rafmacalaba/datause-encoder-data huggingface dataset:
endogenous location of dams, which, as Duflo and Pande (2007) and Strobl and Strobl (2010) demonstrate, can substantially bias the derived benefits, at least for large dams.

In this article, we address the paucity of research on the small versus large dam controversy by estimating and comparing the cropland productivity effects of small and large irrigation dams in South Africa. We believe that our article provides the first comparative quantitative study of the relative impact of large and small dams. In this regard, South Africa is arguably a particularly suitable case study. South Africa has a fairly varied climatology over time and space, allowing for considerable interbasin water transfers via irrigation dams. Additionally, after decades of investment, the country stands out for its operation of the largest number of dams on the African continent, although it is still argued to be operating below its potential, with only approximately 10 percent of the cultivated area equipped for irrigation (Foster and Briceño-Garmendia 2010).<sup>2</sup> To complete the task at hand in this article, we assemble a 20-year annual panel data set of large and small irrigation dams and cropland productivity for river basins in South Africa. Additionally, we employ the instrumental variable (IV) approach suggested by Duflo and Pande (2007), which allows us to address the inherently endogenous nature of dam location. Our analysis reveals a number of interesting results. In line with the literature cited above, our estimates suggest that large irrigation dams improve the productivity of downstream cropland. However, the impact of local large dams crucially depends on controlling for small dams and their interaction with these dams. More 4
**<u>Table 9: Difference-in-differences – Village Infrastructure</u>**

||**w/o**<br>|**FE**<br>|**w/ vill**<br>|**age FE**<br>|
|---|---|---|---|---|
|**Dependent Variable**|**treateff**|**pval**|**treateff**|**pval**|
|Private primary school exists|0.0162|[0.610]|0.0217|[0.627]|
|Private middle school exists|-0.0180|[0.489]|-0.0204|[0.572]|
|Private secondary school exists|0.00849|[0.810]|0.00085|[0.986]|
|Any overhead tank exists|0.0600|[0.317]|0.0600|[0.478]|
|No. of government school|-|[0.00837|-0.230*|[0.0600|
|No. of teachers in govt. schools|-1.340**|[0.0172]|-1.340*|[0.0897|
|No. of anganwadis|0.0600|[0.481]|0.0600|[0.618]|
|No. of employees in anganwadi|-0.0800|[0.738]|-0.0800|[0.813]|
|No. of handpumps|1.475**|[0.0173]|1.510*|[0.0828|
|No. of tubewells|0.0297|[0.979]|0.0441|[0.978]|
|No. of water sources constructed- last 12|-1.054|[0.134]|-1.051|[0.290]|
|No. of roads|0.930|[0.557]|0.930|[0.678]|
|No. of gravel roads|0.310|[0.672]|0.310|[0.765]|
|No. of roads constructed- last 12 months|-1.867**|[0.0158]|-1.869*|[0.0860|
|Number of roads free of garbage|2.521|[0.247]|2.622|[0.399]|
|Number of roads with a drain|0.0500|[0.960]|0.104|[0.942]|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

