rafmacalaba/data-use-mentions-extended huggingface dataset:
increasingly popular solution in which the weights in the PMT are identified from regression coefficients for household consumption or income as a function of readily observed covariates. The regression is calibrated to survey data and then used to make the out-of-sample predictions for the relevant population. This has the intuitive attraction that the dependent variable is a

> 2 This idea appears to have emerged in social policy making in Chile in the 1980s (Grosh, 1994, Ch.5). Grosh et al. (2008) provides a useful overview of PMT and other targeting methods found in practice in developing countries, with details on many examples.

> 3 A popular example of the poverty scorecard was proposed by Schreiner (2010); the <u>Progress out of Poverty Index uses</u> Schreiner’s (2015) method. The scorecard includes 10 easily measured correlates of poverty which are used to form a composite index. Diamond et al. (2016) argue that the predictive ability of such scorecards can be improved by calibrating the variables and their weights to local (sub-national) conditions, for which purpose they advocate econometric methods. 4 This term appears to be due to Grosh and Baker (1995, p. ix), who define PMT as “a situation where information on household or individual characteristics correlated with welfare levels is used in a formal algorithm to proxy household income, welfare or need.”

2
) show that poor institutional quality (as reflected in weak rule of law and a lack of reliable contract enforcement) and uncompetitive banking sectors were associated with less financial inclusion in Latin America (measured as owning an account at a formal financial institution). Using Global Findex data for 2011 and 2014, Dabla-Norris et al., (2015a,b) also find financial inclusion to be positively linked to per capita income, rule of law, and competition in banking. Similarly, using Findex data from 2014, Deléchat et al. (2018) show that individuals were more likely to be financially included in countries with high levels of per capita income and general financial development. 4 Claessens et al. (2018), p. 38.

4
equation (3) with selection controls for formality. In columns 6, 7, and 9, identification comes from the variation in time and information costs of formalizing, proxied by the self-reported distance to the municipal office, the nearest tax office, and whether the firm has had an inspection in the past year.<sup>16</sup> Because the profit equation (1) already controls for the parish-level averages of these variables—which should capture the distance to markets and major clients as well as the likelihood that a firm is located in an inspection-prone area—the firm-level variables should determine a firm’s formality status without having a direct impact on its profitability. However, because the instruments come from information reported by the firm itself, there is still a possibility that they are correlated with the unobservable determinants of profitability. In order to limit this possibility, column 8 of Table 1 shows the results of estimating a specification where parish-level average distances and whether or not a firm has had an inspection are used as instruments to limit a potential bias where less formal firms would tend to report distances less accurately. The results show that the relationship between formality and profitability remains robust, although the standard error of the formality coefficient is larger because the parish-level variables do not capture each firm’s individual circumstances as accurately. It should be mentioned, however, that none of the instruments are fully satisfactory because, unlike similar studies in Bolivia (World Bank, 2009) and Peru (World Bank, 2008), GPS equipment was not available to independently measure the distances to the municipal and tax offices.

The IV estimates reveal that several variables, such as education and guild membership, affect profitability mainly through their impact on formality. For example, unlike OLS estimates, only the highest level of education has a significant impact on profits. However, results from the first stage of analysis reveal that each level of education, beginning with complete secondary, is a statistically significant determinant of formality and the probability of formalizing
Contrary to concerns that reservations may reduce the competitiveness of elections, our data suggest that reserved seats are no less competitive than unreserved ones (table 3 panel 2). In fact, with participation levels of 76% for reserved compared to 72% for unreserved seats, „reserved‟ pradhan elections (in states where pradhans are elected directly) appear to have attracted slightly higher levels of voter turnout.<sup>14</sup> The bottom panel highlights that there is no significant difference in size and structure of budgets between reserved and unreserved panchayats, not unsurprising in a setting where own revenues remain modest and some 90% of local budgets are from central transfers.

Table 4 reports descriptive statistics for individuals‟ assessment of the pradhan‟s leadership attributes, the quality of service delivery in key areas, and the ability to hold officials to account. Figures are reported for the full sample and male as well as female respondents separately for reserved and unreserved panchayats. That reservations impose an additional constraint emerges clearly from the fact that for all of the many areas considered, and irrespectively of respondents‟ gender, reserved pradhans‟ qualifications are always rated below or at most equal to those of unreserved ones. Males and females unanimously view those elected on reserved female tickets as having lower technical qualifications and lower ability to provide local public goods or select beneficiaries transparently. They also coincide in considering those on reserved seats as less honest and fair than those on non-reserved seats, possibly pointing to the fact that in some cases reservation results in females being put up as a front for husbands or relatives who continue to draw the strings behind the scene. Males think that reserved female pradhans are less knowledgeable of national affairs and worse at communicating village problems to higher government. The only area where reserved pradhans are put on par with non-reserved ones relates to the ability to solve local disputes.

