rafmacalaba/data-use-mentions-extended huggingface dataset:
rainfall to be _carried over_ between phases, to try to capture the soil moisture at the start of the phase (see Table 6).

# <u>Consecutive Dry Days (CDD) Index</u>

Another approach to capturing adverse rainfall events is to construct an index equal to the maximum consecutive number of _dry days_ within a specified period, where a dry day is defined as a day with total rainfall below a threshold value.

(3) This cover offers protection for long dry spells and can be sold as a standalone product or in conjunction with other indexed cover, particularly rainfall volume based products. For example, Table 7 characterizes a consecutive dry days product where the index uses a daily threshold of 2.5mm, and with claim payment a step function of the index, with claim payment of ₹750 if the index is between 17 and 24 days, ₹1,500 if the index is between 25 and 29 days and ₹2,000 if the index is greater than or equal to 30 days.

Some consecutive dry days products use a trigger of 0mm (see Table 8), although the agronomic merits of such products are unclear; a dry spell of 30 days, with rainfall of 2mm on day 15 would still most likely result in a large crop loss, but would not trigger a claim payment for the product defined in Table 8.

# <u>Excess/Untimely Rainfall Index</u>

Heavy and continuous rainfall within a short period has the potential to cause severe physiological damage to crops, particularly during the maturity and the harvest phases when excess rainfall makes many crops highly susceptible to attacks by pestilence and disease. The indices that have been designed to capture wet spells are similar in nature to those already described for deficit rainfall, dependent on consecutive rainy days, aggregate rainfall over a period of between two and four consecutive days, or a piecewise linear function of rainfall in each phase. For example Hess et al. (2005) report on a rainy
Figure 8: Impacts on specific land management activities <!-- Start of picture text --> 2011-2012 2013-2014<br>fire breaksfirefightingfencespest controlnurseriesreforestationclean post-harvestpruning patrollingsoil conservationboreholes other fire breaksfirefighting fencespest controlnurseriesreforestationclean post-harvestpruning patrollingsoil conservationboreholes other<br>.65<br>.55<br>.45<br>.35<br>.25<br>.15<br>.05<br>-.05<br>Activity was carried out in the last 12 months -.15<br>-.25<br><!-- End of picture text -->

> 1 Bars show impacts on land cover maintenance activity types as measured in the community survey. Estimates are produced using regressions that include the threshold dummy variable, the first and second order polynomials of the re-centered point score and their interactions with the threshold dummy, as well as state fixed effects.

> 2 Sample sizes for the 2011-2012 cohort are: above threshold 206, below threshold 151, total 357; for the 2013-2014 cohort: above threshold 287, below threshold, total 505.

32
349,414|14,678|
|2014|Near the Syrian Border|3,176,654|10,917|18,575|
|Population|Rest of the Country|17,047,500|417,846|64,973|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _._ However, this should not affect estimates of the number of households.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

20
, in the baseline survey of the policy experiment, we found that, as is common in collectivist societies, behavioral interdependence was broadly practiced and valued. In survey responses, 90% of women agreed or strongly agreed that “when making a decision, it is important to take into account the opinions of other members of your community,” 79% agreed or strongly agreed that “it is your duty to take care of others in your village even if you must sacrifice yourself,” and 89% reported they do what their husbands say with their money rather than doing what they alone want. According to additional data collected among the Study 3 control group, 86% of the respondents reported that they preferred for households in the village to develop together versus independently ( χ<sup>2</sup> (1, _N=_ 1,215)= 612.98, _p<_ .001).