**Table 10: Difference-in-differences - Village Activity** **<u>and Works</u>**

||**w/**<br>|**o FE**<br>|**w/ vil**<br>|**lage FE**<br>|
|---|---|---|---|---|
|**Dependent Variable**<br>|**treateff**|**pval**<br>|**treateff**|**pval**<br>|
|Activity in last 12 mons - Roads<br>|0.0114|[0.913]<br>|0.0245|[0.868]<br>|
|Activity in last 12 mons - Transportation|-0.0410|[0.458]|-0.0396|[0.615]|
|Activity in last 12 mons - Schools|-0.0716|[0.287]|-0.0688|[0.472]|
|Activity in last 12 mons - Health|0.0476|[0.518]|0.0515|[0.624]|
|Activity in last 12 mons - Water|0.0378|[0.716]|0.0397|[0.789]|
|Activity in last 12 mons - Sanitation|0.0629|[0.536]|0.0632|[0.662]|
|Activity in last 12 mons - Electricity|0.114|[0.281]|0.101|[0.503]|
|Activity in last 12 mons - Irrigation|-0.122*|[0.0611]|-0.120|[0.194]|
|Agr. extension officers visit|-0.102|[0.328]|-0.106|[0.472]|
|Taluk Engineer visits|0.0343|[0.775]|0.0447|[0.794]|
|EO visit|-0.0251|[0.824]|-0.0167|[0.917]|
|NGO active in village|-0.0318|[0.662]|-0.0276|[0.790]|
|Traditional Panchayat active|0.108|[0.301]|0.108|[0.467]|
|Male agricultural wage|9.123***|[0.00745]|10.04**|[0.0449]|
|Female agricultural wage|2.620|[0.318]|2.647|[0.499]|
|Oligarchy|0.0108|[0.602]|0.0109|[0.716]|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

34
with those obtained from OLS.<sup>10</sup> Also, clustering standard errors at the weather station level still yields highly significant coefficients.

As mentioned earlier, the dependent variable in this regression was generated by manually matching customers from one year to another, and therefore is likely measured with some error. While there is no reason to believe that this measurement error is correlated with any independent variables in the regression, since the dependent variable in the regression is a dummy variable this can lead to downward bias on the estimated coefficients. In order to get a feel for the potential magnitude of this error I run simulations where I assume that the BASIX data has been matched completely correctly, and then induce ‘measurement error’ by randomly changing the dependent variable of whether people purchased the following year or not. With the introduction of 10% matching errors (with an equal probability of a mismatch for buyer or non-buyers), the coefficient on receiving a payout in the full sample (Column 1) falls from .090 to an average of .072 over 1,000 simulations. For the marketing restricted sample in Column 2, it drops from .222 to .178. In other words, if we assume 10% matching errors, then the estimated coefficients are likely to be underestimated by around 20%. It also may be possible that most of the error came from being unable to identify positive matches, possibly due to different members of a household signing the insurance contract from year to year. Repeating the above simulation but assuming that only people who were found not to have bought the next year could have been errors, the coefficients become underestimated by around 10%. While the exact form and structure of the matching errors cannot be known, it is possible that the reported coefficients are somewhat lower (in absolute value) than the true coefficients.

Overall, the results indicate that receiving an insurance payout correlates with a roughly 9-22% higher chance of repurchasing the next year compared with someone who purchased insurance but did not receive a payout. They also suggest that higher payouts lead to a greater chance of repurchasing, and that very low payouts may actually have a negative effect. The next section will explore some possible mechanisms for this result.

# **4 Explanations for Main Result**

The results of the previous section show a simple correlation that people who receive insurance payouts are more likely to purchase insurance the following year. There are a number of reasons consistent with a neo-classical frameworkwhy this may be the case, and in this section search for support of these explanations in the data.

I first consider the hypothesis that a rainfall shock as opposed to the insurance payout may cause people to be more likely to purchase the following year. This may be the case due to people learning about the effects or shocks, or experiencing changes in wealth or liquidity. To do this I look at villages in the first year they were offered insurance, and see if a rainfall shock the previous year correlates with

> 10Results not shown.

