rafmacalaba/datause-encoder-data huggingface dataset:
**Chart 7. Loans provided by banks and other financial intermediaries (OFIs) (CZK billions)**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>12/05 12/06 12/07 12/08 12/09 12/10<br>Banks OFI<br>Source: CNB<br><!-- End of picture text -->

**Chart 8. Corporate loans by industry (% of total corporate loans)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Agriculture, mining and energy Manufacturing<br>Construction Trade and transport<br>Real estate Other services<br><!-- End of picture text --> Source: CNB

**Chart 9. Credit to nonfinancial corporations by creditor, (%)**

<!-- Start of picture text --> Domestic banks<br>13.0%<br>20.0% Non-residents<br>12.5%<br>Domestic non-<br>1.0% financial corporations<br>Other domestic<br>financial<br>intermediaries<br>Government<br>53.5%<br>Source: CNB, financial accounts<br><!-- End of picture text -->

**Chart 10. Shares of foreign currency bank loans (as of end-2009; as % of total loans to given sector)**

<!-- Start of picture text --> Latvia<br>Estonia<br>Lithuania<br>Hungary<br>Romania<br>Poland<br>Bulgaria<br>Slovenia<br>Slovak Republic<br>Czech Republic<br>0 20 40 60 80 100<br>Households Non-financial corporations<br>Source: ECB<br>Note: Slovak Republic and Slovenia were already members of the<br>euro area in 2009, so their foreign currency loans comprise<br>currencies other than EUR.<br><!-- End of picture text --> The banking system is completely controlled by foreign banks, usually from other EU countries.<sup>3</sup> In 2010, the direct share of foreign banks in total assets was 80 percent, but as some banks (mainly specialized building savings societies) are owned by other banks domiciled in the Czech Republic, but with foreign owners, the share of banking sector under (direct and indirect)

> 3 For a detailed description of the Czech financial and banking system, see CNB (2010).

6
**Figure 2:** Gain in GDP from closing the gender employment gap (%) for each country in MENA— comparing GEGI (long run), short-run (fixed capital) and the LTGM-NR (by 2050)

<!-- Start of picture text --> Aggregate capital does not respond (SR)<br>2050 LTGM<br>Basic GEGI (Aggregate capital fully adjusts) 86<br>79<br>74<br>70<br>68<br>63 63<br>56 56<br>54<br>49 49 48<br>44 46 45 45 43<br>41 41<br>35 37 33 34 36 34 35 37 36<br>29 27 28 30 30<br>2225 23 25 25 22 23<br>19 20 21<br>15<br>12 14 12<br>10<br>7 9 7 6 5 8 7<br>2<br>QAT ARE BHR KWT OMN LBY SAU DJI MAR TUN LBN EGY DZA IRN PSE JOR SYR IRQ YEM<br>100<br>80<br>60<br>40<br>Percentage points increase<br>20<br>0<br><!-- End of picture text --> Note: The blue lines are the Gender Employment Gap Index (GEGI) from Pennings (2022) (ARE, BHR, KWT, OMN, QAT and SAU are adjusted for unequal male-female populations). The green lines are the 2050 gains in output per capita from closing the gender gap by 2050 obtained calibrating the LTGM/LTGM-NR. The red lines are the GEGI adjusted by the labor shares from PWT 10 (or 0.5 if this number is below 0.5 and it is a non-resource rich economy).

increase in GDPPC.

# **3. Transitional gains to 2050 using the Long Term Growth Model (LTGM)**

The analysis of the previous section raises two practical issues in estimating the effect of closing gender employment gaps on GDP growth. First, for most countries, there is a large difference between our short-run and long-run scenarios. How fast is the convergence from the short run to the long run? Second, the policy experiment in the GEGI—a one-off closing of gender employment gaps through higher employment rates of women—represents a fundamental change to the organization of societies in MENA, which can only be achieved over many years. As such, there is not only the issue of the speed of capital accumulation, but also the speed at which gender employment gaps themselves are closed.

