rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Annex 2 Instrumental variables and exogeneity tests_**

All models reported in this paper include one of the variables meant to measure the quality of governance and of public goods in general - _Government Effectiveness_ , _Rule of Law_ or _Satisfaction with the Government_ (see Table A1 for details). Plausibly, same unobserved factors might affect both the (actual and perceived) quality of governance and the level of informal employment in a country. An instrumental-variable approach is used to address this potential endogeneity in each of the equations of the seemingly unrelated models reported in Tables 3, 5, and 7. Each of the tables reports two specifications of equations modeling the levels of informal dependent employment, informal self-employment, and unemployment in three country groups. Thus, we have 18 equations to test.

As instruments we use indicators based on three questions from the same round of ESS survey. These questions ask the respondent to evaluate, at a 0-to-10 scale, (i) Trust in the police ( _POLICE_ ); (ii) Satisfaction with the state of healthcare in the country ( _HEALTH)_ ; (iii) Statement „Most people try to be fair‖ ( _FAIRNESS_ ). For each of these variables, we have tried (on each of the equations) the mean, the median and the 25th percentile, altogether 9 instruments. In theory, the first two variables can influence tax morale, but for 13 cases we could not reject the hypothesis of zero correlation between country-median (9 cases) or country-mean (4 cases) trust in the police and the disturbances in the informality (or unemployment) equations. In remaining cases we have used mean _HEALTH_ (twice), mean _FAIRNESS_ , the 25<sup>th</sup> percentile of _FAIRNESS_ , and, in one case, combination of the _DURATION_ of ESS field work (in months) with the share of local born _MINORITY_ population _._ In most cases, just one of the instruments appeared as convincingly valid and not weak, with one or
<mark>decisions.</mark> _<mark>Climatic change</mark>_ <mark>,</mark> _<mark>138</mark>_ <mark>(3), 439-451.</mark> **[1]** <mark>Kraly, E. P., & Hovy, B. (2020). Data and research to inform global policy: the global compact for safe, orderly and regular migration.</mark> _<mark>Comparative Migration Studies</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(1), 1-32.</mark> **[2]** <mark>Kubik, Z., & Maurel, M. (2016). Weather shocks, agricultural production and migration: Evidence from Tanzania.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(5), 665-680.</mark> **<mark>[</mark> 1]** <mark>Laczko, F., & Aghazarm, C. (2009).</mark> _<mark>Migration, Environment and Climate Change: assessing the evidence</mark>_ <mark>. International Organization for Migration (IOM).</mark> **<mark>[</mark> 1]** <mark>Lai, S., zu Erbach-Schoenberg, E., Pezzulo, C., Ruktanonchai, N. W., Sorichetta, A., Steele, J., ... & Tatem, A. J. (2019). Exploring the use of mobile phone data for national migration statistics.</mark> _<mark>Palgrave communications
|0.018***||0.014**||0.015*||
||(0.006)||(0.007)||(0.008)||
|Domestic credit to private sector (% of GDP)|-0.005|-0.012|0.001|0.004|-0.007|-0.018*|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.012)|(0.010)|
|Depth of credit information index|0.002|0.009**|-0.004|-0.001|0.009*|0.015***|
||(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|
|Rule of Law: Estimate|-0.007|0.000|-0.004|-0.001|-0.017|-0.007|
||(0.009)|(0.007)|(0.010)|(0.007)|(0.011)|(0.009)|
|Constant|-0.008|-0.012|-0.005|-0.005|-0.008|-0.012|
||(0.007)|(0.008)|(0.005)|(0.009)|(0.008)|(0.008)|
|Observations|99|144|99|144|96|141|
|R-squared|0.204|0.184|0.113|0.010|0.215|
<!-- Start of picture text --> 70<br>64 go<br>60<br>50<br>n<br>E<br>re}= 40 35 35 35<br>5 31<br>® 30<br>© 24 25<br>a 20 20<br>20<br>10 10 10<br>10 9 6 5<br>()<br>1 2 3-4 5-24<br>Number of workers in the firm<br>ME ELS 2012 inest. MM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
including also non‐complying households. We control for stratum dummies and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix.

_Source:_ Household data set 2011/2012.

Moreover, Table 8 probes into the question whether _time dedicated to any income generating_ activity increases, which might happen because the higher domestic productivity could set free time for other purposes. We concentrate our analysis on subsistence farmers that constitute 86 percent of household heads and 85 percent of spouses at baseline.<sup>15</sup> Both the head of household and the spouse slightly increase the time they dedicate to income generation (by six and three percent, respectively), but this difference is not statistically significant.

