rafmacalaba/datause-encoder-data huggingface dataset:
enumerator would select the household next door to that one. If the second replacement was also unsuccessful, the enumerator would go on to the next original household (i.e., 10<sup>th</sup> household). viii LFP rates are from the 2013 Labour Force Survey (Bangladesh Bureau of Statistics, 2015).

ix Our definition of “working” does exclude individuals who were in the labor force but not working at the time of the screener. However, the unemployment rate in Bangladesh is low (4.3 percent in the 2013 Labor Force Survey).

> x The employment types were: Paid employee in government; Paid employee in a private entity; Apprentice/Intern/Trainee; Seasonal worker; Day laborer / casual worker; Domestic worker in a private household; Self-employed / business owner with no employees; Self-employed / business owner employing only paid or unpaid family members; Self-employed / business owner employing some non-family members; and Paid or unpaid family member working in a household business.

xi For women who could not be located, we replaced them with men, since we had already targeted all working women in the roster.

xii In the manuscript, we use the terms “written contract” and “contract” interchangeably. For employees who have a Provident Fund, both employers and employees make equal contributions (based on a percentage of the employee’s salary), and the fund is administered by a board of trustees.

xiii Although the survey enumerators emphasized that the respondents should assume that all other attributes were identical, we cannot completely address concerns about unobserved heterogeneity that may arise because of respondents’ prior beliefs about the correlation between included and non-included attributes; for example, if a respondent believes that working longer hours makes her a more likely candidate to get a contract in future, then our measure of the preference for working hours is picking up some preference for a (future) contract. We thank an anonymous referee for pointing this out.

xiv When we piloted the choice experiment during the focus groups, we experimented with both decreases and increases in monthly income, relative to current income. When we offered decreases in monthly income, we found that participants were unwilling to consider any cuts to current income, regardless of what benefits were offered. In contrast, they were willing to consider tradeoffs between differential income increases and benefits. We hypothesize that this difference is driven by the fact that many of the participants are living at or just above a subsistence income, and are therefore unwilling to consider any cuts to current income. Even 30
33

**Table 12:** Visa provision and migration stocks: IV estimation.

|Dep var:|Visa|Mig. stock (ln)|
|---|---|---|
||(1)|(2)|
|Visa<sup>_IV_</sup>|0.139***<br>(0.013)||
|Visa||0.150*<br>(0.085)|
|Schenghen|-0.210***<br>(0.033)|0.037<br>(0.055)|
|GDP diference (ln)|0.010***<br>(0.002)|-0.032<br>(0.006)|
|Estimator|First Stage<br>2SLS|Second Stage<br>2SLS|
|F-stat|116.496||
|Country-Year Fixed Efects|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|
|Sample period|1990-2020|1990-2020|
|Observations|59,587|59,587|
|Two-way clustered s.e.|Yes|Yes|

Note: 2SLS procedure. Standard errors clustered at the destination-year and origin-year. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
have participated in a mentoring or acceleration program before, potentially reflecting a taste or revealed preference for training, or complementarities with existing skills. Finally, companies which use cloud technology were more likely to attend. When asked in our follow-up survey why they did not attend, the most important reasons given were that they did not want to take the time away from their businesses (and in some cases second jobs as employees), and that the locations were too far away.

Out of the 172 participants assigned to the control group, 120 (70%) accessed at least once the online Innovation Readiness Series<sup>TM</sup> platform. However, even conditional on accessing the platform, overall usage was relatively low. Conditional on accessing the online platform, 118 participants viewed at least once the modules’ section and 55 viewed it at least 10 times; the mean number of views of the modules section was 21 and the median 9. Each module last approximately half an hour, so we can approximate that the mean time spent on the modules was 10 hours while the median 4.5 hours. Only 63 (37% of the control group) participated in one of the seven quizzes at the end of a module. A total of 51 control group entrepreneurs passed at least 4 quizzes with 45 attaining the threshold of 70% correct answers in all quizzes, necessary to receive a certificate of completion from the IC<sup>2</sup> Institute at the University of Texas at Austin. The main two correlates of taking and passing the quizzes among the control group are having postgraduate education (positively correlated), and having previously participated in a mentoring or accelerator (negatively correlated). The online courses are thus done by those who have more schooling and have not previously had exposure to such content.

