rafmacalaba/datause-encoder-data huggingface dataset:
# Importance of risk (0 = lowest, 10 highest)

||Risk|Lender|Sponsor|On-Grid|Off-Grid|Domestic|Internat.|
|---|---|---|---|---|---|---|---|
|<x<br>Ww<br>2 @A.<br>|Policyandregulatoryrisk|6.5|6.6|5.6*||i7.5|75|6.2|
|<br>=o<br>© ><br>B. <br><br>|<br> Power sector context risk<br>|5.0<br>|3.6<br>|<br>6.2**<br>|<br>*<br>2.8<br><br>|4.0<br>|4.0<br>|
|Ow<br>6 © C.|Country context risk|4.0|5.3|4.1||<br>4.8|3.8|5.4|
|><br>+<br>6<br>1.<br><br>~|Ease ofmarket entry|6.0|6.5|6.0|7.1|7.0|6.2|
|~<br><br>232.|Clarityofinvestmentpriorities|4.6**|6.4|9.1TM|78|73|5.7|
|<br>Fi¥4.|<br>Sectoraltrackrecord|6.0|29|49|27|39|3.2|
|<br>=5<br>5.|<br>Sectoralgrowth|46|42|7.3|3.2|46|48|
|<br><br>% ©<br>6.<br>o|<br>Firm's personal track record|8.9]|3.7|6.3|2.5|3.6|4.0|
|7.<br><br> <br>|Governance and political risk<br>|7.4*<br>|7.1<br>|5.5<br>|7.5<br>|5.7<br>|7.7<br>|
|><br>=5<br>8. <br><br>|Business environment|3.9|6.8|5.7**|5.3|4.2|7.0|
|Cc<br>3<sup>9</sup><br>9.|Macro-economicframework|3.5**|5.2|4.1|4.6|3.7|5.1|
|<br><br>10|<br>.Bankingandcapitalmarkets|1.2|2.1|1.2|2.0|1.6|1.8|

Statistical significance of difference in means between two groups calculated using a two-tailed t-test: ***p < 0.01, **p <0.05, *p < 0.1. Comparison between lenders and sponsors, off-grid and on-grid, and domestic vs. international. For the sake of readability, the stars are only shown for one of the groups, although the star show the result of the test whetherthe difference in means between two groups (e.g., the importance of the power sector context risk between on-grid and off-grid) is statistically different.
This bilateral relatedness _φi,j_ between products _i_ and _j_ is calculated for 5,016 products using data for 239 countries in 2000 from the BACI<sup>3</sup> world trade dataset (Gaulier and Zignago 2010).<sup>4</sup> The matrix of these proximities characterizes the world product space.<sup>5</sup> Table 1 provides summary statistics of particular HS-2 product categories, and Table 2 presents the proximity measures for some particular product pairs, providing illustrative examples of how products are related to each other.

Digital computers have a proximity value of 0.02 with oil. For the entire sample of countries exporting computers or oil with RCA, only 2% export the other product with RCA at the same time. This low value clearly indicates the distinct requirements needed for the export of the two products. On the contrary, computers have relatively high proximity (0.32) to cars, suggesting that the requirements for computer and car export are quite similar.

Although the Balassa index on which Hidalgo et al. (2007) relies is commonly used to measure countries’ sectorial specialization, it suffers from some empirical distribution weaknesses (Leromain and Orifice 2013; Hinloopen and Van Marrewijk 2001).<sup>6</sup> To ensure that our results are reasonably robust to changes in the definition of RCA, we also compute Hidalgo et al.’s (2007) indicator of proximity using a new RCA index that is based on an econometric estimation procedure recently suggested by Costinot et al. (2012). Their approach relies on pairwise comparisons across exporters and industries instead of aggregate exports. The “revealed” measures of productivity at the country and industry level correspond to fixed effects for a country-sector ( _ck_ ) pair estimated from the following export regression, ln _export_<sup>_k_</sup> _cd_ = _δcd_ + _δd_<sup>_k_+</sup><sup>_δ_</sup> _c_<sup>_k_+</sup><sup>_εk_</sup> _c_<sup>_d_,where</sup><sup>_δcd_,</sup><sup>_δ_</sup> _d_<sup>_k_and</sup><sup>_δ_</sup> _c_<sup>_k_areexporter-importer,importer-industry,and</sup> exporter-industry fixed effects, respectively. Although the Balassa indicator for country _c_ and product _k_ assesses whether _c_ is good at producing _k_ compared to an intuitive but ad 8
what developing countries look for from FDI: scale economies, better technology and managerial know-how. The empirical literature has not reached a consensus about the benefits of foreign ownership on productivity (Caves, 1999). Some found no differences in productivity (for instance, Konings, 2001). Others found some raw differences, which largely disappear when better measurements were provided for input quality. Still others found significant foreign ownership premium in productivity (Yasar and Paul, 2007; Hallward-Driemeier, Wallsten and Xu, 2006; Xu, Zhu and Lin, 2005; Zhang, Zhang and Zhao, 2001).

