<br>0_._10, ** _p <_0_._05, *** _p <_0_._01|
|**bl**<br>L<br>d <br> <br>`o`<br> t<br>le<br> le|u<br>n<br>r<br>ns<br>iv<br>q<br>a<br>_<_|
|**Ta**<br>This table reports results from O<br>monthly repayment: if the require<br>where people pay less or more than<br>The variable `Group Liability L`<br>liability loan. `Loan Amount` refers <br>branches in the sample. The samp<br>errors, clustered at the individual|Gro<br>Loa<br>Beh<br>Co<br>Ind<br>R-s<br>N<br>Me<br>* _p_|

26
Tariffs charged to households with high electricity consumption are generally more profitable (in the case of LYDEC and REDAL). A third factor is geography. Operational costs and investments are generally higher for utilities operating in extended geographical areas, covering scattered clients. Local climate is a fourth important characteristic. Utilities operating in regions in the central part of the country are generally faced with higher operating and investment costs (RADEEMA, RADEEF).

# 2.4.2 Institutions

The difficulty of conducting a detailed assessment of Morocco’s tariff structure and costrecovery ratios of distribution utilities is largely shaped by the absence of a regulator. For a long time, Morocco did not have a regulator until Law N° 48-15 of 2016 established ANRE. It is yet to commence operations and its scope will largely be around the RE law and market, with some oversight over the transmission and distribution grid operators.

At the time of data collection for the Rethinking Project (2015), Morocco had not set up a legal regulatory framework for ANRE. ANRE was eventually set up in 2016 and its head was appointed in December 2018, but it is yet to start shaping the sector’s functioning. Before ANRE, tariffs were still set, quality was monitored, and market entry regulation was carried out considering the significant increase in private sector participation in the sector. Thus, for evaluation purposes here, the regulatory practices are identified and scored. This essentially means that in terms of governance of the regulatory entity, Morocco has no scores for regulatory principles, but does for the substance of regulation as they are carried out in the country. This evaluation is of course based on the system in 2015. Given that the regulator started some operations in 2019, there are bound to be many changes to how regulation is carried out as the new entity creates a formal framework.

The evaluation of the regulatory framework is done from two standpoints. First, the rules and regulations that make the existing framework (as of 2015) in principle. Second, the rules and regulations that the sector’s experts and practitioners believe are implemented in practice.

For a long time, there have been no clear requirements on government entities, especially ONEE to publicly report financial data and meet explicit quality of service requirements. The prolonged absence of a regulator also affected the process of tariff adjustments. Nevertheless, utilities are obligated to connect new customers and these connections are charged. Morocco therefore performs poorly on various indicators for regulatory governance and substance (Figure 19). Compared to an average of 15 countries, there are no scores for Morocco on accountability and autonomy of public operators, the two indicators for regulatory governance.

37
Table 2 presents balance on baseline child-level characteristics for the full sample. About 49% of the children in our sample are female, 94% are living with their biological mothers and 86% are the children of the heads of households. On average, children are 3 years old, with children in the control group being slightly older than children in the treatment group (2.97 years old vs 3.02, _p<_ 0.05). The average child development score in our sample is 85 points out of a total of 152 points, with an average gross motor score of 36 points (ranging from a minimum of 0 to a maximum of 52), an average fine motor score of 20 points (ranging from a minimum of 0 to a maximum of 54), and an average language score of 29 points (ranging from a minimum of 0 to a maximum of 46).

Although our tracked sample, used in the analysis, mainly appears to be balanced on baseline characteristics, we find statistically significant differences at the 5% level in 4 of the 22 baseline household characteristics and 5 of the 18 baseline child characteristics we test, including some key employment and time use variables.<sup>12</sup> To create a scale-free measure of overlap in the distribution of covariates in the two treatment groups, we estimate normalized differences following Imbens and Rubin (2015). The only variable with a normalized difference above their suggested cutoff of 0.25 standard deviations is total hours worked per week, with a normalized difference of 0.27. Given that the public work sites were randomly assigned to receive a childcare center, we view these differences as resulting from chance. We address this imbalance by using an ANCOVA model as our preferred specification in our regression analysis.