# **4. Impacts on Investment Readiness as Scored by Judges**

# **4.1 The Semi-finals and Judging Procedure**

The semi-finals were held in parallel to, and in cooperation with, the Belgrade Venture Forum, an annual venture capital conference that took place from November 12 to 14, 2015. Participants were invited to present in a pitch event that follows the standard format of such events, with firms giving a 5-minute pitch of their business case, followed by 5 minutes of questions from a jury of judges.

Participation required the founder of the firm or a representative to be physically present in Belgrade. To encourage participation, firms received multiple reminders and calls, were sent an invitation letter with a ticket voucher that allowed them one day of free access to the adjoining 15
Measuring Global Economic Activity Using Air Pollution<sup>∗</sup> Irene Ezran<sup>†</sup> Stephen D. Morris<sup>‡</sup> Mart ́ın Rama<sup>†</sup> Daniel Riera-Crichton.<sup>§</sup>

**Keywords:** Nitrogen Dioxide, GDP, Night Lights.

**JEL Codes:** E01, E32, F44, Q53.

> ∗Materials for online publication appear following the end of the paper. The authors gratefully acknowledge Lok Lamsal and Barry Lefer of NASA for assistance with nitrogen dioxide data and Christopher Elvidge and Virgilo Galdo for assistance with night lights. Sam Bazzi provided useful comments. Any remaining errors are our own.

> †The World Bank.

> ‡Amazon.

> §The World Bank. Corresponding author. Email: `drieracrichton@worldbank.org`
The N]PF faces two major challenges. First, there is a need for a greater diversification of its assets in non-government securities (corporate securities and foreign assets). Investing a greater proportion of assets in such securities would require the creation of a more transparent, professional and independent fund governance and asset management structure. The second challenge is to enhance the transparency of its operations, simplify the link between contributions and benefits, and improve its performance as a pension institution. The authorities are considering conversion of the NPF from its current opaque point system to a defined contribution system with individual capitalization accounts, crediting of net investment returns to workers' accounts and purchase of a real annuity on retirement (see the Annex for more details).

# Nationnal Savings Fund (NSF)

The NSF is a defined-contribution scheme that offers covered workers a lump sum on retirement. All employees are required to participate in the NSF, including civil servants and employees of statutory bodies. The contribution rate amounts to 2.5 percent of covered earnings and is paid by employers. The NSF has accumulated resources equivalent to 2 percent of GDP. These are heavily invested in government securities. The NSF does not play a major part either as a pension fund or as a financial institution. It could be merged into the activities of a reformed NPF in the future.

## B. Oc-cupational ]Pension Schemes

Occupational pension schemes cover three main types: the civil service pension scheme (as well as those covering local government employees); pension schemes for various statutory bodies; and pension schemes established by private sector entities. The latter two types operate heavily (but not fully) funded schemes that have accumulated substantial resources, amounting to MUR 14 billion in 2001 and corresponding to 11 percent of GDP. However, the scheme covering civil servants is unfunded and is financed from the budget. Its cost was estimated at 1.3 percent of GDP in 2001.

Table 3: Number of Approved Occupational Pension Schemes (1998-2002)

||1998|1999|2000|2001|2002|
|---|---|---|---|---|---|
|TotalNumber|882|922|967|1007|1095|
|Registered|30|32|35|37|42|
|Insuredand/orAdministered|852|890|932|970|1053|
|Source:Tax Comm|issionera|ndRegist|rarofAs|sociation|s|

Approved pension schemes, outside those covering civil servants and local government employees, increased from 882 in 1998 to 1007 in 2001 (and 1095 in 2002). The number of self-administered and registered funds rose from 30 in 1998 to 37 in 2001 (and 42 in 2002). The vast majority of funds, old as well as new, continues to be insured and/or administered by insurance companies (Table 3). Anglo-Mauritius Life Assurance Company appears to have the lion's share of group pension insurance business, facing competition from a small number of life insurance companies.

6
systematic across age groups. The similarity across pre-reform characteristics is consistent with a non-selectivity of districts into the reform.

