rafmacalaba/datause-encoder-data huggingface dataset:
Figure 13 - FGT(1) measures at Municipality level, ELL simulations and True Census estimates PNAD pseudo-sample POF pseudo-sample <!-- Start of picture text --> 50% 50%<br>R 2 = 0.86<br>R 2 = 0.89<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>0% 10% 20% 30% 40% 50% 0% 10% 20% 30% 40% 50%<br>Census Census<br>'PNAD' 'POF'<br><!-- End of picture text --> Source: Authors’ Calculation.

Figure 14 - FGT(2) measures at Municipality level, ELL simulations and True Census estimates PNAD pseudo-sample POF pseudo-sample <!-- Start of picture text --> 50% 50%<br>40% 40%<br>R 2 = 0.76<br>30% R 2 = 0.76 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>0% 10% 20% 30% 40% 50% 0% 10% 20% 30% 40% 50%<br>Census Census<br>Source: Authors’ Calculation.<br>'PNAD' 'POF'<br><!-- End of picture text --> Given that standard errors accompany poverty estimates in the ELL methodology, a confidence interval can be drawn around each municipality level estimate in each pseudo-survey simulation. Tarozzi and Deaton (2007) focus their attention on this aspect of the ELL methodology – arguing that standard errors are likely to be too small. The smaller the estimated standard errors, the more narrow the confidence interval around each municipality-level poverty estimate. We thus ask whether, for each one of simulated welfare measures, we can verify whether the 95% confidence interval generated encompasses the ‘true’ welfare measure.

There are two ways to proceed with this coverage test as follows:

1. For each one of the 41 poverty maps constructed at the municipality level, we count how many municipalities out of 853 in the state have simulated confidence intervals that contain

24
unemployment increases in proportion to the amount of the raise. It also depends on the number of people affected by the minimum wage: the larger the numbers of workers who are earning close to the minimum wage, the greater the impact on unemployment.

# **a. Impact of the Minimum Wage on Employment and Unemployment**

Much of the empirical literature<sup>3</sup> on developing countries shows that minimum wage increases have negative effects on employment (see, for example, Brown et al., 1982; Neumark and Wascher, 2000 and 2006; Machin et al., 2003 and 2004). However, there are also several studies that have questioned these findings and demonstrated that the policy’s effects on employment can be nil and even positive in cases where businesses have some monopsonistic power over the labor market (for example, Katz and Krueger, 1992; Card and Krueger, 1994, 2000; Dube et al., 2007). The effect of the minimum wage on an economy will depend on the degree of compliance with the law and on the specific characteristics of the labor market in each country. In the case of industrialized countries, the literature indicates that there is usually an adverse effect on the demand for less qualified workers (Neumark, 2006), but the debate around the methodologies used in these studies and the conclusions derived from them continues.

In developing countries, which are typically characterized by high levels of informal employment, the implications of minimum wage policy differ substantially from what can be expected in developing economies, especially because the minimum wage in developing economies is more binding,<sup>4</sup> tends to be higher (defined as 20 to 60 percent above the average wage), and affects a larger number of workers (Maloney and Núñez Méndez, 2004).

As with the developed countries, the findings in the less developed countries in terms of the effects on unemployment and demand are inconclusive. One of the first studies on Latin America was conducted by Bell (1997) based on panel data from manufacturing firms in Mexico and Colombia. The results of this study indicated that during the period 1981‐1987 the firms responded to increases in wage costs by reducing the number of their employees. In Colombia, increases in the minimum wage triggered significant industrial job losses in the country,

> 3 In interpreting most of the results cited in this review, it is important to keep the definition of elasticity in mind. In this document, employment elasticity as it refers to the minimum wage is the percentage change in the level of employment following a 10 percent increase in the minimum wage.

> 4 _<mark>Vinculante</mark>_ <mark>in Spanish [literally ‘binding’], as it is used here, means that the distribution of earned wages tends to be close to the minimum wage.</mark>

7
claims would reflect negatively on wages of smokers, as employers are unwilling to pay for all or part of the higher costs of employing smokers. At the same time, workers who are aware of the negative consequences of smoking might select themselves into occupations that provide better health coverage, accepting lower wages in exchange. The cost of the smoking worker for his employer could also rise given the need for additional facilities for smokers that would increase air cooling, heating, and ventilation cost, increased fire hazard and fire insurance premiums, and smoke pollution leading to larger cleaning and maintenance costs (CHPP 2000).