# **4.2 Use of Childcare Centers**

At baseline, only a very small proportion of households (2%) reported having any child in the household attend a childcare center in the last 24 hours (Table 1). At the follow-up survey, 24% of respondents reported having at least one child attend a childcare center in the last 12 months (12% of respondents in the control group versus 37% of respondents in the treatment group). Asked about the types of childcare centers attended, 12% of respondents reported having at least one child attend a mobile childcare center (less than 1% of respondents in the control group versus 25% of respondents in the treatment group).

> 12We also test for balance in the subsample of households with children 0-2 years old and for children 0-2 years old, using the same baseline variables. Appendix Tables A1 and A2 present baseline balance for the sample of households with a child aged 0 to 2 and for children within this age-range. We find similar baseline differences.

15
# **Table 2.  Components of Direct Cost of Cooking**

||Tra|ditional W|ood Stove||ICS Im|proved Wo|od Stove|
|---|---|---|---|---|---|---|---|
|Cost component|Units|Low|Medium|High|Low|Medium|High|
|(1) Levelized capital cost|$/yr|0|0|0|3|15|50|
|(2) Cooking time|hrs/day|2|3|4|1.4|2.9|6.0|
|(3) Energy content of fuel|MJ/kg|16|16|16|16|16|16|
|(4) Fuel use rate|kg/hr|0.3|0.6|1||||
|(5)<br>Stove<br>heat<br>transfer<br>efficiency|(none)|0.07|0.11|0.15|0.13|0.25|0.4|
|(6) % of fuel purchased||0|0.25|0.5|0|0.25|0.5|
|(7) Fuel price|$/kg|0.03|0.12|0.2|0.03|0.12|0.2|
|(8) Time collecting fuel|hrs/day|0.3|1|3|0.3|1|3|
|(9) Additional time for fuel<br>preparation|hrs/day|0|0|0|0.17|0.33|0.5|
|(10) Wage (cost of time)|$/hr|0.13|0.2|0.5|0.13|0.2|0.5|
|(11) Annual fuel use|kg/yr|219|657|1460|117.9|289.1|547.5|
|(12)<br>Annual<br>fuel<br>cost||||||||
|(purchase,<br>collection,<br>preparation)|$/yr|14.24|74.46|419.75|22.3|87.5|419.8|
|(13) Total annual cost for<br>stove and fuel|$/yr|14.24|74.46|419.75|25.30|102.51|469.75|
|(14) Opportunity cost of<br>annual cooking time|$/yr|94.9|219|730|66.4|208.1|1095.0|
|**(15) Total annual “direct”**||||||||
|**opportunity cost**|**$/yr**|**109.14**|**293.46**|**1149.75**|**91.73**|**310.56**|**1564.75**|

Source: Calculations based on Jeuland and Pattanayak (2012).

Consider first the traditional wood stove. There is no capital or maintenance cost. Lines 2 and 4 give the amount of time spent cooking each day and the fuel use per hour of cooking. Multiplying these two numbers together and then multiplying by 365, we can compute annual fuel use (line 11). Line 10 gives the implicit or “shadow” cost of time based on a nonagricultural wage. Combining lines 2 and 10 and multiplying by 365, can compute the annual opportunity cost of cooking time ( _CTi w_ in equation (1)).

