rafmacalaba/data-use-mentions-extended huggingface dataset:
(irrespective of when), and (iii) those that received support only in Round-2, and not in Round-1. In the context of fixed effects regression, the third group – i.e., those that switched government support status across rounds – serves as the treatment group.

While firm characteristics are captured by the firm fixed effects, the endogeneity and selection concerns remain, as discussed above, which we try to address by adding a wide range of time-varying controls from the COV-ES data (see Section 4.5). To further address the selection issue, we estimate the same model for restricted samples. As discussed in Section 4.6, this is done differently for El Salvador and for Georgia. In particular, for El Salvador, we use firms’ responses to the question on reasons that they did not receive government support and drop firms saying that either that (i) they are not eligible for the support, or (ii) they do not need it, or (iii) they applied but did not get it, or (iv) they were not aware of any such support. The firms that provided one of these responses are unlikely to be a good comparison for the firms that received government support because of the likely discrepancy in the way the pandemic shaped firms’ demand and supply. The question on reasons for not receiving support was not asked in Georgia. There, the firms that did not receive government support but also do not expect to receive it in the next three months are excluded from the sample.

Table 11 and Table 12 report results from our main panel fixed effects regressions for El Salvador and Georgia, respectively. We use linear estimation when the outcome variable is the percentage change of permanent full-time workers and logistic regressions for the remaining three outcome variables, which are binary. For each outcome variable, we estimate fixed effect regressions first using the full sample and next using the restricted sample, hence two columns for each outcome variable. Similar to matching estimation results,
and East Asia. They combine these data with data on per capita GDP, and find that while boys and girls benefit from positive shocks to per capita GDP in a similar way, negative shocks are much more harmful to girls than to boys. On average, a 1 percent change in per capita GDP changes boys’ infant mortality by 0.27 deaths per thousand births, and girls’ infant mortality by 0.53 deaths per thousand births. They show that the association between negative GDP shocks and female infants’ disadvantage in survival holds not just in South Asia, but also in the other regions not usually associated with a preference for sons.

Using a methodology similar to Baird et al. (2007), Friedman and Schady (2009) focus on 30 Sub-Saharan African countries to examine the potential impact of the current economic crisis on infant mortality. Combining data on mothers’ reported births and deaths with per capita GDP growth rates from the International Monetary Fund (actual between 1993 and 2008 and projected for 2009), Friedman and Schady (2009) find that almost all the infant deaths arising from a shock to GDP are concentrated among female infants. A 1 percent deviation in GDP results in about 0.33 more male deaths per thousand children born, and 0.62 more female deaths per thousand children born. They estimate that as a result of the expected growth slowdown in 2009, there will be between 28,000 and 49,000 excess deaths in Sub-Saharan Africa, and most of these deaths will be among girl infants.

Although there is a fairly large literature linking localized income shocks to female health, this relationship between female infant mortality and economy-wide shocks needs further investigation. One possibility is that households reduce health inputs to daughters and protect sons’ health when GDP growth slows down. Another 18
failing.

- **Junior secondary student comprehensive development evaluation (implementation starts with the Grade 6 cohort in 2018).** The evaluation emphasizes student’s experience in selfinitiated research projects and participation in social and vocational activities. The evaluation will be in the form of an essay, which will form a required part for graduation from junior secondary education.

- **Senior secondary education enrollment system (implementation starts with the Grade 6 cohort in 2018).** Nearly half of the enrollment of the leading high schools will be allocated to junior secondary that have no competitive admissions criteria for incoming primary school graduates to enter.

_Source:_ Shanghai Education Commission (https://mp.weixin.qq.com/s/0MbaKjvmEtfkaIlshIiMiw).

