rafmacalaba/datause-encoder-data huggingface dataset:
# **Table S7a: Variations in IV design structure**

|||Using total<br>Variations i|Weighting<br>n city-industr|Using<br>ytrend term||Variations in|citycross-se|ctional term|Using 1990|
|---|---|---|---|---|---|---|---|---|---|
||Base<br>estimation<br>(Column 3,<br>Table 3)<br>|<br>technology<br>trend with<br>all ethnic<br>groups<br>|<br>patents by<br>backwards<br>Anglo-<br>Saxon cites<br>|<br>industry<br>groupings<br>based on<br>mfg roles<br>|Using first-<br>generation<br>immigrants<br>|Using later-<br>generation<br>immigrants<br>|Using<br>bachelor's<br>educated<br>workers<br>|Using non-<br>bachelor's<br>educated<br>workers<br>|<br>ancestry<br>distribution<br>for city<br>weights<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||The<br>P|dependent va<br>anel A: Wei|riable is Δ log<br>ghting bilatera|bilateral exp<br>l routes by th|orts on the int<br>e interaction o|ensive margin<br>f exporter and|by exporter-<br>importer tra|importer-ind<br>de in industr|ustry<br>y|
|ΔLog country-industry labor|1.592**|1.513***|2.812**|2.634*|1.581***|1.699|2.233**|1.607**|1.888**|
|productivity|(0.637)|(0.498)|(1.118)|(1.583)|(0.420)|(1.097)|(1.052)|(0.683)|(0.815)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Panel B: E|xcluding samp|le weights||||
|ΔLog country-industry labor|2.429***|3.342***|3.085**|Invalid|2.645***|2.341**|2.727***|2.812***|2.650***|
|productivity|(0.791)|(1.096)|(1.538)|first stage|(0.733)|(1.062)|(0.988)|(0.962)|(0.825)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
number of days out of the past seven on which it was consumed. The FCS is then a weighted sum of these components. Households are classified as having poor food access if they have a poor or borderline FCS, which is defined as less than or equal to 42 (WFP 2009).

Importantly, the 2018/19 NLSS was carefully implemented to ensure the data used in this paper are of high quality. First, the questionnaire was brought in line with international best practices. For example, food consumption was collected through a set of seven-day recall questions. Second, the 2018/19 NLSS made extra effort to allow households to express their food quantities in non-standard units of measurement to help households better estimate consumption. And third, the 2018/19 NLSS was designed and collected using Computer Assisted Personal Interviewing (CAPI) software on tablet devices. This made it possible to conduct real-time data-quality checks to ensure accuracy. Consequently, the data collected in the 2018/19 NLSS were of significantly higher quality than in previous survey rounds.<sup>23</sup> The basic summary statistics for the 2018/19 NLSS are presented in Table 1 and illustrate a number of important patterns about welfare and food access in Nigeria. First, both poverty and poor food access are widespread in the country. At the national poverty line, 40.1 percent of Nigerians live in poverty. Furthermore, approximately 14.9 percent of Nigerians have poor food access according to the FCS. Given that the threshold commonly used to denote poor food access using the FCS is generally used to target emergency humanitarian assistance and has been associated with calorie consumption well below minimum daily energy requirements (e.g., WFP 2012; Mathiassen 2013; etc.), there is probably an even higher share of Nigerians that have poor food access according to recommended guidelines.

Second, the share of households that have poor food access using the FIES is significantly higher than that of the FCS. Approximately 57.3 percent of Nigerians are moderately food insecure or worse according the FIES, and approximately 25.9 percent

> 23For example, the 2009/10 HNLSS data were marked by implausible seasonal movements in consumption and contradictions of Engel’s Law. These issues were eliminated in the 2018/19 NLSS. For further discussion of data issues in the 2009/10 HNLSS, see World Bank (2016).

14
Table A.1: Indicators for Integrated (I) and Less Integrated (LI) Regions

||LI<br>2001|I|LI<br>2005|I|
|---|---|---|---|---|
|Head count Ratio (Upper poverty line)|53|46|50|33|
|Real per capita expenditure|727|800|1046|1207|
|Electricity in Mouza|67%|63%|80%|83%|
|BD Krishi Bank in Mouza|7%|17%|27%|45%|
|Commercial Bank in Mouza|17%|17%|25%|40%|
|Grameen Bank in Mouza|13%|13%|29%|40%|
|Market/bazar in Mouza|53%|61%|64%|77%|
|Distance to thana HQ (km)|10.7|11.1|9.7|15.5|
|Travel time to thana HQ ('00mins)|0.6|0.7|0.5|0.7|
|Distance to zila HQ (km)|27.7|33.0|28.6|33.5|
|Travel time to zila HQ ('00 mins)|1.1|1.2|1.0|2.0|
|Distance to Dhaka HQ (km)|296.2|169.7|294.4|168.7|
|Travel time to Dhaka HQ ('00 mins)|4.2|3.0|4.5|3.2|
|Anybanks in Mouza|25%|24%|35%|46%|