Consistently for males and females, descriptive data suggest that reservation affects perceived quality of service delivery either negatively (for water, schools, health, and sanitation
student dropout in treated schools by 1.3 percentage points, and we estimate that this effect grows to 3.1 percentage points when we look at the impact on compliers (roughly 3.6% and 8.5% of the baseline dropout rate). The low implementation cost of the program—about 2 USD per student—makes the intervention highly cost-effective. The (conservative) rate of return of the program is estimated to be almost 30%, with a cost-benefit ratio of approximately USD 19 per each 1 USD invested. The evaluation was embedded in a large-scale implementation including almost 17% of all primary schools in Guatemala, making the results more informative for understanding the impacts of the program in ongoing, “real world” conditions.

> 18 See Figure 4 in J-PAL Policy Bulletin (2017) for a cost-effectiveness comparison with these and other programs.

> 19 Data on public education spending from the World Development Indicators.

36
Tanzania (east), Kenya (north), and Tanzania (south). It is a densely populated country with 12.2 million inhabitants in 2017. Its economy has performed quite well over the last 10 years, driven to a large extent by important public investment programs. Its GDP expanded from 3.83 billion (in current USD) in 2017 to 9.14 billion ten years later.<sup>3</sup> This amounts to an annual growth rate of 9.1% per year, which is among the better performances in sub-Saharan Africa. The strong economy is also reflected in a declining poverty rate. In 2005, 67% of the population was living on at most $1.90 (in 2011 PPP) per day, but this had declined to 56% by 2013, the last year for which there is comparable data. Still, with a GNI per capita of 1,990 (in PPP, current international $), which is approximately

> 1 Only one ninth of the firms that register with the business income tax registration, for which we observe sales, also register as employer, and only in that case do we also observe employment.

> 2 A broad overview of the literature studying GVCs using case studies can be obtained from the list of publications of the Global Value Chain Initiative: https://globalvaluechains.org/

> 3 These statistics are taken from the World Bank World Development Indicators online database that can be accessed at http://databank.worldbank.org/data/source/world-development-indicators/.

3
|5|0.40|0.37|2,590|1,040|188|74|35<sup>_d_</sup>|
|**South Sudan**|2.13|2.16|1|5|0.33|0.35|2,808|937|232|78|36<br>|
|**Sudan**|1.41|1.34|1|4|0.10|0.07|2,640|265|107|80|33<sup>_d_</sup>|
|**Uganda**|1.10|1.06|1|3|0.02|0.01|2,016|33|12|56<br>|36<br>|
|**Yemen, Rep.**|3.42|3.26|2|4|0.93|0.89|960|892|35|69<sup>_b_</sup>|14<sup>_b,d_</sup>|
|**Zambia**|1.02|1.01|1|2|0.00|0.00|2,304|0|0|72|32<sup>_c_</sup>|
|**Zimbabwe**|1.54|1.44|1|4|0.17|0.13|2,160|365|136|60|36|
|**Total**|1.60|1.41|1|5|0.16|0.10|37,918|6,143|1,723|1,162|116 **months**<sup>_e_</sup>|

The descriptive statistics are calculated using FEWS NET ratings, netted of humanitarian assistance
out to assess whether there were notable differences in the regulatory and supervisory structure of countries that were engulfed in the recent global crisis and those that were not. More specifically, we analyze the BRSS responses by crisis-hit countries and compare them to the rest of the world, using univariate tests to identify which parts of the regulatory and supervisory framework were different between these two groups of countries. We report differences between crisis and non-crisis countries based on the 2007 and 2011–12 surveys. The reason why we consider both surveys is because regulation and supervision could have changed during the crisis and we do not want to interpret differences found during the crisis as explaining why some countries experienced the crisis more strongly than others.

We identify crisis countries using the database assembled by Laeven and Valencia (2010). For the purpose of our calculations, crisis countries consist of two groups of countries identified in the Laeven and Valencia (2010) database. The first group comprises 13 countries that experienced a _systemic banking crisis_ between 2007 and 2009 (United States, United Kingdom, Iceland, Ireland, Latvia, Belgium, Luxembourg, Netherlands, Austria, Denmark, Germany, Kazakhstan, and Ukraine). The second group includes 8 countries that experienced a _borderline systemic crisis_ in the same period (France, Greece, Hungary, Portugal, Russia, Slovenia, Spain, and Switzerland). All the other countries in our database are treated as noncrisis countries. As a robustness check, we also perform calculations in which only the first group of 13 countries classified as _systemic banking crisis_ is identified as crisis countries, with broadly similar results.

