rafmacalaba/datause-encoder-data huggingface dataset:
Table shows that the number of children in the family is not as strong a constraint for labor force participation as is the need for additional income. As the number of females between 20 and 50 years old in the household increases, the likelihood of nonwage employment decreases, possibly reflecting that the presence of other potential income earners reduces the need for the woman studied to go to work. Consistently, and for the same reasons, a woman's likelihood of employment decreases as the number of males between 20 and 50 years old in her household increases. Additionally, the presence of elders (50 years old and over) lowers women's probabilities of employment in the nonwage sector. The coeffic5.nts on the proxies of the family need for additional income show the expected negative effects on women's probabilities of employment across sectors. Thus, women's probabilities of employment in general decrease as husband's experience and income increase, and as remittances and non-labor income increase. In general, a woman's probability of wage employment (private and public) increases as the husband's schooling increases. Given that more educated spouses have higher permanent income, the positive impact of husbands' education on wives labor force participation does not reflect the need for income in the family, but aspirations for a higher standard of living. Finally, the negative effect of residence in Lima for employment in the nonwage sector indicates, after controlling for all the other exogenous variables, that non-Lima women are more likely to choose nonwage over wage employment.

In summary, the results of this study are largely consistent with prior research in developed countries showing that job-training significantly affects the employment status of urban women. These findings suggest that job-training has an efiect of women's income through a so-called "allocative" effect (Welch 1970) which relates to individuals' capacity to evaluate and make advantageous choices. Job-training may have a non-cognitive effect on Peruvian women which can make them more receptive and self-confident, and consequently more willing to engage in economic. opportunities.

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educational attainment, controlling for rural-urban migration background in the 1987 and 1999 estimations has no impact on the results. The migrant variable itself has no significant effect on labor force participation. Results are also unchanged if we add dummies for both the woman’s and her spouse’s migration background and their interaction, which captures the difference between women migrating to join their husbands’ family versus families migrating jointly. Even if we drop all rural-urban migrants from the sample, the results do not change.<sup>19</sup> Unfortunately, no migration data was collected in the employment surveys after 1999, but the NSSO does provide descriptive statistics on migrants from a special migration survey conducted in 2007–08 (NSSO 2010). Based on these statistics we find that the educational attainment differences between urban female migrants and the total urban female population have been quite stable between 1987–88 and 2007–08. In other words, the educational composition of female migrants has not changed differently from the educational composition of the entire urban female population. We are therefore fairly confident that changes in female migration patterns are not driving the stagnation of FLFP.

# **IV. Decomposition Analysis**

In order to quantify the contribution of different explanatory variables to the observed change— or lack thereof—in the female labor force participation rate, we now turn to a decomposition analysis, using the probit estimates for 1987 and 2011. Following Fairlie’s (2006) extension of the Oaxaca-Blinder decomposition (Blinder 1973; Oaxaca 1973), the labor force participation equation (Eqn. 1) is expressed as Yit = F(Xitβt), resulting in the following decomposition:

> 19 Detailed results for this section are available on request.

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# **Introduction**

The lowering of trade barriers is a visible point of policy change that is often associated with broader changes in the economy and a deeper set of policy reforms. In general, poor countries undertake trade reform because it carries the potential to lessen poverty through the increased opportunities that come with open markets and economic growth. Even so, when changes in trade policy have impact, they are likely to enhance the value of some activities and diminish the value of others. This, in turn, can have important consequences for how natural resources are used and for livelihoods, including those of the poor.

For this reason, trade policies and trade agreements are controversial. The scope for gains from new trade deals and the consequences of existing policies are scrutinized from policymakers, advocates, scientists and social scientists, and the scale of research devoted to these topics across a range of disciplines is large. At the same time, reported outcomes from trade reforms are varied and practical lessons from the literature are elusive. Moreover, when public debate over policy occurs, evidence put forward by advocates of different positions seems inconsistent and contradictory.

In this essay, we discuss findings from a collection of case studies jointly managed by the World Wildlife Fund and the World Bank. The case studies were carried out by local research teams and focus on the consequences of changes in trade policy on poor people living in places with important and sensitive ecologies. The case studies were motivated by a desire shared by both institutions and the local research teams to move the policy debate forward by bringing together accessible and tangible examples of the relationships between global markets, local economic activity and the local ecology. The full case studies are contained in an edited volume (Cook et al., forthcoming).

