rafmacalaba/data-use-mentions-extended huggingface dataset:
investment will be ambiguous.

We test the predictions of the model using data on early childhood stimulation — both the overall amount of stimulation received by each young child, and the amount of stimulation done by different family members (e.g. the mother, the father, the older siblings, etc.). Estimates of the impact of having an older daughter on the overall level of early childhood stimulation are reported in Panel C of Table 3. We find large and statistically significant impacts of older sisters on the level of early childhood stimulation a child experiences. Having an older sister increases the number of different stimulating activities (out of 12) over the three days prior to the survey by between 0.637 (without controls, p-value 0.006) and 0.703 (with controls, p-value 0.002). Among households where the older child is male, the mean number of stimulating activities if 5.147; hence, the estimated treatment effect of having an older sister represents more than a ten percentage point increase in early childhood stimulation. From a theoretical perspective, this suggests that we can rule out the possibility that both _δo_<sup>_G_=</sup><sup>_δ_</sup> _o_<sup>_B_and</sup><sup>_λG_</sup> _o_<sup>=</sup><sup>_λB_</sup> _o_<sup>—sinceweobservea</sup> clear treatment effect of older sibling gender on early childhood stimulation.

Next, we test whether parents are the main channel of impact. In Figure 3, we summarize the estimated treatment effect of having an older sister on 10 different outcome variables. First, we present the treatment effect on the overall level of early childhood stimulation (replicating the specification in Panel C of Table 3 that was discussed above). Then, we present treatment effects on the amount of early childhood stimulation done by parents and the amount done by siblings. Finally, we
- 68 -

Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1992 and source l: _Labor force sample surveys_ and _General household sample surveys._ Mexico Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993. Unemployment data are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993 and source l: _Labor force sample surveys_ and _General household sample surveys._ Nicaragua Unemployment Data are drawn from Country Economic Memorandum for 1992. The figure relates to official unemployment; it may, however, underestimate the actual unemployment since it does not take into account underemployment and informal sector employment. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992.

Central Government, Education and Health employment figures are drawn from the Nicaragua Civil Service Diagnostic Review of September 19,1994 and relates to 1994. Data on GDP at market prices is a 1993 estimate and is taken from World Tables 1995.

Consolidated Central Government wages and salaries are from 1994 and is taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1992.

Paraguay Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Data on military employment include conscripts (12,900), but do not include personnel in paramilitary units, i.e., the Special Police Service (8,000).

Peru All available data are drawn from Public Expenditure Review. Data on military include conscripts (65,500), but do not include personnel in paramilitary units, i.e., the National Police (60,000), the Coast Guards (600), and the Rondas Campesinas (2,000, est), which is a peasant
41

**Table 4: Regression results for postneonatal mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0038|0.0032|0.0025|0.0008|0.0012|0.0037|-0.0000|
|High malaria risk zone (HMRZ)|0.0662***|0.0614***|0.0605***|0.0605***|0.0579***|0.0616***|0.0381**|
|Interaction LMRZ and ITN ownership|0.0080|0.0085|0.0102|0.0108|0.0128|0.0115|0.0122|
|Interaction IMRZ and ITN ownership|0.0045|0.0043|0.0054|0.0092|0.0099|0.0084|0.0041|
|Interaction HMRZ and ITN ownership|-0.0386**|-0.0383**|-0.0373**|-0.0349**|-0.0342**|-0.0379**|-0.0424***|
|2008 survey|-0.0195**|-0.0130|-0.0134|-0.0143|-0.0141|-0.0119|-0.0183*|
|Full immunization (district level)||-0.0417|-0.0415|-0.0420|-0.0509*|-0.0495|-0.
. The economic reasons that, according to the author, explain the differences in the literature include: the bite of the minimum wage, whether the study focuses on affected workers, the existence of labor-labor substitution, and whether the study uses monopsony models. However, the evidence supporting these arguments is at best mixed (as we will discuss in detail in the next paragraphs), with the exception of accounting for monopsony power, which has been shown to be key when studying the effects minimum wage has on employment.

