rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table 3.  Business Outcomes</u>**

||Business<br>Knowledge|New Business<br>CO member<br>involved<br>(1=Yes)|New Business<br>CO member<br>not involved<br>(1=Yes)|<br>Main Busin<br>Failed<br>(1=Yes)|ess<br>Aggregate<br>Business<br>Practices|Aggregate<br>Business<br>Operations|Aggregate Sales<br>and Profits|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A: Intent to Treat Effect**||||||||
|Business Training (1=Yes)|0.066**|-0.006|-0.001|-0.034|0.114**|0.037|-0.035|
||(0.031)|(0.008)|(0.012)|(0.028)|(0.054)|(0.026)|(0.055)|
|Lottery Winner (1=Yes)|-0.011|-0.012|-0.007|-0.002|0.087|0.077**|0.064|
||(0.040)|(0.013)|(0.019)|(0.036)|(0.072)|(0.033)|(0.073)|
|BT and LW|0.105***|-0.004|0.010|-0.014|0.146**|0.041|-0.051|
||(0.038)|(0.013)|(0.018)|(0.037)|(0.069)|(0.033)|(0.067)|
|R-Squared|0.08|0.01|0.01|0.03|0.06|0.09|0.34|
|P-value of t - test of ...||||||||
|BT = BT and LW|0.27|0.88|0.51|0.55|0.58|0.89|0.79|
|LW = BT and LW|0.01|0.53|0.42|0.77|0.43|0.35|0.16|
|BT = LW|0.06|0.59|0.77|0.37|0.70|0.24|0.20|
|**Panel B: Intent to Treat Effects with Gender In**|**teractions**|||||||
|Business Training (1=Yes)|0.070|-0.011|-0.012|-0.061*|0.110|0.057|0.018|
||(0.043)|(0.013)|(0.016)|(0.037)|(0.074)|(0.035)|(0.073)|
|BT x Female|-0.008|0.013|0.023|0.060|0.003|-0.059|-0.144|
||(0.063)|(0.017)|(0.025)|(0.055)|(0.104)|(0.051)|(0.104)|
|Lottery Winner (1=Yes)|0.008|-0.019|-0.015|-0.004|0.057|0.086*|0.030|
||(0.049)|(0.018)|(0.024)|(0.045)|(0.092)|(0.044)|(0.091)|
|LW x Female|-0.044|0.016|0.017|0.000|0.087|-0.03|0.065|
||(0.078)|(0.023)|(0.035)|(0.068)|(0.133)|(0.066)|(0.139)|
|BT and LW|0.101**|-0.016|-0.017|-0.047|0.229**|0.072*|-0.037|
||(0.050)|(0.016)|(0.020)|(0.046)|(0.090)|(0.042)|(0.085)|
|BT and LW x Female|0.010|0.028|0.062*|0.077|-0.203*|-0.078|-0.028|
||(0.073)|(0.024)|(0.036)|(0.069)|(0.122)|(0.067)|(0.128)|
|R-Squared|-0.02|0.05|0.10|0.03|0.06|0.09|0.34|
|P-value of t - test of ...||||||||
|BT = BT and LW|0.51|0.74|0.77|0.75|0.13|0.66|0.44|
|LW = BT and LW|0.08|0.86|0.92|0.40|0.09|0.78|0.52|
|BT = LW|0.22|0.63|0.90|0.20|0.55|0.52|0.90|
|BT + BT x Female = 0|0.18|0.88|0.56|0.98|0.13|0.96|0.11|
|LW + LW x Female = 0|0.58|0.85|0.94|0.94|0.17|0.28|0.38|
|BT and LW + BT and LW x Female = 0|0.05|0.53|0.14|0.58|0.77|0.91|0.52|
|Mean of dependent variable among controls|-0.03|0.05|0.10|0.40|-0.07|-0.02|0.07|
|N. Observations|3494|3494|3494|2137|1312|1310|1181|

Note: The reported mean of the dependent variable is computed using CO members not offered business training nor assigned to be lottery winners.The dependent variables are aggregates of standardized z-scores. See Appendix B for a definition of the aggregates. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline, gender and branch dummies). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
# **_How Might Health Make You Rich?_**

The most obvious reason why healthier people might be richer is that they can work harder, longer, and more consistently than others. In turn, those who are disabled or ill can work less, placing an economic burden on the household. But can better health increase the _rate_ at which income grows?