Taken together, the cases examined in the study suggest that the reason why generalizations about how trade, poverty and the environment relate are difficult to obtain lies in the very specific and local conditions affected by trade. The individual studies, taken from Africa, Asia and Latin America, are set in places where the poor live in close proximity to important and valuable natural resources and focus on the cascading consequences that trade has on the way people interact with the environment to earn their livelihood. The studies illustrate the significance of global markets for important ecologies and the poor who 2
# **2. Data and methods**

# **2.1 Phone survey**

In 2020, as in-person data collection activities of many statistical offices came to a halt, the World Bank started the collection of multiple waves of phone survey data in numerous countries around the world. The aim was to assess the evolution of household wellbeing during the COVID-19 pandemic. The survey comprised modules on different wellbeing dimensions including employment, income, coping mechanisms, access to health, school participation, gender inequalities, and food insecurity. The surveys were designed to be representative at the national level for households and individuals (over 18) who have an active cellphone or a landline at home.<sup>1</sup> Sample sizes typically ranged from 800 to 1,000 interviews in smaller countries, and to over 2,000 interviews in larger countries (including Haiti). World Bank (2021) gives a detailed description of the sampling design and calculation of survey weights. The surveys were administered over the phone to a random sample of individuals aged 18 or older, identified through so-called random digit dialing. As discussed in World Bank (2021), this “methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.” For the countries in our sample, it is either known or reasonable to assume that the vast majority of households has access to a phone. According to the International Telecommunication Union’s (ITU) 2019 core household indicators, 93.9 percent of the households in Belize had a mobile phone and 7.7 percent had a landline. In Suriname, 94.4 and 39.4 percent of households, respectively, had a mobile phone and a landline.<sup>2</sup> For Saint Lucia, our own calculations based on the 2016 Survey of Living Conditions and Household Budgetary Survey indicate that 95.4 percent of households had a cell phone while 28.0 percent of the households had a landline. For Dominica and Haiti, no information is available on the share of households with a phone. However, related indicators suggest that this share is high. According to the ITU, the share of the population covered by a mobile-cellular network is 100 percent in Dominica. Haiti compares

> 1 World Bank (2022) provides an overview of many resources and findings based on the phone surveys.

> 2 See <u><mark>https://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2020/CoreHouseholdIndicators.xlsx (</mark></u> consulted on February 12, 2024).

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These flood risk categories are defined in line with an approximation of the risk to the lives of affected people. Up to 0.15 meter inundation depth, no significant risk to life is expected. Up to 0.5 meter, some risk to life must be expected, especially for vulnerable groups such as children and the disabled. Up to 1.5 meters, a significant share of the affected population could face risk to life, especially if flood waters have a current. Above 1.5 meters, most affected people could face substantial risk to life without rescue measures.

Through this process, each 90 by 90 meter cell of a country will be assigned one of the five risk categories (e.g. a pixel that has an estimated inundation depth of 5 cm is classified as low risk, while a pixel with depth 4.3 meters is classified as very high risk). This is repeated for the world’s landmass of 510 million square kilometers, which implies the processing of 63 billion pixels.

**3. Assign flood risk categories to population headcounts at the pixel level and aggregate to the administrative unit:** As the flood hazard and population density maps are converted into the same spatial resolution, each population map cell can be assigned a unique flood risk classification – these cells can then be aggregated to the administrative unit level (e.g. province or district level). This allows the calculation of population headcounts for each flood risk category and for each (sub-)national administrative unit. A detailed description of how this process is implemented in practice is provided in Section 4.2.

This process yields an estimate of the number and share of people exposed to no-, low, moderate, high, and very high flood risk during an intense flood event. These estimates are available globally with a resolution of 90 meters, but they are also aggregated to administrative units, including for each country and subnational unit. These estimates are also aggregated to yield regional and global estimates.

**4. Compute the number of poor people exposed to flood risk:** While poverty estimates are not available at the pixel level, the World Bank’s GMD database provides them at the subnational level for most countries. These poverty shares are multiplied with the population headcount that is estimated to be exposed to flooding, in order to obtain an estimate of the number of poor people in each administrative unit exposed to flood risk. Similarly, exposure headcount estimates are multiplied with subnational GDP per capita figures to obtain estimates of flood exposed GDP in monetary terms.

