rafmacalaba/data-use-mentions-extended huggingface dataset:
Dasgupta _et al_ . (2010) study the benefits from integrated pest management (IPM) for Bangladesh farmers. IPM uses natural parasites and predators, rather than chemical pesticides to control agricultural pests. As chemicals are expensive, IPM can potentially increase profits as well as benefit the environment. Dasgupta _et al_ . present survey data showing that IPM rice farming is as productive as traditional farming and has lower costs. However, because a neighbor’s use of traditional chemical pesticides can kill natural parasites and predators needed for IPM, individual adoption is not rational. Thus, institutional support for collective adoption is needed.

Several recent case studies note the importance of maintenance and access to finance for successful technology adoption in developing countries. Barry _et al_ . (2011) study the adoption of efficient stoves, small biogas plants, and efficient tobacco barns for commercial farmers in Rwanda, Tanzania and Malawi. They conclude that maintenance must be planned for (including funding) at the outset of the project, and must be kept simple, so that it does not require much additional training. If not, users will abandon a technology as soon as something goes wrong. Because information is spread by word of mouth, having a local champion for a technology is also important. Because of high start-up costs, financing was cited as the main stumbling block for all projects. Thus, providing aid for financing is also important. Reviewing the success of China’s Renewable Energy Development Project, D’Agostino _et al_ . (2011) also cite access to financial credit and quality of after-sales service as important barriers to the adoption of solar home systems in China. Finally, Romijn and Caniëls (2011) find that inadequate on-site technical support holds back adoption of small-scale biomass gasification in India.

Exploring the role of financing further, Brunnschweiler (2010) explores the importance of financial sector development in the adoption of renewable energy. Investment in renewable 31
The latter have no responsibility for the veritable industry that has sprung to life as diskettes spread through the world data that had been put together by patient marriages of snippets of data with uncertain guesses. Cross-country regression results must be viewed critically. As Syrquin notes (1 :7:ZZO), they are reduced-form estimates of far more complicated models and do not provide reliable evidence on causality. Two approaches have been used, which are both dubious. Quick and dirty single regressions have been widely used, but their results are subject to overwhelming "missing variables" biases; complex specifications would be more trustworthy but they are sensitive to the choice of proxies and dummies that take account of hard-to-measure effects. Such regressions may point to interesting mechanisms but to produce reliable conclusions, they should be complemented by deeper study at the country level and validated by a credible theory.

It is crucial to be aware of the extremely poor quality of much of this data. Angus Deaton's chapter 33, which draws on a survey edited by Srinivasan20, discusses evidence on this score. Summers and Hestorn themselves assign a score of D+ to D to data for 66 out of 138 countries (Srinivasan 3:2493). Given the tendency of newspapermen to view development as a GNP race, it is good that the _Handbook_ calls attention the large influence on growth rates on the quite arbitrary estimates of official statisticians on the size and growth rates of the informal sector, and about the lack of reliability of data for the African countries, which account for so 21 many of the data points in cross-country regressions. John Strauss and Duncan Thomas make a similar point in chapter 34.

# 7.3) Survey-Based Studies Data and the New Micro Econometrics

The doubts that are warranted with respect to the macroeconomic data lend importance to the information provided by sample surveys. Here development economists owe a debt to the initiatives of the great Indian statistical school, to which we also owe important theoretical insights. Both Deaton and Strauss and Thomas discuss the figures collected
vs international migrants; liquidity constraints; and migration as adaptation.<sup>4</sup>

# _A. Fast-onset events versus slow-onset changes_

The migration impacts of fast-onset extreme weather events (such as hurricanes, heavy rains, floods, and landslides) related to climate change are usually sudden and direct, resulting mainly in temporary movements over short distances (McLeman and Gemenne, 2018). A specific strand of the literature investigated the role of floods on migration, with mixed results: while Gray and Mueller (2012a) and Bohra-Mishra, Oppenheimer, and Hsiang (2014) document a lack of significant effects of floods on migration in, respectively, Ethiopia and the Philippines, Mueller et al. (2014) find that floods reduce the probability of migrating in Pakistan. Koubi et al. (2016) show that in Vietnam fast-onset shocks are more likely to trigger migration, whereas long-term environmental changes (such as salinization) _reduce_ the likelihood of migration. Robalino, Jimenez, and Chacón (2015) provide evidence that flooding and other hydro-environmental emergencies increase migratory flows in Costa Rica, but also find that more severe emergencies decrease migration. Kaczan and Orgill-Meyer (2020) argue that this may happen as floods and other fast-onset shocks rapidly deplete household assets and resources, leaving households unable to migrate. Using a global data set which combines climatic, census, and night-light data, Castells-Quintana, McDermot and Krause (2021) study the relationship between changes in weather patterns and the spatial distribution of population and economic activity, and find that worse climatic conditions are associated with higher urbanization. Their results suggest that while slow-onset changes in climate may lead to more permanent, and more long-distance, movements, sudden-onset events are primarily associated with temporary displacements and short-term migration to nearby urban areas. Koubi et al. (2022) use survey data from Cambodia, Nicaragua, Peru, Uganda, and Vietnam and document that less educated and lowerincome people are less likely to migrate after exposure to fast-onset shocks, compared to people endowed with higher education and
systems, rapidly developing industrial sectors, and cut-and-burn practices in agriculture are all contributing to heightened concentration levels. In addition, high proportions of physical and outdoor labor mean that more people are faced with heightened exposure. Constraints in terms of the accessibility, availability and quality of health care provision further increase air pollution related mortality in developing countries (Lelieveld, et al. 2020). Evidence on air pollution in India suggests that a one GW increase in coal-fired capacity corresponds to a 14 percent increase in infant mortality rates in districts near versus far from the plant site (Barrows et al 2019). This effect is 2-3 times larger than estimates from the developed world.

