rafmacalaba/data-use-mentions-extended huggingface dataset:
regular channels. In contrast, Hardy and McCasland (2016) use the data from the NAP experiment in Ghana and find no displacement among firms in our sample. Although they do not examine a typical apprenticeship program, Alfonsi et al. (2017) compare the effectiveness of an on-the-job training program with that of a formal vocational training program in Kampala, Uganda. They find that both forms of training improve labor market outcomes, but individuals assigned to formal vocational training had greater earnings growth because of their acquisition of “transferable skills” rather than “firm-specific skills,” highlighting the importance of skills acquisition. Alfonsi et al. (2017) also find evidence that firms are labor constrained. This is consistent with the findings of Hardy and McCasland (2016) for our study context. Rather than focus on firm outcomes, we use the randomized match between trainers and apprentices to identify the characteristics of training providers (firms) that deliver the best outcomes for youth. This novel exercise provides us with a better understanding of the production function for apprenticeship training. To our knowledge, our study is the first to explore this dimension in the context of training programs. This exercise is important, as it can help governments improve the effectiveness of training by introducing a more selective recruitment process for trainers.

Our findings are more pessimistic than those of studies of apprenticeships in European contexts (for example, Acemoglu and Pischke (1998); Fersterer, Pischke, and WinterEbmer (2008). These studies have generally found positive effects of the apprenticeship training on individual participants and have also highlighted the potential for firms to benefit from providing such training. Observational studies of apprenticeship training in Ghana have argued that returns to training are greatest for individuals with low levels of education and are not significantly different from zero for those with more than six years of schooling (Monk, Sandefur, & Teal, 2008; Teal, 2016). Our results are consistent with this notion, since the average duration of schooling in our sample exceeded seven years. Further
# _Data and methodology_

To carry out this analysis, we draw on micro data from 238 household surveys for 15 Latin American countries spanning the period from 1992 to 2013.<sup>4</sup> For these country-years, we use harmonized data from SEDLAC, the Socio-Economic Database for Latin America and the Caribbean (CEDLAS and the World Bank 2015), which include homogenous variables for school attendance, economic activity, and socio-demographic characteristics.<sup>5</sup> We use these data to produce internationally comparable statistics on ninis, defined as those individuals aged 15 to 24 who were neither enrolled in school nor working at the time when the relevant household survey was conducted.

Two qualifications deserve mention before we present the results. First, like the underlying variables from the original household surveys, nini status is self-reported. Second, not all countries are equally represented in the data used for this analysis, since household surveys are conducted with varying frequency across countries and the quality of harmonization varies across country-years. We exclude countries for which the number of years between survey data available is so great that interpolating would produce unreliable results. The table in Annex 1 specifies the particular surveys processed for each country.

To construct the averages, we began with the full period for which SEDLAC data are available— 1980 to 2013 for the region as a whole. However, because several countries have missing data from the beginning and end of this period, particularly for the disaggregated analysis, we confine most of our analysis to 1992-2010, which is the longest time period with reasonably complete data. This year range provides nearly two decades of evolution of the nini phenomenon across the region, while also minimizing the need to interpolate missing years and to extrapolate back for countries that lack survey data in the early years.

The regional figures presented in this note are the unweighted averages for the same 15 Latin American countries throughout. We focus on unweighted averages because they capture more accurately the experience of the average country in the region and avoid letting
- Deaton, A., & Muellbauer, J. (1980). _Economics and consumer behavior_ . Cambridge University Press.

- Douidich, M., Ezzrari, A., Van der Weide, R., & Verme, P. (2016). Estimating quarterly poverty rates using labor force surveys: a primer. _The World Bank Economic Review_ , _30_ (3), 475-500.

- Eckman, S., Kreuter, F., Kirchner, A., Jäckle, A., Tourangeau, R., & Presser, S. (2014). Assessing the mechanisms of misreporting to filter questions in surveys. _Public Opinion Quarterly_ , _78_ (3), 721-733.

- Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. _Econometrica_ , _71_ (1), 355-364.

- Gourlay, S., Kilic, T., & Lobell, D. B. (2019). A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale-Productivity relationship in Uganda. _Journal of Development Economics_ , _141_ , 102376.

- Ha, Jongrim, M. Ayhan Kose, and Franziska Ohnsorge. "One-stop source: A global database of inflation." _Journal of International Money and Finance_ (2023): 102896.

- Jenkins, S. P., Burkhauser, R. V., Feng, S., & Larrimore, J. (2011). Measuring inequality using censored data: a multiple-imputation approach to estimation and inference. _Journal of the Royal Statistical Society Series A: Statistics in Society_ , _174_ (1), 63-81.

