rafmacalaba/data-use-mentions-extended huggingface dataset:
s analysis based on data from the Ethiopian census of 2007 and the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zonespecific linear trends, and a cubic for age. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.23
over a 4-month period between April and July 2019. During this time, the survey team attempted to reach 28,958 credit card clients and completed 2,872 surveys, implying a 10% response rate.<sup>9</sup> The median respondent took 7 minutes to complete the interview. Surveys were offered in English and Malay, and 47% of respondents chose to complete the survey in English, while the remaining 53% responded in Malay. The partner bank shared detailed administrative records for all participants who responded to the survey, as well as a representative sample of clients who were invited to the survey but did not respond.

Table 2 provides descriptive statistics for our sample based on administrative data. Column (1) reports data for a random sample from the universe of the bank’s credit card clients, which includes both survey respondents and non-respondents. In this sample, 62% of clients are male,

> 9. This final sample excludes individuals who started the survey but did not make it to the end. There are only 174 partially complete surveys and we cannot reject the hypothesis that the respondents are missing at random after the information-provision stage of the survey. Our final sample excludes 274 individuals who reported extreme prior beliefs about the nominal exchange rate (above 4.65 or below 3.7 Ringgit per U.S. dollar) because the system used by the surveyors prevents us from knowing the exact expectations of those respondents. We would have excluded those extreme prior beliefs anyway to avoid sensitivity to outliers, as is standard in studies based on expectations data (Fuster et al., 2020).

14
al., 2021). At the other extreme, respondents may also feel burdened if they are contacted too frequently. Garlick et al. (2020) found that phone interviews at a weekly frequency did not induce persistent changes in respondent reporting nor increase permanent attrition but did increase the incidence of missed interviews among microenterprise owners in South Africa. In contrast, Schündeln (2018) finds panel conditioning effects, whereby reported consumption levels are correlated to the number of interviews, for households visited up to 10 times per month during the Ghana Living Standards Measurement Study.

Incentives are key to increase participation rates in phone surveys and were provided in the vast majority of phone surveys discussed in this paper, including those implemented at a national scale. There is a common understanding that monetary payment is important to compensate participants for their opportunity cost of time as well as the cost of airtime and electricity that a phone conversation entails (Glazerman et al., 2020). Furthermore, incentives are provided to encourage engagement in future interviews and are found to increase cooperation and response rates (Gibson et al., 2019; Glazerman et al., 2020; Gourlay et al., 2021; Singer and Ye, 2013). LSMS-supported HFPS provided airtime-based incentives of between $0.70 and $1.83. Gibson et al. (2019) found little importance of the exact amount of the incentive, though Singer and Ye (2013) suggest incentives exhibit diminishing marginal returns as the amount increases. According to a number of studies, survey incentives, ranging in size from $0.10 to $3.00, increase response rate by around 5 percentage points, although this effect ranges from 2 to 10 percentage points depending on context and incentive size (Ballivian et al., 2015; Gibson et al., 2019; Glazerman et al., 2020).

## _Respondent contact protocols and privacy_

Carefully planning how and when to contact survey respondents is another way to reduce burden, increase cooperation and counteract attrition.
.

Total food consumption is an informative measure of wellbeing in itself, but is also important because it is a core component of poverty indicators. To calculate the value of total monthly consumption on food (in Afghani), we map district price data to quantity data from the household consumption module. (Households are asked for the quantity of foods consumed over the past seven days; these quantities are multiplied by 4.2 to get monthly values.) Food consumption data include food bought, produced or obtained through other methods, e.g., food aid, gifts. Prices were matched by month, item, and district. Since not all food items were available in all district markets at all times of the year, we imputed the

> 25 See Kauermann and Carroll (2001) for details on the properties of this estimator.

10
we constructed and tested three alternative measures of tied aid share in estimating both models:

1. the sum of tied and partially tied aid divided by the sum of tied, partially tied and untied aid;

2. the amount of tied aid divided by the sum of tied, partially tied and untied aid; 3. the sum of tied, partially tied, and aid for which tying status was unreported, divided by the total commitment amount<sup>9</sup> .

Several independent variables were also taken from the AidData portal, including the number of donors present in a recipient country, and each donor’s share of the aid given to each recipient. Total aid as a share of recipient GNI, population and GDP per capita were obtained from the World Bank’s World Development Indicators. The LDC dummy is constructed based on the definition in the 2001 DAC recommendation (DAC, 2001).