Second, these interdependent tendencies were not only practiced and valued but also seen as ontologically useful and effective in this context. In order to understand beliefs about women’s economic agency in particular, we asked women to rank the importance of different psychosocial factors as drivers of and barriers to women’s economic success. In each of these questions, we asked participants to rank four factors—two of which related to intrapersonal processes and two to relational. Intrapersonal factors included personal initiative, hard work, persistence, and planning for the future, which are commonly reflected in Western goal setting and self-regulation interventions and other entrepreneurial programs (Campos et al., 2017; Frese & Fay, 2001; Oettingen & Gollwitzer, 2010). The relational factors were derived from our formative qualitative interviews and included social harmony, peacefulness,<sup>6</sup> respect of others, and good social connections. Responses were collected among the control group of the policy experiment in a follow-up survey (N=1,216). We independently collected predictions from a U.S. sample
# **2. Poverty Trends and Poverty Convergence**

Creating a data set to analyze regional poverty merits acknowledgement and discussion. As recent as early 2015,<sup>7</sup> publicly accessible data sets of Turkey’s Household Income, Consumption and Expenditure Survey (HICES) and the Survey of Income and Living Conditions (SILC) did not have regional variables at the NUTS2 level. Econometric techniques were employed to impute welfare measures at the NUTS2 level.

## **<u>Poverty</u>**

Turkey has experienced a large amount of poverty reduction (consumption based) in recent years (Azevedo and Atamanov, 2014). However, most of what we know about welfare, poverty, and shared prosperity is limited to the national level. NUTS2 regional level poverty and welfare measures used in this paper are computed using survey-to-survey imputation methods. Data access limits which data set can be used to analyze poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representitive surveys anually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure poverty, does not have geographic identifiers. The SILC contains geographic identifying variables, but only at the NUTS1 level.

Table 1. Survey Comparison and Data Availability

||Years Available|Income or Consumption|Geographic Identifier|Spatial<br>Deflation|
|---|---|---|---|---|
|HICES|2003-2012|Consumption, income|National, urban/rural|No|
|SILC|2009-2012|Income|NUTS1|No|
|LFS|2009-2013|Imputed Income|NUTS2|Yes|

This paper applies well established econometric techniques of survey to survey imputation (Elbers, Lanjouw, and Lanjouw, 2003) to estimate poverty rates at the NUTS2 level (
# THE ENTERTAINING WAY TO BEHAVIORAL CHANGE:

# FIGHTING HIV WITH MTV

# Abhijit Banerjee

# Eliana La Ferrara

# Victor H. Orozco-Olvera

This research is part of the entertainment-education program of the World Bank’s Development Impact Evaluation Department (DIME), part of the Development Economics Research Group. We thank Oriana Bandiera, Michael Callen, Ruixue Jia, Gaia Narciso, Ricardo Perez-Truglia, Devesh Rustagi and seminar participants at IIES, LSE, Toulouse, Trinity College Dublin, University of Bonn, UCLA, UC San Diego, University of Manheim, University of Oslo, University of Amsterdam, University of Southern California, Yale, UPF, PSE, Wharton and BREAD 2018 conference for helpful comments. Laura Costica and Edwin Ikuhoria did a superb job as research and field coordinators. Tommaso Coen, Viola Corradini, Dante Donati, Francesco Loiacono, Awa Ambra Seck, Sara Spaziani and Silvia Barbareschi provided excellent research assistance. This study was funded by the Bill and Melinda Gates Foundation and the World Bank i2i Trust Fund. La Ferrara acknowledges financial support from ERC Advanced Grant ASNODEV. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

Victor H. Orozco-Olvera Abhijit Banerjee Department of Economics, E52-540 MIT 50 Memorial Drive Cambridge, MA 02142 and NBER <u>banerjee@mit.edu</u> World Bank Development Impact Evaluation Department (DIME) 1818 H Street Washington, DC 20433 <u>vorozco@worldbank.org</u> Eliana La Ferrara Universita' Bocconi Dept. of Economics and IGIER via Roentgen 1 20136 Milano Italy <u>eliana.laferrara@unibocconi.it</u> A randomized controlled trials registry entry is available at AEARCTR0000509
case of most infrastructure SOEs—governments cannot avoid having to bail them out in the event of a negative shock or threatened financial default (Lewis and Mody 1998; Wagner, Musacchio, and Jara-Bertin 2016). As budgets are computed on a cash basis, they rarely contain an estimate of the net present value of those contingent liabilities or the government’s risk exposure to such liabilities.