The third most important user group are children between six and 17 years. They use the Pico‐PV lamp mainly for studying (see Table 7). In order to understand changes in the productivity of studying at home, we first need to analyze children’s study patterns and how they divide their study time between daylight time and evening.

As can be seen in Table 9, in around one‐third of the households with children at school age, children do not study after school. There is no significant difference between households in the control and treatment groups. The share of children studying after

> 15 We distinguish as income generating activities between subsistence farmers, governmental employees, independent occupations, and other dependent occupations. The group sizes of the latter three are small at n=2, n=1, and n=2 for spouses and n=4, n=11, and n=9 for head of households. Therefore, these groups are very unbalanced across treatment and control households at baseline (see Table S6.4 in the supplemental appendix). When estimating effects on time dedicated to income generation including these occupation groups, we find a significant postive effect for overall income generation time for spouses. This difference is driven, however, by these non‐balanced
years earlier (see Table 3.4 above).

# **5.1 Public and Private Sectors**

The results discussed so far do not treat the public and the private sectors separately. We need to turn our attention to this distinction since, within the class of wage earners, principles of wage determination could be expected to be quite different in these two sectors. Glewwe (1996) used the 1988-89 GLSS survey to study earnings functions separately for the two sectors. The standard OLS estimates yielded the result that the coefficient of 'years of schooling' were practically identical -- 0.0751 for the public employees and 0.0738 for private wage earners. But when selectivity of workers entering the two sectors were allowed for through two selection equations determining the choice between wage work and other work, and also between government wage and private wage employment, there were dramatic changes in the return to education. Two alternative methods of estimation yielded the identical result-that "in the government sector the returns to education drop to about 4-6 per cent, and in the private sector they appear to be zero" (p.274). Commenting on this result Glewwe wrote that it is difficult to believe that education had no impact on earnings unless one got a government jobthat there were no return to education in private wage employment. Rather, Glewwe suggests one got this result because the quality of schooling had dropped so much in Ghana at this date, that 'years by education' by themselves did not produce significant earnings increment. The variation in the quality of schooling were probably more 31
337||0.262||0.152||0.129|
|Sunflower|0.344|0.331|||0.249|0.241|0.136|0.150|
|Maize|0.102|0.142|0.243|0.287|||0.280|0.284|
|Soybeans|0.128|0.095|0.073|0.075|0.086|0.088|||
|Cereals|||0.426|0.391|0.247|0.201|0.215|0.207|
|Others|0.227|0.265|0.151|0.148|0.178|0.191|0.197|0.212|
|Area (ha/VC)|1166|1156|1240|1081|844|715|356|425|
|**Panel B: Statistics**|||||||||
|Yield (dt/ha)|34.99|40.28|21.58|24.91|50.47|72.83|17.15|23.83|
|Area per farm (ha)|187.91|189.85|319.58|321.97|301.84|312.15|202.74|190.83|
|Area statistics(F29)|867|894|832|838|604|629|382|345|
|Number of VCs|6,540|6,610|5,356|5,495|4,767|4,785|3,811|3,654|

aYield for cereals is calculated as value of wheat and barley per unit of land divided by average price. Source: Own computation from remotely sensed data for 2016, 2017, and
for potential interruptions, and provision of information on support services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Cumulatively, the survey took about 1-1.5 hours (per person) to administer. We complemented face-toface data collection with a phone survey in April-May 2020 to correct the data on receipt of the 4P benefits: in our initial round of data collection the question about receipt of the 4P benefits was addressed to the respondent of the tracking questionnaire, which resulted in high number of missing values and inconsistencies. The phone survey was administered to target respondents: young women and men who resided in treatment and control barangays at the time of program rollout in 2008.

# 5. Theory of change and outcomes of interest

A key objective of the CCT programs is breaking the cycle of inter-generational transmission of poverty. Thus, we expect that adolescents, who have benefitted from the 4P program at the age of 12.5-14, will enjoy better socio-economic outcomes as adults. we explore this hypothesis by analyzing impacts on education, labor market outcomes and proxies of welfare. For education, we estimate impact on the likelihood to complete schooling, the number of years of education and probability of attending vocational training. Our labor market outcomes include indicator variables for labor force participation, employment, paid employment and underemployment. We collect a rich set of variables on asset ownership and housing conditions and construct three types of indices: an index based on principal component analysis, average z-scores index based on Katz, Kling and Liebman (2007), and an Inverse Covariance Weights (ICW) index obtained by weighting z-scores of variables with inverse of their covariance matrix (Anderson, 2008). The variables used in the construction of the housing conditions index include roof, walls, floor materials, access to piped water and electricity, number of bedrooms, presence of kitchen and type of toilet, type of cooking fuel used. We create asset index based on ownership of residential plot, non-agricultural buildings, farm machinery and implements,
A). Overall, our quasi-experimental setting provides strong evidence of presidential pork in the geographic distribution of federal highways across Mexican municipalities. Before we investigate some of the potential underlying mechanisms, we now turn to examining whether these results are driven by the president alone or the majority party status.