Discrimination against smokers is another potential reason for observed differences in wages between smokers and non-smokers. Employers might discriminate against smoking employees because of the possible effect of second-hand smoking on the other employees and on the customers. Statistical discrimination may also exist if employers assume that an individual smoker is less productive based on the average health effects of smoking<sup>2</sup> .

These three theories linking smoking to wage outcomes propose that smokers, all things equal, will realize lower wages. A fourth theoretical consideration would be that smoking is a proxy for some other personal characteristics that could affect wages. For example, individuals with higher valuation of present versus future consumption may be more likely to smoke and less likely to invest in productivity-enhancing human capital. This theory centers on the observation that the costs of smoking are related to the negative effects of smoking on health which often are realized only in the longer-term. On the other hand, the perceived benefits of smoking are predominantly immediately realized and might include stimulation, enhancement of concentration and short-term memory, and, to a certain degree, weight control (Rohsenow _et al._ 2003). Smokers expect desirable and positive effects of smoking in terms of facilitating of social interactions and relaxation (Brandon and Baker 1991). Mood-altering ability of nicotine tends to alleviate boredom and reduce stress as well as aggressive responses to stressful events (Senagore 2006). So, individuals who smoke are choosing the short-run benefits over the long-run

> 2 Discrimination may be much more relevant in the US context where social norms make smoking less acceptable as reflected in state-level bans against smoking in public spaces compared to Albania where there are no restrictions against smoking. Heineck and Schwarze (2003) make the point that in Germany discrimination against smokers is more moderate than in the US; arguably one could make similar case for Albania where smoking prevalence is even higher.

5
# **FIGURE 1: KENYA'S FISCAL AND EXTERNAL ACCOUNTS 1981 91**

<!-- Start of picture text --> Percent of GDP<br><!-- End of picture text --> <!-- Start of picture text --> 012 -------------------- A-------;<br>lo . . . ..... . .v<br>4 _ ....... .. ........<br>U~~~ . . . . . . . . .<br><!-- End of picture text --> 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

## Fiscal Year

## + Bud Def(-grents)/GDP * Bud Dvf(+grants)/GDP "Cur A Def(-grts)/GDP

<!-- Start of picture text --> 30<br>25<-g . . . . . . . . . ................... .,<br>25.<br>20 1 fi I fi I I *R.omio rf,t Ix. iDP<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991<br><!-- End of picture text --> 73
that investors exhibit a preference for familiar markets, where they face lower transaction costs. Common colonial heritage, however, does not significantly predict the likelihood or size of investing by emerging market firms.

Finally, Models 1 and 2 also include measures of political stability and institutional quality. The estimated coefficients for political stability is statistically significant and has the expected sign. Firms from emerging economies are more likely to invest (and tend to invest more) in destinations where perceptions of political instability and violence are low. The coefficient of quality of government, however, does not reach statistical significance.

In Models 3 and 4, we include our main variables of interest, namely _BIT_ and _PTA_ . In Model 5, both variables are statistically significant in the first stage logistic regression, while the coefficient of BIT is also significant at the 90% level in the negative binomial stage. A PTA between two countries thus appears to influence the likelihood but not the level of investment between two countries. More specifically, all else equal, the odds of receiving an investment from an emerging market firm are 3.7 higher when there is a BIT between the origin and the destination countries, and 1.75 higher if the two countries have signed a PTA. Moreover, a BIT between two countries increases the difference in the logs of expected counts of investments by 1.6.