In the meantime, there has already been evidence of the positive effect of this curriculum reform on the overall student ability well-being. Yu and Mocan (2018) investigated the causal effect of this high school curriculum reform on students’ educational outcomes in university, as well as on their happiness, mental and physical health, self-confidence, confidence in their academic ability, and attitudes toward learning. Using survey data on students from 15 universities and employing a difference-in-differences strategy, the research found that the curriculum reform had a significant and positive effect on all student outcomes analyzed. Specifically, students who were exposed to the new curriculum in high school have better academic performance in university, are more willing to learn and master the course material, are more engaged in social activities in university, and are more confident in their academic and overall ability. In addition, the students who were 44
contract. The former sub-group of informals -characterized by smaller capital endowment per worker- partly explains the lower level and higher variability of wages among informals.

A simple wage gap regression suggests that informal workers receive on average a lower pay per hour, and this difference is statistically significant and robust against the inclusion of job characteristics. We pool data sets from the RLMS that spans 2004-2016 and remove outliers from the wage distribution.<sup>22</sup> Based on results from OLS regressions, there appears to be a wage penalty for informal workers, as suggested by the negative and statistically significant coefficient on being an informal worker, controlling for individual and job-related characteristics, both for salaried and self-employed informals (Table 8, columns 1 and 2). We further control for selection and unobservable characteristics at the individual level using a specification that includes individual fixed effects. Now, we find a statistically significant wage gap only for informal salaried workers (Table 8, columns 3 and 4). This means that, despite controlling for region, year and person fixed effects, there remains a systematic difference in pay between formal and informal workers, even after controlling for all observable productive characteristics. This is an important departure from existing literature that has previously suggested that the labor market is largely integrated with possible weak segmentation only at the bottom of the distribution (Lukiyanova, 2015).

The wage gap has widened in recent years, and is statistically significant for both types of informal workers. Using data for year 2016 only, the wage regressions show a much larger coefficient and strong statistical significance for both salaried and self-employed informal workers (Table 8, columns 5 and 6). In contrast with the regressions using pooled surveys, which showed a wage gap of ranging between 2 and 8 percent, the most recent data shows a wage gap between 15 and 19 percent. This confirms the widening informal-formal wage gap (described in foregoing paragraphs and shown in Figure 10). This wage gap is significant even after controlling (for a
Table 2: Discriminant Analysis

|Variable set used<br>Opportunity<br>Necessity<br>in classifcation<br>correctlyclassifed(%)<br>correctlyclassifed(%)|Classifed as<br>opportunity (%)|Classifed as<br>necessity (%)|
|---|---|---|
|**Panel A: Full sample**|||
|Measures of performance only (1)<br>52.37<br>62.85|40.37|59.63|
|Business characteristics only (2)<br>59.18<br>56.19|46.48|53.52|
|Cognitive skills only (3)<br>59.06<br>56.45|47.17|52.83|
|Non-cognitive skills only (4)<br>57.13<br>53.53|48.78|51.22|
|All variables combined<br>60<br>62.75|42.06|57.94|
|**Panel B: Opportunity group and frstquartile of self reported daily profts of Nec**|**essity group**||
|All variables combined<br>60.00<br>74.11|43.13|56.87|
|**Panel C: Opportunity group and fourthquartile of self reported daily profts of N**|**ecessity group**||
|All variables combined<br>60.00<br>49.46|56.92|43.80|

Measures of performance (1) include Composite Business Practice score; Business characteristics (2) include: age of entrepreneur, age of business, proportion with one worker or more and monthly salary expenses; Cognitive skills (3) include raven (standarized) test score, digit span (standarized) test score and
.6 Key covariates: Conflict_**

Our focus covariate of concern is conflict. From the UCDP data (Pettersson & Öberg, 2020; Sundberg & Melander, 2013), we calculated total fatalities for each calendar year. We matched and merged this conflict data to the girl-age analysis sample based on the DHS cluster location (second level administrative geography from GADM) and the calendar year corresponding to a particular time-varying year of age for that girl. Since the UCDP data are calendar year data, we match calendar years to years of age based on the birth year<sup>12</sup> plus the year of age for that observation. This means that if, for example, a girl was born on March 1, 2000, age zero is considered to have occurred in 2000, age one in 2001, etc. Although age zero actually spanned March 2000-February 2001, this means that conflict data always overlap with or slightly precede the year of age (never after that age). We excluded from our analyses observations where the calendar year was equal to the year of the interview, since these would be only a partial year. The appendix and Table 12 provide an illustration and further discussion of this data structure.