The burden of air pollution not only falls disproportionately on poorer countries; within countries poorer and more marginalized communities also tend to be exposed to higher pollution levels (Jbaily et al. 2022; Bell et al 2013). This inequality in exposure is well documented in the United States, where data on socioeconomic characteristics is available with high spatial disaggregation (Fann et al, 2018; Bell et al, 2013; Colmer et al, 2020; Kioumourtzoglou et al 2016; Bell & Ebisu, 2012; Mikati et al. 2018).

Patel et al. (2021) show that in the US, industrial planning policies have disproportionately placed polluting industries in areas with large Black, Hispanic and Asian populations. As a result, these minority groups face systematically higher pollution levels. Tessum et al. (2021) show that the disproportionate exposure to high PM2.5 exposure by racial-ethnic minorities in the US is not limited to industrial emitters. The study documents that the disproportionate pollution exposure is consistent across all major emission categories, across states, urban and rural areas, income levels, and exposure levels. Studying the period from 2004 to 2016, Jbaily et al (2022) show that in addition to ethnic minorities, low-income populations also face systematically higher PM2.5 pollution levels – and these exposure disparities have been increasing over time. Air quality is also documented
domestic food prices. The average pass-through effect for the region as a whole is 0.25, only slightly lower than the „average‟ estimate of the effect in the literature (0.3). The slightly lower average effect is consistent with the high prevalence of policies intended to cushion consumers and limit movements in domestic food prices in nearly all MENA countries.

**Table 3. Food Price Pass-Through Coefficients**

|Countr and rou|6-month foodpricepass-through|12-month foodpricepass-through|
|---|---|---|
|y gp|Worldprice increase Worldprice decreas|e<br>Worldprice increase Worldprice decrease|
|Bahrain|0.050<br>0.057<br>(0.057)<br>(0.036)|**0.349**<br>0.051<br>(0.113)<br>(0.034)|
|Kuwait|0.107<br>0.016|**0.279**<br>0.020|
|s|(0.081)<br>(0.029)|(0.128)<br>(0.029)|
|Oman<br>trie|**0.341**<br>0.079|0.213<br>0.075|
|oun|(0.142)<br>(0.063)|(0.130)<br>(0.074)|
|Qatar<br>C c|**0.286**<br>**0.182**|**0.355**<br>**0.220**|
|GC|(0.125)<br>(0.085)|(0.161)<br>(0.099)|
|Saudi Arabia|0.144
I

# Cents and Sociability: Household Income and Social Capital in Rural Tanzania'

Beyond apparently now old fashioned 'physical" capital, human capital, natural capital, institutional capital and social capital all clamor for attention. With capitalism all the rage, perhaps the proliferating rechristenings as 'capital" of otherwise perfectly serviceable concepts is understandable. But fashion aside, the popularity of 'capital'isms is due in part to the robust usefulness of the underlying metaphor: stuff that augments incomes but is not totally consumed in use. The attraction of investigating the incomes of households by examining their ownership of the various "capitals" is obvious. However, while obvious, an exclusive focus on households is seriously incomplete. Factors both at the national level of policies and institutions (Olson, 1996) and at the community level affect the fortunes of households and are potentially as important as the household's own capitals.