To determine whether there are significant differences in regulation and supervision in crisis versus non-crisis countries, we conduct a series of mean t-tests on responses to individual survey questions. From this analysis, summarized in Table 2, five main findings stand out. First, 7
Korea, but is there a causal relationship? The answer hinges critically on whether we can evidence that the domestic of Korean MNCs declined more find employment relatively than that of non-MNCs after the policy changes. A disproportionate drop in domestic employment in MNCs after the labor market shock can be interpreted as task substitution between domestic workers and foreign factors of production within their internal network. Formally, we test whether Korean firms that already had foreign affiliates before 2017 reduced their domestic employment to relocate tasks abroad in response to the exogenous domestic labor market policies. The task reallocation would take place primarily through foreign affiliates in nearby low-wage countries if cost saving was the key motive. A subsequent assessment therefore is centered on whether MNCs with foreign affiliates in those countries experienced a steeper decline in domestic employment. Since our data set comprises a nontrivial number of Korean firms with affiliates in China and the ASEAN countries prior to the labor policy changes, the current economic, geographic, and policy circumstances all provide a unique setup for our empirical test.

# **3 Data**

Our primary data set is the Survey of Business Activities (SBA) from Statistics Korea, an annual that was carried out in 2006. The data set covers firm-level panel survey first firms that conduct business activities in Korea as of the survey reference date, employing at least 50 full-time employees and reporting a capital stock value of at least 300 million Korean won (about 267,869 USD using the 2010 exchange rate). The sample size in each year is approximately 13,000 firms in Korea, which represent 80% of the total output of Korea in the year 2008.

The data set provides typical firm-level variables such as employment, tangible assets, sales, purchases, value added, wage bill, exports, and imports. In addition, it allows us to identify each firm’s foreign affiliates and their locations (if any), along with equity shares.<sup>15</sup> For each firm, we can also identify the region,
Lees (2022), and Angelov and Waldenström (2021) highlight the role tax administrative data can play in measuring economic activity in the private sector. Data is collected continuously, often at high frequencies, and typically covers a high share of formal firms. Similar to the aforementioned research based on surveys, analysis of tax administrative data has highlighted the pandemic’s negative impacts on firms’ sales and employment levels. Many firms have struggled to stay profitable, and at times have exited the market completely. For example, Mascagni and Lees (2022) use VAT returns to outline the severe repercussions of COVID-19 lock downs in Rwanda. Despite reporting low numbers of COVID-19 cases, VAT-registered firms in Rwanda experienced a 30 percent drop in aggregate sales over the lockdown period. Over this period, losses in sales amounted to 5.2 percent of GDP, with smaller firms suffering the most in proportionate terms. Mascagni and Lees (2022) also show that firms belonging to the accommodation, food, and transport sectors have struggled in particular since the onset of the pandemic.

There is also a relevant strand of the literature that goes beyond measuring the crippling impacts of COVID-19 by examining what characteristics of firms are associated with their recovery from crises. Jin et al. (2018) studied the recovery of firms’ performance in the aftermath of the 2007–08 global financial crisis. They found that firms with stronger financial constraints experienced a more sluggish recovery from the crisis relative to firms with weaker constraints. Additionally, well-developed financial institutions and structures were able to contribute significantly to firms’ recovery by easing their financial constraints. Amin and Viganola (2021) corroborate these findings: firms with better access to finance experience a less significant drop in sales, albeit results are highly heterogeneous. The reduction in likelihood of a decline in sales is much higher for firms that have a stronger and longstanding relationship with prominent stakeholders such as skilled workers and input suppliers. Cowling et al. (2018) also analyzed the 2008 global financial crisis and found that
interactions. Each cell is defined as a standalone treatment variable (so that all impacts are measured relative to the control group) and p-values from F-tests are provided at the bottom of the table for the joint effect of the three UCT arms and the two training arms. Interestingly, however, we see quite a different pattern of impact on business investment, with the UCTs actually leading to a significant _decrease_ in agricultural income, and rather than a significant improvement in enterprise income we see a negative effect on both income and assets with t-statistics above 1. Hence, something distinct seems to be occurring with the TASAF beneficiaries; they are failing to translate individual-level cash transfers into improvements in household enterprise outcomes.<sup>15</sup> Table A8 (using an R3 ANCOVA regression model with an interaction term to test the difference between TASAF members and the ENBs) shows that the UCT-receiving TASAF members are achieving less enterprise income at the household level than are the ENBs who get UCTs.

