rafmacalaba/datause-encoder-data huggingface dataset:
to better accommodate the needs and demands of women clients could improve performance and outcomes considerably.

Market research could usefully test savings and loan products and training offerings demanded by different categories of women clients (by occupation and income levels, for instance), contributing to the design of products that are more appropriately tailored to women entrepreneurs and farmers.

# _Female Autonomy Helps_

Interventions seem to work best when women micro and small producers and entrepreneurs have economic independence or autonomy and few social restrictions. Female autonomy means that women have physical mobility and can make independent choices, both of which are requirements to succeed in business. Autonomy allows women to seek and benefit from productive services and resources directly, increasing the likelihood that they will be able to switch from low- to high-value production. Autonomy is either measured directly or is indicated by the fact that women are unmarried, divorced or widowed heads of household or sole income-earners and, therefore, do not have a male partner who can control their choices. Soleincome earners were a significant proportion of the ultra-poor women in Bangladesh who transformed their occupations as result of the capital and training provided by BRAC. Female headship was a characteristic trait of women farmers in Malawi and urban entrepreneurs in Uganda who successfully crossed over from lower-value to high-value production, (Dimova and Gang 2013; Campos et al. 2013).

An alternative explanation of the above evidence is that these women entrepreneurs and farmers do not necessarily lack autonomy, but they access fewer productive services because 22
difference of the log of real GDP per capita and the first difference of the log of real nonagricultural value added per capita we included only an intercept in the test equation. In contrast, when testing for the stationarity of the first difference of the log of real agricultural value added per capita and the log of terms of trade we set the intercept to zero in the test equation.

In contrast to the country-by-country unit root tests, the panel unit root tests of specific time series assume as the null hypothesis that a unit root exists for all of the countries, with country distinction coming only from having separate deterministic terms for each country (i.e. different intercept effects or different intercept effects as well as different trend effects for each country). The difference between the Levin, Lin, and Chu (2002) and Im-Pesaran-Shin (2003) panel unit root tests resides in the form of the alternative hypotheses assumed by the tests. In the Levin, Lin, and Chu test the alternative hypothesis takes the form of a common stationary first-order autoregressive coefficient across all of the countries whereas the Im-Pesaran-Shin test assumes all of the first-order autoregressive coefficients are stationary but that they can possibly take on different stationary values. Both tests are, of course, all-or-none tests in the sense that test results imply that either (1) all of the countries’ given series have unit roots in them or (2) all of the countries’ series are stationary of the same degree (as in the Levin, Lin, and Chu test) or different degrees (as in the Im-Pesaran-Shin) test. The benefit of the panel unit root tests are that, in the case of short time series in the panel, the power of the unit root tests are increased when one or more of the panel series are non-stationary as compared with country-by-country unit root tests.

The results of the above unit root tests applied to the four series are summarized in Table III.1.<sup>5</sup> The left half of the table pertains to unit root tests of the non-trending series (log of real agricultural value added per capita and log of terms of trade) while the right half of the table pertains to the unit root tests of the trending series (log of real GDP

> 5 All of the results reported in Table III.1 were produced by EViews 6.0.

21
to arms (Region*PassTercile). We also adjust for an indicator that the school’s baseline average test score exceeds 50 percent (B50i). Our main specifications are therefore: Phase 1: Testi1_e = β1_0 + β1_1*TI_onlyi + β1_2*TI_SIi + δ 1_0*B50i +Σrjκ1_rjRegionr*PassTercilej + ε1_i (1) Next, we investigate heterogeneity in impacts using two approaches. First, in order to explore crossschool heterogeneity, we rely on machine learning to identify the covariates associated with treatment heterogeneity.<sup>20</sup> Guided by the machine learning results, we include an indicator variable for whether the baseline average score for the school was above 50 percent (B50i1) and interact that indicator with the indicators for each study arm. Roughly 30 percent of schools are above that threshold. The specifications we estimate are therefore: Phase 1: Testi1_e = β1_0 + β1_1*TI_onlyi + β1_2*TI_SIi + δ 1_0*B50i1 + δ 1_1*B50i1*TI_onlyi + Second, in order to explore cross-student heterogeneity, we re-estimate equations (1) and (2) using quantile regressions. Specifically, we estimate the impacts on students at the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> , and 90<sup>th</sup> quantiles of the distribution of ε1/2_i, namely the test score conditional on all the variables capturing the