# _Human Capital Accumulation_

A recurring theme in the literature is that health leads to income growth through its effect on human capital accumulation—and particularly through education—provided that people have sufficient food and satisfactory educational opportunities.

First, children who are healthy and adequately nourished may spend more time at school and be better learners while there, preparing themselves to earn higher incomes. Along these lines, Sachs and Malaney (2002) describe a number of channels through which malaria can compromise educational attainment, including by hampering fetal development, reducing cognitive ability, and lowering school attendance.

Second, the health status of adults affects human capital accumulation by their children. A large proportion of human capital investment decisions are made by parents on their children’s behalf. But if parents die, they cannot invest in their children. Orphans do not necessarily suffer a complete withdrawal of adult support, given the social networks in many societies, but they are likely to receive less than when their parents were alive, an issue that is discussed below in the context of the economic impact of illness. Lorentzen, McMillan, and Wacziarg (2005), using an instrumental variables approach, find that the adult mortality rate affects growth less through its influence on investments in education than through its influence on fertility and physical capital investments.

# _Physical Capital Accumulation_

A population in better health may accumulate physical capital more quickly. The most obvious route is through savings, as higher life expectancy (for example) increases the expected length of retirement. Indeed, Bloom, Canning, and Graham (2002) attribute the rapid growth of East Asia to precisely this mechanism. Alsan, Bloom, and Canning (2006) and Sachs and Malaney (2002) highlight the impact that better population health has on inflows of foreign capital, as opposed to increases in domestic savings; this effect is usually thought to operate in situations in which foreign (direct) investment and expatriates (either in the role of staff or consumers) are highly complementary. Tourism is the most commonly cited example, as the threat of communicable diseases such as SARS (severe acute respiratory syndrome) deters visitors and investment, at least in the short term, because it suggests high-risk environments (Bell and Lewis 2004).

# **_Trends in Health and National Income_**

The economics and population-health professions were brought together empirically only in the last 30 years. Preston (1975) presents data on per capita income and on population health status 7
leadership; and the strength of the private sector (see Section 6.1). The case study narratives also brought to our attention two additional variables that contributed to the uptake of reform: the role of CSO voice; and inter-district competition and diffusion of knowledge. We discuss these alternatives explanations in Section 8.

— Fieldwork was carried out in four regencies North Luwu, Sijunjung, — Madiun, and Lamongan and two cities, Makassar and Balikpapan. To select districts, we used data from a scan of procurement reform efforts at the sub-national level to create a database of all districts that have initiated procurement reform. From this sample, we selected pairs of cases that have different values on a key explanatory variable but similar values on control variables. From the subset of candidate districts, we selected cases that are relatively accessible and where either B-Trust or The Asia Foundation has contacts (see Table 2).

Table 2: Case Selection

|**Variables**|**Low**|**High**|
|---|---|---|
|Leadership Quality|North Luwu|Sijunjung|
|Private Sector Competitiveness|Madiun|Lamongan|
|Public Financial Management|Makassar|Balikpapan|

We selected two districts, North Luwu and Sijunjung, which differ on the quality of the leadership of their mayor or regent but have similar values on other explanatory and control variables (e.g. population size and whether the district is urban or rural). We measured leadership quality using data from the 2007 and the 2010/2011 Local Economic Governance Surveys (KPPOD and The Asia Foundation), which covered 12 and 20 provinces, respectively. These surveys probed business owners about their perceptions of the integrity and capacity of the Mayor/Regent. The survey asked respondents about whether they agreed that the: (1) mayor/regent takes strong action against corruption cases, (2) mayor/regent is not corrupt; and (3) the mayor/regent has strong leadership.