In this section, we use a related Solow-Swan neoclassical growth model—the Long Term Growth Model (LTGM; Loayza and Pennings (2022))—to estimate a more realistic transition path for each MENA country. Specifically, our policy experiment in the LTGM is that the gender employment gap is fully closed by 2050 in each economy, with a linear progression towards that goal. This means, for example, that if there is a 60 percentage point (ppt) gap in employment rates—male employment rate is 78% and the female employment rate is 18% as for Saudi Arabia (with slightly outdated data)—then over a 30 year horizon to 2050 the female employment rate would increase by 8
and asset ownership, along with individual-level information including age, gender, schooling, and occupation. Using this data in the ranking stage, planners estimated predicted household welfare following a proxy-means testing (PMT) approach. PMT formulas were constructed based on districtspecific consumption regressions to explicitly account for heterogeneity across regions. The full set of variables used in this stage is available in TNP2K (2014).

Although the PMT approach can be a cost-effective means of identifying beneficiaries of social programs, it is also prone to errors (Grosh and Baker, 1995). In particular, targeting errors may occur due to weak predictive performance of the consumption models within the estimation sample (e.g., due to constraints on the set of socioeconomic variables available for use in the PMT regressions). The Indonesian PMT models have a predictive performance that appears similar to PMT regressions in other countries. On average, the PMT models used to rank households in the UDB have an R- squared of 0.5, a rank correlation between actual and predicted consumption of 0.67, and predicted model targeting error rates at the 40<sup>th</sup> percentile cutoff of about 30 percent (Bah, 2013). Below, information outside the UDB is used to examine the potential targeting improvements associated with increasing the predictive accuracy of the PMT.

Before proceeding, Figure 1 summarizes the multi-stage process of creating Indonesia’s UDB registry of 25 million households. The remainder of the paper investigates the overall accuracy of this unified targeting registry.

# **II. Empirical Strategy: Assessing Targeting Accuracy**

Targeting accuracy is measured based on the discrepancy between intended and actual recipients. Researchers typically identify these discrepancies using data on household expenditures (or income) and receipt of government benefits from a single survey (see, e.g., Coady et al., 2004). Indeed, many evaluations rely on self-reported program receipt and poverty status after programs have begun.

Building upon Coady and Parker’s (2009) innovative work on targeting effectiveness in Mexico, we evaluate the UDB’s targeting performance using actual administrative data on household eligibility 10
Analytical solutions that accurately delineate the real world economy are thus difficult to obtain since the problem formulation would need too many dimensions – more countries, sectors, and factors – to be able to solve with any ease or confidence.

In addition to the difficulties in obtaining clear analytical solutions, the power of services trade to move wages and factor use patterns are undoubtedly also influenced by non‐tariff trade frictions. Unlike the goods trade, there is no easily measured “transportation” cost for services. However, as shown by Grossman and Rossi‐Hansberg (2008), there is a cost to outsourcing a service, with the cost depending on how difficult it is to fragment production and trade the fragments. For Mode 1 services, the barriers to trade are the limitations of the technology available, such as the resolution of the digitization software or quality of the internet connection for electronic data transfers. If the technology improves, then we would expect an increase in the volume of Mode 1 services trade. There may also be non‐tariff trade barriers (NTBs) like information protection legislations and accounting standards that may increase the cost of “transporting” Mode 1 services.

There is thus an absence in the literature of any generalized analytical solutions. While several empirical studies have used econometric methods to examine the historical impacts of outsourcing on the USA (see Feenstra and Hanson, 1996; Amiti and Wei, 2005, 2009; Canals, 2007), there have been no equivalent studies to shed light on the impacts of outsourcing on India. A reason for the absence of such statistical analyses of outsourcing impacts in India is that of data limitations. The USA‐focused studies were able to leverage highly disaggregated panel data for specific industries and inputs from a variety of sources, such as the Annual Survey of Manufacturers and the Current Population Survey. While the data that are collected on India, by both public and private organizations are considerable (e.g. the annual Manpower Profile India yearbooks from the Institute of Applied Manpower Research), these data are inappropriate for statistical analyses in the tradition of Feenstra and Hanson (1996).