Source: HES 2000 and 2005
# **Table A5: Robustness of Profits Impact to Attrition Assumptions**

|||||||<br>Ass|<br>umingattritors|<br>earn:|
|---|---|---|---|---|---|---|---|---|
||Base<br>Specification|PDS<br>Lasso|Month<br>Fixed Effects|Lee lower<br>bound|Lee upper <br>bound|personal<br>max|average of<br>one-time only|no profits<br> (are closed)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Assigned to personal initiative|90.6***|96.0***|91.6***|-41.5**|125.1***|91.3***|75.8***|87.8***|
||(32.2)|(24.5)|(35.2)|(19.1)|(33.6)|(31.9)|(27.4)|(27.6)|
|Assigned to traditional training|27.6|33.2|18.8|-49.8***|46.4|23.9|19.4|26.8|
||(30.4)|(24.8)|(34.3)|(18.7)|(31.6)|(29.0)|(25.6)|(25.9)|
|Sample Size|1337|1337|1250|1278|1278|1500|1500|1500|
|Assumed Control Mean|173|173|187|173|173|204|172|147|

# Notes:

All regressions include randomization strata fixed effects and lagged baseline total profits. Dependent variable is total profits in all businesses. Robust standard errors in parentheses. *, **, *** denote significance at 10, 5, and 1 percent levels respectively. Column 1 shows base specification. Column 2 uses PDS Lasso to select additional controls. This selects no controls for either treatment but selects baseline monthly sales, weekly profits, and capital stock as additional controls that predict the outcome.

Column 3 introduces fixed effects for the month of interview. Columns 4 and 5 provide Lee bounds by dropping the top (column 4) or bottom (column 5) 36 firms from PI training and 23 firms from traditional training in terms of profits.

Columns 6, 7 and 8 instead fill in missing profits under different assumptions of what attritors could be earning. Column 6 assumes they would earn the maximum profits they have ever earned over the baseline and four short-term follow-up rounds; Column 7 assumes they earn 167 USD, the average for those answering the long-term follow-up only once; Column 8 assumes that all attritors are closed and hence earn zero profits.

33
health facilities are at locality, and therefore would be difficult to have both treatment and control groups in the same small locality. In selecting localities, the probability of being chosen is population weighted. This means that the PROGRESA household data subsamples are self-weighting. Since PROGRESA is targeted primarily at poor families, criteria for selection into the experiment are based on empirical measures of poverty within the locality. After selecting a subset of localities to participate in the evaluation, it was determined using poverty-related criteria, which households within each locality would be eligible to receive PROGRESA services. Finally, each locality is randomly assigned to be a member of either the treatment or control group. In localities assigned to treatment, all eligible households within the locality are offered and usually take advantage of PROGRESA benefits/services. In localities assigned to the control group, none of the households receive PROGRESA benefits/services. There was a 60 percent probability of being assigned to treatment and a 40 percent probability of being assigned to the control group; 320 localities ended up being assigned to the treatment group and 185 to the control group.

# **6. Descriptive Statistics**

Child labor is higher among indigenous-monolingual populations.<sup>18</sup> Child labor incidence decreased from 16.3 percent to 7.4 percent between 1997 and 1999 (see Table 1). An overall decrease is noticed, which is higher among indigenous-monolingual children, while Spanish-monolingual children’s labor supply decreased 8.2 percent, it decreased 8.9 percent among indigenous-monolingual children. As children grow older, child labor increases. The steepest change occurs in 1997 in the indigenous-monolingual group from 13 to 14 years with a 13.3 percent increase (see Table 2 and Figure 1). In 1997, indigenous-monolingual children have higher child labor incidence in all age groups except 16. Bilingual children have the lowest child labor incidence in all groups in 1997. The indigenous-monolingual group has a higher incidence for children between 8 and 12 years. An increasing child labor with age pattern is still noticed when we separate children in the treatment and control groups (see Table 3).

> 18 An “indigenous-monolingual population” is a location where more than 30 percent of households report a household head that is indigenous-monolingual.

19
- Mu, R. and D. van de Walle (2011). Rural roads and local market development in vietnam. _The Journal of Development Studies 47_ (5), 709–734.

- National Geospatial Intelligence Agency (2015). World Port Index 2015. National Geospatial Intelligence Agency, Springfield, Virginia.