We selected two cases, Madiun and Lamongan, which have different levels of private sector competitiveness but have similar values on other variables. We used the 2009 Regional Gross Domestic Product (RGDP) as an indicator of private sector competitiveness. For districts with missing 2009 RGDP values, we used 2008 data.

10
reform eliminating the _tiliu_ but receiving only 60 percent in revenue replacement transfers in 2004, villages had to rely more heavily on the latter, especially land-related fees and the sale of land use rights and other assets. Among 100 surveyed villages, the number selling land rights increased from 3 to 4 per year in the early 1990s to 24 in both 2003 and 2004, or nearly one-quarter of our sample. Other assets, including buildings, equipment, and timber, were increasingly sold as well – we found almost a tripling in the frequency of sales, from 10 times in 1998 to 29 times in 2004. Revenue from these sales in 2003 and 2004 that went to the village (after netting out compensation to households) averaged RMB 35,000 per village, in addition to the RMB 35,000 for land-related transactions.

Nationwide, revenues from land transactions have become an important part of the income of local governments at the municipal, county, township and village levels. Official data show that from 2001 to 2003, total revenue from land-use right transfer fees was as high as RMB 910 billion (World Bank 2005a). In some municipalities and counties, these revenues were greater than the governments’ fiscal revenues. In addition, local governments are increasingly taking out loans using requisitioned land as collateral through the creation of “land banks,” and these loans have become an even greater source of funds than the land-use right transfer fees in many areas. Together, a joint State Council Development Research Center-World Bank study found these land transaction-related revenues to be financing 80 to 90 percent of total investment in infrastructure in many areas (World Bank 2005a, Zhang 2007).

Aside from selling assets, local governments are borrowing. Our survey found total village liabilities amounted to more than RMB 250 per villager in 2004.<sup>35</sup> This implies a total debt of all villages in China equal to about RMB 240 billion. Table 20 shows the size and sources of village debt in 2004.

> 35 There are large differences across provinces in the magnitude of these debts (Table 16). The highest debt was in Shaanxi, at RMB 462.1, followed by Jilin (RMB 397.2), and then Hebei (RMB 338.6), Jiangsu (RMB 145.2), and Sichuan (RMB 45.4).

36
14

**Table 1:  Competitive Elections and Public Policy**

|(_p_-<br>|valuesinpare<br>|ntheses, stand<br>|ard errors clustered<br>|by country)<br>|<br>|
|---|---|---|---|---|---|
|Dependent<br>variables:|Gross<br>Secondary<br>School<br>Enrollment|Gross<br>Primary<br>School<br>Enrollment|Private<br>Investment/GDP|Bureaucratic<br>Quality<br>(ICRG)|<br>Corruption<br>(ICRG)|
|Competitive|2.97|5.60|-0.02|0.47|0.43|
|elections, 0-1|(0.15)|(0.04)|(0.98)|(0.00)|(0.00)|
|Income/capita,|1.30|-1.62|0.69|0.63|0.38|
|real, ppp-|(0.31)|(0.25)|(0.66)|(0.00)|(0.00)|
|adjusted, in<br>$10,000||||||
|Fraction|-31.14|-18.39|0.32|0.27|-0.11|
|population rural|(0.00)|(0.08)|(0.92)|(0.50)|(0.81)|
|Fraction|-211.47|-33.17|-31.17|-4.76|-2.53|
|population<br>young|(0.00)|(0.07)|(0.00)|(0.00)|(0.02)|
|Land Area,|1.11|-0.13|-0.32|0.02|0.00|
|1,000,000 sq.|(0.31)|(0.87)|(0.18)|(0.72)|(0.46)|
|km.||||||
|Total|-0.18|0.12|-0.00|0.01|-0.00|
|population, 10<br>millions|(0.04)|(0.09)|(0.87)|(0.29)|(0.22)|
|Observations|2,243|2,491|2,374|3,036|3,036|
|R-squared|0.8|0.1|0.1|0.6|0.3|

Note: Results of ordinary least squares regressions of public policy and investment variables.