8
|(10)|-0.0052**<br>(0.0024)<br>-0.0116***<br>(0.0013)<br>0.0002***<br>(0.0001)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>specifc<br>months<br>the|
|---|---|
|(9)|-0.0052**<br>(0.0024)<br>0.0013**<br>(0.0006)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>des country-<br>s death at 12<br>s clustered at|
|(8)|-0.0049**<br>(0.0023)<br>0.0331***<br>(0.0021)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>olumn (1) inclu<br>y, is redefned a<br>t standard error|
|(7)|-0.0050**<br>(0.0024)<br>-0.0027<br>(0.0038)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>me trends. C<br>ant mortalit<br>0.16. Robus|
|(6)|-0.0056**<br>(0.0024)<br>-0.0040<br>(0.0024)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>ry-specifc ti<br>t variable, inf<br>s are equal is|
|(5)|-0.0051**<br>(0.0024)<br>-0.0005<br>(0.0021)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>d linear count<br>the dependen<br>oint dummie|
|(4)|-0.0045*<br>(0.0023)<br>1,704,061<br>499,950<br>56<br>Yes<br>No<br>ed effects, an<br>n column (3),<br>Completion P|
|(3)|-0.0053**<br>(0.0025)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>birth-year fx<br>racteristics. I<br>on Point and<br>0.1.|
|(2)|-0.0057**<br>(0.0022)<br>1,770,705<br>501,800<br>56<br>Yes<br>No<br>ontrols, cohort-<br>her-specifc cha<br>ng if the Decisi<br>* p_<_0.05, * p_<_|
|(1)|-0.0074***<br>(0.0025)<br>1,635,743<br>463,836<br>54<br>No<br>Yes (see notes)<br>ude exogenous c<br>effects and mot<br>he F statistic testi<br>s. *** p_<_0.01, *|
|Dependent Variable:<br>Infant Mortality|Decision Point<br>Completion Point<br>MDRI<br>Original Decision Point<br>Short Birth Interval<br>Age<br>Age2<br>Number of Observations<br>Number of Mothers<br>Number of Countries<br>Mother FE<br>Country FE<br>Notes: All estimations incl<br>mother’s-birth-cohort fxed<br>or earlier. The p value of t<br>country level in parenthese|

33
5 not. But what is the benchmark against which firms in a given country are expected to rate the severity of a particular constraint? Except possibly for multinationals active in many economies, firms will have no “absolute” scale against which the effect of individual constraints can be assessed. Further, any simple quantitative criterion (how much could sales increase if a particular constraint is relaxed?) can mislead in cases where constraints bind firms simultaneously. In such cases, even if all constraints together are important, no one constraint may be important on its own, and we might expect to see a cluster of constraints identified.

There is no simple answer to such questions. Firms’ responses are likely to embody a blend of rating and rankings—constraints will be stressed if seen as seriously problematic, and particularly if seen as serious relative to other constraints experienced by the firm. To help firms discriminate in this way, without forcing an absolute ranking, the procedure advocated by McCarty and Shrum seems the most appropriate. First introduce a “showcard” with all alternatives to help firms to consider the most and least important ones, then ask for ratings. The Enterprise Surveys discussed below do not formally use the showcard method. However, we understand that in practice managers are usually informed about the list of constraints before being asked to rate their importance, so that the procedure in general conforms to the McCarty-Shrum recommendation.<sup>2</sup> Another important issue raised by Iarossi (2006) and others concerns the ordering of alternatives. This has been shown by studies to potentially influence responses. One approach to this problem could be to randomize the order of options. This is not done in the ICA surveys; however, the ordering of the options is the same across all countries, so that differences in response cannot be ascribed to order changes. Finally, Bourguignon (2006) notes the importance of distinguishing between the perception of a constraint and the impact on performance if this constraint is relaxed. This bears on how the question should be framed and the importance of confirming opinion-based indicators with

> 2 From discussions with World Bank staff fielding survey instruments, it is clear that in some cases, enumerators are instructed to show managers the entire list.  In other case, they may do so anyway in order to speed up the response process.
**Table 1. VAT rates in OECD countries as at 1 January 2022**

