rafmacalaba/datause-encoder-data huggingface dataset:
be explained partially through the increased health risk.<sup>20</sup> Also of note is the 72 shilling (US $1.03) return to company, which likely re‡ects unobserved characteristics of clients that request company. Column 1 shows that unprotected anal or vaginal sex is associated with a 42 shilling (US $0.60) increase in the price. As the total average price paid is 488 shillings, this amounts to a premium of about 8.6%, which is substantially lower than the risk premium of 23% calculated by Gertler, Shah, and Bertozzi (2005). However, the market for sex work in Mexico, where sex work is legal and regulated and where average incomes are higher, is likely to di¤er greatly from the Kenya context. The lower risk premium may also be the result of di¤erences in data - Gertler, Shah, and Bertozzi (2005) collect recall data on the last three to four client transactions rather than a longer panel. In addition, only 11.7% of their sample has variation in condom usage with clients, while 71% of our sample has variation in condom usage.

Columns 2 and 3 run the same speci...cation among the regular and casual client samples separately. The di¤erences between regular and casual clients in the prices paid for most activities are not signi...cantly di¤erent (tests not shown), but regular clients actually pay more for unprotected sex than do casual clients (though the di¤erence is not signi...cant). In fact, the premium for casual clients is insigni...cant (though we can not make much of this, as the standard errors in all speci...cations are quite large). However, the fact that the risk premium is positive and signi...cant on the full sample suggests that, on average, women have some discretion over the activities that they perform and that women are (at least partially) compensated for the health risks that they take. Further, the fact that regular clients also pay premia for various activities suggests that women have similar discretion even within these relatively longer-term relationships.

Columns 4-7 restrict attention to the Round 2 data. First, Column 4 shows the risk premium for the round 2 sample: generally the results are very similar to that of the entire sample. Column 5 separately estimates the risk premia to unprotected anal and vaginal sex. Though the standard 20 Studies on HIV transmission through male-to-female anal sex are very rare, and causality is di¢ cult to establish, but sex workers in South Africa that supply anal sex (either protected or unprotected) have been found to have a 10-120% increase in the risk of HIV infection (Karim and Ramjee, 1998). Similarly large increases have been estimated for couples in Europe (European Study Group on Heterosexual Transmission of HIV, 1992). We are not aware of studies that estimate the per-act transmission probability for heterosexual anal sex in Africa.

16
<!-- Start of picture text --> 2<br>romBo 36 H:<br>BE<br>525& H<br>2s<br>Be 3 i<br>Qo<br>Ux :<br>Bs<br>3<br>& 25<br>-1 -5 0 Re} 1 1.5<br>Stringency index<br>(standard deviations from Apr20-Dec21 mean)<br>— Non-urban/rural Urban<br>N=148,769; C=37<br><!-- End of picture text --> <!-- Start of picture text --> Do<br>-€é5S 6 ::<br>ana<br>SsPe=: 6 —_—S :<br>5 55<br>oe :<br>os<br>Be :<br>ge 4<br>Sx<br>a g A :<br>-1 -5 0 Re} 1 1.5<br>Stringency index<br>(standard deviations from Apr20-Dec21 mean)<br>— Male Female<br>N=36,673; C=30<br><!-- End of picture text -->
In particular, electricity consumption refers to electricity purchased from PLN, which accounts for the majority of electricity expenditures and that purchased from other providers. Fuel consumption includes diesel, gasoline and lubricants, which together to electricity account for over 80% of all energy consumption.<sup>21</sup> Since the focus of this paper is on the distinction between fossil fuels and electricity, we categorize each energy type used by the plants into one of the two groups. To do so, we convert consumption of fuel in kWh equivalents and compute plant-level quantity shares in order to capture the relative importance of each source.<sup>22</sup> The shares sum to one within each category. To avoid potential endogeneity of timevarying proportions, for each plant we fix shares in the first year of observation and then drop that year from the sample.<sup>23</sup> One of the key challenges of the Statistik Industri data is the lack of complete series of capital stock. Earlier studies tried to re-construct capital stock series applying the perpetual inventory method (PIM) to the first year of capital stock data reported by the plant (Amiti and Konings, 2007; Javorcik and Poelhekke, 2017). However this imputation method crucially relies on the capital value selfreported by the plant the first year the data are available, which is not necessarily accurate.<sup>24</sup> One potential advantage of using PIM is that purchase and sales data might be more accurate relative to the self-reported value of the stock, requiring an appropriate calculation of market values and depreciation. However, PIM needs to rely on measures of capital depreciation, which are difficult to accurately estimate. To mitigate such trade-off, we have adopted a hybrid strategy. We first clean the self-reported data, adopting an algorithm which keeps only observations that fulfill a battery of tests.<sup>25</sup> Then, we apply the PIM only to fill the gaps between the