The most recent studies on the employment effect of the minimum wage (with the most rigorous methodologies and highest-quality administrative datasets) do not find substantive negative employment effects. For Germany, where the minimum wage was introduced in 2015, Dustmann et al. (2022) find no disemployment effect since affected workers reallocate to more productive firms, and Ahlfeldt et al. (2022) find an almost negligible reduction in employment of 0.3% in an equivalent period (2011–2016 of the former; 2011–2018 of the latter). Moreover, Ahlfeldt et al. (2018) find no reduction of employment in the first 2 years following the introduction of the minimum wage even in German low-wage regions. For the United States, Cengiz et al. (2022) find that the number of low-wage jobs remained essentially unchanged over the 5 years following an increase in the minimum wage, pointing toward negligible employment effects not only in the short run but also in the longer run. Similarly, Cengiz et al. (2019) use a differences-in-differences approach to measure the effects of

> 12Hurst et al. (2022) use a putty-clay technology model to study the (long-run) welfare impact of different policies on low-wage individuals in which employment cannot be adjusted immediately after the minimum wage rise. The authors find within the lenses of the model that minimum wage has a small employment effect in the short run, but a larger negative one in the long run and thus adverse welfare consequences. From their estimation,
MONGOLIA'S INFORMAL SECTOR Appendix B: Methodologies and Descriptive Statistics 45

# Informal Sector Surveys of Outdoor Markets and Street Informals

121. Two hundred and seventeen additional informals were surveyed in June of 1997. The purpose of the Generalized Informal Survey was not to provide estimates of overall value added, as little was known _a priori_ about the population of the informals that pack the various outdoor markets and offer services on the streets. Rather, we sought to illuminate the ,great diversity of the informal sector, by providing some contrast to taxicabs and kiosks. It was the quest for contrast that drove the sampling procedures.

122. At least 40 informals were interviewed from each of Ulaanbaatar's three largest outdoor markets, the "Black" Market, the Technical Market, and the _Harhorin Market._ Also interviewed were 80 informals who made their livings on the streets of Ulaanbaatar.<sup>9 5</sup> The parameters of sample selection were simple: (i) We only interviewed informals who occupy their space on a daily basis; if they work from any sort of structure they were excluded from the sample. (ii) We only interviewed informals whose operations appeared to be "small." At the "black" market, for example, containers clearly failed this test and were excluded from the sample. (iii) We only interviewed people that appeared to be engaging in the activity regularly. This criterion excluded people who attend the markets occasionally to sell unneeded household items. (iv) No one under age 16 was interviewed. A large number of informals were thus excluded, but we felt that children were not likely to understand many of the questions. The sample for the Generalized Informal Survey is, then, not a random sample. It is probably biased toward low income - low productivity informals. The bias was intentional, designed to provide contrast to the high income - high productivity taxicabs and kiosks. The composition of the sample is presented in Table B4.

> Table **B4.
|-|137|1434|-0.30|0.19|0.00|
|Trust in Formal System|82|1798|-0.01|-0.03|0.31|137|1434|-0.28|-0.14|0.00|
|Months as Member|82|1798|16.48|28.74|0.00|137|1434|20.28|24.98|0.45|
|Holds office in Group (Yes=1)|82|1798|0.48|0.20|0.00|137|1434|0.06|0.18|0.01|
|Eligibility|82|1798|0.49|0.63|0.00|137|1434|0.53|0.63|0.00|
|**_Household Characteristics_**|||||||||||
|Household size|82|1798|7.88|7.93|0.70|137|1434|7.45|7.00|0.00|
|Years of Education of Spouse|82|1798|3.87|2.67|0.02|137|1434|4.93|4.88|0.91|
|Number of children under 9|82|1798|1.78|1.97|0.40|137|1434|1.69|1.40|0.05|
|Land|82|1798|4.38|6.01|0.39|137|1434|4.48|2.58|0.30|
|Fraction of Members of same Zaat (caste)|82|1798|0.41|0.43|0.12|137|1434|0.45|0.21|0
/mark>_ <mark>(4), 857-899.</mark>

- Hanushek, E. A., & Rivkin, S. G. (2006). Teacher quality. Handbook of the Economics of Education, 2, 1051-1078.