||Standard rate|Reduced rates**|
|---|---|---|
|AUS|10.0|0.0|
|AUT*|20.0|10.0/13.0|
|BEL<br>|21.0|0.0/6.0/12.0|
|CAN*|5.0|0.0|
|COL*|19.0|0.0/5.0<br>|
|CHE|7.7|0.0/2.5/3.7|
|CHL|19.0|-|
|CRI|13.0|1.0/2.0/4.0|
|CZE|21.0|10.0/15.0|
|DEU|19.0|7.0|
|DNK|25.0|0.0|
|ESP*|21.0|4.0/10.0|
|EST|20.0|0.0/9.0|
|FIN|24.0|0.0/10.0/14.0|
|FRA*|20.0|2.1/5.5/10.0|
|GBR|20.0|0.0/5.0|
|GRC|24.0|6.0/13.0|
|HUN|27.0|5.0/18.0|
|IRL|23.0|0.0/4.8/9.0/13.5|
|ISL|24.0|0.0/11.0|
|ISR*|17.0|0.0|
|ITA|22.0|4.0/5.0/10.0|
|JPN|10.0|8.0|
|KOR|10.0|0.0|
|LTU|21.0|5.0/9.0|
|LUX|17.0|3.0/8.0/14.0|
|LVA|21.0|5.0/12.0|
|MEX*|16.0|0.0|
|NLD|21.0|9.0|
|NZL|15.0|0.0|
|NOR|25.0|0.0/12.0/15.0|
|POL|23.0|5.0/8.0|
|PRT*|23.0|6.0/13.0|
|SLV|22.0|5.0/9.5|
|SVK|20.0|10.0|
|SWE|25.0|0.0/6.0/12.0|
|TUR|18.0|1.0/8.0|

Source: OECD (2022)

# **Table notes**

*Austria: a standard rate of 19% applies in Jungholz and Mittelberg. Canada: provincial sales taxes also apply. Colombia: 0% rate applies to hygiene products in the department of Amazonas. France: rates of 0.9%, 2.1%, 10%, 13% and 20% apply in Corsica; rates of 1.05%, 1.75%, 2.1% and 8.5% apply to overseas departments excluding French Guyana and Mayotte. Israel: a rate of 0% applies when an Eilat resident dealer buys goods from Eilat non-residents. Mexico: 8% reduced rate applies in border regions. Portugal: rates of 5%, 10% and 18% apply in the Azores; rates of 5%, 10% and 22% apply in Madeira. Spain: rates of 0%, 2.75%, 3%, 7%, 9.5%, 13.5% and 20% apply in the Canary Islands; rates of 0.5%, 1%, 2%, 4%, 6%, 8%, 9% and 10% apply in Ceuta and Melilla. Temporary COVID-19-related rates are not specified in Table 1 (see OECD, 2022, for details). **Includes zero rates on domestic supplies. Excludes zero rates on exports and other supplies subject to similar treatment such as international transport or supplies to embassies, international organisations and diplomatic missions.

5
**Figure 5: Informal employment rates estimated using ESS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>ESS informal Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <u>Source: Authors’ calculation using ESS.</u> <u>Note: In the ESS, informal employment is defined as working without a contract in the main job. Rosstat’s official measure of</u> informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted. 95% confidence intervals are shown using dotted lines.

**Figure 6: Informal employment rates in 2016 across different surveys**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Official Rosstat ESS LFS (broad) RLMS (primary) LiTS<br><!-- End of picture text --> <u>Source: Rosstat and authors’ calculation using various surveys.</u> <u>Note: Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in</u> their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. In the LFS, informal employment includes workers that are employed but not at an enterprise in their primary job, workers employed without a written contract in their primary job, and selfemployed workers without registration. In the LiTS, informal employment is defined as working without a written contract in the main job. In the ESS, informal employment is defined as working without a contract in the main job. Rosstat’s official measure of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are from 2016 surveys and weighted.

Informal Employment and Worker’s Wellbeing in Russia / June 2019 34
The previous results are now used to shed some light on the evolution of the wage premium between 1996 and 2015.