The proportion of poorer countries with competitive elections is significantly lower than that of richer countries (approximately 40 percent versus 70 percent in the case of the education samples; approximately 30 versus 59 percent in the case of the private investment sample). This could lead to upwardly-biased estimates of the elections variable in the Table 1 estimations, since rich democracies (as the next sections show) are more likely to exhibit omitted arrangements that facilitate citizens‟ collective action. Table 2 therefore reports the same regressions, but in a sample confined to countries with incomes less than the median country‟s per capita income in each year.
**Table 2: Random effect probit estimations - Determinants of renegotiation**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Reneg|Reneg|Reneg|Reneg|Reneg|Reneg|Reneg|
|Price cap|1.274***|1.077***|0.938***|0.975***|1.067***|0.965***|0.955***|
||(0.364)|(0.407)|(0.328)|(0.325)|(0.404)|(0.324)|(0.323)|
|Road|-0.285|-0.207|-0.303|-0.224|-0.139|-0.222|-0.215|
||(0.329)|(0.338)|(0.299)|(0.303)|(0.341)|(0.300)|(0.301)|
|Rail|-0.217|-0.068|-0.374|-0.246|-0.028|-0.274|-0.255|
||(0.367)|(0.372)|(0.341)|(0.334)|(0.374)|(0.338)|(0.335)|
|Air|-0.776**|-0.615|-0.660*|-0.652*|-0.489|-0.590|-0.583|
||(0.392)|(0.397)|(0.356)|(0.357)|(0.404)|(0.361)|(0.361)|
|Duration|-0.039***|-0.032**|-0.038***|-0.042***|-0.024|-0.032**|-0.031**|
||(0.013)|(0.013)|(0.013)|(0.013)|(0.015)|(0.014)|(0.014)|
|Investment|0.000|0.000**|0.000***|0.000**|0.000**|0.000**|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Project age|-0.036***|-0.066***|-0.052***|-0.054***|-0.064***|-0.055***|-0.057***|
||(0.012)|(0.015)|(0.013)|(0.014)|(0.017)|(0.014)|(0.014)|
|Guarantee|0.652***|0.795***|0.728***|0.695***|0.801***|0.661***|0.662***|
||(0.157)|(0.173)|(0.158)|(0.174)|(0.178)|(0.164)|(0.164)|
|IRA|0.119|0.173|-0.000|0.024|0.215|0.018|0.017|
||(0.146)|(0.168)|(0.152)|(0.156)|(0.175)|(0.155)|(0.155)|
|GDP growth (lag)|-0.055***|-0.037*|-0.033|-0.038*|-0.048**|-0.039*|-0.038*|
||(0.018)|(0.022)|(0.020)|(0.021)|(0.023)|(0.022)|(0.022)|
|Corruption<sup>17</sup>|-0.072|-0.155*|-0.128|-0.112|-0.165*|-0.131|-0.120|
||(0.074)|(0.087)|(0.082)|(0.083)|(0.094)|(0.087)|(0.090)|
|Leverage1 (lag)||-0.041||||||
|||(0.168)||||||
|Distress1|||0.247*|||||
||||(0.149)|||||
|Distress2||||-0.082<br>(0.134)||||
|ROA (lag)|||||0.153|||
||||||(0.291)|||
|Performance1||||||0.105<br>(0.208)||
|Performance2|||||||0.111|
||||||||(0.210)|
|Observations|1104|748|630|727|669|830|747|
|Projects|102|99|91|95|97|99|97|

Standard errors in parentheses. The columns are identical with the exception of the financial variables included in the bottom rows. *** p<0.01, ** p<0.05, * p<0.1 Such results are only indicative. They do not imply that firms cannot renegotiate when in financial difficulty; only that being in difficulty does not increase the likelihood of renegotiation. To address this, we can look in more detail at those firms that have particularly high leverage, or particularly poor profitability, and analyse the renegotiations that occur.