- Hanushek, E. A., & Rivkin, S. G. (2010). Generalizations about using value-added measures of teacher quality. _American Economic Review, 100_ (2), 267-271.

- International Labour Organization. (2012). _International Standard Classification of Occupations: ISCO08_ . Geneva: ILO. Retrieved from

<u>https://www.ilo.org/public/english/bureau/stat/isco/docs/publication08.pdf</u> International Labour Organization. (2020). ILOSTAT: The leading source of labour statistics. Retrieved from. <u>https://ilostat.ilo.org/data/</u> Iwu, C.G., Ezeuduji, I.O., Iwu, I.C., Ikebuaku, K., Tengeh, R.K., 2018. Achieving quality education by understanding teacher job satisfaction determinants. Social Sciences 7.

https://doi.org/10.3390/socsci7020025 Jackson, C. K. (2012). Recruiting, retaining, and creating quality teachers. _Nordic Economic Policy Review, 3_ (1), 61-104.

Kontagora, H. L., Watts, M., & Allsop, T. (2018). The management of Nigerian primary school teachers. International Journal of Educational Development, 59, 128-135.

Kumar, S., and Kumar, N. (2019, January 25). Striking Tamil Nadu teachers denounce state and central governments _. The World Socialist Web Site._ Retrieved from

- <u>https://www.wsws.org/en/articles/2019/01/25/tnin j25.html</u>
<sup>29</sup> In an analysis where firm growth is measured by change in overall employment, it would be misleading to consider plant TFPQ of the major product of the plant.<sup>30</sup>

# **_Distances to Cities/Urban Agglomerations_**

This work uses GIS software to compute distances between every town in the LMMIS data and five major Ethiopian cities that are considered to be centers of broader agglomerations. The five cities/urban agglomerations are Addis Ababa, Awassa, Bahir Dar, Dire Dawa and Mekele. Besides the agglomeration center itself, the following distance bands are defined from the nearest agglomeration center à la Ghani et al. (2016): 0-50 kilometer (km), 50-100 km, 100-150 km, and the residual category of towns that are located over 50 km away.

# **_Measuring Spatial Clustering_**

The Ellison-Glaeser index (EGI) measures the extent of spatial concentration in a given industry. It takes negative values when the industry is more dispersed than what random assignment would lead to, while positive values where industries show greater levels of concentration.<sup>31</sup> The EGI for industry _s_ at time _t_ is defined as:

> 27 The project also experimented with alternative definitions of TFP and found the results to be robust to such changes. Another problem related to TFP estimation is measurement errors; these are arguably all the more pervasive in the context of developing countries.

> 28 Examples of such products include bricks of clay, cement blocks, nails, sugar, bread and wheat flour.

> 29 Since most data-sets do not have disaggregate information on inputs by products of the firms, the share of inputs feeding into the major product is extrapolated.

> 30 About 50 percent of observations pertain to single product plants but restricting to homogenous products, only 26 percent of observations are suitable for TFPQ estimation.

> 31 The main advantage of the index is its robustness to the level of spatial aggregation, provided that the agglomeration
preliminary of estimates. This “waiting period” may be substantial; one to two months among advanced economies and a quarter to two quarters among emerging markets. Daily NO2 data is made freely, publicly available by NASA online just a few days after measurement. Given the quick availability of this resource, there is also the potential for applications to real-time nowcasting, and the reduction in GDP revisions, with substantial benefits for policymaking.

# **References**

- **Alstadsæter, Annette, Niels Johannesen, and Gabriel Zucman** , “Tax evasion and inequality,” _American Economic Review_ , 2019, _109_ (6), 2073–2103.

- **Baragwanath, Kathryn, Ran Goldblatt, Gordon Hanson, and Amit K Khandelwal** , “Detecting urban markets with satellite imagery: An application to India,” _Journal of Urban Economics_ , 2019, p. 103173.

