rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Air pollution and climate change represent serious threats to human health. In 2016, air pollution was responsible for about 7 million deaths from various life-shortening diseases, including heart disease, lung cancer, and stroke, according to the World Health Organization (WHO).<sup>2</sup> The public health and economics literatures have established that air pollution increases mortality, especially among the most vulnerable groups, including infants and older adults, and that it leads to large morbidity costs.<sup>3</sup> In addition to local air pollution, climate change resulting from emissions of greenhouse gases is expected to cause far-reaching and sweeping economic and societal changes that are likely to affect agriculture, biodiversity, economic growth, geopolitics, human health, and world peace.<sup>4</sup> Many developing countries, especially rapidly growing countries, are experiencing pressing environmental challenges as a result of the dramatic increase in fossil fuel consumption to meet the need for consumption and production, limited access to clean technologies, and the lack of stringent and well-enforced environmental regulations. The populations in these countries are particularly vulnerable to adverse environmental conditions because of the lack of effective government interventions, and the costs of and limits on options available to individuals to prevent or mitigate the effects of pollution.

Road vehicles are important sources of fine particulate matter (PM2.5), nitrogen oxides, carbon dioxide (CO2) and other pollution. Panel (a) of Figure 1 depicts the level of PM2.5 across the globe. It shows that concentrations tend to be higher in low- and middle-income countries. The United States and Japan were historically the world’s major sources of CO2 emissions. Panel (b) shows that over the past two decades, emissions from developing countries such as China and India surged to catch up. In 2006, China surpassed the United States to become the world’s largest emitter of CO2.

> 2 WHO Global Ambient Air Quality Database: https://www.who.int/airpollution/data/cities/en/. 3 Studies on the mortality impact of air pollution include Chay & Greenstone (2003), Currie & Neidell (2005), Currie & Walker (2011), Knittel et al. (2016), and Deryugina et al. (2019). Studies on morbidity costs include Moretti & Neidell (2011), Deschênes et al. (2017), Barwick et al. (2019), and Williams & Phaneuf (2019).

4 Recent papers on the impacts of climate change include Nordhaus (2006) and Dell et al. (2012) on economic growth; Mendelsohn et al.(1994), Schlenker et al. (2005), Deschênes & Greenstone (2007), and Burke & Emerick (2016) on agriculture; Deschênes & Moretti (2009), Deschênes & Greenstone (2011), and Barreca et al. (2016) on mortality; and Miguel et al. (2004), Hsiang et al. (2011), and Jia (2014) on social conflict.

2
tasks such as preparing a budget, opening a bank account, paying a loan, or buying insurance. Such counseling may be more effective in changing behavior as it provides guidance specific to the needs of the participant.

Medical and public health studies have found individualized or segmented counseling to be effective in promoting better health behaviors. For example, individualized risk counseling for women with a family history of breast cancer has been shown to improve understanding of their personal risk (Lerman et al. 1995). Similarly, Proper et al. (2003) find positive and significant effects of individual counseling on physical fitness. In the financial context, Dalal and Morduch (2010) find that having an insurance representative present after trainings significantly improves take-up rates. Similarly, Bertrand et al. (2006) find that allowing banking workshop participants the opportunity to complete account opening paperwork as part of the learning workshop and having a bank representative present on-site significantly improves take-up and adoption of complementary banking products such as ATM cards, direct deposit, and electronic fund transfers. Finally, psychologists have long advocated the benefits of human interaction in individualized counseling over inanimate information sources such as pamphlets, text messages, or computer messages (King, 2007).

The counseling treatment in our study was randomly assigned among financial education participants. Half were randomly selected to receive an offer of financial counseling, independent of their goal setting treatment status. Specifically, within one month of the classroom sessions, financial counselors visited the counseling treatment group in their homes to provide individualized financial counseling services. The financial counselors assisted participants on several issues – including, but not limited to, preparing a budget, opening a bank account, paying off or re-financing loans, and purchasing an insurance policy – depending on their individual needs. Financial counselors were trained rigorously by our partner research organization in India, the Center for Microfinance, prior to visiting respondents.<sup>4</sup>

> 4 See Appendix 2 for more details on the services provided by the financial counselor.