**Table 3.** Baseline models. Hurdle negative binomial regressions

||(1)||(2|)|(|3)|(|4)|
|---|---|---|---|---|---|---|---|---|
||Logit|NB|Logit|NB|Logit|NB|Logit|NB|
|_GDPpcj_|0.537***|0.309|0.229*|0.145|0.0150|0.0170|0.349|0.240|
||(0.192)|(0.285)|(0.137)|(0.220)|(0.153)|(0.222)|(0.213)|(0.284)|
|_Populationj_|0.561***|0.501***|0.689***|0.473***|0.578***|0.557***|0.432***|0.549***|
||(0.103)|(0.147)|(0.0912)|(0.138)|(0.101)|(0.140)|(0.112)|(0.161)|
|_Distanceij_|-0.00023***|-0.00013***|-0.00025***|-0.00014***|-0.0002***|-8.15e-05|-0.00017***|-7.12e-05|
||(6.24e-05)|(4.90e-05)|(6.38e-05)|(4.27e-05)|(6.73e-05)|(5.03e-05)|(6.54e-05)|(5.60e-05)|
|_Tradeij_|4.71e-08**|-7.28e-09|2.59e-08|-9.75e-09|5.08e-08*|-1.18e-08|8.38e-08**|-7.47e-09|
||(2.40e-08)|(8.80e-09)|(1.75e-08)|(8.26e-09)|(2.89e-08)|(8.38e-09)|(3.59e-08)|(9.04e-09)|
|_Contiguityij_|0.163|0.701|0.604|0.392|0.649|0.421|0.178|0.712|
||(0.690)|(0.591)|(0.641)|(0.502)|(0.661)|(0.487)|(0.690)|(0.517)|
|_Colonyij_|0.531|0.0844|0.300|0.176|-0.0696|-1.097|0.0180|-0.340|
||(0.876)|(0.801)|(0.928)|(0.696)|(0.778)|(0.930)|(0.801)|(0.843)|
|_Comlangij_|1.320***|2.126***|1.253***|2.229***|1.748***|3.557***|1.890***|2.556***|
||(0.373)|(0.478)|(0.340)|(0.488)|(0.363)|(0.907)|(0.390)|(0.797)|
|_Gov qualityj_|-1.521|1.743|||||-1.933*|1.903|
||(1.091)|(1.455)|||||(1.151)|(1.508)|
|_Poll stabilityj_|||0.349*|0.472**|0.323*|0.680***|||
||||(0.192)|(0.234)|(0.196)|(0.239)|||
|_BITij_|||||1.306***|1.612*|1.420***|0.567|
||||||(0.431)|(0.836)|(0.472)|(0.799)|
|PTA_ij_|||||0.568*|0.467|0.450|0.684|
||||||(0.335)|(0.398)|(0.355)|(0.426)|
|Constant|-16.68***|-15.44***|-16.67***|-11.36***|-13.64***|-11.78***|-13.30***|-15.98***|

14
Wilde, G.J.S. (1998). The theory of risk homeostasis: Implications for safety and health. _Injury Prevention, 4,_ 89-91.

Woodward, R.T. (1998). _Should agricultural and resource economists care that the subjective expected utility hypothesis is false?_ Meeting of the American Agricultural Economics Association, August 2-5, 1998, Salt Lake City, Utah. http://agecon2.tamu.edu/people/faculty/woodward-richard/paps/AAEA98Uncertainty.pdf. (23 January 2006).

World Climate Summit (2010). _The road to global green growth: Key recommendations by global business, finance, philanthropy and government leaders._ Cancun Report, downloaded 9/19/2011 at http://www.wclimate.com/world-climate-summit/ Zentall, T.R., Jr. Galef, B. G., and Zentall, T. R. (Eds.) (1988). _Social Learning: Psychological and Biological Perspectives._ Lawrence Erlbaum.

48
education, even though with primary education compared with no education, the coefficient is not significant in column 2 of panel A. This set of results is suggestive evidence that the positive relationship between education, urban residence and mortality during the 1994 genocide in Rwanda was general and was not only driven by the fact that the Tutsis - who were more educated and more likely to live in urban areas, were targeted by the genocide.

# **6. Conclusions**

This paper uses the maternal mortality module in Rwanda’s 2000 Demographic and Health Survey to analyze how excess mortality during the 1994 Genocide in Rwanda was distributed in the population. We find that adult males were most likely to die, indicating that violent deaths represented a very large share of the excess mortality. Using the characteristics of the respondent sister as a proxy for the socio-economic status of the family, we also establish that individuals with an urban or educated background were more likely to die.