# **The Supply of Skilled Workers Increased, but So Did Misallocation**

To set the stage, table 12 presents census data to track the evolution of the size and type distribution of firms and employment between 1998 and 2013. Notice first that average firm size _fell_ . Second, the number of small and very small firms increased more than the number of medium and large firms; in parallel, the number of formal firms _fell_ (by almost 8 percent), while that of informal firms _rose_ quite substantially, by 64 percent. In parallel, recall from section 2.4 that the share of six-digit sectors where informal firms are a majority increased during this period in manufacturing, commerce and services.

**Table 12. Size and Type Distribution of Firms and Employment, 1998–2013**

|||_thousands_<br>||
|---|---|---|---|
||**1998**|**2013**|**% change**|
|**Total**||||
|Firms|2,693.5|4,099.1|52.2|
|Employment|11,805.0|17,394.3|47.3|
|Average firm size|4.38|4.24|0.0|
|**Firms by size**||||
|1–5|2,460.77|3,754.0|52.5|
|6–10|118.7|186.4|57.0|
|11–50|91.5|127.9|39.7|
|51+|22.8|30.6|34.2|
|**Firms by type**||||
|Formal*|443.2|408.0|−7.9|
|Informal**|2,249.0|3,691.0|64.1|
|**Employment by firm size**||||
|1–5|4,309.8|6,903.4|60.1|
|6–10|884.9|1,374.2|55.2|
|11–50|1,879.7|2,663.8|41.7|
|51+|4,730.5|6,452.7|36.4|
|**Employment by firm type**||||
|Formal*|7,291.2|7,710.2|5.7|
|Informal**|4,513.8|9,684.0|114.5|

39
**Table 2 Percentage of estimated** **_Zakah_ proceeds to GDP in selected Muslim countries**

||Z1<sup>~~20~~</sup>|Z2|Z3|
|---|---|---|---|
|Egypt,Arab Rep.|2.0|3.9|4.9|
|Indonesia|1.0|1.7|2.0|
|Pakistan|1.6|3.5|4.4|
|Qatar|0.9|3.7|3.2|
|Saudi Arabia|1.2|3.7|3.4|
|Sudan|4.3|6.3|6.2|
|Syria|1.5|3.1|3.1|
|Turkey|1.9|4.9|7.5|
|average|1.8|3.9|4.3|

Source: (Kahf 1989)

**Table 3 Resource shortfall to fill the poverty gap**<sup>21</sup>

|**(1)**<br>**Country**<br>**name**|**(2)**<br>**Survey**<br>**year**|**(3)**<br>**GDP (PPP)**<br>**current USD**<br>**(million)**<br>**Group**<br>38487887|**(4)**<br>**Total pop.**<br>**(million)**<br>**1: countries**<br>2747|**(5)**<br>**Poverty gap**<br>**at $2 a day**<br>**(PPP) (%)**<br>**with moderate re**|**(6)**<br>**Poverty gap**<br>**at $1.25 a day**<br>**(PPP) (%)**<br>**source shortfall (**|**(7)**<br>**Resource**<br>**shortfall**<br>**under $2 per**<br>**annum**<br>**(million)**<br>**<=1.0 percent of**<br>3208|**(8)**<br>**Resource**<br>**shortfall**<br>**under $1.25**<br>**per annum**<br>**(million)**<br>**GDP)**<br>000|**(9)**<br>**Resource**<br>**shortfall**<br>**under $2**<br>**per annum**<br>**as % of**<br>**GDP**|**(10)**<br>**Resource**<br>**shortfall**<br>**under**<br>**$1.25 per**<br>**annum as**<br>**% of GDP**|
|---|---|---|---|---|---|---|---|---|---|
|**Malaysia**|2009|,.|.|0.16|0.00|.|.|0.01|0.00|
|**Kazakhstan**|2007|169,633.06|15.48|0.26|0.05|29.39|3.53|0.02|0.00|
|**Jordan**|2006|26,142.82|5.54|0.62|0.10|25.08|2.53|0.10|0.01|
|**Albania**|2008|26,449.68|3.14|0.85|0.19|19.50|2.72|0.07|0.01|
|**Azerbaijan**|2008|76,730.04|8.68|1.52|0.25|96.31|9.90|0.13|0.01|
|**Maldives**|2004|1,197.43|0.29|2.53|0.14|5.33|0.18|0.44|0.02|
|**Iran**|2005|643,503.42|69.09|1.80|0.34|907.80|107.17|0.14|0.02|
|**Kyrgyz**<br>**~~Reublic~~**|2007|10,620.40|5.23|5.46|0.08|208.65|1.91|1.96|0.02|
|**~~p~~**<br>**Syrian**<br>**~~Arab~~**|2004|70,017.06|18.51|3.28|0.20|443.24|16.89|0.63|0.02|
|**Gabon**|2005|17,839.03|1.37|5.02|0.90|50.18|5.62|0.28|0.03|
|**Turkey**|2005|781,243.40|71.17|2.64|0.88|1,371.57|285.74|0.18|0.04|
|**Egypt,**<br>**~~Arab Rep~~**|2005|333,218.41|77.15|3.45|0.39|1,943.13|137.29|0.58|0.04|