A computable general equilibrium simulation analysis calibrated on sparse, yet real world, data can fill these gaps, and provide a numerical solution of how prices and factor uses in India may adjust to increased Mode 1 services exports resulting from technological improvements<sup>8</sup> . This paper provides such an analysis and a better understanding of the sensitivity of factor incomes in India to the services trade.

> 8 Markusen (2006) successfully used simple stylized simulation models to explain a variety of theoretical foundations perspective on outsourcing.

8
These findings are consistent at the country level, but with some exceptions. For example, in Ethiopia and Nigeria, male NEETs and male students aged 15-18 exhibit greater educational aspirations than their female peers ( **Figure 1** ). Conversely, in other countries, female youths aged 19-25 display greater educational aspirations than male counterparts. Moreover, in Malawi, female NEETs and female students aged 19-25 express higher educational aspirations than their male peers ( **Figure 2** ).

**Figure 2. Share of youths aged 15-18 who would like to complete university or post-graduate studies, by sex and current activity.**

**Figure 1. Share of youths aged 19-25 who would like to complete university or post-graduate studies, by sex and current activity** _._

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100% 95%<br>91%<br>88%<br>90% 85% 90%<br>82%<br>79% 79%<br>80% 80%<br>69%<br>70% 70% 65%<br>62%<br>59% 59%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity.

Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024

# **4.2. Employment status and career aspirations**

We estimate that overall, 25 percent of youths is employed as of May/June 2020 and 30 percent of them is not working in their ideal job. Nigeria and Ethiopia report the highest proportions of youths who are not currently engaged in their ideal work activity, accounting for 41 percent of youths in Ethiopia and 31 percent of youths in Nigeria. This share decreases to 10 percent in Malawi ( **Table A2 in the Annex** ). Nonetheless, approximately 81 percent of youths believe they can achieve their dream job, with percentages ranging from 86 percent of youths in Ethiopia and Nigeria to 61 percent in Malawi ( **Table A2 in the Annex** ). Furthermore, about 70 percent of youths know someone from their community who holds their desired occupation, ranging from 72 percent and 69 percent in Nigeria and Ethiopia respectively to 64 percent in Malawi ( **Table A2 in the Annex** ).

10
Table 7: Impacts on Labor Market Outcomes (cohorts 2002-2006).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Variable|Empl|oyed|Self-Em|ployed|Wage|worker|
|Years after entry|2|4|2|4|2|4|
|||Panel A<br>|Panel A<br>1: Not Em<br>|: Head<br>ployed befo<br>|re 2002<br>||
|Eligibility (ITT)|-0.002|-0.009|0.010|-0.014|-0.012|0.005|
||(0.013)|(0.016)|(0.012)|(0.015)|(0.007)|(0.010)|
|Participation (IV)|-0.005|-0.054|0.058|-0.082|-0.063|0.028|
||(0.069)|(0.097)|(0.062)|(0.088)|(0.035)|(0.057)|
|Control Mean|0.2|35|0.1|83|0.|060|
|Observations|43,269|31,395|43,269|31,395|43,269|31,395|
|||Panel|A2: Emplo|yed before|2002||
|Eligibility (ITT)|-0.005|-0.004|-0.001|0.001|0.007|-0.005|
||(0.005)|(0.006)|(0.006)|(0.008)|(0.006)|(0.008)|
|Participation (IV)|0.027|-0.022|-0.007|0.006|0.035|-0.028|
||(0.025)|(0.034)|(0.031)|(0.045)|(0.030)|(0.043)|
|Control Mean|0.8|51|0.6|28|0.|219|
|Observations|155,195|134,294|155,195|134,294|155,195|134,294|
|P-Value: HA:β<sup>_iv_</sup><br>0 <sup>_>_ β</sup><sup>_iv_</sup><br>1|0.272|0.524|0.252|0.797|0.960|0.191|
|||Panel B1:|Panel B:<br> Not Empl|Spouse<br>oyment bef|ore 2002||
|Eligibility (ITT)|-0.004|0.010|0.004|0.006|0.001|0.004|
||(0.005)|(0.010)|(0.005)|(0.008)|(0.003)|(0.007)|
|Participation (IV)|0.022|0.057|0.017|0.038|0.004|0.019|
||(0.028)|(0.049)|(0.024)|(0.040)|(0.017)|(0.035)|
|Control Mean|0.1|16|0.0|82|0.|032|
|Observations|103,112|63,536|103,112|63,536|103,112|63,536|
|||Panel B2|Not Empl|oyment bef|ore 2002||
|Eligibility (ITT)|0.005|0.007|-0.002|0.003|0.007|0.005|
||(0.025)|(0.032)|(0.025)|(0.032)|(0.016)|(0.022)|
|Participation (IV)|0.014|0.021|-0.013|0.009|0.027|0.012|
||(0.133)|(0.213)|(0.132)|(0.209)|(0.086)|(0.148)|
|Control Mean|0.4|24|0.3|00|0.|111|
|Observations|13,746|10,201|13,746|10,201|13,746|10,201|
|P-Value: HA:β<sup>_iv_</sup><br>0 <sup>_>_ β</sup><sup>_iv_</sup><br>1|0.516|0.62|0.412|0.188|0.604|0.680|