The econometric strategy does not allow us to conclude that we completely solved the omitted variable bias due to the absence of the ethnic variable in the 2000 DHS. Age at marriage is only an imperfect proxy for being Tutsi. The coefficients on education and the type of residence in table 3 may still be picking up part of the ethnic effect. Nevertheless, we have gone one step further in addressing the omitted variable bias and our evidence suggests that, over and above targeting Tutsi, mortality in 1994 in Rwanda was more likely to be concentrated among the urban and educated groups of the population. We can certainly conclude that the genocide disproportionately affected educated and urban groups and therefore, over and above the lost lives, had a further 15
9 growth and market access is insufficient to raise food production and farmers’ incomes, beyond those of their parents, agricultural involution would be observed instead (Geertz, 1963).

Figure 3: Fallow land has virtually disappeared, except in Tanzania <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>7.8<br>10 1.0 3.0 0.1 5.0 1.2<br>0<br>Malawi Niger Nigeria Tanzania Uganda Average<br>Share of land in fallow (%)<br><!-- End of picture text --> Source: Binswanger‐Mkhize and Savastano, 2017.

The authors set out to explore the relevance of the BR framework in understanding contemporaneous modern input use in SSA (acknowledging that longer term panel data would be needed for proper testing) and find only partial support. They establish that fallow areas have virtually disappeared (on average the rate of fallow in the six countries is 1.2 percent (Figure 3)), an important finding that has not been systematically documented so far. They also observe a response of modern input use (fertilizer, agro‐ chemicals and improved seeds) to their newly developed exogenous measure of agro‐ecological potential, which is correlated with population density, and controlling for market accessibility (as measured through their newly developed measure of urban gravity), but not with other measures of intensification, such as irrigation or crop intensity. Overall, they conclude that the existing use of organic and inorganic fertilizer is insufficient to maintain soil fertility when fallow practices cease. And the weak response of crop intensity and irrigation is also not consistent with the BR framework. In light of the promising outcomes suggested by the BR framework, the process of intensification across these countries appears to have been too weak according to the authors.

The conclusion by Binswanger‐Mkhize and Savastano is consistent with the prevailing notion of underutilization of modern inputs in Sub Saharan Africa. But it remains unclear what an acceptable level of intensification should be, given Africa’s current state of population pressure and domestic market access. Intensification is clearly starting to happen in some of the more densely populated landlocked countries and areas within these countries, and has also been accompanied by a decline in poverty, as in Ethiopia. Globalization and Africa’s resource boom of the past two decades have further enabled governments and farmers to meet food needs through an expansion of food imports and rural‐urban migration (as in Nigeria), which may also have raised the levels at which population pressure really starts to bite and governments start to act upon it.<sup>12</sup> So is the glass half full with respect to Africa’s agricultural intensification, as the

> 12 Gollin, Jedwab and Vollrath (2016) document how Africa’s commodity boom during the 2000s, fueled economic growth in many resource rich countries as well as urbanization, in particular the emergence of consumption cities,
nearby villages or rural towns may be more readily accessible to the poor given lower thresholds to migrate and better compatibility with their skill sets (because of higher local demand for unskilled and semi-skilled versus skilled labor). In addition, urbanization affects poverty also indirectly, through positive spillovers on the rural economy. There is, _a priori_ , no reason to believe that these indirect effects of urbanization on rural poverty would be the same for less and more concentrated urbanization patterns.

Different literatures have so far focused on subsets of these three channels (agglomeration economies, rural off-farm employment, and urbanization externalities) and their effects on either growth or poverty, but typically not both, and not in a comparative or comprehensive framework. The new economic geography literature, for example, emphasizes how urbanization fosters economies of scale and agglomeration, which in turn are found to spiral economic growth (World Bank, 2009). The existence of localized external economies of scale has been documented for several industries such as heavy industries, and more modern manufacturing sectors such as transport and high tech. Externalities arising from producers locating close to suppliers and service providers as well as consumers and knowledge interactions in dense interactive locations can further add economies of agglomeration, especially beneficial to high tech industries (Henderson, 2010). Economies of scale and agglomeration would thus favor urban concentration, provided it also maximizes employment generation, especially for the (unskilled) poor.

Another longstanding literature has highlighted the positive role of rural nonfarm activities in poverty reduction, with rural towns, which mediate the flow of inputs, goods and services between rural hinterlands and large urban centers, seen as the most effective generators of nonfarm employment for the poor (Haggblade, Hazell, and Reardon, 2007; Lanjouw and Murgai, 2009). Faster rates of poverty reduction from secondary town development than from metropolitization might be understood within the standard Harris-Todaro (H-T) framework,<sup>5</sup> if for example, the probability of being employed for the

> 5 In the Harris-Todaro framework people migrate based on their (discounted) expected income streams (correcting for migration costs).