In this paper we show that associational relationships and social norms of villages in rural Tanzania are both _capital_ and _social._ After outlining the various concepts of social capital in the introduction we tell how (and why) we created data on social capital using a large The Social Capital and Poverty Survey was conducted as part of a Participatory Poverty Assessment led by Deepa Narayan as a joint activity of the government of Tanzania, the University of Dar Es Salaam, and the World Bank, funded by the British Overseas Development Agency. The Human Resource Development Survey data used in this paper come from a nationally representative survey of 5,000 households in Tanzania which was a joint effort undertaken by the Department of Economics of the University of Dar es Salaam, the Government of Tanzania, and the World Bank, and was funded by the World Bank, the government of Japan, and the British Overseas Development Agency. We would like to thank Jonathan Isham, and Sushenjit Bandopadhyay for collaboration in the early stages of the research, Christiaan Grootaert, Dean Joliffe, Michael Kremer, Peter Lanjouw, and Jonathan Morduch for useful comments, and
mark><sup>4</sup> <mark>Specifically, Recommendation 5 suggests that poverty estimates should be based on a total error approach, evaluating the possible sources, and magnitude, of error, particularly non-sampling error and the error introduced by the process of determining the international poverty line. There are many factors that can affect the total error of international poverty rate estimates, such as incomplete coverage of the country’s population, errors in measuring consumption data, errors in calculating the poverty line, using the CPI to deflate prices that may not be consistent with the consumption pattern of the poor, and geographic differences in prices. Because total error may be significant, the report recommended that the Bank provide a margin of error to help policy makers understand how accurate the reported extreme poverty numbers are. Although it would be difficult to implement, the World Bank’s response concurred that reporting total error with the poverty estimates is one of the most important recommendations to implement.</mark> South Asia is a useful laboratory to study how methodological differences in poverty measurement can contribute to total error. This is mainly because South Asia is home to about one-third of the international extreme poor, but consists of only eight countries, making the analysis both significant from a global perspective and tractable. In South Asia, all eight countries compare consumption per capita against the poverty line to identify the international extreme poverty status of an individual.<sup>5</sup> This paper provides new evidence from South Asia on how differences in the construction of the welfare measure in each country contributes to total error in international poverty measurement. To study this in detail, we look at how countries in the region construct the consumption aggregate to assess poverty. A key source of ‘total error’ is the collection and aggregation of household-level

> 4 World Bank (2016) and World Bank (2017).

> 5 This contrasts with what most countries do in Latin America and the Caribbean and Eastern and Central Asia that
To consider this possibility, we performed similar panel regressions, but with goods grouped at their highest level of disaggregation: 4- and 6-digits. Even at the highest level of disaggregation, which resulted in 1232 commodity groups and 65315 observations in the fixedeffects negative binomial panel regression, the coefficient estimates for lnGDP per capita and lnGDP per capita squared were 13.22 and –0.77, which are well within the confidence intervals from the pooled regression. We also performed the same tests with the SITC data, performing a fixed effects negative binomial regression with industry panels at the highest level of disaggregation (3-digits, 64 products per country). Both the 1990s cross-section and the pooled 1970s, 1980s and 1990s data had tipping points extremely similar to those of the pooled estimations reported in Table 5 ($4656 compared to $4765, and $3478 compared to $4278, respectively). These findings are quite robust across data types and time periods.

Therefore, unless one can assert that even at these highly disaggregated levels, the different commodity classifications do not represent goods requiring significantly different factors of production, the conclusion holds: the factor-endowments analysis of production across income levels is not closely related with discovery, and developing countries are not limited to discoveries in certain sectors based on their level of development. We now turn to the third theoretical perspective on the discovery process-- the role of market failures.

# **6. Discovery and the Role of Market Failures**

As discussed in section 2, we have proposed a framework for evaluating the importance of market failures in the discovery process. We will add to the basic model variables that are 29
# **9.3 Direct transfers**

**The PECS only provides aggregate data of households’ income from social protection (e.g., government assistance).** This includes one variable for “total cash” and another variable for “total in kind”, the latter labeled as near-cash transfers in our model. Hence, it was not possible to disaggregate transfers households received for each of the four main specific social protection (cash transfer) programs in the country. Currently, the model infers that the cash-transfers reported in the PECS are from the CTP program based on cross-validation with other administrative data. However, if the team had access to the PMT formula of the CTP and threshold or the allocation methods for other programs, it would have been possible to do a more detailed simulation of social protection programs in the West Bank and Gaza.

## **9.3.1 Education**

**Data limitations in the PECS 2016/17 required that we made assumptions about beneficiaries of public education benefits.** The PECS 2016/17 survey does not have information on “current grade of education”; instead, this study inferred beneficiaries combining self-reported information on “highest grade of education achieved (plus one year)” and “currently enrolled in public education”. The analysis also imputes an average education benefit calculated at the national level; implicitly, this assumes that students in the West Bank and Gaza receive the same benefits across regions at each education level. Information on public expenditure by education level, disaggregated by region, would improve the calculation of education benefits. Lastly, due to PECS data limitations, it was not possible to analyze distributional effects of public expenditure from other levels of education (pre-primary and tertiary).