After thus calculating the total number of fatalities at the second-level administrative geography in each year, we normalized relative to the second-level administrative population.<sup>13</sup>

> 11 In our data, children marry as young as age zero (N=24). When modeling (see below) the hazard of marriage at each age is accounted for; there are lower hazards for very young ages. Since the model is effectively multiplicative, including all ages does not bias our estimates.

> 12 In countries (Bangladesh, Egypt, Indonesia, Pakistan) where the women’s questionnaire was only asked of evermarried women, we use the current age from the household roster, the day and month of the interview, and randomly assign a month of birth to calculate the year
competition. But over a longer period, other factors can affect growth, and the degree of competition may change over time.<sup>11</sup> Using the same regressions as before, the results using the 1980—1997 data are reported in Table 5.

The results are very similar to those reported in Table 4, with greater financial development and more banking system competition each exerting a positive effect on sectoral growth in all regression specifications, and with coefficients of similar magnitude. Banking system concentration remains insignificant in all specifications.

> 11 We checked the stability of the competition measure over time by splitting the sample period for which competition was estimated into two equal periods, 1987—1991 and 1992—1996.  See Appendix Table III for the specific measures.  We find a high correlation between the competition measures estimated for each period, 0.81, and strong correlations between the competition measures estimated for each period and the degree of competition for the whole period, 0.85 for the first and 0.99 for the second period.  We also find no obvious trend, as the average competition measure for the first period is 0.75 and for the second period is 0.71, while the overall average is 0.72. We do not resort to using the _H_ -statistic estimates for the period 1987—1991 because these estimates are available for eight countries only; we require a sample of at least 20 bank-year observations for each country in order to estimate _H_ .

27
4|12157|26.8|6.0|50.0|

*For Indonesia and Korea, expressed in trillions of local currency units. For all others, in billions of local currency units "**Estimates.

Columns 2, 3 and 4 of Table 6 report total non-performing bank loans as a percentage of the monetary base, real output and central government revenue, respectively. Table 7 reports the analogue percentages for the liabilities associated with non-bank foreign borrowing. Note that regardless of which measure we use, Korea and Thailand exhibit high levels of total implicit liabilities. For example, in 23Our data on domestic lending are from the IMF International Financial Statistics. We have attempted, as best as possible, to net out lending from one type of financial institution to another (for example, lending by a deposit money bank to some other type of financial intermediary).

32
an analysis of how foreign direct investment (FDI) is treated in the macro-literature to suggest a methodological approach, and outlines how we address specific data needs. Section 3 presents our cross-sectional data on land demand, outlines the econometric approach, and briefly discusses relevant descriptive statistics. Key econometric results in section 4 support the importance of food import demand as motivations for countries to seek out land abroad ( ̳push factors‘) and of agro-ecological suitability as key determinants for the choice of destination ( ̳pull factors‘). They also highlight the extent to which weak land governance seems to encourage rather than discourage transnational demand for land. Section 5 concludes by highlighting a number of implications for policy.

# **2. Conceptual framework and empirical evidence**

In a historical perspective, the surge of demand for land in the wake of the 2007/2008 commodity price was but one of a series of booms in farm land. Putting it into context also helps to understand the role of technical, economic, and institutional factors in shaping the nature and eventual impact of such 3
<u>Table 2 (Cont.)</u>