Note: See table 3 for the specification used in rows (ITT) and table 4 for rows (IV). The coefficient estimate in rows (ITT) is the indicator of eligibility, _Eim_ , and the coefficient estimate in rows (IV) is the indicator of participation in CS, _CSim_ . The p-value in the table concerns for the null hypothesis that the effect for those not employed prior 2002 equals the effect on those employed, H0: β<sup>_iv_</sup> 0<sup>= β</sup><sup>_iv_</sup> 1<sup>, against the alternative that the effect</sup> is larger for those not employed prior to 2002, HA: β<sup>_iv_</sup> 0<sup>_>_β</sup><sup>_iv_</sup> 1<sup>.</sup> Robust standard errors are reported in parenthesis clustered at municipality of residence when eligibility is evaluated. * significant at 10%; ** significant at 5%; *** significant at 1% (the critical values for inference are adjusted for multiple hypotheses testing using the procedure in Algorithms 4.1 and 4.2 of Romano and Wolf, 2005).

33
15 results. We next add measures of potentially confounding national policy changes.<sup>18</sup> In Column (3) we control for fossil fuel subsidies (as % of GDP), since governments in more exposed countries may have been more likely to respond to the crisis by subsidizing more affected firms. Including this control slightly increases the magnitude of our coefficient estimates, consistent with the targeting of subsidies to dampen the energy price impact lowering our results. In Column (4) we control for national industrial policy support for solar and wind energy and electric vehicles, using policies appearing in the Global Trade Alert database targeting HS codes for these technologies as outlined in Rosenow and Mealy (2024). We find that while the number of these industrial policies is correlated with green technology-related job postings, controlling for the former does not substantially weaken the estimated impact of pre-crisis exposure in differential establishment adoption of green technologies. Finally, to address concerns with the use of the IHS transformation, notably around coefficient interpretation, following Chen and Roth (2023), the specification in Column (5) repeats the baseline analysis but with a dependent variable that equals one if an establishment had any green technology-related job postings in year-month _t_ and zero otherwise. The statistically significant result at the 1 percent level indicates that the impact of the energy shock on demand for green technologies, as evidenced by related job openings, is also strong at the extensive margin. The results at this margin have a more straightforward interpretation:

# **4.2 Event study results: Timing and magnitude of effects**

We next examine how the increase in green technology-related hiring evolved throughout the 2022 energy crisis. Event study results presented in Figure 3 plot estimates of the coefficient on the interaction between the country- and firm-level exposure measures and a year-month indicator, illustrating the effect on establishments’ demand for green technology-related hiring in each month (relative to the 2021Q2 omitted period).