## **9.3.2 Health**

**The PECS does not have information on access to public health services.** By using the health-insurance approach (focusing on individuals reporting coverage of government health insurance in the
it is affecting a portion of the population that might well be considering shifts between the formal and informal sectors.

# **3. Literature Review**

There have been several studies that examine the impact on informality of large social programs that may affect the incentive workers have to exit formal jobs. This section first explores the

> 4 Sosa-Rubi et al. (2007) analyzed the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT), focusing on the responses of 3,890 women who delivered babies during 2001-2006. They find that the _SP_ program had a robust, significantly positive impact on access to obstetrical services.

6
services.<sup>6</sup> Poverty and inequality measures then are derived under pre- and postfiscal income measures. Thus, by comparing these welfare indicators for the distribution of each

> 5 Postfiscal income concepts include net market Income, disposable income, consumable income, and final income. See Figure 7 and accompanying text.

> 6 The primary microdata source used for this study is the 2015 Household Income, Expenditure, and Consumption Survey. Administrative and budgetary data are used to determine the magnitude of the fiscal policies allocated.

3
nearly overlapped with the subsequent crisis and thus complicates efforts to link layoffs directly to the financial crisis. Nonetheless, evidence from quarterly data suggests that shocks associated with the crisis, which hit in the third quarter of 2008, may have had a stronger effect on gross domestic product (GDP) and employment than earlier policy shocks.

The events of September 2008 raised concerns that China would face a sharp drop in GDP. The global financial crisis changed the growth trajectories of all major world economies and plunged Japan, the United Kingdom, and the United States into deep recession (Figure 1b). By the first quarter of 2009, annual growth rates were negative in major developed economies; and while the growth rates of China and India were still positive, they dropped steeply in both countries. In comparison to other major economies of the world, China experienced one of the largest changes in annual growth rates from 2007 to 2008. From the first and second quarter of 2008 to the fourth quarter of 2008 and the first quarter of 2009, China’s annualized quarterly growth rate fell from more than 10 percent to 6 percent (Figure 1b).

Responsibility for the slowdown in China’s GDP lies squarely with the global financial crisis, given that GDP growth declined well _after_ contractionary domestic monetary and employment policies were implemented and that the timing was consistent with a sharp drop in export demand: the negative export demand shock contributed more to the contraction in aggregate demand that slowed China’s economy. Customs data on China provides useful evidence on the scale of the shock to its exports. The aggregate decline in trade volume shown in Figure 2 is driven by decreased exports to the European Union and the United States of 22.1 and 17.1 percent, respectively, over the same periods in the previous year. Given these sharp declines, it was apparent that China’s economy would be hit hard by the decline in exports if no alternative sources of demand for goods and services emerged.

As shown in Figure 3,
prices – as shown by the last two rows of Table 3 – the cost of a consumption basket for these households rises.<sup>24</sup> Consequently, as illustrated in Figure 5 **,** households in the left tail of the distribution (percentile 17 and under) suffer a real income loss of 1.3 percent relative to the BaU scenario.<sup>25</sup>

> 24 This result should be taken with caution since we are assuming that consumption baskets are fixed although prices are changing. Moreover, the household-specific price index that we are using only allows for differences in the shares of food to non-food expenditures between households. One would expect that the basket of food consumed by the poor differ substantially from the basket of food consumed by the nonpoor.

25 Real incomes of the families under extreme poverty would increase 1 percent under the assumption that the real value of the exogenous components of income remain constant. In other words, if the government 19
<!-- Start of picture text --> 15.0%<br>10.0%<br>5.0%<br>0.0%<br>-5.0%<br>-10.0%<br>Current Account Deficit GDP growth<br>Source: Authors’ calculations using data from the IMF World Economic Outlook and Haver Analytics<br>Figure 3: Current account balance, Turkey and comparator groups<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>Turkey Non-NR UMICs NR UMICs UMICs<br>Source: Authors’ calculations using data from Haver Analytics<br>Notes: NR: Natural resource; “Non-NR” defined as a country less than the average natural resource rents as a<br>percent of GDP within that group in that year.<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**Turkey’s CAD has been roughly comparable to the average of all upper middle-income countries (UMICs), and in most years, smaller than the average for non-natural resource dependent UMICs.** At an average of 4.5 percent of GDP per year, Turkey’s CAD since 2002 has similar to the average of (today’s) UMICs of 4.2 percent and less than the average for non-natural resource dependent UMICs (-6.9 percent) of which Turkey is one (Figure 3).

# **_Driven by import expansion, particularly of intermediate goods_**

**The CAD expansion was primarily driven by a