The obvious explanation for this divergent pattern is that the TASAF group members have a unique non-household vehicle for investing cash transfers, namely the group business. We have two distinct ways of approaching this question. The first is individual; we can bring the data collected in the group survey about individual contributions to and receipts from the group together with the treatment status from the UCT experiment to ask whether those who received cash behave differently in the groups. The results of this analysis, presented in Table 8, show that indeed recipients of UCTs engage more strongly with their groups. We see a 10% increase over the UCT control group mean across the board per $100 transferred, which is only statistically significant for labor inputs. In each case these treatment effects at the average transfer represent increases of about 20% over the no-UCT mean. Hence, those receiving the UCT are putting more effort towards

> 15 Appendix Table A7 shows that there are also no significant impacts on secondary outcomes from the UCT for TASAF beneficiaries.

29
results are comparable to the findings in Frankenberg and Thomas (2001).

# **3 Data**

This study uses the Indonesian Family Life Survey (IFLS) for the main empirical analysis. The IFLS is an ongoing longitudinal survey. So far, there are five waves which were conducted in 1993, 1997, 2000, 2007, and 2014. The fifth wave of the IFLS is primarily used, since the paper is interested in adult outcomes of children born between 1984 and 1994.

IFLS asks the village leaders and the head of the Village Women’s Group about the availability of any village midwife in the community, and the year a midwife started serving the community. This information is again verified with the volunteers at the Village Health Post, where women obtain prenatal and obstetric care for the consistency of reporting on the village midwife’s presence in the community (Cas, 2012). While the first wave of the IFLS has information on whether there is a village midwife in the community, it does not have the start year information. It is only from the second wave that the information on both the availability and start year is available. Since the empirical strategy requires both pieces of information, this paper uses the availability of midwife information from wave one and subsequent waves, and the placement year of midwives from the follow-up waves for the IFLS communities.

Birth year information is available on the roster, as well as book 3A of the fifth wave.<sup>12</sup> The sample is limited to individuals born between 1984 and 1993 and are listed in the first wave and individuals born in 1994 and listed in the second wave. This process eliminates any adults who may have joined the panel household later in their adulthood.

A notable feature of the IFLS is that it provides various measures on health, cognition, economic, and math skills. Since there are multiple outcomes under each broad category of outcome— including health, cognition, economic, and math skill— a summary index
so called Shapley decomposition, which highlights changes in average productivity, employment or labor reallocation by sector (** World Bank, 2007b **)**<sup>**23**</sup> **.** It is confirmed that agriculture and, to a lesser extent, services, mostly absorbed the main part of the increased labor force between 1981 and 2005, combined with an increased (+17%) but still low labor productivity in agriculture and significantly lowered productivity (‐64%) in services (Figure 13). Labor productivity was highest within the industry sector, growing by 6% during the period, Even though growth in employment was high for the industry sector itself, the absolute number of job opportunities created was small. In 1981, agriculture

> 23 Historic employment data in Togo is quit scarce. In the case of value added GDP, this decomposition necessitates of at least two data points for each sector value added variable and for the employment levels, as well as two data points for indicators of population, working age population, labor force and employment. As one is interested in long term trends, it is recommended for data points to be at least 10 years apart, and that they correspond to normal (non‐shock) years. Fortunately, employment data by sector in Togo is available for 1981 and 2005 by combining information from the World Bank DDP data base and the CWIQ 2006 survey, and real Value Added data by main economic sectors from Government of Togo.

17
green task statements for both the green enhanced skills occupations and the green new and emerging occupations.<sup>36</sup> The green enhanced skills occupations have 870 tasks in the database, of which 113 were identified as green. In addition, 196 tasks were generated for these occupations. The green new and emerging occupations, on the other hand, had no task in the database since all of these were new occupations to the O*NET-SOC taxonomy. In total, 626 task statements were generated for this group.

Two major advantages stand out from the O*NET GEP green classification. First and above all, O*NET is a highly reputable agency whose data is trusted and used by academics all over the world, as well as government agencies, training institutions, and the private sector in the U.S. Second, the O*NET green data constitutes an addition to the existing occupational database that contains detailed descriptions of almost 1,000 occupations in the O*NET-SOC taxonomy, including the importance and level of their tasks, skills, abilities, knowledge, and other work requirements. The O*NET task approach, as well as the O*NET green occupation classification, have been prominently used by academics to estimate the consequences of the greening of the U.S. economy.<sup>37</sup> Despite its advantages, the green occupational database of O*NET has methodological limitations. First, O*NET GEP limited the analysis to occupations in 12 green sectors, excluding green jobs in the rest of the economy that contribute to the sustainability of the environment. Second, the methodology relies

> 35 The O*NET-SOC is based on the U.S. SOC, by assigning the 6-digit SOC code plus an additional level. 36 O*NET (2010).

> 37 Consoli et al. (2016); Bowen et al. (2018); Vona et al. (2018); Vona et al. (2019);