# **_The Data Set_**

Our data sets, the investment climate surveys conducted by the World Bank over the past decade, take a snapshot of a country's business environment, including access to finance, physical and regulatory infrastructure, corruption, and the necessary accounting information to estimate productivity. The SADC sample that we use consists of the following countries: Angola (two waves), Botswana (two waves), Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia (two waves), South Africa, Swaziland (two waves), Tanzania (two waves), and Zambia. For each wave, the number of firms range from 75 (Lesotho) to 1057 (South Africa). The firms were randomly sampled from the universe of firms for each country. The surveys have been conducted over the past five years. The sample periods differ by countries, but all are between 2005 and 2009. The data set is cross-sectional in nature.<sup>5</sup> Table 1 reports the definitions of the variables used in the firm-level regressions, including some of the variables measured at the country level. Table 2 shows the differences in sample characteristics for foreign and domestic firms in the SADC region. Foreign firms are defined to be those with positive foreign ownership.<sup>6</sup> Foreign firms tend to be significantly larger--the median foreign firm in SADC is 70 log points (or 100 percent) larger than domestic ones. The countries they are located in tend to be richer by almost 28 log points. Surprisingly, host countries tend to have higher firing costs. A possible explanation is that, in response to opening up for foreign ownership, countries

> 5 When there are two waves for a country, the data are repeated cross sections.

> 6 Other thresholds for defining foreign ownership produced qualitatively similar results.

5
important being the government paper. In 1997, 10 percent of bank assets were in the form of treasury securities.

Another complication arising from high inflation is that it creates measurement problems with regard to bank profitability and capital adequacy. With chronic and volatile inflation, nominal profits do not mean much if the erosion in the value of their capital is taken into account. We take this factor into account by adjusting profits for the loss in the value of bank capital which, of course, reduces their profitability.

# **III. BANKING MARKET DEVELOPMENTS AND THE ENTRY** PROCESS

In this section we provide a review of the banking market in Turkey, focusing on foreign bank entry, comparative performance of local and foreign banks, market structure in terms of concentration and some regulatory issues. Table 1 presents the financial market composition in Turkey. At the end of 1997 there were 59 commercial banks, 13 investment and development banks, more than 100 brokerage houses, and some sixtyfour insurance companies. Focusing on the banking sector, it can be seen that since 1980 there has been a significant number of entries and exits. State-owned commercial banks declined in number from eight in 1980 to four in 1997. This decline has been due to the privatization or merger of some of the smaller public banks. Private banks increased in number from 24 in 1980 to 38 as of end of 1997, a net entry of 14.

As can be seen in Table 1, the most significant increase has been in the number of foreign banks. In 1980 there were only four foreign banks - by 1990 there were 23. This number then declined to 17, mostly as a result of some of the foreign banks merging with smaller Turkish banks. This is a net entry of 13 banks almost matching the number of de novo entries by local banks. Judging by this it seems that relaxing entry barriers 7
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**Table 4:** Bilateral visa provision and migration from the rest of the world.