# **3 Data**

The World Bank Infrastructure SOEs Database was compiled as part of a broader research project to analyze the fiscal costs and risks of infrastructure. The data come from financial statements from SOE websites, government websites that include SOE financial statements, annual reports, and other sources, such as the EMIS Intelligence database and stock exchange websites. The database covers all SOEs operating infrastructure assets in the power (generation, transmission, and distribution) and transportation (roads, railroads, and airlines and airports) sectors for 19 countries between 2000 and 2018. The countries were selected based on data availability and to maximize sectoral coverage. They include the following: 8
sampling weights as provided by the Enterprise Survey Indicator Database. We also perform a t-test which tests for the null hypothesis that there is no significant difference between the core and non-core regions for a given indicator.<sup>7</sup> We do this separately for manufacturing and services firms. Our analysis covers 33,113 manufacturing firms which represent more than 80 percent of the overall sample (see _Appendix 3_ ).

> 5 We dropped these, since the database only included nine high-income countries which were not representative of high-income countries (some OECD countries, some non-OECD countries, and one oil exporter).

> 6 We applied a threshold of three and not two regions, because in many countries with only two regions these had similar relative sizes rendering the determination of core regions very difficult.

> 7 Since some strata only had one observation, we did not use stratification. This affects the standard errors, which in general should be larger than when the strata is specified, or in other words, this should result in a more conservative test.

8
It is impossible to develop a general rule about the relative accuracy of different models, because their relative accuracy depends on the nature of the data. This is especially true when comparing across different sources of auxiliary data. For example, census data aggregated to the target area level may be preferable to geospatial data available at the sub-area level because it is more predictive of welfare, but current geospatial data may be preferable to old census data even if the latter is available at the household level.

Nonetheless, when considering a single source of auxiliary data, the household and sub-area models enjoy the important advantage of using auxiliary data at a more disaggregated level. The use of more spatially disaggregated data can lead to more accurate estimates of non-linear functions such as headcount poverty rates, though these gains in accuracy may be negligible in some contexts. An additional benefit from using more spatially disaggregated estimates is the additional precision gained by exploiting the additional variation across sub-areas within areas. Whether the benefit comes in the form of increased accuracy, precision, or both, when considering a single source of auxiliary data it is generally preferable to use a household or sub-area model rather than an area-level model when possible.

Although the general principal of using the most spatially disaggregated auxiliary data possible seems straightforward, there is not yet full consensus on this point in the literature. For example, Corral et al. (2021) argue that, when considering model-based bias, household models with aggregate variables suffer from omitted variable bias, and therefore recommend using an area-level model rather than a household-level model when the auxiliary data consist solely of sub-area or area-level means. Newhouse et al. (2022), however, show that this source of omitted variable bias is equally present in area-level and household models that use auxiliary data drawn from the population, such as census or administrative data. Furthermore, this source of model-based bias disappears when considering design-model bias, taking the expected value of the predictions prior to drawing the sample. Omitted variable bias is therefore not a relevant concern when
typically include population size (Rozenfield _et al._ , 2011; Dijkstra and Poelman, 2014; Aubrecht _et al._ , 2016), other definitions may include other geospatial information such as land cover (Mertes _et al._ , 2015), night time lights (Balk _et al._ , 2006; Zhang and Seto, 2011; Brecht, Deichmann and Wang, 2013), travel time (Uchida and Nelson, 2010; Day _et al_ ., 2017) and/or commuting flows (Duranton, 2015; Nelson and Rae, 2016; OECD, 2012; Sanchez-Serra, 2016). Specifically, this paper reestimates the relationships depicted in Figure 1 using two alternative methods that have been proposed in the literature for the consistent definition of urban areas across countries which can be feasibly implemented at a global scale using readily available population and travel time data.<sup>9</sup> The first method – the Agglomeration Index (AI) – was originally proposed by Uchida and Nelson (2010) and delineates urban areas on the basis of population densities and travel times to large urban centers.<sup>10</sup> Meanwhile, the second method, which, for brevity, we shall refer to as the “cluster method”, has been developed by Dijkstra and Poelman (2014) and relies on the identification of dense spatially contiguous clusters of population.<sup>11</sup> Having re-estimated the relationships, the paper identifies whether LAC countries continue to exhibit significant differences of the sort portrayed in Figure 1. In doing so, we are able to assess the extent to which Latin America’s apparent “overurbanization” qualifies as one of Satterthwaite’s urban myths.