- Nunn, N. and D. Puga (2012). Ruggedness: The blessing of bad geography in africa. _Review of Economics and Statistics 94_ (1), 20–36.

- Ofori, G. (2007). Construction in developing countries. _Construction Management and Economics 25_ (1), 1–6.

- Ofori, G. (2011). The construction industries in developing countries: strategic review of the book. In _New perspectives on construction in developing countries_ . Routledge.

- Olken, B. A. (2007). Monitoring corruption: Evidence from a field experiment in indonesia. _Journal of Political Economy 115_ (2), 200–249.

- Olken, B. A. and R. Pande (2012). Corruption in developing countries. _Annual Review of Economics 4_ (1), 479–509.

- Pesendorfer, M. (2000). A study of collusion in first-price auctions. _The Review of Economic Studies 67_ (3), 381–411.

- Rebosio, M. and P. E. Wam (2011). Violent conflict and the road sector: Points of interaction. Technical report, The World Bank.

- Shiferaw, A., M. Soederbom, E. Siba, and G. Alemu (2012). Road networks and enterprise performance in ethiopia: Evidence from the road sector development program. IGC Working Paper 12/0696.

- Stifel, D., B. Minten, and B. Koro (2012). Economic benefits and returns to rural feeder roads: Evidence from a quasi-experimental setting in ethiopia. ESSP II Working Paper 40.

- Svensson, J. (2005). Eight questions about corruption. _The Journal of Economic Perspectives 19_ (3), 19–42.

29
21 _Fiscal Crisis in Central Europe_ unemployment rate remained much smaller, at 6.6 percent in 1991 and 5 percent in 1992 (it was 13.6 percent in 1992 in Poland). This would suggest that the pressure on the social securicy system might also have been very high; nevertheless, the outcome in terms of number of new pensions was very different than the one just seen for Poland. While the growth rate of disability pensions - which had been stable in previous years - more than doubled in 1990 and increased by another 60 percent in 1991, the overall numbers remained considerably below Poland and Hungary - in the latter, for example, the annual growth rate of total non-farme-s pensions increased in 1991, one year after the reform, by more than 400 percent, compared with the average annual growth rate in 1986-90. On the whole, inflows into the pension system after 1990 diverged sr:, little from the long-term trends that had prevailed until then.

The different outcome is probably explained by differences in the social security legislation across the two countries, as well as the administrative application of the existing rules. A detailed analysis of such differences is outside the scope of this paper, although the relevance of such study is enhanced by our results. We only note here that, more generally, the political and social environmnent in which the reforms took place was also completely different in the two countries.

||P<br>Rea<br>(Poland, 19|Ta<br>oland and<br>l Average<br>86=100; C|ble 4.2<br>Czechoslo<br>Pension, 1<br>zechoslov|vakia:<br>986-92.<br>,akia, 1987|=100)|||
|---|---|---|---|---|---|---|---|
||1986|| 1987|1988|1989|1990||<br>1991|| 1992|
|Poland 1/|95.8|100.0|101.4|105.6|92.3|103.1|94.4|
|Czechoslovakia 2/|..|100.0|104.2|117.6|107.5|80.0|71.2|

Source: Biuletyn Statystyczny and Rocznik Statystyczny, several issues; Statisticka Rocenka 1989, 90 and 1992, and Czech R-public Bureau of Statistics.

1/ Data refer to average across all pensions (old age, disability, surviving).

2/ Czech Republic only (which, however, accounted for about three fourths of CSFR pensions).

Data refer to average old age pension.
Ederington, J., J. Minier and K. Troske (2010), “Where the Girls Are: Trade and Labor Market Segregation in Colombia,” University of Kentucky, Lexington, KY, USA.

Elson, D. (1996), “Appraising Recent Developments in the World Market for Nimble Fingers” in A. Chachi and R. Pittin (eds.), Confronting State, Capital and Patriarchy: Women Organizing in the Process of Industrialization. Basingstoke: Macmillan.

Elson, D. (1999), “Labor Markets as Gendered Institutions: Equality, Efficiency and Empowerment Issues”, _World Development_ , 27(3): 611-627.

Elson D, and R. Pearson (1981), “The Subordination of Women and the Internationalisation of Factory Production,” In Young K, C. Wolkowitz and R. McCullagh edited _Of Marriage and the Market: Women’s Subordination in International Perspective_ , CSE Books: London.

Fisman, R., & Svensson, J. (2007). Are Corruption and Taxation Really Harmful to Growth? Firm Level Evidence. _Journal of Development Economics_ , 83 (1): 63 – 75.