- **Bazzi, Samuel, Martin Fiszbein, and Mesay Gebresilasse** , ““Rugged individualism” and collective (In) action during the COVID-19 pandemic,” _Journal of Public Economics_ , 2021, _195_ , 104357.

36
We opted to use the earliest available survey for each country to limit mortality bias in the estimated completed schooling and then the most recent survey to capture the completed schooling for the youngest cohorts in the country. In total, we used 188 surveys from 111 countries to generate 4,670 cohort observations covering almost 4 million individual observations. We further disaggregate the cohorts by gender, and if possible, also by urban and rural residence. For each birth cohort, we computed average years of schooling, average earnings, and incidence of marriage. To compare lifetime earnings across countries, we require a common unit of time. Across the 188 surveys, wages are measured per hour, day, week, month, quarter, or year. These surveys are internally consistent, so a survey dummy variable standardizes both time units and currency units. The survey dummy variable also controls for country-specific effects. In effect, the source of identification in our estimates is the variation in schooling and earnings across cohorts within surveys.

The data on life expectancy at birth by country were compiled from 1950 from the United Nation’s population database.<sup>10</sup> For earlier birth cohorts, GapMinder constructs a measure of life expectancy at birth for almost 200 countries back to 1900. Figure 1 shows the pattern of life expectancy at birth by birth cohort starting in 1910. Worldwide life expectancy has risen from 38 to 72 years over the 90-year period. Over that same period, the global average years of schooling rose from 6.8 to 12.4 years. As shown in Figure 2, these patterns are common across regions and income groups.

As life expectancy increases, the fraction of the birth cohort that enters working age increases. If workers of different ages are not perfect substitutes for one another, members of unusually large working-age cohorts will face depressed earnings (Welch, 1979). We use the

> 10 The UN database is available at http://data.un.org/Default.aspx.

15
# **3 Empirical Framework**

## **3.1 Data**

There are three main types of data that we use in this paper. First, to study road traffic crashes (RTCs), we use data from the National Police Service that we digitized from paper situation reports. Police officers generate these reports for each crash that they record where there was an injury, fatality, or significant property damage. Second, we use data from Flare, the largest emergency response dispatch platform in Kenya with over 800 ambulances available on call, 200 of which are in Nairobi. Finally, we use several big data sources to study mobility and congestion including video-generated traffic counts, Waze, Uber and Google Maps.

Our main source of data on road traffic crashes comes from the Kenya National Police Service (NPS). We construct a unique, detailed dataset on the time and location of RTCs which previously did not exist for Kenya by working with NPS to digitize all the Situation Reports for the 14 traffic police stations in Nairobi. These are paper records that are produced for each crash that involves an injury or notable property damage. We use 5,075 crash reports from January 1, 2017-December 31, 2020 that we have manually digitized. While this data does not include minor crashes reported to the police or crashes that were not reported to the police, it is the most comprehensive set of data on the location, casualties and time of crashes for the city of Nairobi. We aggregate total crashes occurring per hour for Nairobi.

We cross-reference this dataset with data on road traffic crashes from Flare, an emergency response dispatch platform that aggregates emergency response fleets into a single platform and runs a 24/7 dispatch center much like 911. The majority of emergency calls made to Flare are for RTCs. Of the calls coming in, almost half come from good Samaritans who witness a situation requiring an ambulance. The other half of the calls are evenly split between calls coming from the police who show up on
, A. G., Toscanelli, C., & Arnett, J. J. (2021). The neglected 95% revisited: Is American psychology becoming less American? _American Psychologist_ , _76_ (1), 116–129. https://doi.org/10.1037/amp0000622

- Thomas, C. C., & Markus, H. R. (2023). Enculturating the Science of International Development: Beyond the WEIRD Independent Paradigm. _Journal of Cross-Cultural Psychology_ , 54(2), 195–214. https://doi.org/10.1177/00220221221128211