||(1)<br>Wome|(2)<br>n 25-55|Women with<br>kid 4-6<br>(3)|<br>Latino-<br>barometro 2018<br>(4)|Urban areas<br>only<br>(5)|
|---|---|---|---|---|---|
|_Local labor market variables_||||||
|Male/Female employment ratio in the municipality|-0.0155|-0.0157|-0.0144|-0.0142|-0.0157|
|Log of municipality GDP per capita|[0.00374]***<br>0.0475|[0.00366]***<br>0.0487|[0.00374]***<br>0.0443|[0.00246]***<br>0.0342|[0.00398]***<br>0.0412|
||[0.0245]*|[0.0245]**|[0.0255]*|[0.0130]**|[0.0329]|
|Crimes in the municipality / Total population in the municipality|-1.55|-1.865|-1.768|-1.523|-2.37|
||[1.743]|[1.769]|[2.081]|[1.369]|[1.970]|
|Share of male employment in primary (municipality)|0.00393|0.00526|-0.0344|-0.00259|0.0198|
||[0.0541]|[0.0539]|
Financial Regulation and Government Revenue In this section, we discuss three issues that are useful in our accounting exercise:

-

- 1. compliance banks responded by purchasing NBE bills as prescribed by the regulation (Table 1);

-

- 2. safety overall safe asset purchase increased one-to-one with the NBE bill purchase (Table 2 and Figure 1);

3. profitability – banks’ profit growth slowed down, but did not turn negative (Table 3 and Figure 2).

# **Compliance**

In this subsection, we show that banks started to purchase this bond in proportion to lending (0.27 : 1) as prescribed by the regulation. For this task, we use the monthly bank balance-sheet information and present a simple fixed effect regression, highlighting that the policy had an actual effect on the behavior of private banks. For this reason, we use the volume of NBE bills purchased by bank _i_ in month _m_ and year _y_ , and regress it over a dummy variable taking unit value after April 2011, _Policymy_ , interacted with the monthly bank-specific volume of lending, _Lendingimy_ , and bank, month, and year fixed effects. The null hypothesis of this test is _b_ = 0 _._ 27, given the 27% rule. In the presentation of the main results, we exploit a variety of variations by including and excluding certain sets of fixed effects. Under no circumstances can we reject that banks start purchasing the government bonds as prescribed by the regulation.

In Table 1, we report the results of regression (1) for the whole variation in column (1). We exploit only within-bank variation by including only bank fixed effects in column (2), and then by removing year-specific shocks in column (3) and month-specific shocks in column (4). Finally, we replace the last two with non-parametric effects for every month of every year in column (5). Though the point estimates move slightly across these estimations, the null hypothesis _b_ = 0 _._
# **INTRODUCTION**

Indonesia, the fourth most populous country and tenth largest economy globally (World Bank, 2022), has made remarkable economic progress since the Asian Financial Crisis. <mark>Between 2000 and 2019, GDP grew by a little over 5 percent per year, propelling the country to upper middleincome status. S</mark> ustained economic growth has lifted millions of Indonesians out of poverty, with an estimated 10 percent of individuals living below the national poverty line in 2019 (World Bank, 2022). While the COVID-19 pandemic reversed some of the country’s economic gains, Indonesia remains committed to achieving high-income status by 2045.

To accomplish its objective, Indonesia needs to address its long-standing development challenges. This includes the informal economy, defined as ” _all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements_ ” (International Labour Conference, 2002, p. 53). Based on estimates from SAKERNAS, Indonesia’s informal economy accounted for an average of 36 percent of GDP between 2011 and 2019 (Hapsari, Yu, Pape, & Mansour, 2022) and nearly 75 percent of total employment in 2019 (Wihardja & Cunningham, 2021).<sup>2</sup> The latter figure is considerably higher than the share of informal employment in neighboring Southeast Asian countries and other countries at a similar level of economic development.