> 20 We use the _generalized random forests_ machine learning algorithm (Athey and Wager 2019) to identify the sources of variation in conditional average treatment effects. Observations are clustered at the school level with a minimum cluster size of 20 students. Across the three treatments, the most important variable driving treatment heterogeneity is the school average baseline average (aggregated across all three subject tests) with a variable importance between 24 and 70 percent. In particular, there is a sharp difference in treatment effects between schools below and above a baseline average score of 50 percent. In the full sample, other variables of importance include the 2012 Form 2 exam pass rate, baseline student performance and wealth. In the restricted teacher-student matched sample, class size and whether headteachers observe instruction are among the other top 10 most important variables.

11
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sri lanka country risk profile 15 between a speedy recovery and a long-term poverty trap [6, 7, 8]. These costs and resources all impact households’ ability to maintain consumption after a shock, or their _consumption losses_ .

Over the long term average, and inclusive of all districts for which flooding data are available, nearly 6, 000 Sri Lankans face transient consumption poverty due to precipitation flooding every year. This is equivalent to 0.5% of national poverty incidence, with significant variation across districts. In some districts, the number of individuals pushed into subsistence represents a significant fraction of the HIES incidence of subsistence. This result is most notable in greater Colombo, reaching 3.5% of the chronic poverty incidence there. These results suggest that precipitation flooding events may be major drivers of extreme poverty in certain districts of Sri Lanka. In these areas, disaster risk management strategies can be a highly efficient tool to decrease poverty and subsistence incidence, either by reducing asset losses, or by enhancing households’ abilities to recover from disasters.<sup>3</sup>

# Socioeconomic characteristics and disaster recovery dynamics

Asset losses (and the foregoing income poverty analysis) describe individual households’ status in the instant after a hazard occurred, and provide useful insights into how governments and other first responders should target humanitarian relief. However, another important question is whether disaster-affected households become mired in poverty or achieve a speedy recovery. In contrast to asset losses, income and consumption losses can inform policy makers on how to manage the duration of disaster impacts.

Expanding our analysis of the 2017-like floods, Figure 4 on the following page maps the expected time to recover 90% of the assets destroyed when a 50-year precipitation strikes each district. The indicates that coastal districts in the northern flood map half of the country take the longest to recover. When disasters strike communities in these districts, many of which are national minorities, recovery is expected to proceed

> 3 Consumption losses are net of household savings, which can be used to smooth consumption after a shock, or to finance each household’s recovery from the initial event. In the absence of reliable data on household savings, we assume that each household has "precautionary" savings–i.e., liquid savings available for consumption– equivalent to one month’s worth of wages.
mines or coal power plants) or situations (e.g., long-term exposure to heavy noise and vibrations in power plants, oil refineries) and their health gets affected adversely. A large number of studies (e.g., Attfield and Seixas, 1995; Kuempel et al. 1995; Attfield and Kuempel, 2008) have confirmed various types of diseases are caused due to long term exposure in coal mining. Examining a group of 3,194 underground bituminous coal miners and ex-miners between 1985 and 1988, Attfield and Seixas (1995) finds that miners who work for 40 years in bituminous coal mines in the United States have a higher risk of having progressive massive fibrosis (type of severe pneumonia). Examining the quantitative relationship between exposure to respirable coal mine dust and mortality from nonmalignant respiratory diseases in a group of 8,878 working male coal miners who were medically examined from 1969 to 1971 and followed to 1979, Kuempel et al. (1995) finds increasing standardized mortality ratios with increasing cumulative exposure category. The study concluded that miners exposed at or below the current U.S. coal dust standard of 2 mg/m3 over a working lifetime, have an elevated risk of dying from pneumoconiosis or from chronic bronchitis or emphysema. Evaluating mortality experience over 22–24 years of 8,899 working coal miners initially medically examined in 1969–1971 at 31 U.S. coal mines, Attfield and Kuempel (2008) shows that exposure to coal mine dust leads to increased mortality, even in the absence of smoking.