- Kilic, T., & Sohnesen, T. P. (2019). Same question but different answer: experimental evidence on questionnaire design's impact on poverty measured by proxies. _Review of Income and Wealth_ , _65_ (1), 144-165.

- Kilic, T., Moylan, H.
Table 2: Reported assets by the wealthiest 2% in 2015 and 2016

|Average reported a|ssets(in 2015 USD|)||
|---|---|---|---|
|**2015**|**2016**|_%_∆|_% of total_∆|
|p98–p99<br>62,283|70,208|13%|3%|
|p99–p99.5<br>121,491|150,029|23%|5%|
|p99.5–p99.9<br>304,985|515,821|69%|28%|
|p99.9–p99.95<br>586,091|1,234,090|111%|11%|
|p99.95–p99.99<br>1,455,901|3,510,129|141%|27%|
|p99.99–p100 (top 0.01%)<br>8,097,634|15,804,161|95%|26%|

_Notes:_ this table compares the average assets (in 2015 US dollars) reported by the wealthiest 2% of tax units (individuals aged 20 and above) in 2015 and 2016. The table decomposes the top 2% into bins of increasing assets all the way to the top 0.01%. The last column expresses the differences relative to the total change in reported assets between 2015 and 2016. Individuals below the top 1% had moderate increases in their average assets after the 2016 amnesty. By contrast, the wealthiest 0.5% of taxpayers declared substantially more assets after the program. The number of tax units is 28,764,680 in 2015 and 29,164,076 in 2016. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

37
SSA commodity price index––by OLS with an AR(1) model. Panel B of Table 1 reports the results. The quarterly autocorrelation of the commodity price in the NK model is set to 0.975 (which implies a half-life of 6.7

> 21 There is a larger steady state transfer to the hand-to-mouth households to equalize steady state consumption across households, given that the Ricardian households also profit from firms.

> 22 Appendix Table 1 lists the two most important commodities for each country.

> 23 As is well known, estimates of persistence are biased on finite samples. However, usually a long time series of commodity price data––typically more than a century––minimizes the size of the bias and hence the severity of the problem.

> 24 Appendix Table 2 also reports the data source and the length of each time series in columns (2) and (3), respectively. Columns (4) and (5) report the OLS point estimate of the autocorrelation and error standard deviation ( ), respectively. pp σσ

14
917|0.795|***|0.891|0.804<br>**|-0.026|0.009||
|Private elementary school|0.018|0.038||0.154|0.154|0.136|0.117||
|Public high school|0.228|0.192||0.312|0.308|0.084|0.116||
|Private high school|0.040|0.032||0.123|0.121|0.083|0.089||
|Health center|0.784|0.596|***|0.766|0.619<br>**|-0.018|0.023||
|Public library|0.024|0.020||0.046|0.031|0.022|0.011||
|Post office|0.036|0.017||0.026|0.038|-0.010|0.020|**|
|Number of observations|62|39||62|39|62|39||

Source: TEEP Tracking Survey. Note: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

35
|0.334|0.212|0.100|0.177|0.284|0.469|0.664|
|Outage|638|0.575|0.269|0.220|0.340|0.578|0.832|0.933|
|Web Intensity|642|0.440|0.239|0.114|0.241|0.448|0.618|0.766|
|Security Cost|642|0.018|0.018|0.004|0.008|0.014|0.022|0.037|
|Capacity agglomeration|648|0.268|0.146|0.077|0.161|0.260|0.354|0.456|

In order to include pollution measures in our analysis, we need access to the GPS location of each of the firms of the survey. Because this information is confidential, we only have access to a masked location for each firm, randomly assigned within a radius of 2km of the true firm location. Although not all firms in the WBES data set have GPS locations, comparing the distribution of the key variables between the whole sample and the sample of geo-referenced firms shows no significant differences (see Annex I for details). We have a sample of 56,977 georeferenced firms.