# **4.2. Stepwise computational process**

The following steps are repeated for each subnational region where data are available.

## **Merge coastal, pluvial, fluvial flood hazard maps**

- Crop fluvial and pluvial flood rasters to subnational boundary

- Crop then virtually warp the coastal flood raster, using a nearest neighbors resampling method, to perfectly match the resolution and extent of the fluvial and pluvial rasters

- Merge fluvial, pluvial, and coastal rasters using a maximum value method

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significantly affected, we include lagged dependent variable in all regressions in the main text. Appendix 1 shows that our results are robust to the exclusion of Ycpt−1 . αpt are province-year fixed effects. City characteristics include average years of schooling, population, average age, population share of Han ethnicity in the structural transformation regression. We drop average years of schooling when the dependent variable is school enrollment rate. We add additional city characteristics, including GDP per capita and rural population share in robustness checks. These variables are not included in the baseline regression because controlling these variables will reduce our already small sample size.

As our dependent variable is either change in structural transformation variables or change in school enrollment, it automatically removes all time-invariant factors that affect structural transformation and educational outcomes at the city level. We further control province-year fixed effects to account for province-specific time trends. Essentially, we examine how increased local FDI presence affects the pace of structural transformation and school enrollment among cities in the same province. In all regressions, standard errors are clustered at the city level. Observations are weighted by initial population in each city.

A natural concern in Bartik IVs is that city-specific pre-trends are correlated with initial industry composition. As FDI deregulation is not assigned randomly across industries, it can introduce omitted variable bias if they are correlated with underlying trends that are not controlled for in the econometric model. McCaig (2011) discussed options to control for such underlying trends, one option is to include relevant initial conditions at the observation level. Therefore, we include initial sectoral employment share, school enrollment rate, foreign firms’ employment share, average years of schooling, population, average age, population share of Han ethnicity, GDP per capita so any underlying trends associated with these variables are controlled for, and different trajectories across cities are allowed.

# 2.3 Data

This paper combines various data sources to explore whether FDI catalyzes local structural transformation and human capital accumulation.

## **Population census**

This paper uses population census data in 1990, 2000 and mini-census data in 2005 to estimate city level employment share in the three broad economic sectors and high school/college enrollment rate. The population census data also provides rich information on individuals’ age, gender, migration history, employment status, industry, and ethnicity. We aggregate such information at the city level as control variables. In addition, the paper uses the initial industry employment composition from population census to construct the Bartik shock IV for city level FDI inflows.

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1 input-output relations such as the number of workers per client or connection. The _frontiers_ recognise the much more complex nature of interactions between inputs and outputs. The _cost frontiers_ show the costs as a function of the level of output (or outputs) and the prices of inputs and is generally much more useful to regulators who are assessed to assess the wedge between tariff and minimum costs. The _production frontiers_ reveal technical relations between inputs and outputs of firms and provides a useful backup when cost frontiers are not easy to assess due to lack of data.' The inclusion of control variables in the specification of the functional relations estimated ensures that the various operators of a same activity are effectively comparable. Indeed, once the frontier has been estimated, the efficiency of a specific operator can be assessed in relation of the performance of the best operators in the industry when these are confronted with the same factors constraining the performance of the operator being assessed.<sup>2</sup> The productivity indicators although theoretically inferior to efficiency frontiers are quite commonly used by regulators to assess the performance of utilities. They are useful complements to efficiency frontiers, but seldom good substitutes. However, in most countries, data limitations makes them the only game in town so they tend to be used in conjunction with various types of quality indicators to obtain a multidimensional snapshot of a firm's performance. Moreover, the experience suggests that efficiency frontiers are not flawless. Even when the data controlled by the firm can be requested from the operators, it is not seldom that the data needed to identify the specific constraining characteristics of the activity analysed. This results in an impossibility to decompose the degree of efficiency that the firms can control from factors that influence costs but l In choosing between the estimation of a production or a cost function, the regulator needs to take into account the specificities of the sector he/she is working on. An important characteristic in regulated utilities is that in general, the firms are required to provide the service at a preset tariff. In other words, the firms are required to meet the demand and are not allowed to pick the level of output to supply. Since output is exogenous, the regulated firm maximizes benefits simply by minimizing its costs of producing a given level of output. Under this situation, in principle, the specification of a cost frontier is often the natural choice. 2 In Chile (water sector) and Spain (electricity), the frontier is calculated on the basis of engineering data instead of relying on best practice.
installments, in accordance with the schedule, in the nearest NRSP village branch and take a repayment receipt.