# _4.2. Empirical Findings_

Results for model 4.1 are presented in table 3, for each of the three alternative definitions of tied aid. Consistent with the model’s predictions, coefficients for a donor’s share in the total amount of aid are significantly negative. Using either definition 1 or 2, each percentage point increase in a donor’s share in total aid is associated with a reduction of 0.4 percentage points in the share of tied aid. To illustrate, an increase in aid share from the mean value of 3% to 8% other things equal would be associated with a decline in tied aid from the mean of about 20% to 18%. For definition 3, the coefficient on aid share is somewhat larger (in absolute value).

The LDC coefficient is also significantly negative, consistent with the DAC recommendation on aid untying. Other things equal, the share of tied aid is 7 percentage points lower in LDCs, or 10 percentage points based on results in equation 3. Tied aid tends to be higher in larger countries: the coefficient
**|**(8)**|**(9)**|
|**Ln bilateral distance**|-2.118***|-1.535***|-0.582***||||-2.052***|-1.487***|-0.565***|
||(0.052)|(0.034)|(0.032)||||(0.053)|(0.035)|(0.033)|
|**Ln bilateral tariffs**||||-3.395***|-2.470***|-0.925***|-1.134***|-0.831***|-0.302***|
|||||(0.776)|(0.559)|(0.234)|(0.260)|(0.184)|(0.117)|
|**Exporter fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Importer fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|2980|2980|2980|2980|2980|2980|2980|2980|2980|
|**R-squared**|0.742|0.834|0.439|0.585|0.637|0.393|0.744|0.837|0.44|

Notes: Robust standard errors in parentheses; *** and * indicate significance at the 1 and 10 percent percent confidence levels, respectively. The dependent variables used in the regressions are measures in the country-year-destination level dataset
in the quality of electricity or roads are associated with reductions in the employment share in agriculture and increases in manufacturing and services. In fact, focusing on the GAM estimates, for a 1 percentage point increase in the quality of electricity (roads), the employment share in agriculture declines by 0.13 (0.04) percentage points while manufacturing and services employment shares increase by 0.19 and 0.28 (0.19 and 0.15) percentage points, respectively. This result is consistent with Szerman et al. (2022), who find strong gains along the intensive margin in Brazilian agriculture when electricity access is improved.

The interacted term is always negative and significant in all sectors. For agriculture, this suggests that the complementarities in infrastructure investment help reduce even further the agricultural employment shares.<sup>23</sup> For manufacturing and services, the estimated coefficient

> 23Through the lens of our model, this complementarity acts through a labor push mechanism (AlvarezCuadrado and Poschke, 2011), as increased productivity in agriculture reduces the employment share in the primary sector. It is also consistent with Vanden Eynde and Wren-Lewis (2021), who find that roads and electricity are complements in dry season agricultural production and suggest the effect is through productivity, which is in line with our results.

34
global patterns that are consistent with patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank.<sup>20</sup>

**Sample Size.** I2D2 includes about 1,500 survey/census samples. However, wages are only reported for two-thirds of them. We restrict our analysis to workers aged between 18 and 67 with data on age and education. The baseline sample that we obtain includes 24,437,020 individuals from 1,073 surveys and 11 censuses in 145 countries from 19902016 (median = 6; mean = 7.5; min = 1; max = 44). We include surveys from the 1990s to increase sample size. The 145 countries comprise 95% of the world’s population.

**Variables.** Data are available on the monthly wage, age, and number of years of education. For a substantial subsample, information is available for the hourly wage, the number of hours worked, and the employment status (e.g., self-employed or paid employee). Note that I2D2 calculates the wage of self-employed individuals as the amount of salary taken from the business. Next, we calculate _potential work experience_

> 17We use the expanded December 2017 vintage of the database.

> 18World Bank (2013) writes that I2D2’s aim is “to provide the World Bank and its client countries reliable micro economic data that is in an easy to use format. The data will be used to calculate a variety of indices and in the future can be used for complex economic analysis. The micro level data will be strictly confidential; however, the aggregate data can and will be used for information and research purposes.” 19World Bank (2013) writes: “In practice we frequently see samples sizes of 10,000.”

> 20They find that the samples are globally representative in terms of per capita income, age structure, education, and self-employment. The Mincerian R-squareds are also not too different
the process: an agent first needs to obtain information before actually applying, and the agent must then apply before being assessed for eligibility. The second key feature is the interdependence between stages: at each stage, the agent’s decision is responsive to the agent’s expectations about the decisions and outcomes in the subsequent stages. In the spirit of rational expectations, we assume that the agent has unbiased beliefs about the decisions and outcomes in the following stages. This assumption ties the estimation of each stage to that of the subsequent stages. The model thus provides a framework for studying the participation decisions-making process.