Figure 3 shows that more energy-intensive establishments in countries with higher levels of natural gas dependence saw a relative increase in green technology adoption after the onset of the energy crisis in February 2022, in line with the difference-in-difference results in Table 1. Specifically, we show that the differential increase in job openings began in March 2022, just one

> 18 See full results and discussion in Supplementary Appendix Section A.5.1.
this region is carried out solely in urban areas and therefore explicitly does not capture rural price levels.<sup>45</sup> Similar to the LAC region, PovcalNet also draws its consumption and income aggregates in ECA from a regional database, ECAPOV. This regional database, maintained by the World Bank, also embeds spatial-price adjustments into many of its consumption aggregates. ECAPOV adopts, whenever information on households’ purchased quantities is available, a spatial Paasche index based on unit values from food consumption, which is then applied to all consumption items (including non-food items). Unlike the LAC region, however these prices are not expressed at the urban level, but adjusted to reflect national prices.<sup>46</sup> Table 5 recaps the various spatial price adjustments embodied in PovcalNet data. From the perspective of comparability, it would be desirable to have more consistent methods to adjust for within-country differences in prices. As one example, the adjustments for rural-urban differences in the cost of living made for China, India and Indonesia differ from those made for other countries, and the overall poverty count is sensitive to the assumptions underlying these adjustments. Jolliffe and Prydz (2015) note that the approach followed for these three countries has the overall effect of lowering the poverty estimates, relative to a benchmark of simply applying the national PPPs to national distributions.

While further research is needed to help identify a more uniform approach to accounting for within-country, spatial-price differences when estimating global poverty, we believe the approach followed for the 2012 estimates offers two desirable properties. First, this approach allows us to separately estimate the extent of rural poverty in each of these countries, which is useful given the sheer size of the rural population in each of these countries. Second, and perhaps most importantly, the approach we follow for adjusting the 2011 PPP factors for China, India and Indonesia is the same approach followed by the World Bank for the previous estimates based on the 2005 PPP conversion factors. The only change made is that the rural-urban scaling factors have been updated to reflect more recent data. This approach allows us to continue monitoring

> 45 Maintaining these adjustments is also helpful for consistency between regional and international measurements of poverty.

> 46 Another way of stating this is that in ECAPOV, mean consumption levels are not changed when adjusting for spatial differences in prices.

26
|Carrying out the cost data-collection and<br>analysis is costly to the projects||3|5|8|
|---|---|---|---|---|
|Obtaining cost data can be challenging|2|4|1|7|
|Measuring the impact or benefit is the<br>evaluator'sfirst-orderconcern|2|3|1|6|
|Large institutions implement VFM in IEs<br>inconsistently across their programs||3|2|5|
|Institutional conflicts and interests stand in the<br>way of applying VFM to institutional decision-<br>making|2|1|1|4|
|The skills of VFM and IE experts differ and<br>there is not a lot of integration across projects<br>or within institutions|1|2|1|4|
|The importance of VFM will increase as more<br>large-scale randomized evaluations are carried<br>out||2||2|

_Source_ : Analysis of unstructured and semi-structured interview responses to the question: “Thinking about the field, generally, why do you think VFM is so infrequently incorporated into impact evaluations?” Even so, producers suggested that the political calculus differed greatly depending on the amount and on the quality of the evidence provided. Validating this point is the fact that impact evaluations have flourished at current levels of policymaker interest, and that quantitative analysis of the policymaker survey data<sup>7</sup> showed that their willingness to pay for causal information on benefits is not much higher than their willingness to pay for Cost information.

Analysis of the policymaker survey of 407 respondents in the study sample revealed that respondents are willing to pay about 10 percent more for an impact evaluation study as they are for a Cost study, but the observed difference is not statistically significant. Likewise, the average willingness to pay for VFM is also higher than willingness to pay to know a projects effects alone, but again this difference is not statistically significant. However, respondents indicated they are willing to pay a 13 percent premium for a study that presents VFM (Costs and Benefits) than for a Cost study alone – a statistically significant difference at the _p_ <0.01 threshold. Together, these results imply that although decisionmakers are most interested in _what works_ , there is also clear demand to know _bang for buck_ .

> 7 Based on a 14 percent response rate of nearly 4,000 individuals on the mailing list of the World Bank’s Strategic Impact Evaluation Fund. These individuals self-selected for the mailing list based on their interest in impact evaluations—which underscores the parity found between willingness to pay for effectiveness information and Cost information. Because a sampling frame of those involved in the policy-making process does not exist, the external validity of these results, as with all findings in this evaluation, are indicative even if not conclusive.

16