<mark>Frankel, Jeffrey A., and Davind Romer (1999), “Does Trade Cause Growth?”</mark> _<mark>American Economic Review</mark>_ <mark>, 89(3): 379-399.</mark> Fussell, E. (2000), “Making Labor Flexible: The Recomposition of Tijuana’s Maquiladora Female Labor Force,” _Feminist Economics_ , 6(3): 59–79.

Gaddis, I. and J. Pieters (2012), “Trade Liberalization and Female Labor Force Participation: Evidence from Brazil,” IZA Discussion Paper No. 6809.

Galor, O. and D. N. Weil (1996), “The Gender Gap, Fertility, and Growth,” _American Economic Review_ , 86(3): 374–387.

Ghani, Ejaz, Anandi Mani and Stephen O’Connell (2013), “Can Political Empowerment Help Economic Empowerment? Women Leaders and Female Labor Force Participation in India,” Working Paper No. 6675, World Bank, USA.

Gladwin, Christina ed. (1991), _Structural Adjustment and African Women Farmers_ , Gainsville, FL, University of Florida Press.

Glaeser, E.L. and B.I. Sacerdote (1999), “Why Is There More Crime In Cities,” _Journal of Political Economy_ , 107(S6): S225-S258.

Goldin, Claudia (2006), “The Quiet Revolution that Transformed Women’s Employment, Education, and Family,” _American Economic Review_ , 96(2): 1-21.

Gray, Mark M., Miki Kittilson and Wayne Sandtholtz (2006), “Women and Globalization: A Study of 180 Countries, 1975-2000,” _International Organization_ , 60: 239-333.

34
# II. Database and Methodology

# II.1. Database

The analysis is conducted at the monthly frequency for January 1970-October 2022 and is therefore constrained in its country sample and choice of variables. In particular, to ensure a balanced sample for all measures of inflation, we focus on G7 countries as our baseline country group. In our baseline model, inflation, , is measured by headline CPI inflation; output growth, , is measured by the growth rate of industrial production ii tt (which, in contrast to real GDP growth, is available at a monthly frequency); and the ii ππ tt interest rate, , is measured by the first-differenced 3-month Treasury Bill yields (or YY shadow rates after the global financial crisis).ii<sup>4</sup> Oil price growth is measured as the growth tt rate of nominal oil prices (average of Dubai, WTI and Brent benchmark oil prices). All RR variables are month-on-month, seasonally adjusted, log-differenced, demeaned changes and are stationary.

For robustness, we also consider an alternative (unbalanced) sample of 30 countries, including advanced and emerging market economies, and alternative measures of inflation (producer price inflation and core CPI inflation), output growth, oil prices, and interest rates in Sections V and VII.

# II.2. Dynamic Factor Model and FAVAR Model

Dynamic Factor Model. Global inflation, global output growth, and global interest rates are estimated by the following three dynamic factor models: g g g g g where , , and represent inflation, output growth, and interest rate in country i in g g g g g g month t, respectively, while ii ii ii , , and are the global common tt tt tt factors for inflation, output growth, and the changes in the interest rate in month t, ππ YY RR ππ,g YY,g RR,g tt tt tt respectively. The error terms (ff ) are assumed to be uncorrelated across countries at all ff ff leads and lags. The error terms and factors follow an autoregressive process. The model ii tt is estimated using Bayesian techniques as described in Kose, Otrok, and Whiteman (2008). ee FAVAR Model. In its structural form, the FAVAR model is represented by:

> 4 The shadow interest rate is estimated as in Wu and Xia (2016) as the shortest maturity rate based on the shadow yield curve. It is essentially equal to the policy interest rate in “non-lower” bound or unconventional monetary policy environments.

4
- Bos, J., Berman, J., Kane, T. J., & Tseng, F. (2012, November 8). _Does the Source Program Increase Access to Financial Aid and Support Four‐Year College Enrollment?_ Presented at the APPAM Fall Research Conference. Retrieved from https://appam.confex.com/appam/2012/webprogram/Paper2589.html

- Bruce, D. J., & Carruthers, C. K. (2014). Jackpot? The impact of lottery scholarships on enrollment in Tennessee. _Journal of Urban Economics_ , _81_ , 30–44.

- Bulman, G. B., & Hoxby, C. M. (2015). The Returns to the Federal Tax Credits for Higher Education. _Tax Policy and the Economy_ , _29_ (1), 13–88.

- Carneiro, P., & Heckman, J. J. (2002). _The Evidence on Credit Constraints in Post‐Secondary Schooling_ (Working Paper No. 9055). National Bureau of Economic Research.