To compute the cost of acquiring fuel, we first apply the percentage in line 6 to calculate the fuel portion of purchased fuel and then multiply by the price of fuel in line 7. This gives us _fiQim_ in equation (1). For fuel that is collected, line 8 provides the time spent in collection activity, while line 10 gives the shadow value of that time. Together these give _FTi w_ in equation (1). Combining the cost figures for purchased and collected fuelwood and multiplying by 365, we obtain the total

**9 |** P a g e
enumeration areas (EAs), 580 EAs in GLSS 5, and 300 EAs in GLSS 4.<sup>8</sup> Based on the location information, we match households to districts of regions with oil and non-oil regions. The GLSS 5 data includes 110 districts while GLSS 6 includes 170 districts.<sup>9</sup> The number of districts in Ghana has increased over the years from 110 districts in 2000 to 170 districts in 2010. With information on both the original districts that have been split and newly created districts, we are able to match the new districts back to the old districts that existed in 2000.

Data on household income is taken from the Rural Income Generating Activities project (RIGA), which is compiled by the Food and Agriculture Organization of The United Nations.<sup>10</sup> The RIGA income data is constructed using household survey data (GLSS), with the idea of including all sources of household income that are often neglected in the computation of income. Such sources include income from selfemployment and income from own-farm production of crops. Additionally, we use information on the location of oil fields from Petroleum Commission of Ghana to determine the oil and non-oil regions. The data contains information on the number of offshore oil fields, wells, location, year of discovery and status of operation, which is whether the oil field is currently producing, under appraisal or exploration.

The empirical analysis focuses on regions in Southern Ghana by comparing households and individuals in treatment and control areas. We focus on the southern part of Ghana for two reasons. First, we limit our focus on offshore oil. The oil discovered in 2007 as well as the subsequent discoveries are offshore. We define treatment areas as districts in regions that face offshore oil or districts in regions that have oil in its offshore boundaries. Second, districts considered in our analysis are more similar to each other compared to districts in the north. One of the regions in the south that has oil, i.e. the Central region, is excluded, because the oil field in that region was

> 8 GLSS is geocoded at the locality level.  The 6th wave (GLSS 6) contains a total of 1200 localities but our sample includes 818 EAs out of the total, because GPS information on 382 localities was missing. The majority of the missing EAs are located in the Northern regions. These regions are not included in our main analysis since we focus on the south. However, we use EAs in the Northern regions with GPS information to show that excluding these regions does not affect our results.

> 9 Districts are second level sub-national administrative units in Ghana after regions.

> 10 Rural Income Generating Activities (RIGA) project- Food and Agriculture Organization, United Nation. <u>http://www.fao.org/economic/riga/riga-database/en/</u>

9
23 the size of trade in parts. The first most integrated group consists of Hungary, Estonia and the Czech Republic. The share of parts in manufactures ranges between 19 and 21 percent (Table 10). The second group with shares ranging from 12 to 16 percent comprises Latvia, Poland, Slovakia and Slovenia. These countries are at intermediate level. Bulgaria, Romania and Lithuania form the third least integrated group.

Table 10: Trade in parts with the EU and share in **trade of manufactures, 1998**

||Exports (in<br>million of US<br>dollars)<br>_**____________**__|Share of parts in<br>exports of<br>manufactures (in<br>percent)<br>|Imports (in<br>million of<br>US dollars|Share of parts in<br>imports of<br>manufactures (in<br>percent)<br>__________|Share of turnover<br>in parts in<br>turnover in<br>manufactures (in<br>percent)|
|---|---|---|---|---|---|
|Bulgaria|64|<br>3.5|<br>246|<br>11.1|<br>7.6|
|Czech Republic|2,992|<br>20.4|<br>3,449|<br>20.8|<br>20.6|
|Estonia|343|<br>27.9|<br>423|<br>17.2|<br>20.8|
|Hungary|2,551|<br>17.4|<br>3,980|<br>25.8|<br>21.7|
|Latvia|23|<br>3.7|<br>171|<br>11.3|<br>9.0|
|Lithuania|32|<br>2.7|<br>237|<br>11.3|<br>8.2|
|Poland|1,521|<br>10.5|<br>4,296|<br>16.1|<br>14.1|
|Romania|266|<br>5.0|<br>718|<br>11.6|<br>8.6|
|Slovakia|715|<br>12.8|<br>1,650|<br>28.7|<br>20.9|
|Slovenia|923|<br>17.2|<br>938|<br>14.8|<br>15.9|
|CEEC10 with EU|<br>9,431|<br>14.5|<br>16,107|<br>18.9|<br>17.0|

Source: Computations based on EU data as reported to the UN COMTRADE.