11
15 bargained model of household behavior also predicts differential consumption effects of nonearned income depending on who controls it. The bargaining framework suggests why **women may** engage in separate jobs from their husbands to enhance their control over resources. **Indeed,** this pattern **is** particularly notable in subSaharan Africa and South East Asia (Sct.ultz, 1988a), although women **are** still mostly wrrking **as** unpaid family workers.

In parts of Africa husband and wife often cooperate in the joint production of scue crops, while other crops or parts of the production process--e.g. marketing--ar **e** ntirely the responsibility of one meber. The neoclassical model of the family leads to the expectation that the wife allocates her time between the joint crops and her own crops to equalize the value of her marginal product across all activities. The bargaining model, however, predicts **that** s e would work more on her own fields, because **the** value of her marginal product there is more under her control. Jones (1986) confirmed these predictions of the bargainLg -model with survey data collected from North Cameroon. Allocatl.ve incentives within these Cameroon families, therefore, may not achieve an overall efficient use of labor but advance other individual interests of family members,7 Another implication of the deomnd model is that the income-compensated cross-substitution effects should be symetric or equal. This restriction of the unified family demand model implies in allocating labor supplies that the husband and wife agree on the value of each other's nonmarket time. It is possible to imagine, **as** an alternative hypothesis, that a husband would assign a higher value to his nonmarket time than does his wife to his time. In the case of their valuations of the wife's nonmarket tiwe, the wife might also value her own tim more highly than does her spouse. An individualistic
**Figure 10:** Trade-off between spending on infrastructure and health and education: secondary gross completion rate in 2025 versus average growth over 2010-2025

<!-- Start of picture text --> 7.5<br>INF<br>7.0<br>6.5 HE+INF<br>6.0<br>HE<br>5.5<br>5.0<br>25 30 35 40 45<br>Secondary gross completion rate in 2025 (%)<br> Source: Authors’ calculations<br>Average growth over 2010-2025 (%)<br><!-- End of picture text --> Regarding the effect on employment and wages, additional investments in infrastructure lead to the highest increases in real wages. The effect on employment levels, on the other hand, is mixed. The scenario GOVEFF-INF has a similar effect on employment levels as the baseline scenario, whereas increased spending in health and education implies more demand and employment growth for the highly educated (mainly teachers and doctors) and less employment growth for those with secondary education and less, compared to the baseline scenario <!-- Start of picture text --> Figure 11: Development of real wages by educational level Figure 12: Development of employment by educational level<br>(average annual growth rate 2010-2025) (average annual growth rate 2010-2025)<br>6.0 5.0<br>5.0<br>4.0<br>4.0<br>3.0<br>BASE BASE<br>3.0<br>HE 2.0 HE<br>2.0<br>HE+INF 1.0 HE+INF<br>1.0<br>INF INF<br>0.0 0.0<br>Labor Labor Labor Labor Labor Labor<br>(< secondary (secondary (tertiary (< secondary (secondary (tertiary<br>education) education) education) education) education) education)<br>Source: Authors’ calculations Source: Authors’ calculations<br><!-- End of picture text --> The findings highlight that increased government efficiency can lead to significant improvements in outcomes, but the type and extent of improvement depend on which areas of government spending and services are scaled up as a response to increased government efficiency. The results also point to tradeoffs between achieving higher growth and less poverty versus improving health and, especially, education outcomes.