|Dep var:|_Mig_<sup>_R_</sup><br>_it_|<sup>_oW−j_</sup>|
|---|---|---|
||(1)|(2)|
|Visa|-0.001**|-0.001**|
||(0.000)|(0.000)|
|RTA|-0.000|-0.000|
||(0.000)|(0.000)|
|Schenghen||-0.000|
|||(0.001)|
|GDP diference (ln)|0.000*|0.000*|
||(0.000)|(0.000)|
|Estimator|PPML|PPML|
|Destination-Year Fixed Efects|Yes|Yes|
|Origin-Year Fixed Efects|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|
|Sample period|1990-2020|1990-2020|
|Observations|198,806|198,806|
|Pseudo R-squared|0.99|0.99|

Note: The dependent variable is the total migrant stock in country _j_ originating from the rest of the world (RoW), except from country _i_ . Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
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quintiles of per capita expenditure, independently of where they live, it is indeed true that female LFPR declines with household income. But to a large extent this is a composition effect, resulting from the higher share of better-off households who reside in urban areas, where female LFPR is lower (figure 4). The relationship between household income and female LFPR is much flatter within both rural and urban areas.

Figure 4: Female participation rates decline with household income at the aggregate level, less so within either rural or urban areas <!-- Start of picture text --> 50<br>40<br>30 All<br>Rural<br>Urban<br>20<br>10<br>Q1 Q2 Q3 Q4 Q5<br>Quintiles of real monthly per capita consumpt (poorest to richest)<br>LFPR (Usual Status)<br><!-- End of picture text --> _Notes:_ Estimates for 2011-12. Quintiles drawn on monthly per capita expenditure, after correcting for cost of living differences between states and rural-urban areas using the official (Tendulkar) poverty lines.

The observed decline in rural female labor force participation may be overstated by the arbitrariness of the administrative rural-urban divide. Much of the growth in India’s urban population in recent years has taken place in administratively rural areas. Thus, what appears to be a decline in rural female LFPR may to some extent be the consequence of urbanization, not a genuinely rural phenomenon.

The Census categorizes urban areas into statutory towns and Census towns. Statutory towns are places with a municipality, corporation, cantonment board or notified town area committee. Census towns are rural panchayats with urban characteristics. They have a minimum population of 5,000, a density of at least 400 inhabitants per sq. km. and at least 75 percent of their male workers engaged in non-farm work. There has been a multiplication of these Census towns in recent years, with 2,542 of them created between 2001 and 2011, compared to 242 new 6
the formal sector, using only data form the 1994/95 Census. The differences are particularly stark later in the life cycle where we find older plants to be much larger while they find older plants to be declining in size. We discuss the reasons for the discrepancy below in section IV.E.

In Table 5, we use pooled OLS regressions to explore the age effects in detail. In col. 1, we regress Establishment Size on age dummies and find all the age coefficients to be positive and significant, suggesting that older firms are larger than younger firms. For ease of comparison with the youngest firms, we add the constant (which presents the average mean size for firms younger than 5 years old) to each of the age coefficients and divide the sum by the constant. These ratios are reported in bold in square brackets below the coefficient and standard errors. Thus we see that the average 40+ year old firm is 3.775 times the size of the average firm that is younger than five years old. In col. 2, we repeat the analysis in col. 1 using 3-digit NIC industry dummies and state dummies. While we are not allowing for time-varying changes across states and industries, the dummies help us control for any time-invariant unobserved heterogeneity across states and industries. Once again we find that all the age coefficients are positive and significant. Figure 10 plots the coefficients for the age dummies and the 95% confidence bands and shows that even after controlling for period effects, location, and industry, older firms are larger than firms younger than five years old (reference sample).

In columns 1 and 2, we are comparing the mean size of firms that are 40+ years old in the pooled sample with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. Figure 11 plots the mean employment and the 26
A total of 4,228 women and 3,695 children under two years old were surveyed at baseline. Of these, 3,152 women and 2,783 children were in the counties surveyed at endline, which will be primarily used in the analysis. After attrition and trimming outliers, the primary sample consists of 2,338 women and 1,953 children.

The baseline, midline, and endline surveys were nearly identical, and each included three separate modules to measure information on the household, the eligible mother, and the eligible infant. The household component of the survey was intended to be answered by either the household head or eligible mother, and measured a household’s composition, assets, annual income, monthly expenditures, and daily food intake. The mother then answered questions about herself and her children, which measured her knowledge of maternal health and nutritional best practices and her actual behaviors with her youngest child while pregnant and breastfeeding. The survey concluded with anthropometric measurements of the child and a measure of cognitive development as measured by the Ages and Stages Questionnaire (ASQ).