> 21The Indonesian survey asks manufacturers about expenditures and quantities of PLN and non PLN electricity, gasoline, diesel, kerosene, coal, gas, LPG, lubricant, oil diesel, oil burn, charcoal, firewood, coke plus a category labelled “other fuels”. However, exception made for PLN, non-PLN electricity, diesel, gasoline and lubricants, all other sources have been included in “other fuels” in some years. Therefore, to minimize noise, we limit the analysis to the energy sources that have been separately identified in every year in the sample.

> 22We used the following standard conversion factors: 1 litre of Diesel corresponds to 10 kWh; gasoline: 9.1 kWh; lubricants: 11 kWh.

> 23A similar strategy can be found in Marin and Vona (2017).

24In particular, there is no a priori reason to believe that the quality of the self-reported capital stock the first year is necessarily better than the value in other years.

> 25The procedure is described in the appendix.

16
institutions in Africa and elsewhere. In Africa, banks hold 42.1 percent of microfinance assets compared with 48.6 percent in the rest of the developing world (Table 3). NGOs actually comprise a smaller share of the institutions in Africa than outside Africa (26 vs. 35 percent), though the African NGOs hold a larger share of total microfinance assets (20.4 vs. 11.6 percent). Non-bank financial institutions (NBFIs) appear to play a somewhat more important role outside Africa, while credit unions and cooperatives play a larger role in Africa. In all, the range of institutions in Africa appears to be about as diverse as elsewhere, and the reliance on more commercially oriented forms of microfinance similar. That conclusion is further supported in that group liability loans typically favored by less commercialized institutions represented only 15 percent of microfinance assets and 26 percent of loans in Africa in 2009 based on the MIX data, compared with 15 percent of assets and 20 percent of total loans in the rest of the developing world. Beck et al. (2011) also note that African MFIs shifted away from the group liability lending mechanisms made famous in Bangladesh under Muhammad Yunus early in their development. In West Africa, they argue that only individual liability mechanisms were used.

**Table 3: Share of Microfinance Assets, By Institutional Type, 2009**

||<br>_Afri_|<br>_% of Assets, by_<br>_ca_|<br>_MFI Type_<br>_Non-A_|<br>_frica_|
|---|---|---|---|---|
||_# of institutions_|_% of Assets_|_# of institutions_|_% of Assets_|
|Bank|12|42.06%|59|48.57%|
|Coops/Credit unions|39|23.71%|109|8.39%|
|NGO|44|20.42%|297|11.60%|
|NBFI|70|13.46%|320|29.68%|
|Rural bank|3|0.33%|55|1.65%|

<!-- Start of picture text --> 0.33%<br><!-- End of picture text --> <!-- Start of picture text -->
1.65%<br><!-- End of picture text -->

<!-- Start of picture text --> Rural bank<br><!-- End of picture text --> <!-- Start of picture text --> 3<br><!-- End of picture text --> <!-- Start of picture text --> 55<br><!-- End of picture text --> The growth in borrowers coincided with a declining trend in the financial performance of African MFIs as measured by the MIX’s Operational Self-Sufficiency (OSS) index, a trend that was more pronounced than in other developing regions (Figure 6, panel B).<sup>ix</sup> In part, this reflects the arrival to the MIX of new African MFIs that were less profitable than existing 23
CHARLS main respondents are a random sample of people over the age of 45, of which both main respondents and their spouses are asked for detailed information on themselves and on their families. In this paper, we are particularly interested in the transfers between the respondents and their adult children.<sup>5</sup> CHARLS has information on all living children of each respondent and spouse, no matter where they live. In order to fully employ the rich information on each of their children, the basic sample of interest are the children of the CHARLS respondents. Specifically we first chose the 789 households in which either the main respondent or his/her spouse was over 60.<sup>6</sup> The 2,667 adult children (25 or older<sup>7</sup> ) of the 789 respondents are then treated as our study sample.