The key to addressing the challenge of informality in Indonesia and elsewhere is understanding its drivers. As Chen (2005) argued, the underlying causes of informality determine the kind of policies needed – if indeed they are needed at all. Dualism, for example, implies that the informal economy would naturally disappear with economic development (Kanbur, 2017), while other theories call for greater government intervention such as stricter enforcement of taxes and labor laws. Policies may also contradict rather than complement each other. As a case in point,

> 2 Wihardja and Cunningham's (
**Empirical Specification** To investigate the role of institution on the process of resource allocation, we map the measures of business climate from the ES to our census data. Since there is no firm-specific policy variable in our data, we first construct policy variables from the ES by taking the averages of survey responses of firms located in the same region. The underlying assumption of this approach is that policies vary across regions within a country, which is plausible since firms facing the same _de jure_ policy are likely to face different policy implementation across regions (Acemoglu and Dell, 2010; Hallward-Driemeier et al., 2010).<sup>26</sup> The estimation equation is then given by:

where _yisrc_ is firm-specific measure of distortion of firm _i_ in industry _s_ region _r_ and country _c_ . _Dc_ and _Ds_ are country and industry dummies, respectively. _xic_ is a vector of firm characteristics that may be correlated with distortions (age and size). The policy variables ( _BE_ ) are region-average of the respective measures of business environment. For example, ‘Finance’ measures the region-average of fraction of firms with no access to short-term finance. Corruption – the region-average of losses due to informal payments. Electricity - average of losses due to electrical outages in each region. Regulation – the region-average of the fraction of time that the senior managers spent dealing with government regulations.

The results of this regression are reported in Table 6. In a frictionless world, _TFPRsi_ should be equalized across all firms within industries, and we would expect all the coefficients of the policy variables to be not significant if these policies are orthogonal to other factors that may possibly affect _TFPR_ . To the extent there is a positive and significant coefficient of the policy variables, this can be taken as evidence that the business environment affects idiosyncratic distortions.

Although it cannot be interpreted as causal evidence, the results in table 6 shows that business environment indicators are found to be significantly correlated with firm-specific distortions.<sup>27</sup>
sectors.

While there are large gaps in average productivity, allocative efficiency requires a convergence in marginal productivity across sectors. Assuming Cobb‐Douglas production technologies at sector level together with perfect competition in factor markets, the measurement of marginal productivity reduces to adjusting the average products of a sector with the sector‐specific labor share of income. The table below shows the marginal productivity in industry and services relative to agriculture for Bangladesh which have been computed using labor shares of income at sector level from the GTAP database.

||**Margin**<br>|**al Productivity of La**<br>|**bor Relative to Agr **<br>|**iculture**<br>|
|---|---|---|---|---|
||**2000**|**2005**|**2010**|**2015**|
|Industry|4.38|4.41|4.19|4.32|
|Services|7.93|9.68|11.20|11.56|

> 8 Please see appendix A.2 for a brief discussion on factors that are important in driving TFP growth. This discussion is a summary of the survey on productivity by Syverson (2011). The interested reader should read the paper as the discussion here highlights only some of the important factors that can lead to productivity growth and is in no way provides a comprehensive listing of such factors.

23
|2.1|2.0|1.9|2.4|
|Total factor productivity (index)||-0.1|1.3|1.5|1.7|1.9|2.1|2.3|2.4|2.4|1.9|
|Real exchange rate (index)||-0.3|0.4|0.4|0.3|0.2|0.1|0.0|-0.1|-0.1|0.2|
|Headcountpovertyrate(%)|27.3|21.4|19.3|17.4|15.7|14.2|12.9|11.6|10.6|9.5||

Note: the column 2008 shows values in million lei; the final column shows the average growth rate for the period 20122020; the other columns show annual growth rates (%).

The fact that, for the full simulation period, absorption grows at a slower pace than GDP indicates that exports have to increase more than imports due that non-trade items in the balance of payments does not fully keep up with overall growth. Among the domestic final demand items, growth in private consumption is relatively fast, growth in government consumption relatively slow, while both private and government investment growing at rates similar to those of total absorption. Total factor productivity grows moderately. Our assumption of increased full-time employment is important for reaching the base growth rates. If employment growth would not materialize, it would mean that labor scarcity would be even more severe making additional productivity growth necessary unless a lower rate of GDP growth is imposed. The real exchange rate is almost unchanged, showing that domestic productivity, export and import prices, and non-trade items in the balance of payments together evolve in such a way that additional adjustments in export and import incentives are not needed to maintain external