> 17 A higher value indicates less corruption.

14
Table 1 also gives a regional breakdown of the estimated impacts of differential mortality on the 2001 headcount index. Since the headcount index is not (of course) around 20% in all regions, the estimates of _DRD_ from the DHS data do not strictly apply to each region separately. One would be drawn toward lower values of DRD for the poorer regions. But even focusing on _DRD_ =1.2, it is clear that the impacts are highest for Sub-Saharan Africa. The trend rate of poverty reduction in SSA has been close to zero over 1984-2001 (indeed Chen and Ravallion report a regression coefficient of the region’s $1 a day headcount index on time of 0.0!). So it is not meaningful to express these impacts as a proportion of the annual rate of poverty reduction. However, the impacts are clearly sizeable. Indeed, these calculations suggest that without differential mortality, the headcount index in SSA would be rising at a non-negligible rate over time, at about 0.2 percentage points per year at _DRD_ =1.2, rising to 0.8 at _DRD_ =2.

The region with the second highest impacts is South Asia. At _DDR_ =1.2, differential mortality adds 0.06 percentage points to the “$1 a day” headcount index in South Asia, representing 15% of the long-run trend since the mid-1980s implied by the Chen-Ravallion estimates. This rises to 0.3 points per year at _DRD_ =2 though this appears to be an implausibly large value for South Asia.

Turning next to the impact of differential fertility, the calculations in the previous section also suggest a range for _BRD_ of 1.2 to 2. Table 2 gives the corresponding results for the impacts of differential fertility (analogously to Table 1). At _BRD_ =1.2, the poverty rate in 2001 under random fertility would have been reduced by 0.1 percentage point, representing 16% of the trend rate of poverty reduction. Again, the impacts are large in South Asia and (especially) Africa, though evidence presented in Section 3 suggests that we should probably be using a lower value 17
Enduring Impacts of Aid Quality on Job Choices: The Case of the 2004 Tsunami in Aceh<sup>1</sup> Manabu Nose<sup>2</sup>

**<mark>JEL classification:</mark>** Q54, H84, J43, O12

**Keywords:** Rural development, Natural disaster, Aid effectiveness, Coping strategy, Inequality

**Sector Boards** :  POV, SDN, PSD

> 1 I am grateful to Vernon Henderson, Andrew Foster, and Sriniketh Nagavarapu for their guidance and supports on this paper. I thank Ari Kunkoro, Zakir Machmud, Ifa Isfandiarni, and Hamdan Bintra of the University of Indonesia for the supervision of the Aceh tsunami survey. Useful insights from Glenn Loury, Blaise Melly, Pedro Dal Bo, Oded Galor, Nathaniel Baum-Snow, Mark Pitt, and Adam Storeygard, and Takashi Kurosaki are greatly acknowledged. I also acknowledge the support from World Bank Jakarta office for the access to Indonesian household surveys. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent

> 2 The World Bank, 1818 H Street, N.W., Washington, DC, 20433 USA; Email: <u>mnose@worldbank.org; Tel:</u> +1-(202)294-5370
controls are very similar. In addition, Appendix Tables B10 and B11 indicate that results are analogous when using covariates without the lasso procedure. We estimate heterogeneous treatment effects with respect to predicted malaria risk using the following specification: where _highict_ and _lowict_ are dummy variables identifying patients with above/below median predicted malaria risk, **xict** is the vector of clinic and individual-level controls (again selected using double lasso), and _λ_ **t** are date fixed effects. Since _highict_ and _lowict_ are generated regressors, we bootstrap standard errors, clustering at the clinic level.<sup>28</sup> We now turn to the data to assess our theoretical predictions empirically.