# 3.2 Empirical strategy

Given the “quasi-experimental”nature of the 1997 Amhara land redistribution and the availability of census data both before and after its implementation, we employ a difference-in-differences approach to estimate its impact on fertility and other related outcomes. A simple cross-sectional comparison would not be appropriate for identifying any causal effects as, even in the absence of the implementation of the reform, fertility rates between the 1997 reform and nonreform districts might differ as a result of persistent cultural norms (e.g., as to polygyny, the patriarchal system of family and inheritance, early marriages, and the stigmatization of unmarried and divorced women) and environmental factors (such as soil quality, land degradation, and climatic conditions). These variables are diffi cult to observe and hence to control for in a cross-sectional analysis. In contrast, a difference-in-differences estimation strategy taking into account district level fixed effects wipes out any persistent influences from norms and environmental factors on fertility outcomes at the level of the empirical analysis. The difference-in-differences estimation equation, with district level fixed effects, can be written as where Yjt captures the fertility outcome for district j = 1, ..., 212 at time t = 0, 1. AFTERt is a binary indicator equal to one for the time period after the implementation of the 1997 reform and zero for the time period before the reform. We use two alternative indicators to capture the land reform represented by REFORMj, in district j. The first one is a binary indicator equal to one if at least one of the peasant associations belonging to district j implemented the land reform and zero otherwise. The second one is an indicator for the intensity of the reform, defined as the proportion of peasant associations in district j that carried out the 1997 land redistribution. It should be noted that since the reform was implemented at the district level, typically almost all the peasant associations were covered in the areas where the land redistribution was implemented.

The coeffi cient β3 on the interaction between AFTERt and REFORMj estimates the average difference-in-differences between the 1997 reform and nonreform districts, Xjt consists of time varying control variables which may be correlated with fertility outcomes, and dj contains district fixed effects which absorb persistent unobserved differences between reform and non-reform areas.

The inference is generally based on the standard Huber/White estimator of the variance matrix. It is valid for the within estimator controlling for clusterspecific fixed effects with data aggregated at the cluster level (Arellano, 1987; Cameron and Miller, 2015). All the main findings are robust to other specifications of the standard errors, such as clustering at the zonal level (the zone 11
|Table B.3: Fiscal (pro)cyclicality of individual income tax rates,|whole sample (2000-2016): Impacts of rigidity, fisc|al space and business cycle||
|---|---|---|---|
|Dependent variable: Individual income tax|(1)|(2)|(3)|
|Cyclicality of real GDP|0.052|55.509|-2.194|
||(0.188)|(35.893)|(3.394)|
|**Business cycle**||||
|Crgdp*good times|-5.960e+07**|-5.781e+09|2.369e+08|
||(22995752.015)|(5.978e+09)|(3.385e+08)|
|Crgdp*good times*rigidity||1.431||
|||(1.227)||
|Crgdp*rigidity||-13,856.972||
|||(8,135.989)||
|Rigidity of public spending||-0.089||
|||(0.061)||
|Crgdp*good times*fiscal space|||-0.015|
||||(0.017)|
|Fiscal space|||116.726|
||||(170.358)|
|Crgdp*fiscal space|||-0.001*|
||||(0.001)|
|good times|1.740|3.339|1.306|
||(1.310)|(3.066)|(1.100)|
|Constant|0.375|42.418***|1.648|
||(1.731)|(5.691)|(1.373)|
|Adjusted R-squared|0.876|0.781|0.880|
|Rmse|4.300|5.520|4.230|
|Joint significance (p-value)|0.00|0.002|0.000|
|CD test (p-value)|0.000|0.000|0.000|
|Order of integration|I(0)|I(0)|I(0)|
|Cluster|country|country|country|
|Covariates|Yes|Yes|Yes|
|Observations|826|399|823|
|# countries|50|26|26|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

32
education spending is smaller (2.049), but still large compared to existing studies (the last column of Table 5).

# **5.2.4 Event Study**

Figure 3: Event Study (Dynamic Effects) <!-- Start of picture text --> (a) Attending College (b) Years of Education<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>Year Year<br>(c) Education Spending<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>Year<br>.05 .2<br>0 0<br>-.05 -.2<br>DID Estimates DID Estimates<br>-.1 -.4<br>-.15 -.6<br>.5<br>0<br>-.5<br>DID Estimates -1<br>-1.5<br>-2<br><!-- End of picture text --> _Source_ : Iranian Households Income and Expenditure Surveys (HIES). Note: Figure reports the DID estimates of dynamic effects on outcome variables (coefficients of the interaction Treat _×_ year in Eq (3), with 95-percent confidence interval). While the estimated coefficients are not significantly different from zero before 2007, they turn significantly negative after the sanction imposed in 2007. Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.