# **B. Ruling Out Alternative Political Outcomes**

To further substantiate the evidence that the interaction between the executive and the legislature matters for the distribution of public capital, we separately analyze how federal highway construction responds to: (1) alignment with the majority party in the Chamber of Deputies for legislative elections, and (2) presidential election outcomes. Formally, we examine new versions of equations 2 and 3 using legislative (as before) and presidential election outcomes. We extract the information on presidential elections compiled at the municipality level from the IFE database for the years 1994, 2000, and 2006. We then match these presidential election outcomes with changes in road length over the periods 1993-1998 (1994), 1998-2008 (2000), and 2008-2012 (2006). In both the presidential and legislative cases, we compare as before changes in road outcomes in municipalities barely won by the ruling party (the president or the majority party in the Chamber of Deputies) vs. municipalities barely won by opposition candidates. For consistency and ease of comparison with previous results, we consider the polynomial 2. specifications similar to those reported in Panel A of Table As shown in Table 4, there is no evidence of a disproportionate allocation of new federal highways in municipalities narrowly won by the president or the House majority party. Our RD estimates even point to fewer highways, albeit statistically insignificant, associated with both the and status. Taken with our president majority party together previous findings in the previous subsection, these results confirm that the president’s influence over the distribution of federal spending is inherent to the legislative process.

19
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|
|Education Spending|(27.5, 30]%|70|101|65|87|162|485|
|as % of Revenue|(25, 27.5]%|75|112|77|106|165|535|
||25%|303|214|95|99|130|841|
||Total|520|546|356|506|855|2,783|

_Notes:_ 25% of revenue is the imposed minimum expenditure on education, and thus the lowest expenditure category. Predicted net beneficiaries are municipalities that, as of 1997, would receive more from FUNDEF than they were required to pay in if the rules of FUNDEF were applied to 1997 enrollment and revenue levels.

_Sources:_ Author’s calculations based on data from Tesouro Nacional (1997).

Table 4: Correlation Matrix, Measures of Income and Inequality

||(1)|(2)|(3)|
|---|---|---|---|
|(1) Average income per capita, 10,000s of 2005 Reais|1.0|||
|(2) Median income per capita in 2000, 10,000s of 2005 Reais|0.72|1.0||
|(3) Gini coefcient in 2000|-0.04|0.01|1.0|

_Source:_ IBGE (1995-2008).

42
_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Acknowledgments: This paper was prepared as a background paper to _Beyond the City: The rural contribution to development_ . We are grateful to Andrew Nelson and Juan Robalino (who contributed to a precursor versions of this work), and thank Uwe Deichmann and Deborah Balk for helpful comments. We are very grateful to CIESIN for sharing the GPW3 data. The denominations used and the boundaries shown on maps herein do not imply, on the part of the World Bank, any judgment on the legal basis of any territory or any endorsement or acceptance of such boundaries.
significant at 5% level, *** - significant at 1% level. Abbreviation: DI - decentralization index. Corruption perception index (source - Transparency International), GDP per capita (source - WDD), GG consumption (source - Penn World Tables correspondingly), Start of business, # proc. (source - WE Doing Business), Democracy score (source - Polity IV), Durability of regime (source - Polity IV) are from 2005. CGI - citizen-centric gowermance indicators (source vanyna and Shah (2009)), 4 civil conflicts (source -WDI) are averages in 2000-2005.
Zingales (1998).  The index is based

on the assumption that the well-developed US financial markets have fewer market

> 14 Appendix tables A1 and A2 provide the full list of countries in the Orbis and ES datasets, as well as when they are used as treatment or control in the regression using credit bureau reforms.

> 15 Unlike a standard difference-in-difference specification, the pre and post periods are not defined for the control group in our analysis. The interaction term we refer to as “CB reform”, however, is well defined throughout, and is the variable of interest.