Developments in 1998 as compared in 1997 suggest that this trade has been gaining momentum. CEEC exports increased by 31 percent (from US$7.2 billion to US$ 9.4 billion) and imports by 36 percent (US$ 12 billion to US$ 16 billion). Except for Lithuania (6 percent increase) and Latvia (contraction of 55 percent in terms of value), other CEEC recorded the increase in exports of at least 25 percent in 1998. The share of turnover in parts in turnover of manufactures increased significantly for each CEEC except Latvia and Lithuania. The increase was particularly significant for Bulgaria (from 6% to 8%) and Romania (7% to 9%).

# 4. COMPARATIVE ADVANTAGE IN **PARTS**

The concept of 'revealed comparative advantage' to identify a country's strong and weak sectors (products) in terms of export performance has been widely used in empirical economic studies. A country's sector has a comparative advantage if its exports are more important to this country than in exports of the group of reference countries. In order to assess a country's export specialization in selected markets, for instance in the EU, one may use EU exports. In other words, reference countries then include all exporters from the EU.

Since the purpose of this analysis is to identify a country's 'revealed comparative advantage' in production sharing, we modify this approach in three ways. First, since our focus is
# **Acronyms**

|AyA|CostaRicaInstitutefor Aqueducts and Sewers|
|---|---|
|ABT|ABT Associates,Inc.|
|ALIDES|AllianceforSustainableDevelopment|
|ARESEP|Public ServiceRegulatoryAuthority|
|ASOTEM|Association fortheManagement oftheTempisqueRiver Basin|
|CATIE|Center for Tropical AgronomicResearchandEducation|
|CCAD|Central American Environmental and Development Commission|
|CEDARENA|Center for Environmental Law and Natural Resources|
|CNFL|National PowerandLight Company|
|CNP|National ProductionCouncil|
|COMCURE|Reventazón River Basin Management Commission|
|CRAC|RegionalCommitteesforConservation Areas|
|CRGT|Coordinating Commission forthe Grande deTárcolesRiver Basin|
|ESPH|Public Service Company of Heredia|
|FECON|CostaRican Federationof EnvironmentalGroups|
|FONAFIFO|National Fundfor ForestryFinancing|
|FUDEU|Foundation forUrban Development|
|FUNDECOR|Foundation for Development ofthe CentralVolcanicMountain Range|
|GDP|GrossDomesticProduct|
|IADB|Inter-American DevelopmentBank|
|ICE|CostaRican Institute of Electricity|
|ICT|CostaRican Tourism Institute|
|IFAM|Institutefor Municipal PromotionandAdvising|
|ITCR|Technological Institute ofCostaRica|
|INEC|National Statistics and Census Institute|
|INVU|National Housing and Urbanization Institute|
|JICA|JapaneseInternationalCooperation Agency|
|MAG|Ministry of Agriculture andLivestock|
|MIDEPLAN|Ministry of Planning|
|MINAE|Ministry of Environment andEnergy|
|MINSA|Ministry of PublicHealth|
|MIRENEM|Ministry ofNatural Resources,Energy andMines|
|NGO|Non-GovernmentalOrganization|
|PLAMA<br>VIRILLA|Improvement Plan for the Virilla River Basin|
|PLAMAGAM|Environmental Improvement Plan for the Greater Metropolitan Area|
|SENARA|<br>NationalUnderground Water,IrrigationandDrainage Service|
|SETENA|National Environmental TechnicalService|
|SINAC|NationalSystemofConservation Areas|
|SNE|National Electricity Service (defunct)|
|UCR|University ofCostaRica|
|UNA|NationalUniversity ofCostaRica|