13
# **Table 9The Evolution of Evasion Gaps**

||**The**<br>|**Evolution of Evasi**<br>|**on Gaps**<br>||||
|---|---|---|---|---|---|---|
||_Log_|_Log_|_Log_|_Log_|_Log_|_Log_|
|_Dependent Variable:_|<br>_Value Gap_|<br>_Quantity Gap_|<br>_Price Gap_|<br>_Value Gap_|<br>_Quantity Gap_|<br>_Price Gap_|
||V|Q|P|V|Q|P|
||(1)<br>**coef/se**|(2)<br>**coef/se**|(3)<br>**coef/se**|(4)<br>**coef/se**|(5)<br>**coef/se**|(6)<br>**coef/se**|
|Post*Log (Tariff+1)|0.004|0.010|-0.013|-0.057**|-0.053*|-0.015|
||(0.022)|(0.024)|(0.012)|(0.026)|(0.03)|(0.016)|
|Post*Ben Ali %|-0.051|0.120|-0.221*|-0.282|-1.123*|0.765*|
||(0.179)|(0.194)|(0.123)|(0.469)|(0.619)|(0.420)|
|Post*Offshore %|0.045|-0.007|0.048*|-0.474**|-0.518**|-0.041|
||(0.062)|(0.068)|(0.029)|(0.230)|(0.248)|(0.119)|
|Post*Public %|0.196|-0.116|0.366***|-0.805|-1.440|0.589*|
||(0.130)|(0.161)|(0.081)|(0.600)|(0.877)|(0.301)|
|Post*Ben Ali %*Log (Tariff+1)||||0.076|0.382**|-0.283**|
|||||(0.141)|(0.179)|(0.120)|
|Post*Offshore % *Log (Tariff+1)||||0.150**|0.152**|0.025|
|||||(0.063)|(0.068)|(0.032)|
|Post*Public*Log (Tariff+1)||||0.307*|0.397|-0.069|
|||||(0.176)|(0.255)|(0.087)|
|Post* Log (NTB + 1)|-0.028|-0.020|0.006|-0.022|-0.020|0.006|
||(0.021)|(0.024)|(0.012)|(0.022)|(0.025)|(0.012)|
|||_Ben Ali%, Offshor_|_e%,Public%, l_|_og (NTB+1), l_|_og(Tariff+1)_||
|_Simple Controls_|_(all held cons_<br>Yes|_tant at their last o_<br>Yes|_bserved pre-R_<br>Yes|_evolution level_<br>Yes|_s in the post 20_<br>Yes|_10 period)_<br>Yes|
|_Interacted Controls_|_Ben Ali%_<br>_(all held cons_|_*Log (Tariff+1), O_<br>_tant at their last o_|_ffshore%*Log_<br>_bserved pre-R_|_(Tariff+1),Pu_<br>_evolution level_<br>|_blic%*Log (Ta_<br>_s in the post 20_<br>|_riff+1)_<br>_10 period)_<br>|
|||||Yes|Yes|Yes|
|Country*year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Country*product|Yes|Yes|Yes|Yes|Yes|Yes|
|N|55933|54823|54823|55933|54823|54823|
|Number of products|1217|1217|1217|1217|1217|1217|
|R2|0.524|0.237|0.358|0.525|0.500|0.358|
|Note: Standard errors are clustered<br>AdjustedR2|byproduct. ***<br>0.470|p=<0.01, ** p<0.0<br>0.217|5, * p=0.10.<br>0.283|Sample is con<br>0.470|fined to product<br>0.441|-<br>0.283|

Note: Standard errors are clustered by product. *** p=<0.01, ** p<0.05, * p=0.10. Sample is confined to productsource-year combinations (i) in which imports reported in Tunisia and exports reported in partner countries are strictly positive and (ii) which were imported both before and after the revolution, (iii) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (iv) products which account for at least 0.01 percentage points of import value (either as declared by partners or as declared by Tunisian customs) over this period.

46
- 4 -

Despite the long-neglected maintenance, some countries have been increasing efforts toward reviving railway assets in recent years. Ethiopian Railways Corporation embarked upon a phased program for the construction of 5,000 km of rail lines to connect Addis Ababa to the country’s main gateway port, Djibouti, as well as other major cities. In Kenya and Uganda, new standard gauge rail construction was started to connect Mombasa to Nairobi in 2014. Tanzania is rehabilitating the Central Corridor connecting inland cities, such as Kigoma and Mwanza, and neighboring countries, such as Burundi and Rwanda, to the Port of Dar es Salaam (“Big Results Now” program). These long-distance rail services could dramatically reduce transport and trade costs in inland areas and landlocked countries in the region. In Africa, there are 16 landlocked countries.