Originally, we have air pollution data for two pollutants, nitrogen dioxide (NO2) from NASA OMI satellite and PM 2.5, from NASA MODIS and MISR Aerosol Optical Depth. Both data sets are in raster format, with a resolution of 0.1x0.1 degrees, approximately 11km by 11km at the equator. Each pixel contains the annual mean of the corresponding pollutant. For nitrogen dioxide, the data span between 1997 and 2011, while for PM 2.5, the period is 1998 to 2015. Because different models may differ in how they estimate the PM
had, however, an unintended effect: it increased the enrollment rate of the upper castes! Thus, aid targeted to Dalits did not, as policy-makers had expected, narrow the schooling gap between the Dalits and the rest of society. The increase in high-caste enrollment maintained the relative superiority of the high caste in years of education.<sup>11</sup> Finally, an observational study in Pakistan identifies a causal impact of high-caste dominance and low enrollment of low-caste children in school. This finding is consistent with our conjecture that cues to the caste order evoke a mental frame that impairs low-caste individuals’ ability or desire (or both) to achieve in the classroom. Jacoby and Mansuri (2011) analyze data from a survey of over 3000 households and 1000 elementary schools in rural Pakistan. They define a settlement as _high-caste-dominant_ if a high caste owns the majority of land in the settlement, giving them the power to enforce the traditional caste order. They show that low-caste children are deterred from enrolling in schools in high-caste dominant settlements. In fact, the very low enrollment of low-caste children<sup>12</sup> for whom the closest available school is in a high-caste-dominant hamlet can account for the entire enrollment gap favoring high-caste over low-caste children The following responses from low-caste women to the question, “Do children receive the same treatment from teachers,” illustrate the kinds of exclusionary norms imposed on low-caste individuals: “They let the daughters of [high castes] use the latrines, but tell our daughters to use the fields because you stink.” “The teachers make the daughters of Zamindar Zaats [high castes] sit inside the rooms, under the fans. Our poor children are outside, under the sun and dust” (Jacoby and Mansuri, p. 7).

In sum, these two observational studies —the only studies of which we are aware that examine the effect on achievement of increasing or decreasing the salience of the caste order— indicate that when one’s traditional status in
Bankscope|
|Non-deposit short-term funding|Share of non-deposit short-term funding in total deposits and short-term funding.|Bankscope|
|Interest expense|Interest expense over bank liabilities excluding non-interest bearing debt|Bankscope|
|Deposit growth|Growth rate of a bank's customer and short term funding after dividing by the GDP deflator in percent|Bankscope and<br>WDI|
|Equity|Ratio of equity to assets|Bankscope|
|Liquidity|Ratio of liquid assets to total assets|Bankscope|
|Overhead|Ratio of personnel and other non-interest expenses to assets|Bankscope|
|Short term debt|Bank's customer and short term funding divided by total interest-bearing debt|Bankscope|
|Investment bank|Dummy variable that equals 1 if bank is investment bank or securities firm, and zero otherwise|Bankscope|
|Non-banking credit institution|Dummy variable that equals 1 if bank is non-banking credit institution, and zero otherwise|Bankscope|
|Other bank|Dummy variable that equals 1 if bank is cooperative bank, Islamic bank, medium and long term credit bank ,|Bankscope|

23
............. 22|
|Presentation of policy information on the screen ...................................................... 24|
|Description of Excel and CSV downloads of policy information.................................... 25|
|Presentation of the STRI results on the screen ......................................................... 25|
|Description of Excel and CSV downloads of the results of the selection for the STRI ..... 26|
|Description of the file "All World Bank STRI data (including 2008-11)" ........................ 26|
|**5 CONCLUDING REMARKS ............................................................................... 27**|
|**REFERENCES ...............................
politically connected. While only 1.1 percent of firms in our data set were politically connected, they accounted for 15.8 percent of total output on the eve of the crisis. Last but not least, Suharto appointed mayors were allowed to finish their terms, which generates quasi-exogenous variation in the degree to which old-regime elites could capture local power (Martinez-Bravo et al., 2017,) and maintain anticompetitive privileges.

The main hypothesis tested in this paper is that disruption in political connections associated with Indonesia’s democratization had a pro-competitive impact on Indonesian manufacturing industries. If political turnover resulted in a reduction in anticompetitive practices that conferred advantages on companies connected with Suharto, one would expect companies connected to him to lose their privileges and market share after his removal from power. The impact of Suharto’s fall on firms with connections to him may be attenuated in districts where mayors appointed by him stayed in office longer. One would also anticipate greater competition and more vibrant firm dynamics in industries in which his family and cronies had extensive business interests, with greater improvements in industries where connected firms accounted for a higher market share.