In addition to re-examining the key basic stylized facts of urbanization, development and structural transformation, this paper also makes two additional contributions to the literature. First, by implementing both the AI and cluster methods to defining urban areas, the paper provides the first systematic comparison of results between these two
**Figure 10a: Prevalence of Adequacies, Resource‐poor households**

<!-- Start of picture text --> Adequacy status - Resource-poor<br>Recent year<br>Bangladesh (HK, 2010) Bangladesh (IFPRI, 2011) Bolivia (2008) Cambodia (2010)<br>3%11% 3%11% 9% 19% 22%<br>42% 34% 9% 36%<br>38%<br>41% 3% 52% 5% 20% 32% 10%<br>Ethiopia (2011) Indonesia (RKD, 2010) Nepal (2011) Peru (2012)<br>6% 3% 25% 1%12% 1% 21%<br>33% 55% 33% 24% 49% 40% 7%<br>6% 1%6% 31% 14% 32%<br>Zimbabwe (2010)<br>None<br>11%<br>Food only<br>29% 53% Care only<br>Environmental health only<br>7% Two of three adequacies<br>All three adequacies<br>Source: Author estimates. See Table 5 for the specific components used in each country for the adequacy measures.<br><!-- End of picture text -->

**Figure 10b: Prevalence of Adequacies, Resource‐rich households**

<!-- Start of picture text --> Adequacy status - Resource-rich<br>Recent year<br>Bangladesh (HK, 2010) Bangladesh (IFPRI, 2011) Bolivia (2008) Cambodia (2010)<br>5% 3% 16% 15% 15%<br>24% 28% 37% 9% 26% 32%<br>6% 25% 12% 2% 41% 39% 11%10% 4% 25% 12%<br>Ethiopia (2011
# 1 Introduction

In the early 1990s, Argentina tried to develop a homegrown auto industry, hiding behind an average tari¤ of more than 13 percent. Over the past two decades, Argentina’s auto exports have stagnated at a dismal 0.2 percent of global auto exports. Around the same period, General Motors (GM), one of the world’s largest automakers, set up GM Poland to import Opel cars for the large Polish domestic market. In 1994, production activities of GM Poland started, and today, Poland is one of the world’s major auto exporting countries. Similar to the auto industry in Poland, Vietnam’s electronics sector expanded sharply in less than a decade. Today, Vietnam is the world’s second largest smartphone exporter, producing 40 percent of Samsung’s global mobile phone products and employing 35 percent of its global sta¤. Ten years ago, Vietnam barely exported electronics products.

What sets Argentina, Poland, and Vietnam apart is their very di¤erent participation in global value chains (GVCs). In fact, the meteoric rises of Poland and Vietnam and the faltering of Argentina are not unique. China’s World Trade Organization (WTO) accession in 2001 ushered a new wave of GVCs which gave rise to “Factory Asia” (Baldwin, 2016), while a large part of the Africa, South Asia and Latin America regions is being left behind with little integration into GVCs (see Figure 1).

As discussed in Antràs (2016), what distinguishes GVC trade from traditional trade are the intense ...rm-to-...rm interactions characterized by contracting and specialized products and investment. What factors determine GVC participation across countries? Do factors that a¤ect traditional trade have di¤erential impacts on GVC trade? This paper studies the determinants of GVC participation based on empirical evidence stamped from a panel data set covering more than 100 countries over the past three decades. The time period re‡ects the growing international
2011, 2012): License, Localization, Herfindahl, Education and Vocational Training.