Several sample restrictions are further applied according to different purposes of the study. For the estimation on transfers, we restrict the analyses to non-resident children (2,202 observations) because transfers within the household are not clearly specified conceptually and CHARLS, like other aging surveys, does not attempt to measure them. In analytical models exploiting family fixed-effects, only those families with at least two non-resident adult children are included (2,068 observations).

In order to use individual information on both parent and child, we match individual child and parental characteristics. We choose the information of the main respondent parent

> 5 The incidence of transfers between the respondents and their elderly parents are quite small (only 13.4%), so we did not take them into consideration in our analysis. Family transfers can entail interactions among members of three or even four generations, but it is beyond the scope of this paper to give a comprehensive treatment of this issue. 6 Only 16 main respondents do not have any children in the sample, who are dropped for the purpose of studying parent-child transfers.

> 7 We choose age 25 because many adult Chinese people younger than 25 are full-time college students who are incapable of supporting their parents.

9
42. (d) <u>Barriers to growth: About 75 percent of formal and 81 percent of informal</u> enterprises wanted to expand their business, and listed financial constraints as being the most daunting (see Table 10). Others were lack of adequate demand and (mainly for formal enterprises) lack of appropriate workers. The barrier of adequate financing indeed appears to be a key source of concern since, startup capital is overwhelmingly from private, non-institutional sources, for both types of enterprises. Underdeveloped credit markets – despite the impressive this growth in recent years - and lack of aggregate demand both appear to be barriers to firm growth. Given the different policy implications of the two, warrants further investigation.

<!-- Start of picture text --> Figure 17: Distribution of Start-up Capital and Annual Turnover of Informal<br>Enterprises<br>90<br>Startup Capital<br>80 (Dotted Line)<br>70<br>60<br>Percent<br>50 Annual Turnover<br>40 (Solid Line)<br>30<br>20<br>10<br>0<br><1,000 1,001-5,000 5,001-10,000 >10,000<br>USD<br>Source: UES, 2004<br><!-- End of picture text --> 43. (e) <u>Non-wage Benefits for Workers: The survey asked about several non-wage</u> benefits (see Table 9). As might be expected, overall, there is significant divergence in non-wage benefits claimed to be provided between formal and informal enterprises. Employers in formal enterprises claim to provide more substantial benefits than would be expected of such small ventures. However, this picture of non-wage benefits in urban enterprises must be taken with an important caveat. Since the survey asks questions of owners and managers, they may well have tailored their responses in the context of the prevailing laws and procedures. Therefore, further research is needed before conclusive statements can be made.

**Table** **<u>8: Constraints to Growth Reported by Urban Enterprises</u>**

|**Constraint**|**% of Formal Enterprises**<br>**Reporting “yes” **|**% of Informal Enterprises**<br>**Reporting “yes” **|
|---|---|---|
|Financial|60.1|81.0|
|Demand/Market|38.1|40.0|
|Appropriate workers|14.6|2.3|

|Note: Enterprises reported more than one constraint|
|---|

Source: UES, 2004 44. With this caveat, we find that less than half the formal enterprises give a written contract to their employees. A little over one-third give a contract to all their employees. Almost none of the informal enterprises provide a written contract and this is consistent with their “informal” status. This is not surprising given the findings presented in Section 1 on the time and costs of contract enforcement, but may also be a reflection of high levels of social capital in Timor-Leste. However, surprisingly, severance pay appears to be the norm among formal enterprises, as the large majority of them give severance pay to their employees. In fact only 7.5 percent of them do 21
Table 3: Determinants of School Enrollment: male youngest cohort, with and without test scores