Figure 3 shows the difference-in-difference (DID) estimates of dynamic effects on outcome variables: college attendance, years of schooling, and education spending per child (coefficients of the year treatment interaction terms in Eq (3), with 95-percent confidence interval). The estimated coefficients fluctuate around 0 before 2007 thereby providing evidence that there is no significant difference between children of households in treated and control groups in terms of these outcome variables before the sanctions. As Figure 3 panel (a) shows, coefficients for college attendance turn significantly negative immediately after the sanction imposed in 2007. The decline in college attendance led to a decrease in the years of education of affected children. As Figure 3 panel (b) shows, this reduction happened with one-year lag. Moreover, Figure 3 panel (c) shows a decline in education spending of households working in treatment industries relative to households in control industries. This negative effect temporarily decreased in 2011. That decrease may have been the result of a targeted subsidy plan institute in December 2010. The goal of the subsidy reform was to replace energy subsidies with targeted social assistance. As 26
Table B2: Robustness check using the sample of the aged 60 and below—Observable abilities

||Sc|hooling (ye|ars)||Age at hir|e|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|-3.515<sup>_∗∗∗_</sup>|-2.851<sup>_∗∗∗_</sup>|-1.382<sup>_∗∗∗_</sup>|-1.146<sup>_∗_</sup>|-1.304<sup>_∗_</sup>|-0.070|
||(0.311)|(0.338)|(0.263)|(0.667)|(0.782)|(0.891)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.222|0.146|0.095|0.059|0.067|0.071|
|N|3763|3763|3763|3763|3763|3763|
||Pri.|school: fat|her|Pri.|school: mo|ther|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-0.092<sup>_∗∗_</sup>|-0.054<sup>_∗_</sup>|-0.062<sup>_∗_</sup>|-0.157<sup>_∗∗∗_</sup>|-0.085<sup>_∗∗∗_</sup>|-0.081<sup>_∗∗_</sup>|
||(0.042)|(0.033)|(0.034)|(0.040)|(0.033)|(0.040)|
|Schooing in years|0.031<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.024<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|0.024<sup>_∗∗∗_</sup>|
||(0.004)|(0.004)|(0.006)|(0.005)|(0.005)|(0.007)|
|Age at hire|-0.002|-0.004<sup>_∗_</sup>|-0.004<sup>_∗_</sup>|0.002|0.001|0.000|
||(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.167|0.099|0.065|0.189|0.118|0.062|
|N|3763|3763|3763|3763|3763|3763|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

46
earnings possible when migrating to New Zealand. Delavande and Kohler (2008a) elicit expectations about HIV/AIDS in Malawi. Individuals were tested for HIV at the time of the survey, and many did not know their HIV status at the time of the expectations questions. They do find that the elicited expectation of having HIV/AIDS varies with socioeconomic factors in a way that matches the relationship with true status, and that individuals who were later revealed to be HIV positive are more likely to expect that they have it. Nevertheless, they find that the subjective expectation of being infected with HIV remains low for those who are later found to have been infected. In the same study, they show that individuals dramatically overestimate the likelihood of a rare event (death in the next year or next five years) occurring.

# _5.2. Do subjective expectations predict economic behaviors?_

Several studies provide evidence that subjective expectations do affect economic behavior. Some of the most striking evidence is found in studies which look at perceptions about the returns to education. The first of such studies is Jensen (2006), who finds that while the measured returns to schooling in the Dominican Republic are high, the returns perceived by students are extremely low. He finds that when students at a randomly selected subset of schools were informed of the returns estimated from earnings data, their perceived returns increased when re-interviewed 4 to 6 months later, and that four years later they had completed on average 0.20 years more schooling. He finds further that the effects are non-existent for poorer households, but large for students from households with above the median income per capita. Nguyen (2008) uses a similar approach in Madagascar, and finds providing statistics on actual returns reduces the dispersion in perceived returns, and improves test scores, particularly for those underestimating the returns. Related evidence is also seen in Attanasio and Kaufmann (2008) who find expected returns, perceived employment probabilities, and earnings risks to be significant predictors of college and high school attendance choices in Mexico, but only for richer individuals.

Agriculture is the other main area where expectations have been elicited in the developing country context, and the existing literature has found expectations to be associated with agricultural decisions. Hill (2007) finds that Ugandan farmers’ expectations about future coffee prices are significantly associated with the share of labor allocated to coffee. Her respondents were asked to allocate twenty beans into three price categories in accordance with

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