The current paper aims at estimating the impact of rail transportation on firm productivity with micro data collected in Tanzania. The literature suggests that with better transport infrastructure, regardless of mode, firms can improve their productivity. Firm inventory can be decreased by highway investment in the United States (Shirley and Winston, 2004). International trade reduces with transport costs (Limão and Venables, 2001). Because of those effects, firms prefer to be located along major highways (e.g., Holl, 2004). Several empirical challenges exist in estimating the impacts of large-scale infrastructure investment, including endogeneity, self-selection and contamination: While inherently productive firms may choose to be located where particular transport infrastructure exists, policy makers may invest more (or less) depending on where those firms are located. Therefore, the possible impacts of transport infrastructure may or may not be attributed to the investment itself. To avoid this problem, the current paper uses the instrumental variable technique and the threestage least squares (3SLS) estimator.

The remaining sections are organized as follows: Section II provides an overview of recent developments in transport infrastructure in Tanzania. Section III develops our empirical model and presents data. Section IV presents main estimation results, and Section V discusses policy implications. Then Section VI concludes.
# The Long-Awaited Rise of the Middle Class in Latin America Is Finally Happening

Maurizio Bussolo, Maryla Maliszewska and Elie Murard<sup>1</sup> World Bank Keywords: Inequality, Middle Class, Skill Premium, Latin America JEL codes: D31, D58, I24, J11 1 This paper is a product of the Global Modeling and Analytics Unit, Global Development Prospects Department/Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at mbussolo@worldbank.org. This paper was a background paper to the World Bank publication "Economic Mobility and the Rise of the Latin American Middle Class", and the authors would like to acknowledge comments received by Jamele Rigolini, Francisco Ferreira, and others (J. Messina, L.-F. López-Calva, M. A. Lugo and R. Vakis) from the World Bank team who has worked on the publication.
cohorts. Equation (3) implies: The share of individuals that attain a level of schooling equal or higher than _s_ ̄ therefore increases with the net benefits of schooling _b_ . One key implication at the aggregate level is that, as the net benefits of schooling rise, educational attainment increases and the mean scholastic performance of individuals that attain a given level of schooling decreases. In Appendix A1.3, we show hat this result holds empirically, using US data.

We now introduce the possibility that _Gz_ ( _._ ) and ̄ _z_ differ between genders. Let _zm_ and _zf_ be random variables that denote the scholastic performance of males and females, respectively. The scholastic performance threshold _z_ ̄ is also allowed to vary by gender and denoted _z_ ̄ _m_ and _z_ ̄ _f_ for males and females, respectively. In our framework, the fraction of the population in the cohort that attains a given level of schooling ̄ _s_ , assuming the total population of males and females is the same, is given by: and the female-to-male ratio among individuals that choose a given level of schooling, denoted _R_ ( ̄ _z_ ), can be expressed as: The implications of our model at the aggregate level are fully summarized by Equation 4 and 5. In the remainder of the paper, _Gzf_ and _Gzm_ are assumed to be characterized by their first two moments, where _μ_ and _σ_<sup>2</sup> denote the mean and variance of the distributions, respectively.