- Carrell, S. E., & Sacerdote, B. (2013). _Why do college going interventions work?_ (No. 19031). National Bureau of Economic Research.

- Carruthers, C. K., & Fox, W. F. (2016). Aid for all: College coaching, financial aid, and post‐ secondary persistence in Tennessee. _Economics of Education Review_ , _51_ , 97–112.

- Carruthers, C. K., & Özek, U. (2016). Losing HOPE: Financial aid and the line between college and work. _Economics of Education Review_ , _53_ , 1–15.

- Castleman, B., Arnold, K., & Wartman, K. L. (2012). Stemming the Tide of Summer Melt: An Experimental Study of the Effects of Post‐High School Summer Intervention on Low‐ Income Students’ College Enrollment. _Journal of Research on Educational Effectiveness_ , _5_ (1), 1–17.

- Castleman, B., & Goodman, J. (2014). _Intensive College Counseling and the College Enrollment Choices of Low Income Students_ (SSRN Scholarly Paper No. ID 2493103). Rochester, NY: Social Science Research Network.

- Castleman, B., & Long, B. (2013). _Looking Beyond Enrollment: The Causal Effect of Need‐Based Grants on College Access, Persistence, and Graduation_ (NBER Working Paper No. 19306). National Bureau of Economic Research, Inc.

- Castleman, B., & Page, L. (2015). Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low‐income high school graduates? _Journal of Economic Behavior & Organization_ , _115_ , 144–160.

- Castleman, B., & Page, L. (2017). Parental Influences on Postsecondary Decision Making: Evidence From a Text Messaging Experiment. _Educational Evaluation and Policy Analysis_ , _39_ (2), 361–377.

- Castleman, B., Page, L., & Schooley, K. (2014). The Forgotten Summer: Does the Offer of College Counseling After High School Mitigate Summer Melt Among College‐Intending, Low‐Income High School Graduates? _Journal of Policy Analysis and Management_ , _33_ (2), 320–344.

- Choitz, V., & Reimherr, P. (2013). _Mind the Gap: High Unmet Financial Need Threatens Persistence and Completion for Low‐Income Community College Students_ . Center for Law and Social Policy.

- Clotfelter, C. T., Hemelt, S. W., & Ladd, H. F. (2018). Multifaceted Aid for Low‐Income Students and College Outcomes: Evidence from North Carolina. _Economic Inquiry_ , _56_ (1), 278– 303.

36
# **I. Introduction**

Although most countries have experienced per capita growth in the 20<sup>th</sup> century, extreme income poverty and undernourishment is still widespread. In 2001, GDP per capita, corrected for purchasing power, in the world was on average about $21 a day. However, in the same year, more than half of the world population lived on less than $2 a day and more than 1 billion lived on less than $1 a day, whereas daily per capita income in a typical developed country exceeded $60. Income poverty is but one measure of (the lack of) development, another measure is the presence of hunger or undernourishment.<sup>1</sup> Unfortunately, undernourishment followed a similar, dispersed pattern. On average, about 20 percent of the world population was undernourished in the 1990s. However, undernourishment’s prevalence was 70.5 percent in Eritrea, compared to 2.5 percent in Poland. Moreover, a lot of countries have eradicated undernourishment.<sup>2</sup> A large literature is devoted to analyzing the links between economic growth, inequality, poverty and hunger. General findings are that higher growth and lower inequality are associated with lower levels of poverty and hunger. Another established literature has found robust evidence that financial sector development spurs economic growth, primarily because it puts capital to its most productive use. Moreover, this line of research shows that financial development reduces inequality because it levels the playing field and enables (poorer) individuals and (smaller) firms to participate in the formal economy and grow. New research has tied together these two literatures and found beneficial relationships from financial sector development to poverty reduction.

> 1 One is considered undernourished when one’s food intake falls below the minimum requirement or when one’s food intake is insufficient to meet dietary energy requirements continuously. During the Millennium Summit in September 2000, 189 nations unanimously adopted the Millennium Declaration. The Declaration contains eight specific Millennium Development Goals (MDGs). The main aim of the Declaration is to eradicate extreme poverty around the world by 2015. As such, the MDGs are the most ambitious and most broadly supported development goals the world has ever established. One of the most important is the Poverty-MDG, which consists of two parts: 1) reduce income poverty by cutting in half the fraction of the population who live on less than 1$ a day and 2) reduce hunger by cutting in half the fraction of the population who suffer from undernourishment.

> 2 Of the 171 countries for which we have GDP per capita data, all but one of the 29 countries with GDP per capita higher than $16,000 have no undernourishment (the exception is United Arab Emirates, which has a 3% prevalence of undernourishment).

2