3
2014) and to 15 years (since 2015). Starting with 2016, this record is increasing by 6 months a year until it reaches 20 years.

The design of the pension system ensures substantial income redistribution. First, an old age pension is equal to 55% of the “wage base”, but not less than the minimum old age pension.<sup>6</sup> . Second, one year of work record above 25 years for men and 20 years for women adds 1 percentage point to the 55%, but no more than 20% of wage base (or minimum old age pension). And the most serious redistribution comes from the method of the wage base calculation: where _w_ is average wage of an individual accounted for pension calculation and _w_ is the average wage in the economy in the same period. The redistributive effect is provided in the table below.

|Actual wage,% of average wage in the economy|50|100|130|400|500|
|---|---|---|---|---|---|
|Wage base, % of average wage in the economy|29.65|52.15|65.65|92.65|92.65|
|Wage base,% of actual wage|59.30|52.15|50.50|23.16|18.53|

As a result, the Gini index for old-age pensioners<sup>7</sup> in 2015 was 0.126, while for employees<sup>8</sup> – 0.269.

# **3. Methodology of the analysis**

# **3.1. Data available and CEQ approach to income concepts construction**

The analysis is based on the Household Budget Survey (HBS) data. This survey is conducted each year starting from 1995. It covers all oblasts and Minsk city, and includes observations from around 50 towns and rural councils. The sample of the survey is expected to be 6,000 households (0.2% of general population). In 2015 the actual sample included 6,269 households, including 313 households with zero sampling weights, as they did not provide basic information about their income and expenditures. The remaining households represent 9.1 million people or 96.3% of total population. The sample does not cover collective households, i.e. care homes, students’ dormitories, specialized institutions, etc. As any other survey of this kind, it does not properly represent the richest households and the most marginalized households, which refuse to participate in the survey. The sample is structured to be representative at the country level for key population groups and for total population at the oblast level. Still it inevitably has some distortions. For instance, the sample overestimates rural population by 10.1% and underestimates urban population by 7.8%.

**Figure 5. Ratio of HBS data and data of national accounts, %**

<!-- Start of picture text --> Consumption<br>Disposable<br>Income<br>Rural population<br>Urban population<br>Total population<br>0 20 40 60 80 100 120<br><!-- End of picture text --> 6 The minimum old age pension is equal to 25% of the subsistence minimum plus 20% of the average wage. As of 2017, it is 5.5% below the subsistence minimum.

> 7 HBS data: women 55+ and men 60+ who have pension income (total – 2,448,272 individuals).

> 8 HBS data: for those who received wages during 12 months, sum of all wage-related incomes (total – 3,186,139 individuals).

10
MULTILINGUAL CHILD ASSESSMENT <mark>We hypothesized that caregiver literacy in both languages at baseline would be significantly associated with child receptive and expressive language at follow-up, and that the association between the baseline measure of child receptive vocabulary in Luo and English vocabulary at follow-up would be strongest among children whose parents had lower English literacy.</mark>

# **Methods**

# **Study design and sample description**

The measures described in this paper were developed for an ongoing cluster-randomized trial in Kenya’s Kisumu and Homa Bay counties that is designed to evaluate the effectiveness of a book distribution and parenting training program on child development (see trial registry: <u>https://doi.org/10.1186/ISRCTN68855267</u> and pilot results: Knauer, Jakiela, Ozier, Aboud, & Fernald, in press). Families with at least one child between the ages of 24 and 83 months were recruited from a set of nine primary school catchment areas in rural communities within two hours’ drive from Kisumu. A total of 357 primary caregivers (one per household) and 510 children were assessed during household visits (average 1.43 children per household); five child assessments were incomplete, resulting in an analysis sample size of 505. A total of 442 children were assessed at follow-up (5–6 weeks later), with 68 children lost due to relocation or difficulty in making contact.

# **Measures**

# **Overview of child assessments**

To develop our test battery, we used adoption, adaptation, expansion, and creation of new <mark>tests for different developmental domains. A</mark> ll assessments were translated to Swahili or Luo and then back-translated to English by a different team of translators (two for each language) who 7