A number of literature have also investigated the occupational impacts of coal mining on human health in the nearby areas (Coggon and Tylor, 1998; Hendryx and Ahern, 2008; Hendryx and Ahern, 2009; Hendryx et al. 2008). Using data from a survey of 16,493 West Virginians, Hendryx and Ahern (2008) finds that high levels of coal production are associated with worse adjusted health status and with higher rates of cardiopulmonary disease, COPD, hypertension, lung disease, and kidney disease. Through a national county-level analysis to identify contributions of smoking rates, socioeconomic variables, coal-mining intensity and other variables to age-adjusted lung cancer mortality in Appalachia, Hendryx et al. (2008) finds that lung cancer mortality for the years 2000–2004 is higher in areas of heavy Appalachian coal mining after adjustments for smoking, poverty, education, age, sex, race and other covariates. Comparing data from 3,141 counties for years 1979-2005 including counties in Appalachia with levels of coal mining above the median, Appalachian counties with levels of mining below the median, non-mining counties in Appalachia, and other counties in the nation, Hendryx and Ahern (2009) finds 1,736 to 2,889 excess annual age-adjusted deaths in coal mining areas. Reviewing a large body of literature 13
GDP from the Bureau of Economic Analysis to examine how another important determination of migration – access to economic opportunity – interacts with hurricane exposure.

We estimate the impact of experiencing a storm in a linear regression model that allows for separate intercepts for each county in our data. We also include year and state fixed effects to account for correlated shocks to migration with states or years (e.g. the Great Recession or the Bakken Shale boom) and state-year trends that account for long-term state-specific trends in migration. Our main model estimates the change in migration that occurs in the year a county experiences a storm relative to the migration that county experiences in a non-storm year. We also apply a model with lags that estimates the change in migration in the years following a storm compared to non-post storm years. We estimate similar models for the change in net migration and the average risk of the counties that receive migrants from a storm-impacted county in storm years versus non-storm years.

# **Results**

## **Out-migration does not increase after storms**

On average, migration out of impacted counties declines by -0.42% in years in which a storm occurs ( _t_ -stat: -0.44 ). This is, in effect, a precisely estimated null effect of storms on outmigration (Figure 1 Panel C & Table SI-1). We can reject positive changes in out-migration of greater than 1.5% at 99% confidence. A 1.5% change in out-migration represents less than a tenth of the standard deviation in year-to-year out-migration over our sample period.

The change in out-migration that occurs in a storm year is both economically and statistically insignificant. Our central estimate suggests that out-migration may decline in storm years, but even the most positive numbers in the 99% confidence interval represent an economically insignificant change in the out-migration rate in storm years relative to non-storm years.

As Panel C in Figure 1 shows, when we allow for five years of lags in the impact of storms on out-migration, we see effects that are not different from our primary result. Changes in out-migration remain small and on average negative for the five years following a storm. The sum of lags indicates no meaningful change in migration in the year of and five years following a storm.

We find that net migration is positive on average in storm years and in the years following a storm. We define net migration as in-migration minus out-migration, such that a positive number indicates more individuals moving into a county than out of a county. In storm years, net migration increases by 10.28% relative to non-storm years ( _t_ -stat: 3.28 , Figure 1 Panel D & Table SI-1). This effect persists for the year following a storm, before declining to near zero in years 2 through 5. In total, the sum of the lags in a 5-lag model suggests that net migration 5
“poor” or “not-poor.” The international poverty line for a given country can then be defined as the money needed to achieve a globally common level of welfare.<sup>5</sup> If individual welfare depends on both own income and relative income then differences over time and space in the comparison group’s level of living will require adjustments to any welfare-consistent monetary poverty line. In principle, the relative comparison might be upwards or downwards; in the former case, one is deemed to be relatively deprived if one is poorer than the average for some comparison group, while in the latter case one may be gratified in knowing that one is better off than that group. But the key point is that the income-poverty line becomes relative—specific to circumstances at each date and place.

This perspective immediately casts doubt on some of the prevailing stylized facts about poverty in the world. There is evidence that the incidence of absolute poverty—judged by a wide range of fixed real-income thresholds—is declining in the developing world, as shown in Chen and Ravallion (2004, 2010, 2013). Economic growth has played an important role, but it is less clear that this is also true when one takes account of relative income; indeed, the Easterlin paradox suggests otherwise. Is poverty also falling in growing economies when a welfareconsistent allowance is made for relative incomes? Similarly, it is widely believed that poverty is a much greater problem in the developing world than in today’s rich world. Some observers have even been tempted to claim that there is really little difference between rich and poor countries in the personal experience of “poverty” once one takes account of the social effects on welfare.<sup>6</sup> Is that still true when one allows for relative poverty?