# **4 Two Main Hypotheses for the Gender Gap Reversal**

Within this theoretical framework, we formulate two main hypotheses for the gender gap reversal: the tail dynamics hypothesis (or tail hypothesis), and the mean dynamics hypothesis (or mean hypothesis). The mean dynamics hypothesis itself can be formalized in two alternative ways: 12
23 increase in the minimum wage rate, and allowing for the fact that 1988/89 was a good ab,ricultural year. Our results suggest a structural break in the process of employment determination, consistent with the introduction of rationing. Assuming that our model adequately captures the notional demand function for EGS work, we estimate that the EGR met only 43% of that demand in the 12 months after the wage increase. 86% of the difference between mean employment per month in 1988/89 and that for the preceding 13 years was due to rationing; otherwise we would have expected to see an attendance figure some 3.3 million persons higher in 1988/89. We do not know whether there was any systematic pattern in the rationing (whether, for example, it tended to be the poorest participants). An investigation of the spatial pattern of rationing across districts may throw light on this question.

ii) A further test is possil..e for a three year period up to early 1990 for which we have better employ-f,ent data and a monthly series on the number of EGS works in progress. For this recent period, we find little evidence to suggest that the observed series of EGS employment can be interpreted as the workers' notional demand for local work on the scheme. The number of works in progress has a sizable and significant effect on EGS employment, which is not what one would expect if the scheme was simply accommodating the notional demand for local EGS employment. Also, unlike the period prior to the increase in minimum wage rates, we find a seemingly perverse response of emplovment to EGS wage rates. And agricultural wage rates do not appear to have influenced employment on EGS in recent years.

iii) Finally, we investigated the effect on agricultural labor markets. If EGS really was absorbing the excess supply of labor to alternative work then one would expect to see a substantial impact on wages for that work
“unintended consequences” on domestic demand of a potentially large redistribution of income at the expense of wage earners.

To be sure, competitiveness does matter for economic performance. The impulse response functions of our model suggest that changes in external competitiveness, as measured by changes in the real exchange rate or unit labor costs, are positively associated with changes in real GDP growth: A real exchange rate appreciation (depreciation) slows (boosts) GDP growth over an initial period of 2-3 years — and similarly for an increase (decrease) in unit labor costs. Consequently, a loss of competitiveness could well lead to slower growth and loss of jobs, although the impact of this development on the external balance could be, according to our results, muted.

Ensuring that labor cost growth remains in line with productivity growth would be an enabler of sustained growth over time by preventing a loss of competitiveness when wage costs come under pressure in the face of booming domestic demand. Structural reforms that improve productivity and productivity-enhancing and cost-saving investments seem, however, to hold the key to sustained improvements in competitiveness and stronger growth over the medium term (see World Bank, 2012). This broader structural reform agenda, as well as the complementary, productivity-enhancing public investments in physical and human capital, should not be lost from sight by the excessive focus on labor cost competitiveness as a remedy to external imbalances in the drive to prevent future crises.

25
_Social Capital, Household Welfare and Poverty in Indonesia_ 16 (2) **Heterogeneity index.** The LLI questionnaire identifies the three most important associations for each household. For those associations, a number of supplementary questions were asked including about the internal homogeneity of the group. This was rated according to eight criteria: neighborhood, kin group, occupation, economic status, religion, gender, age, and level of education. On that basis, we constructed a score ranging from 0 to 8 for each of the three associations (a value of one on each criterion indicated that members of the association were "mostly from different" kin groups, economic status, etc.). The score of the three associations was averaged for each household and the resulting index was re-scaled from 0 to 100 (whereby 100 corresponds to the highest possible value of the index).<sup>9</sup> The index of heterogeneity shows distinct regional and socio-economic patterns (Table 2). Associations in Jambi are much more homogeneous than in the other two provinces. Associations to which Protestant households belong are the most heterogeneous. The index follows a U-shaped pattern in relation to education and income quintile: Heterogeneity rises with education and with income except at the very bottom of the distribution.

- 9 We also considered alternative weighting schemes: (i) weights based on a principal component analysis of the heterogeneity criteria; and (ii) giving larger weights to the economic criteria (occupation, economic status, education) on the assumption that an association of people with e.g. different occupations presents greater opportunities for information sharing than e.g. a group with different ages. The empirical results on the importance of the heterogeneity index were not altered substantively by changing the weights. We are grateful to Jonathan Isham and Michael Woolcock for having suggested these alternatives and for helpful discussions on the issue of heterogeneity.