- Thomas, C. C., Otis, N. G., Abraham, J. R., Markus, H. R., & Walton, G. M. (2020). Toward a science of delivering aid with dignity: Experimental evidence and local forecasts from Kenya. _Proceedings of the National Academy of Sciences_ , 117(27), 15546–15553. https://doi.org/10.1073/pnas.1917046117

- Thomas, C. C., Rathod, S. D., De Silva, M. J., Weiss, H. A., & Patel, V. (2016). The 12-item WHO Disability Assessment Schedule II as an outcome measure for treatment of common mental disorders. _Global Mental Health_ , _3_ , e14. https://doi.org/10.1017/gmh.2016.7

- Thomas, C. C., Walton, G. M., Reinhart, E. C., & Markus, H. R. (2023). Mitigating welfarerelated prejudice and partisanship among U.S. conservatives with moral reframing of a universal basic income policy. _Journal of Experimental Social Psychology_ , 105, 104424. https://doi.org/10.1016/j.jesp.2022.104424

- Tingley, D., Yamamoto, T.,
# 6) Conclusion

In modern production processes, the parts and components comprising a product are produced in multiple locations, crossing borders multiple times before they are assembled into the final product. Firms therefore increasingly rely on imported inputs to produce competitively. In fact, several studies have shown that the use of imported intermediates has been associated with improved firms’ productivity and diversification patterns. Trade policy reform aimed at reducing the impact of tariffs in upstream sectors can therefore go a long way in improving firms’ competitiveness and help them play a more active role in global value chains.

This paper presents UTAS: a novel tool developed by the World Bank to help policy makers assess the impact of tariff reforms on production costs across a variety of sectors in a flexible and time‐efficient manner. It also allows users to compute effective protection rates. This is important because high levels of effective protection can result in a significant anti‐export bias and reduce firms’ incentive to export. Based on a simple modeling framework, the tool is intended to allow policy makers to consider how different tariff structures intertwine with firms’ production costs, with the ultimate goal of supporting an informed discussion of the possible consequences of trade reform for the private sector. It allows users to combine country‐specific input‐output data across sectors with information on tariffs by HS code to quantify the impact of different tariff reform scenarios across different sectors, building on a user‐friendly Microsoft Excel environment. The tool can be operated with only basic Excel knowledge and allows users to specify different assumptions to gauge the impact of direct and indirect effects of tariff reform scenarios on the costs of inputs across sectors.

22
ture has found it hard to adjudicate between these two worldviews. One strand of the empirical literature has attempted to test particular theories of poverty traps. For example, Strauss and Thomas (1998) review studies which look for nonlinear relationships between health and productivity, and McKenzie and Woodruff (2003) test for non-convexities in returns to microenterprise investment. These studies generally have not found support for poverty traps caused by the particular mechanism being studied<sup>3</sup> , but leave open the question of whether poverty traps may still arise due to the non-studied processes.

A second strand of recent literature has therefore attempted to look directly at the dynamics of income, expenditure, or assets in order to test for nonconvexities and poverty traps. Lokshin and Ravallion (2004) use a six-year panel from Hungary and four-year panel from Russia to carry out nonlinear estimation of the relationship between current and lagged income. Almost one half of their sample has attrited by the end of the panel, and so they use a systems estimator which explicitly models attrition as a function of initial observed characteristics of the household. They do find the mapping from lagged income to current income to be nonlinear, but find no evidence of low-level threshold effects which would be associated with poverty traps. Jalan and Ravallion (2004) obtain similar findings using a six-year panel of income from four provinces in China.

Carter and Barrett (2005) criticize the use of short panels of income or expenditure to test for poverty traps by claiming that they are unable to distinguish between structural poverty and short-term transitory movements into and out of poverty.<sup>4</sup> This can be exacerbated by measurement error, which can lead a household to be mis-classified as poor in one period and correctly classified as non-poor in the next. They further note that many theories of poverty traps are based on an asset threshold, and propose study of the dynamics of asset poverty. Such an approach is followed by Lybbert et al. (2004), who use 17