We conduct an empirical application of the model using data from the participation process in _Oportunidades_ , a prominent social safety-net program in Mexico that combines means testing with self-selection by households. The survey data that we use contain detailed information on the different stages of the participation process as well as the socioeconomic household characteristics used to determine eligibility and the level of transfers a household would receive if deemed eligible. Information on the different stages of the process and the characteristics of eligible and ineligible households has not generally been available in previous work. Our data allow us to separately estimate the potential participant’s decision to apply and the program officials’ decision regarding whether to enroll the applicant. Because we have detailed information on the official criteria for eligibility, we can determine whether enrollment decisions are based exclusively on these criteria. Information on the benefit entitlements of participants allows us to explore the relationship between expected benefits and the different stages of the participation process.

In accord with the model presented, we estimate the stages of the participation process in reverse order. In particular, at the information acquisition and application stages, we consider the expected benefit for the potential participant of going through the next stages of the participation process. The estimation indicates that the stages of the participation process are interdependent: whether a household becomes 3
org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-general-de-la-population-et-de-lhabitat-delagriculture-et-de-lelevage.

Based on the imputed model in the census, the national vulnerability rate is 55.7 percent. This includes 38.2 percent of individuals who are vulnerable because of poverty (poverty-induced vulnerability) and 17.5 percent who are nonpoor but exhibit a high probability of falling into poverty (risk-induced vulnerability). Survey estimates of vulnerability are consistent with the small area estimates, with a 11
development tend to be location-specific. Following the diagnostic approach to growth (Hausman et al. 2005), we employ firm level data to identify key constraints to development (Lin and Monga 2010). Our presumption is that, even within a country, there are sufficient variations in the city-level aggregates that we can use to gauge the effects of the policy and business environment on firm-level productivity.

Our firm-level data are from comparable samples of manufacturing businesses in the two countries, namely, the World Bank–sponsored Investment Climate Surveys conducted in 2003. The Indian survey covers 1,860 manufacturing establishments sampled from the country’s top 40 industrial cities and major exporting industries. The Chinese survey covers 2,400 enterprises sampled from 18 cities covering 15 provinces and 5 geographic regions. In addition to collecting annual data on each sample firm’s characteristics, financial accounts and operations between 2000 and 2002, the survey also collected data on the local business and policy environment that each sample firm faced

> 2 We calculate daily income based on GDP per capita, measured using purchasing-power-parity (PPP) exchange rate and 2005 constant international dollar, as reported in the 2010 World Development Indicators published by the World Bank. Per capita GDP in China was $1.43 per calendar day in 1980.

2
.0212)|(0.0572)|(0.0545)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.8094***|2.3786***|1.4655***|4.4929***|5.0903***|0.5021|1.6602***|0.0642|-0.0288|9.1221***|8.9677***|
||(0.1221)|(0.4986)|(0.4972)|(0.4803)|(0.3533)|(0.3749)|(0.4398)|(0.0743)|(0.0601)|(0.1666)|(0.1519)|
|Observations|12,154|12,154|12,154|12,154|12,154|12,154|12,154|12,154|12,154|4,536|2,346|
|R-squared|0.298|0.159|0.149|0.214|0.238|0.120|0.159|0.130|0.105|0.192|0.213|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

63
**Teacher shortages.** The probability of hiring the teacher in a high management private school is higher than the probability of hiring the teacher in an intermediate management public school (via teacher selection). In the numerical example shown in the top panel of Figure 7, the area of the grey region is bigger than the area of the unshaded region.

The PISA dataset does not contain objective information on school-level vacancies, so we use a series of 4 questions in the school principal questionnaire that ask the principal whether he/she feels that the school’s capacity is hindered by a lack of qualified teachers in each of math, science, language and ‘other subjects’.<sup>22</sup> It is worth emphasising that these questions are open to considerable interpretation. For instance, a principal might answer ‘a lot’ because he/she feels that the school needs more new posts even if there are few vacancies for existing posts. Conversely, he/she might answer ‘not all’ because of a belief (or desire to say) that the school is coping despite there being vacancies.<sup>23</sup> With this caveat in mind, it can still be instructive to examine the data. Column (1) of Table 3 shows that, consistent with the theory, the teacher shortage index is 0 _._ 535 standard deviations _lower_ among private schools than public schools, significant at the 1 percent level. Column (4) repeats the specification but with the people management index instead of the private school dummy. The relationship is consistently negative, suggesting that a one standard deviation increase in the people management index is correlated with 0 _._ 062 _lower_ teacher shortage in schools, marginally significant at the 10 percent level.