# **4.2 Impacts on Antimalarial Prescriptions and Purchases**

**Overall Impacts.** Table 3 shows treatment effects on voucher use, antimalarial prescriptions, and antimalarial purchases. We report the coefficients on the treatment dummies _βP_ and _βD_ and the p-value of a two-sided test of _H_ 0 : _βP_ = _βD_ . We also indicate the theoretical mechanism being tested (existence of gatekeeping costs, doctor-driven, or patient-driven overprescription – GC, DD, or PD) and whether we find significant evidence of that mechanism or not.

The first column looks at _voucher redemptions_ . We find strong, significant evidence in favor of gatekeeping costs: voucher redemption is 9 percentage dummies, illness duration (top-coded at the 99th percentile), patient age, an under 5 dummy, patient gender, a dummy to identify pregnant women, a dummy to identify patients who were also survey respondents, survey respondent gender, respondent ethnicity, education, literacy and knowledge of French, and a dummy variable identifying patients interviewed in the home follow-up survey. Missing values are dummied out and recoded to the sample mean. We also include squared terms for patient age and illness duration, as well as pairwise interactions between the aforementioned individual-level controls. We partial out date fixed effects, so they are included in all regressions.

> 28First we use double lasso to select covariates. Then we create 1,000 bootstrapped samples by sampling 60 clinics with replacement. Then we re-estimate predicted malaria risk in each bootstrap sample, reconstruct _highict_ and _lowict_ based on the new risk estimate, and run the regression. Standard errors are based on the distribution of coefficients across bootstrapped samples.

25
Due to the flexible nature of casual work, they also saw a quick recovery to pre-pandemic shares of employment. Section 4 discusses how transitions between employment types during the pandemic were likely a distress signal in the labor market.

# **3 Methodology: Fixed cohort analysis using CMIE panel structure to follow the same cohorts over time**

In this section, we introduce a fixed-cohort analysis that allows us to study the same cohort of individuals over time. This approach has two advantages. First, it eliminates the concern over different composition of individuals in different snapshots of time from the cross-sectional analysis, which confounds any analysis of trends in labor market outcomes. Second, it allows us to follow the labor market trajectory of individuals up to 16 months afterwards.

We define a Covid-affected cohort as the group of workers employed at the baseline period (Nov 2019-Feb 2020), right before the pandemic. The CPHS revisits households once every 4 months, therefore we can track this group of workers 4 months later (Mar 2020-Jun 2020), 8 months later (Jul 2020-Oct 2020), 12 months later (Nov 2020-Feb 2021), and 16 months later (Mar 2021-Jun 2021). We consider only workers who can be tracked consistently over all four periods so that the composition of workers stays the same for each time period.

We create a pre-Covid cohort for comparison. It consists of workers who were employed in Nov 2017-Feb 2018 and those who were employed in the Nov 2018-Feb 2019 period as baseline and follow them for 16 months and 12 months respectively (12 months for the second group to avoid overlap with the Covid period). The goal of having this cohort, for a period that is as close as possible to (and yet distinct from) the Covid period, is to have a comparator group with a likely ‘normal’ labor market trajectory for workers in India during a time period without Covid. If we observe any difference in terms of labor market trajectory between the Covid cohort and pre-Covid cohort, these differences can reasonably be attributed to the Covid shock.

We formulate the above comparison in a difference-in-difference regression framework, estimating the change in labor market outcomes with respect to baseline for each time period separately for both the Covid and the pre-Covid cohorts. For worker _i_ from cohort _k_ (pre-Covid vs Covid cohort) at time period _t_ , we run the following regression specification: _Outcomeikt_ = _βkt × CovidCohortik × Periodt_ + _Fixed Effects_ + _εikt_ We estimate _βkt_ separately for each of the 4 post-baseline periods. We are particularly interested in the difference in the coefficients between the pre-Covid and Covid 8