|||**With**|**test scores**|||**Without**|**test scores**||
|---|---|---|---|---|---|---|---|---|
||Public<br>general|Public<br>vocational|<br>Private<br>general|Private<br>vocational|Public<br>general|Public<br>vocational|Private<br>general|Private<br>vocational|
|Junior secondary test scores|||||||||
|Middle third|13.7***|8.3*|-3.4|-18.6***|||||
||(4.8)|(4.2)|(4.6)|(4.2)|||||
|Top third|23.6***|16.4***|-17.4***|-22.7***|||||
||(6.5)|(5.6)|(3.9)|(4.8)|||||
|Personal characteristics|||||||||
|Repeated grade in junior high|13.3|-9.6|-0.4|-3.4|9.7|-11.4**|0.6|1.1|
||(12.4)|(6.0)|(11.8)|(11.7)|(12.1)|(5.0)|(13.2)|(13.9)|
|Lived in small town at age 12|5.4|-0.0|-9.0**|3.6|4.8|0.7|-9.4**|3.9|
||(4.2)|(3.2)|(4.1)|(4.5)|(4.4)|(3.5)|(4.2)|(4.8)|
|Lived in big city at age 12|4.3|4.6|-9.1**|0.2|1.1|5.2|-7.9*|1.6|
||(5.6)|(5.2)|(4.6|(5.8)|(5.0)|(5.2)|(4.7)|(5.7)|
|Height|-0.5|0.2|0.0|0.3|-0.4|0.2|-0.0|0.2|
||(0.3)|(0.3)|(0.3)|(0.3)|(0.4)|(0.3)|(0.3)|(0.3)|
|Parental education|||||||||
|Father graduated elementary|-7.2|11.9|-9.2|4.5|-0.4|0.2|-0.0|0.2|
||(9.2)|(11.0)|(7.6)|(9.0)|(0.4)|(0.3)|(0.3)|(0.3)|
|Father graduated junior secondary|-2.5|7.9|-7.2|1.8|-1.9|7.5|-6.6|1.0|
||(10.7)|(10.0)|(9.1)|(9.4)|(9.9)|(11.4)|(8.2)|(10.5)|
|Father graduated senior secondary|-8.7|6.6|5.4|-3.3|-1.9|7.5|-6.6|1.0|
||(10.3)|(9.4)|(11.5)|(10.2)|(11.6)|(10.3)|(9.5)|(10.8)|
|Father graduated university|-1.6|4.1|3.4|-5.9|-7.2|7.4|6.0|-6.2|
||(11.4)|(9.4)|(13.7)|(10.6)|(11.7)|(10.4)|(12.3)|(10.7)|
|Father attended vocational school|6.3|8.6|-12.8**|-2.1|0.1|4.3|3.1|-7.5|
||(8.8)|(8.1)|(5.4)|(7.9)|(12.7)|(10.1)|(14.3)|(10.9)|
|Mother graduated elementary|4.9|-1.5|3.8|-7.2|5.7|8.5|-12.8**|-1.4|
||(5.4)|(5.2)|(5.7)|(6.1)|(9.2)|(8.4)|(5.6)|(8.6)|
|Mother graduated junior high|0.5|-4.3|12.5|-8.7|0.9|-4.0|11.5|-8.4|
||(6.5)|(5.3)|(8.7)|(7.9)|(6.8)|(5.6)|(9.0)|(7.9)|
|Mother graduated senior high|13.7|-4.9|-2.6|-6.1|14.7|-4.9|-3.1|-6.7|
||(9.6)|(7.0)|(8.4)|(9.8)|(10.8)|(7.2)|(8.7)|(10.4)|
|Mother graduated university|22.8|-8.0|-5.8|-9.0|29.0**|-6.2|-8.8|-14.0|
||(15.3)|(7.8)|(9.8)|(16.9)|(14.0)|(9.1)|(8.1)|(13.8)|
|Mother attended vocational|0.9|-10.2*|5.3|4.0|1.7|-9.7|4.2|3.7|
||(7.8)|(5.4)|(11.6)|(10.2)|(8.4)|(5.9)|(12.0)|(10.4)|
|Base case probability|28.4|0.3|46.1|25.3|44.1|0.7|39.2|15.9|
|Observations|||745|||7|37||
|R-Squared|||0.199|||0.|158||

Notes: *** 1% significance, ** 5% significance, * 10% significance; figures are marginal effects in percentage points; estimation includes province of junior secondary graduation fixed effects and all variables listed in Appendix 1; standard errors in <u>parentheses, they are robust to heteroskedasticity and clustered at subdistrict level.</u> 13
<!-- Start of picture text --> ed<br>w+<br>ba<br>=-<br>co<br>¢<br>§<br>r at<br>g<br>a ol<br>Zo<br>6 oN<br>S in —— Upper Bound<br>g 2 — PDF<br>2 —— Lower Bound<br>=<br>-3 -2 -1 a 1 2 3<br>nonexceedance probability<br><!-- End of picture text -->
# **b.** Skills, Training and Innovation<sup>23</sup>