A crucial identifying assumption is that the collapse of the Suharto regime reduced the value of connections to Suharto and attendant anticompetitive privileges received by politically connected firms. Fisman (2001) provides strong support for this assumption, showing significant movement in the stock prices of firms with connections to Suharto in response to news about his health. The online appendix B provides additional supportive evidence by showing that Suharto connections do not predict stock price movements to political events in the post-Suharto era.<sup>1</sup> Empirically, the main challenge is to isolate the impact of Suharto’s fall from potentially confounding changes caused by contemporaneous events such as the financial crisis, deregulation policies, and decentralization. Toward that end, we adopt two main strategies. To start with, we use difference-indifference and event study approaches in which we control for industry, year and district fixed effects as well as for Suharto era
country (basket of the home and foreign goods) and the Producer Price Index (PPI) as the price index of a unit of the home good (basket of all good varieties). The real ER (RER) is defined as the price of the foreign good (converted to the home currency) over the CPI. The terms of trade is defined as the price of the foreign good (converted to the home currency) over the PPI. As in Galí and Monacelli (2005), the law of one price holds at the variety level: the price of a given variety in different countries is identical once expressed in the same currency. In the presence of home bias, the law of one price carries over to the PPI level but not to the CPI level. Note that the UIP implies that, in the linearized model, the return of the domestic bond is equal to the world interest rate plus the debt-elastic spread plus the expected devaluation of the nominal ER. 16 Naturally, agricultural commodities are renewable, and so a constant s is less controversial. 17 Appendix Figure 3 shows an index of the evolution of the reserves of the main natural resource QQ produced in 13 large commodity exporters from 2000 to 2017. The index is equal to 100 in 2000 and is built with data from the BP-Energy dataset and the US Geological Survey. One can see that reserves increased from 2000 to 2017 in 10 of 13 commodity exporters. For some countries, such as Peru, Venezuela, and Brazil, the increase is quite remarkable, with reserves tripling over 2000– 11
understanding the complex process of economic development.

> 20 The legal data are available for 44 countries in our sample. In section II, we did not use these as instrumental variables because legal codes, enforcement quality, and accounting standards may be influenced **by** economic development. Thus, we feel comfortable arguing **-** and specification tests did not reject this argument **-** that legal origin is an exogenous endowment. We feel a bit less comfortable arguing that economic growth does not affect accounting standards. Nonetheless, we employ these legal data as instruments below to show that particular features of the legal environment are

> strongly linked with both financial sector performance and long-run growth.
- **Control and Intervening Variables:** Based on the literature, and as highlighted in the conceptual framework above, it is evident that a number of control/intermediating variables should be included, which can affect the relationships between the key independent and dependent variables. While many additional factors can and should be considered, the statistical analysis will only operationalize the most obvious enabling factors, including the following:

- **Publicity Condition:** This index relies on the IIAG sub-index for “Rights” which averages scores for freedom of expression, media freedom, media impartiality, censorship, freedom of association and assembly, trade unions, civil liberties, human rights, and the like.

- **Political Agency Condition:** This index relies on the IIAG sub-index for “Participation” which averages scores for political participation, freedom of political parties, CSO/NGO participation, free and fair elections, election monitoring, legitimacy of the political process, and the like.

- **Public Management Condition:** This index relies on the IIAG sub-index for “Public Management” which averages scores for public administration capacity, statistical capacity, budget management, fiscal policy, revenue collection, HR management, and the like.

- **Client Power Condition:** This research proposes a customized index that relies on a variety of sub-indicators related to CSO/NGO freedom, IT literacy, mobile phone and internet penetration, media censorship, freedom of association and assembly, and access to information.

- **Log Per Capita GDP:** In order to remove the income effects between countries, as well as to control for inflationary effects, this research uses log per capita GDP harmonized to 2011.

# **V. STATISTICAL FINDINGS**

Using the multi-year data set created, each premise in the conceptual framework was tested in order to estimate associations between the principle independent variable (OGD) and the respective dependent variables (accountability and service delivery). In addition to these aggregates, key relationships
caution. However, overall more than two third of urban area rayon level estimates can be considered fairly reliable.

# **2.4 Results at administrative level**

Moving below the rayon level, the standard errors become wider and the estimates of headcount poverty become unreliable. However the analysis suggests that the estimates of mean adult equivalent consumption remain fairly robust at the level of administrative districts, but not at the level of individual census enumeration areas. Although the results are available for all the administrative units for both urban and rural areas, we only visually plot the results for administrative units which contain at least 1000 households. Only a quarter of the administrative units in rural areas contain at least 1000 households, with an average of 5000 individuals. Urban administrative units tend to be much larger in size, and only a third are not sufficiently large. Currently there is no digital map of Azerbaijan showing boundaries at the administrative level. Thus in order to visually represent the results for administrative units for urban and rural areas we have created a map based on the centroid of the Thiessen polygons for each administrative area (see Section 5 for details about the Thiessen polygons map). We then created a buffer (which is a zone of a specific distance around a feature or point) of size proportional to the population size of each administrative polygons. We then assigned to each buffer the estimate of the adult equivalent consumption for each administrative unit. Thus the map does not represent the actual administrative boundaries, which are still under discussion by the Government. It does, however allow a visual representation of poverty at this lower level of geography.

Figure 5 and 6 show the mean adult equivalent consumption expenditure at administrative level, for urban and rural area respectively. Although the results for administrative units might not be as reliable as the rayon level estimates, there are nevertheless some mporatnt findings. In particular the results at administrative level suggest that there is a significant degree of variation 15