11
1999); increased foreign competition that reduces rents at the margin (Ades and Di Tella, 1999); and more international investors who are particularly sensitive to the costs imposed by corruption (Wei, 2000). However, the opposite case has also been proposed. Ades and Di Tella (1999) argue that the cost of corruption is higher in less open societies, implying that such societies devote more resources to combating corruption. Similarly, Tanzi (1998) reports that trade liberalization has created new opportunities for corruption and paying bribes gives greater advantages in obtaining privileged access to markets. Empirical evidence on the issue largely favors a negative relationship between corruption and trade openness (see for example, Krueger 1974, Ades and Di Tella 1999, Wei 2000, Gatti 2004, Gokcekus and Knorich 2006), although a few studies find a positive or no significant relationship between the two (see for example, Tanzi 1998, Torrez 2001, Majeed 2014). Thus, if the degree of trade openness also varies systematically with size of the country, the possibility of our main results suffering from the omitted variable bias problem cannot be ruled out. We guard against this possibility by controlling for trade openness defined as the ratio of merchandise exports plus imports to GDP (Merchandise Trade). The data source is WDI.

Our next set of controls includes two historical factors, ethnic fractionalization and the legal origin of the country. The existing literature suggests that countries with higher ethnic fractionalization might be more corrupt (Shleifer and Vishny 1993, Mauro 1995). One reason for this is lack of obedience with respect to the state as civil servants and politicians aim to exploit their positions to favor members of their own ethnic group. Another reason suggested in the literature is that divided societies tend to under-provide public goods, and this in turn creates opportunities for corruption rents (Pellegrini and Gerlagh 2008). There is a large literature that argues that the kind of legal codes that are in place in a country affect the quality of governance including corruption.
quarter (28.7 percent) are female headed. They are physically isolated. Only about half of the sample live within three kilometers of a food market and just five percent live within three kilometers of a bank. While the sample is evenly spread across the four regions (Tigray, Amhara, Oromia, SNNP), there is a slightly lower proportion of observations in earlier rounds (2006, 2008) compared to the later rounds (2012, 2014).

Data on receipt of PSNP payments were recorded during the survey. Enumerators recorded payment data directly from “client cards”, a document held by PSNP beneficiaries that lists their monthly payments. When these cards were not available, respondents were asked to recall their payments by month. Payment information includes whether the payment is made as cash or as food – and if food, what type of food – as well as the total amount of payment received. Comparisons of payment data based on client cards and from self‐reports show that levels, distributions and timing of payments are comparable. Further, limited access to PSNP payroll data confirms that the self‐reported data correlate with the payroll data (Berhane et al, 2013). By converting the quantities of food payments received by beneficiaries using the market price data, it is possible to calculate the share of payments made in food and in cash. In 2006, cash payments accounted for about 60 percent of all payments.<sup>10</sup> This increased to 71 percent in 2008, fell in 2010 but rose thereafter with cash accounting for 73 percent of the value of all payments in 2014. At the household level, 85 percent of the households in the sample report having received at least one food payment and 89 percent report receiving at

> 10 Similar patterns emerge when data are disaggregated by beneficiary type (public works or direct support). These results are not reported but are available upon request.

12
because of the uneven coverage of policy uncertainty and other indicators across countries. Our panel sample includes 18 of the 22 countries for which the policy uncertainty index is available.<sup>3</sup>

**Table 1.** **_Data sources_**

|Indicator|Sources and computation|
|---|---|
|Real GDP|IMF WEO|
|Real effective exchange rates(REER)|IMF IFS|
|Importprice deflator|WTO Merch. Indices|
|GDP deflator|IMF WEO|
|Goods and services import volume|IMF WEO|
|Goods import volume|IMF WEO|
|Goods import volume, by main end use (BEC<br>classification)|ratio of UN Comtrade (WITS) values to "import price<br>deflator"|
|Backward GVC participation: intermediate<br>imports embodied in exports|ratio of WIOD ‐ derived values to "import price<br>deflator"|

_Notes:_ IMF WEO: International Monetary Fund World Economic Outlook, Oct. 2016; WB GEM: World Bank Global Economic Monitor database, Feb 2017 download; IMF IFS: International Monetary Fund International Financial Statistics; WTO Merch. Indices: World Trade Organization merchandise values annual dataset; WTO Services values: World Trade Organization services annual dataset; UN Comtrade (WITS): United Nations Commodity Trade Statistics Database – data downloaded via World Integrated Trade Solutions (WITS).

In Section 5, we extend the analysis using the World Trade Uncertainty (WTU) index (Ahir et al. 2019) as an alternative to the EPU index. The WTU index captures uncertainty related to trade policy for a set of 143 developed and developing countries from 1996 onwards and is constructed based on the Economic Intelligence Unit (EIU) country reports, by counting the number of times “uncertainty” is mentioned in proximity to a word related to trade. In principle, the fact that the WTU index is specifically designed to capture trade related uncertainty could give it