**Teacher motivation.** The expected intrinsic motivation of a teacher hired to a high management private school is higher than the expected intrinsic motivation of a teacher hired to an intermediate management public school (via teacher selection
**Figure 3:** Trends in Non-Farm Employment by Gender and Secondary Education

<!-- Start of picture text --> Worker Type Worker Type<br>Secondary Educated No Secondary<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>Male Female Male Female<br>Self-Employed Casual Self-Employed Casual<br>Salaried Salaried<br>Sectors Sectors<br>Secondary Educated No Secondary<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>Male Female Male Female<br>Manufacturing Trade Manufacturing Trade<br>Construction Services Construction Services<br>.2 .2<br>.1 .1<br>0 0<br>Fraction of Workers Fraction of Workers<br>.2 .2<br>.1 .1<br>0 0<br>Fraction of Workers Fraction of Workers<br><!-- End of picture text --> _Notes:_ The above figures shows trends in non-farm employment categories and sectors, conditional on participation in the labour force, and disaggregated by gender and secondary education. The left-hand figures restrict the sample to rural workers who have completed secondary education; the right-hand panel restricts the sample to rural workers without secondary education. NSS survey rounds 55, 61, 66 and 68 correspond to the 1999-00, 2004-05, 2009-10 and 2011-12 years.

38
factors, country-specific proxies of economic activity, and various policy-related variables.

We document three novel findings regarding the relationship between fiscal rules and sovereign spreads. First, we show that countries with fiscal rules in place faced lower sovereign spreads relative to countries without rules before and after the onset of the COVID-19 pandemic.<sup>4</sup> Specifically, our estimates imply that through the pandemic, the presence of fiscal rules is associated with 350 basis point lower sovereign spreads, on average. Second, we illustrate that the difference in sovereign spreads for countries with and without rules widened in the

> 1 We consider budget balance rules, debt rules, expenditure rules, and revenue rules in our empirical analysis.

> 2 An important strand of the literature has theoretically studied the fiscal rules’ tradeoff between commitment and flexibility. See for example, Angeletos et al. (2006); Halac and Yared (2014); Halac and Yared (2018); and Halac and Yared (2022).

> 3 In a robustness test, we investigate the spread-compressing effect of fiscal rules during the global financial crisis (GFC) of 2008-09. The sample covering the GFC timeframe includes 26 countries and uses data covering January 2007 through December 2009.

> 4 Importantly, we show that the spread-compressing effect of fiscal rules is robust to the GFC of 2008-09.

2
school performance were positive and significant.

Wayne and Youngs (2003) conducted an extensive review of the existing empirical literature until the early 2000s, when most of the evaluation studies of the effects of the education of teacher passes using student-level longitudinal data and involve broader sets of variables. Most of the work considered indicates that secondary school students learn more math when their teachers have higher training in mathematics, and no effect in relation to the lower grades and to other areas of knowledge. Moreover, the survey indicated that the quality of teacher education (measured by the raking of the institution) has positive relationship on the performance of the students.

Based on a review of a wide range of empirical studies, Rice (2003) reports positive effects on high school mathematics and science achievement of students whose teachers have obtained advanced degrees in these subjects, and reports mixed evidence regarding the impact of advanced degrees at the elementary 7
# The Effects of Subsidizing Social Security Contributions: Job creation or Informality Reduction?<sup>*</sup>

Güneş Aşık<sup>†</sup> , Laurent Bossavie<sup>‡§</sup> , Jochen Kluve<sup>**</sup> , Efşan Nas Özen<sup>‡</sup> , Metin Nebiler<sup>‡</sup> , Ana Maria Oviedo<sup>‡</sup> JEL codes: H32, J23, J32 Keywords: employment subsidies, formal employment, employer social security contributions

* We would like to thank the Social Security Institution of the Republic of Turkey for providing access to the administrative data used in this study, and the Social Security Institution and the Turkish Employment Agency for their continued support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. They also cannot be attributed in any way to any institution of the Republic of Turkey.

† TOBB University of Economics and Technology.

‡ World Bank § Corresponding author: lbossavie@worldbank.org

> ** KFW <mark>Development Bank, IZA, Humboldt University.</mark>