- 2) Data for the following variables is taken from the Welfare and Monitoring Survey (2011): Electricity, Transport and Water.

|Variable Name|Variable Description|
|---|---|
|Dependent variable|Growth of employment|
|License|Share of employment in licensed enterprises|
|Localization|Log of Localization|
|Herfindahl|Log of Hirschmann Herfindahl index)|
|Education|Average years of school education|
|Vocational Training|% of labor force with TVET training|
|Electricity|% of households using electricity for lighting|
|Transport|Log of households’ average distance to access public transport|
|Water|Log of households’ average distance to access drinking water|
|Year|Year fixed effects|
|City|Cityfixed effects|

# **5 Policy and Conclusions**

The distribution of economic activity and employment over physical space (i.e. economic geography) is central to economic development. Geographical variations in industrialization are an important factor affecting geographical variations in jobs. And so the question of which kinds of industries and cities are creating employment and why is vital to an understanding of economic growth and development. City leaders care increasingly about creating and maintaining employment within particular locations by encouraging industries and firms. They are interested in understanding what sorts of policy levers affect economic outcomes – is it a set of infrastructures? Fiscal incentives? Good business environment? Or could it be agglomeration – the compounding effect of existing industries and firms? The primary objective of this study is to investigate what drives employment and economic growth in Ethiopian cites so as to inform leaders how best to prioritize policy decisions.

Ethiopia has a small, yet significant, window of opportunity to exploit its demographic dividend. Its labor force doubled over the last two decades, and it continues to have a very large youth bulge entering the labor market in the next 20 years. Many of these young people will
, the mean level of poverty at a given urban population share is allowed to vary by region or country....Both poverty measures tend to decline as the urban population share rises, although the effects are generally smaller (but more significant) for the country data.... Among the six regions of the developing world, sub‐Saharan Africa is an exception to our finding that urbanization has been accompanied by falling overall poverty.... All we can reasonably claim from these results is that the data are consistent with the view that the economic
# **References**

<mark>Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public high schools.</mark> _<mark>J</mark> ournal of Labor Economics_ , _2_ _<mark>5</mark>_ <mark>(1), 95-135.</mark> <mark>Adelman, M., Haimovich, F., Ham, A., & Vazquez, E. (2018). Predicting school dropout with administrative data: new evidence from Guatemala and Honduras.</mark> _<mark>E</mark> ducation Economics_ , _2_ _<mark>6</mark>_ <mark>(4), 356372.</mark> <mark>Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>113</mark>_ <mark>(27), 7353-7360.</mark> <mark>Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about.</mark> _<mark>Annual Review of Economics</mark>_ <mark>,</mark> _<mark>11</mark>_ <mark>, 685-725.</mark> <mark>Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education.</mark> _<mark>arXiv preprint arXiv:1606.06364</mark>_ <mark>.</mark> Azam, M. and Kingdon, G.G. (
.554***|0.545***|0.205|0.509***|0.535***|
||(0.108)|(0.182)|(0.183)|(0.177)|(0.181)|
|Perm|0.164*|0.719***|0.203|0.667***|0.706***|
||(0.099)|(0.095)|(0.133)|(0.098)|(0.097)|
|Sing|-0.075***|-0.214***|-0.089**|-0.282***||
||(0.021)|(0.062)|(0.044)|(0.060)||
|Ftim||||0.302***<br>(0.046)||
|Sat|||||0.324***<br>(0.092)|
|Pseudo-_R_<sup>2</sup>|0.243|0.247|0.225|0.249|0.248|
|Number|6,375,646|6,375,804|6,375,826|6,375,804|6,375,804|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

Foster methodology, this paper has offered a benchmark analysis of quality of jobs across Latin American and over time (1995-2012) by taking advantage of harmonized – microdata for 17 countries. In addition to labor income approximated with labor earnings that are high enough to keep a worker above the poverty
Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, replacing the 2011 dummy variable by the 2009 year dummy variable. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **denotes significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.