The ASQ is a screening mechanism that asks a mother if her child can perform – a specific task in one of five skill categories communication, gross motor, fine motor, problem solving, and personal social. These questions are age specific, and can be asked of children ranging from 1-60 months old.<sup>7</sup> Each of the five modules consists of six yes or no questions. For example, one of the gross motor questions for children ages 11-12 months old is “When you hold one hand just to balance your baby, does she take several steps forward?” The raw score on each module is simply the number of “yes” answers. For ease of interpretation, within each age cohort, we standardize an individual’s score so that the total population has a mean of 0 and a standard deviation of 1 on each module.

In the first survey wave, there was statistically significant differential attrition across treatment groups, with only 8% of women interviewed at baseline in the info plus cash group not found at endline, and 15% of the control group not found at endline. These data were collected during the final two months of the intervention to capture the contemporaneous effects of the cash transfer on household expenditures, given that these effects might quickly fade after the cash transfer stopped. However, conditionality of the cash transfer (needing to attend the meeting) may have led some individuals from the cash group to be found who would not have been in the absence of the cash transfer, which may bias results. Ap-

> 7The age intervals of the questions are 2 months for children under 2 years old, 3 months for children from 2-3 years old, and then 6 months for children over 3 years old.

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monitoring of methane emissions from the high-priority second-level administrative units that account for 50% of total emissions.

Figure 5 provides a detailed illustration of our approach for an important methane emissions cluster in Iraq. Figure 5(a) overlays a map of Iraq governates with 10 km EDGAR cells whose methane emissions place them among 21,057 cells (Table 4) in the top five deciles (accounting for 50% of global emissions). The cells are color-coded by decile. For this exercise we highlight the emissions cluster in the east-central part of Maysan governate. Enlarging the provincial map (Figure 5(b)) reveals that the principal cluster is in one second-level administrative unit, Al Amarah district. Within this district the emissions cells are roughly clustered along a north-south central axis.

The key to effective monitoring in this context is mobilization of high-resolution Sentinel-5P observations within a methane emissions cluster area. The measurement area should be large enough to ensure a robust sample of high-quality satellite measures,<sup>9</sup> but small enough to ensure that area-average measures reflect cluster activities. For this exercise, we adopt the approach illustrated in Figure 5(c). Using the R programming language, we compute the convex hull that includes all EDGAR 5-decile sites in Al Amarah district. We construct a 20-km buffer around the hull to enlarge the sample of proximate Sentinel-5P observations. Then we intersect the buffered polygon with the boundaries of Al Amarah to define the sampling area (with blue boundary) for Sentinel-5P observations.

# **3.3 Global Monitoring Areas**

We extend the previous illustration to global coverage in a multi-step exercise. For terrestrial emissions, we adopt GADM level 2 as the default standard. For countries without GADM 2 units, we adopt the highest GADM level available (0 or 1). For emissions from offshore gas and oil sites, we create geographic units of comparable scale by intersecting countries’ exclusive economic zones (EEZs) with a global grid whose cells have sides of 250 km. In all, this combined terrestrial/offshore exercise yields 51,976 global geographic units (GGUs). We compute total EDGAR emissions in the latest available year for each GGU, order by total emissions, compute cumulative emissions, and divide units by decile. Table 5 displays the results.

**Table 5:  Global geographic units by EDGAR emissions decile**

|Decile|GGUs|Cum.<br>GGUs|
|---|---|---|
|1|39|39|
|2|93|132|
|3|174|306|
|4|303|609|
|5|522|1,131|
|6|888|2,019|
|7|1,674|3,693|
|8|3,411|7,104|
|9|7,164|14,268|
|10|37,708|51,976|

> 9 Image quality can be degraded by extensive cloud cover or random technical problems.