# **Appendix B: Variable definitions**

Data used in this paper come from two surveys: a baseline conducted in November 2006, a follow-up survey in December 2008. We also used administrative data about loan takeup, obtained from NRSP’s internal records.

# _<u>Treatments and Take Up (from administrative records)</u>_

- _Female picture brochure_ takes the value of 1 if the member was shown a brochure with female business owners on the cover, 0 if the brochure showed the same businesses with men.

- _Eligibility_ , takes the value of 1 if member is eligible for the loan lottery, 0 otherwise.

- _Business Training_ , a dummy taking the value of 1 if the individual was offered business training, 0 otherwise.

- _Applied for larger loan_ , 1 if the member actually requested a loan, 0 otherwise.

- _Approved_ , conditional on applying for a loan, takes a value of 1 if the member was approved by NRSP, 0 otherwise.

- _Borrowed_ , takes a value of 1 if the individual actually borrowed money from NRSP; while conditional on approval, not all those that applied and were approved took actually a loan.

- _Amount borrowed_ , is measured in thousands of Rupees.

# _<u>Baseline characteristics</u>_

# _<u>Individual</u>_

- _Female_ equals 1 for women and 0 for men.

- _Age_ is respondent’s age in years.

- _Years of education_ is years of completed schooling, and is top-coded at 16.

- _Married,_ a dummy taking the value of 1 if member is married, 0 if single, divorced or widowed.

- _Digital span recall_ reports the number of digits correctly recalled after being shown an eight digit number for 30 seconds.

- _Member of a mixed group,_ dummy takes the value of 1 if the member belongs to a borrowing group with mixed gender, 0 if the group is of the same gender.

- _Index of female mobility and No purdah index_ are principal components of several variables with negative values indicating less mobility (or observing more types of purdah).

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# **Conclusion**

The continuing geographical concentration of the rural poor in ecological fragile and remote areas remains one of the biggest development challenges facing many low and middleincome economies. As argued in this paper, the result is two interacting poverty traps that inhibit widespread rural development: a poverty-environment trap at the household and community level and a spatial poverty trap for any isolated region and its local markets. That such poverty traps remain an important obstacle to progress in alleviating rural poverty is evident from recent trends.

According to the World Bank (2008), rural poverty rates in developing economies have declined over the past decade but remain high in South Asia (40%) and Sub-Saharan Africa (51%). Reductions in rural poverty in most countries and regions are largely due to rural development and not rural-urban migration. In Brazil, Ecuador, Thailand, Malawi and Vietnam poverty rates are higher in remote areas, and in general, poverty incidence is greater in rural areas with less favored land. These findings confirm that “the extreme poor in more marginal areas are especially vulnerable, and until migration provides alternative opportunities, the challenge is to improve the stability and resilience of livelihoods in these regions” (World Bank 2008, p. 49).

Thus, the key ecological scarcity problem facing the rural poor located in resource-poor and remote regions is a vicious cycle of declining livelihoods, increased ecological degradation and loss of resource commons, and declining ecosystem services on which the poor depend. That is why addressing this vicious cycle calls for a new policy strategy that does not just focus on improving natural capital in general, but attempts to address the key elements that are the root cause of the poverty-environment and spatial-poverty traps. To be effective, such a pro-poor strategy needs to target the rural poor where they are geographically concentrated – in remote and ecologically fragile areas. The key elements of the strategy in such areas involve:

- Improving the productivity of marginal agriculture and resource productive activities.

- Overcoming the constraints on smallholder market participation.

- Fostering well-functioning and affordable markets for credit, insurance and land.

- Generating off-farm employment opportunities.

- Enhancing environmental protection and management of resource commons.

- Improving public services and infrastructure, including roads, communications, marketing, education and health services, and research and extension.

As indicated in this paper, considerable advances have been made in recent years in mapping the geographical location of the poor and assessing how poverty-environment and spatial traps emerge. Such tools and analysis are now helping to guide and direct policy interventions to overcome these geographical dimensions to rural poverty in the developing world (see Higgins et al. 2010, for a review). A more concerted effort is required to expand the lessons learned from such studies into a more comprehensive strategy to alleviate rural poverty throughout the developing world.

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