4
# **5. Conclusion**

While the dataset presented in this paper certainly has room for improvement, the figures emerging from it seem meaningful and provide valuable insights on some of the employment issues more hotly debated in South Asia these days. The contribution of the dataset is to provide employment information that is curated in a systematic way from individual responses to over 60 primary data sources in seven countries from 2000 to 2017. The coding of the dataset is fully transparent and replicable, as we report how the different questionnaires have been used to generate information on the employment status, the sector of activity, the type of job and the institutional sector of all respondents. We show that employment outcomes are sensitive to data sources, even within the same country. But we also show that comparable data from our new dataset shed light on some of the ongoing debates in South Asia.

Thus, we show that the agricultural sector still employs the most people in all countries in the region with the exception of Sri Lanka. We also show that the employment share of the manufacturing sector is relatively low across the region, and in many countries is even matched by the employment share of the construction sector. Probably related to this pattern, only around one-tenth of the working-age population has a regular job across South Asia and even casual wage employment is relatively uncommon. Instead, self-employed and unpaid family work are the predominate forms of employment. In addition, a high share of regular wage employment is accounted for by the public sector in all South Asian countries.

The dataset confirms that the number of people at work in South Asia has not increased in line with the working-age population and that employment rates mostly declined during the last decade. This contrasts with the experience of many East Asian countries, in which the employment rate increased during the same period. The decline in overall employment rates was mostly driven by declining employment rates of women.

Last but not least, our dataset provides valuable insights on the relationship between economic growth and employment growth in South Asia, both in the short term and in the long term. We show that seasonal growth patterns affect the composition of employment, while non-seasonal changes in short-run growth affect its overall level. In the long-run, every percentage point of economic growth leads on average to an increase of employment by 0.34 percent.

The new dataset presented in this paper can be used to deepen these insights. For example, it would be interesting to analyze the determining factors of the employment structure and the drivers of structural employment changes. In addition, researchers may be interested in studying how the relationship between employment growth and economics growth is influenced by the institutional characteristics of countries, and the composition of their economic growth. We hope that our dataset will help provide reliable answers to these and other pressing employment-related questions debated in South Asia these days.

18
providing detail on the trajectory of an effect. Such data are also incredibly valuable for structural modeling, and for constructing comparable control groups in non-experimental impact evaluations. Moreover, with a large number of time periods on a smaller number of firms, one can move from standard estimators that rely on large-N asymptotics to estimators that rely on large-T. Bloom et al. (2011) provide an example of this, using over 100 observations per firm and the Ibramigov and Muller (2010) method to implement a t-statistic based estimator that is robust to substantial heterogeneity across firms as well as to considerable autocorrelation across observations within a firm.

# **4.3 Recognize What Experiments Can and Cannot Do**

Employing the two recommendations offers the potential to estimate the average treatment effect of a firm policy for the types of firms that one has restricted the analysis to. Thus if we reduce the C.V. to a manageable level by focusing on firms with 10 to 100 workers and baseline sales below $100,000, this is the group for which we are able to estimate the policy impact. If there are only a couple of firms in the whole country with much larger sales and employment, an experiment is not going to be able to reveal the impact of the policy on these two firms, and pooling these firms in with the remainder of the firm population will make it hard to detect the impact for _any_ type of firm. Note that this is not a limitation of experiments alone – if there are really only two firms like this in the entire country with no firms comparable to them, then nonexperimental methods of impact evaluation are not going to be informative about these largest firms either.

Then what can we say about the effects of a program on the largest firms? This is where I believe time series analysis and structural modeling can play their most important role provided that _lots_ of data is collected on such firms. If we have daily production data on the same firm for a decent period of time pre-intervention, it is more credible to believe that we can approximate well its production function than is the typical case of only a couple of periods of data on many firms. Detailed data on a lot of different dimensions of firm behavior also allows for construction of a spatial and temporal causal chain, and testing of falsifiable predictions of competing theories.

16