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# technology.<sup>15</sup>

For solar sources, the calculation is straightforward and intuitive. One could think of a fee‐for‐service arrangement where households pay for the fixed cost over several months. No additional direct costs occur (abstracting from maintenance costs, which are assumed to be small enough to be neglected) because electricity production is free. For the grid electricity, both the fixed and variable costs have to be borne by the household. Therefore, we add operational costs for a hypothetical consumption of 250 kW per year.<sup>16</sup> We assume no external cost of finance and thus do not include interest rates and loan service fees in calculations of the amortization period.

**Table 9: Amortisation period**

|**Technology**|**Approx. WTP**<br>**electricity**<br>**consumption level**|**Assumed**<br>**investment**<br>**costs in**<br>**USD**|**monthly**<br>**consumption**<br>**fee**|**Amortizatio**<br>**Burkina**<br>**Faso**|**n period in months**<br>**Rwanda Senegal**|
|---|---|---|---|---|---|
|Grid||||||
|Electricity|level 3|1100|4.58|219|<br>150<br>62|
|SHS|level 2|250|0|35|<br>28<br>16|
|Solar Lamp|level 1|25|0|6|<br>5<br>2|

_Note:_ Investment costs are based on World Bank (2009), EWSA (2012), and GOGLA and A.T. Kearney (2014). Monthly consumption fees assume an average consumption of 250 kWh per year and an electricity tariff of 0.22 USD per kWh. Amortization period is determined by dividing the investment cost of electricity access technologies by the estimated households’ monthly willingness to pay.

Table 9 shows that amortization periods vary from 2 months for a solar lamp in Senegal to 219 months for a grid connection in Burkina Faso. In general, amortization periods seem reasonable only for solar lamps if the investment costs had to be entirely financed by the households. The relatively higher WTP for higher household income and consumption levels does not keep pace with the rapidly increasing investment costs of the higher‐tier electricity access technologies. As point estimates

> 15 An alternative approach would be to calibrate and simulate the model described in section 2. This approach is, however, obscured by the lack of reliable estimates of some of the model's parameters, resulting in a loose calibration problem. We have, nonetheless, tried simulating the model for a range of plausible parameter values, and results were similar to the ones presented in this section.

> 16 250 kWh per year is the minimum electricity consumption defined by IEA (2011) for their Universal Access investment cost calculations.

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Table B2: Robustness Tests for 1<sup>st</sup> Prenatal Visit before Week 13

|(1)|(2)|(3)|
|---|---|---|
|Intervention Period|Post‐Intervention<br>Period I<br>(Jan 2011 – March 2012)|<br>Post‐Intervention<br>Period II<br>(April – Dec 2012)|
|**A. Results from Table 4**|||
|Treatment<br>0.11**<br>(0.04)|0.08**<br>(0.04)|0.08**<br>(0.04)|
|Large Sample_p_‐value<br>0.01|0.02|0.04|
|Wild Bootstrapped_p_‐value<br>0.03|0.05|0.06|
|Control Group Mean<br>0.31|0.34|0.27|
|Sample Size<br>769|1,296|710|
|**B. Estimates Using Restricted Sample**<br> <br>|||
|Treatment<br>0.09**|0.10**|0.10*|
|(0.04)|(0.04)|(0.06)|
|Large Sample_p_‐value<br>0.03|0.01|0.08|
|Wild Bootstrapped_p_‐value<br>0.08|0.02|0.11|
|Control Group Mean<br>0.31|0.33|0.36|
|Sample Size<br>760|1,326|425|
|**C. Difference‐in‐Differences Estimates**<br> <br>|||
|Treatment<br>0.09*|0.07|0.07|
|(0.05)|(0.05)|(0.06)|
|Large Sample_p_‐value<br>0.08|0.11|0.23|
|Wild Bootstrapped_p_‐value<br>0.13|0.17|0.24|
|Control Group Mean<br>0.31|0.34|0.27|
|Sample Size<br>4,015|4,015|4,015|

Notes: This table reports LATE estimates of the treatment effect of the modified fee schedule an indicator of whether the 1<sup>st</sup> prenatal visit occurred before week 13 of pregnancy. The _p_ ‐values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 15‐month period following the end of the intervention (January 2011 – March 2012). Column (3) reports the results for the 9‐month period after the change in coding of the first prenatal visit (April 2012 – December 2012). Standard errors in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

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