> 20 (Kahf 1989): “Z1 was estimated accordance with the majority traditional view according to which _Zakah_ was levied on agriculture, livestock, stock in trade, gold, silver and money. Z2 was based in according with the views of contemporary Muslim scholars where _Zakah_ can be deducted from net returns of manufacturing concerns and building rents and from net savings out of salaries. Z3 was based on Malikite views, where _Zakah_ base includes buildings and other fixed assets except those assigned for personal and family use.

> 21 (2)-(6): Data source: World Bank Databank; (7) = 2*(5)*365*(4)/100; (8) = 1.25*(6)*365*(4)/100; (9) = (7)/(3)*100; (10) = (8)/(3)*100

19
# **_remains competitive, and many overqualified jobseekers compete on jobs that require education that is lower than their qualifications._**

The number of vacancies by education requirements, illustrated in Figure 8, shows that the online job portal at Rozee.pk is primarily for jobs that require undergraduate and postgraduate education. Of the vacancies, 75% require an undergraduate or postgraduate education. Thus, the portal is mostly for undergraduate and graduate-level jobs and may have a potential for improving job matching of postsecondary education graduates, whose unemployment rate is high (Bossavie et al., 2018). On the other hand, the tightness (i.e., vacancies per jobseeker) by education indicates that finding jobs for graduate degree holders is not easy because the tightness is low at 0.05.<sup>16</sup> (That is, 100 jobseekers with graduate degrees compete for five jobs that require graduate degrees.) By the same token, while the total number of job vacancies that require undergraduate degrees is the greatest, the tightness at the undergraduate level is as low as 0.17 because of the large number of jobseekers who have undergraduate degrees. By contrast, the tightness at the secondary or less education level is the highest at 0.27 since jobseekers whose highest education level is secondary or lower are few.

**Figure 8. Labor demand, supply, and market tightness by education**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : The number of vacancies is the total posted since January 2012. The number of jobseekers is the total registered since January 2012.

The lower tightness at the higher education levels may induce high-educated jobseekers to look for low-education jobs, since high-education jobs are less available. In fact, the data show that for jobs that require secondary education or lower level of education, 85% of the applicants have undergraduate and postgraduate degrees (Figure 9). Similarly, for jobs that require a higher secondary education level, 87% of the applicants have undergraduate and postgraduate degrees. Thus, the majority of applicants for secondary-level jobs are overqualified in terms of education. A considerable number of mismatches in terms of education may occur. The key finding from this analysis is that there is an insufficient number of jobs in the online job portal that are suitable for bachelor and graduate degree holders.

> 16 In March 2018, the same ratio for the United States was 1.01. The lowest during the last 10 years was 0.15 in July 2009 (https://fred.stlouisfed.org/series/DHIDFHVTUR).

9