There appears to be a critical mismatch between the current skills of workers in many ECA countries and the types of skills demanded by the region’s employers: (i) education and training institutions in the region provide inappropriate types of skills to their graduates; and (ii) large proportions of students lack basic competencies in literacy and numeracy (World Bank, 2012b). Figure 13 below illustrates that a large share of employers in many ECA countries rate insufficient or inappropriate worker skills as “major” or “very severe” constraints to growth. Skills constraints appear most severe in CIS countries (Belarus, Kazakhstan, Moldova, Russian Federation and Ukraine). In other countries, such as Montenegro and Hungary, constraints appear the least severe. In other words, maintaining the status quo in their education and training policies risks leaving many of the region’s economies lagging in the rapidly changing global environment. These mismatches are likely to get accentuated as the type of skills demanded by firms continues shifting as a result of a greener economy.

**Figure 13 – Lack of Appropriate Skills Hinder Business in ECA**

Distribution of firms in ECA region that consider worker skills a “major” or “very severe” constraint, 2008 <!-- Start of picture text --> 9<br>Mean = 30.2<br>8<br> TJK<br>7 EU10+1 and Turkey<br> UZB<br>Western Balkans<br>6<br>AZE ARM CZE LI CIS<br>5 GEO EST MI CIS<br>BIH<br>4 KGZ HRV<br>KOS<br>ALB LVA UKR<br>3 MKD<br> POL MDA<br>2 SRB BGR<br> SVK<br>KAZ<br>MNE SVN TUR LTU<br>1 RUS<br>HUN ROM BLR<br>0<br>x=% of firms (in respective countries) who consider education as an obstacle<br>Countries<br><!-- End of picture text --> Source: World Bank (2012b). Note: LI = low-income, MI = middle income Though students, workers and firms are the principal actors involved in accumulating skills and utilizing knowledge in the labor market, governments play an important enabling role in the process<sup>24</sup> . Government policies shape the environment and set the rules in which industrial and labor-market interactions take place; therefore, government’s role is crucial for ensuring that such interactions produce

> 23 This section was prepared for this paper by Igor Kheyfets (ECSH2)

> 24 De Ferranti, et al. (2003)

26
|Land Allocation for Social and Economic Development|Mudita Chamroeun|
|---|---|
|Land Allocation for Social and Economic Development|Mudita Chamroeun|
|Land Allocation for Social and Economic Development|Mudita Chamroeun|
|Land Allocation for Social and Economic Development|Mudita Chamroeun|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|IN Maharashtra Water Sector Improvement Project|Jun Matsumoto|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Assam Agricultural Competitiveness Project|Manivannan Pathy|
|Support to Brazil's National PolicyDialogue IDF|Deborah L. Wetzel|
|Support to Brazil's National PolicyDialogue IDF|Deborah L. Wetzel|
|EnergyEfficiencyGEF Project|Peter Johansen|
The number of doctors per 1,000 inhabitants is extremely low in Mozambique, even after controlling for income per capita, as shown in Figure 4. Shortages of medical assistants (middle-level degree) and nurses are also acute. The incentive system is to blame since the salaries of those medical assistants are substantially lower than those of doctors, so students pursue the 6 years of the medicine undergraduate degree rather than graduate with a bachelors degree after 3 years. However, such middle-level professionals can be crucial for the provision of affordable medical services as shown in Box 1.

**_Table 7: Private Medical Services Providers in 2006_**

||Maputo|Rest of Country|
|---|---|---|
|Health Post|31|67|
|Medical Post|3|1|
|Health Center|31|14|
|Clinic|11|5|
|Doctors' Office|42|18|
|Institute|2|0|
|Laboratory|10|1|
|Pharmacy|62|121|

Source: Ministry of Health.

**_Figure 4:    Health Inputs and Outputs_**

<!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2004<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>Panel A. Physicians<br>per Capita<br>Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>5<br>4<br>3<br>2<br>1<br>in 2004<br>Physicians (per 1,000 people)<br>150<br>100<br>in 2006 50<br>0<br>-50<br>Mortality rate, infant (per 1,000 live births)<br><!-- End of picture text --> <!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2003<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>150<br>100<br>in 2003 50<br>Births attended by skilled health staff (% of total)<br><!-- End of picture text --> Panel B. Births Attended by Skilled Health Professional Panel C. Infant Mortality Rate Source: World Development Indicators.

23