There are already measures in the literature that we might consider turning to in addressing these questions. Explicitly relative poverty lines appear to have been first proposed by Fuchs (1967) who argued that poverty lines for the US should be set at 50% of the current median. While not adopted officially in the US, a version of the Fuchs proposal has become the most common official method of measuring poverty in the OECD and Eurostat, and is used by many national governments in the OECD (though 60% of the median is more common than

> 5 The definition of the poverty line as the point on the consumer’s expenditure function corresponding to a reference level of utility needed to not be poor appears to have originated in Blackorby and Donaldson (1987). For further discussion see Ravallion (2016a, Chapter 5). 6 For example, with reference to case studies (mainly using qualitative methods) in China, India, Norway, Pakistan, the Republic of Korea, Uganda and the United Kingdom, Walker (2014, p.14) claims that “...while material circumstances vary enormously across the case-study countries, poverty feels very similar in all settings; people simply cannot afford to live up to their own expectations and those of others.”

3
On the causality issue, Levine, Loayza, and Beck (2000) provide strong, though not uncontroversial, evidence that financial depth generates larger economic growth. Working with a panel of cross-country and time observations, the paper uses both external instruments (legal origin) and internal instruments (past observations of explanatory variables) to isolate the exogenous impact of financial depth on growth, finding it to be statistically and robustly positive. Beck, Levine, and Loayza (2000) and Benhabib and Spiegel (2000) go one step further and find that the mechanism through which finance improves growth is mainly through its positive effect on total factor productivity.

Studies using primarily cross-country or longitudinal panel data mainly test the causality direction from finance to growth, confirming in most cases its significance. On the other hand, papers using more intensively the time-series dimension of the data (whether for a single country or for many), test the causality in both directions. Although specific results vary, they tend to find that causality may run in both directions. See, for example, Berthelemy and Varoudakis (1996), Luintel and Khan (1999), Shan, Morris, and Sun (2001), Khalifa Al-Yousif (2002), Calderon and Liu (2003), and Ghirmay (2004).<sup>2</sup> The second area of controversy has been the homogeneity of the growth effect of financial deepening. Although the original papers did not pretend to measure other than average effects (although they did consider non-linearities through a log-linear regression specification), they have been criticized for advocating a one-size-fits-all approach. A large literature has emerged trying to understand how the effect of financial deepening varies according to the level of economic development, the level of financial development, and other country characteristics. See, for example, Arestis and Demetriades (1999), Deidda and Fattouh (2002), Rousseau and Wachtel (2002), Rioja and Valev (2004), Aghion, Howitt, and Mayer-Foulkes (2005), DemirgucKunt, Feyen, and Levine (2011), Arcand, Berkes, and Panizza (2011), and Barajas, Chami, and Yousefi (2016). Pasali (2013) provides a comprehensive review of this strand of this literature. The paper finds little consensus on whether more economically advanced countries benefit more from financial deepening than developing countries do; however, it does find an emerging

> 2 A few papers, however, reject bi-directionality, favoring causality from finance to growth. See Christopoulous and Tsionas (2004), Liang and Teng (2006), and Ang and McKibbin (2007).

4
||**A**|**Appendi**<br>**ll**|**x Table 1: A**<br>**B**|**ttrition**<br>**oys**|**G**|**irls**|
|---|---|---|---|---|---|---|
||Application|2008 Survey|Application|2008 Survey|Application|2008 Survey|
|Treatment|0.04|0.03|0.03|0.03|0.03|0.04|
||[0.01]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|
|Mean of Y among|||||||
|Control|0.47|0.52|0.53|0.53|0.52|0.51|
|_Notes_: The table in<br>Observations|estiates wheth<br>4,349|er there were di<br>3,776|ferential rates<br>2,038|f attrition betw<br>1,964|en treatment a<br>1,876|d control The<br>1,810|

_Notes_ : The table investigates whether there were differential rates of attrition between treatment and control. The dependent variable is an indicator for whether the child was interviewed in the 2016-17 survey with the incentivized experiments. The odd columns measure attrition relative to the sample of 4,349 children which was drawn for the study based on the information available in the applications. The even columns measure attrition relative to the first survey conducted in 2008. Robust standard errors between brackets.

36