rafmacalaba/datause-encoder-data huggingface dataset:
both powerlooms and handlooms are small-scale private sector enterprises. Handloom weavers use the most primitive technology. Cotton yarn is either sold to powerlooms and handlooms weavers or woven by composite mills themselves. The handloom workers, who are the largest group in termns of employment in the whole textile sector, are the poorest, even more impoverished than poor cotton farmers in India. In the past, several surveys have revealed the remarkable income difference between mill workers and handloom workers **(Table 3).**

**Table** 3: **_Average Daily Wage Comparison Among Sectors_**

|Year|Survey|Composite Mill|Powerloom|Handloom|
|---|---|---|---|---|
|1988|Goswami|Rs.75|Rs.40||
|1985|ATIRA|Rs.50|Rs.10 - 25||
|1990|GOI|Rs.50|Rs.25|Rs.10 - _15_|
|1982|SITRA|-|Rs.12.5|Rs.9.5|

Sources: Goswami (1990), Uchikawa (1998) etc.

## **_Compromise to Cotton Farmers_**

While the cotton textile industry is the largest manufacturing sector in terms of employment in India, cotton is the most important commercial crop in India's agriculture in tenns of cultivated area. Therefore, while suppressing cotton prices by restricting exports, the government has also needed to satisfy farmers. In the past, the government has provided them a number of benefits, such as income tax exemption, concessional power rates, and fertilizer subsidies. Particularly, the fertilizer subsidization policy has been used as an important tool to satisfy politically influential wealthy farmers, as they have had more access to these subsidized fertilizers. Another way to keep cotton farmers content was to allocate most of the cotton export quotas to the public cotton traders who rebate their economic rents to cotton farmers in various ways such as by providing them high-quality cotton seeds.

# 2.2. COTTON EXPORT RESTRICTION SCHEME

## _Roles of_ **_the Textfile Commissioner and the_** _Cotton_ **_Advisory Board_**

India's cotton exports are handled by their Ministry of Textiles. Every year, the Textile Commissioner recommends an export quota to the Minister of Textiles, based on crop estimates made by the Cotton Advisory Board which is a representative body of the government, cotton growers, traders, and spinning and _5_
**Table 4**

**Aid and banking crises: economic variables**

|Equation|**1**|**2**|**3**|**4**|**5**|**6**|**7**|
|---|---|---|---|---|---|---|---|
|Number of years from crisis start|‐0.038**|‐0.048***|‐0.080***|‐0.047|‐0.039***|‐0.036**|‐0.037**|
||(0.017)|(0.017)|(0.015)|(0.036)|(0.014)|(0.016)|(0.018)|
|Number of years from crisis start squared|0.002***|0.002***|0.003***|0.002*|0.002***|0.002***|0.002***|
||(0.0004)|(0.0004)|(0.0003)|(0.001)|(0.0003)|(0.0004)|(0.0005)|
|Log per capita income|3.215***|3.391***|4.193***||3.126***|2.878***|2.963***|
||(0.697)|(0.660)|(0.630)||(0.716)|(0.686)|(0.752)|
|Log population|‐0.267|0.061|‐0.976|0.208|‐0.179|‐0.202|‐0.615|
||(1.338)|(1.480)|(1.588)|(2.965)|(1.194)|(1.260)|(1.288)|
|Lagged gov. exp. as a share of GDP|0.003<br>(0.003)|<br>|<br>|<br>|<br>|<br>|<br>|
|Lagged budget surplus/deficit (% of GDP)||‐0.023**<br>(0.009)|||<br>|<br>|<br>|
|Unemployment rate|||0.044***|‐0.012||||
||||(0.008)|(0.033)||||
|Inflation rate|||||‐0.031**|||
||||||(0.017)|||
|Real exchange rate (USA 2000 = 100)||||||0.008***<br>(0.001)||
|Trade share of GDP|||||<br>|<br>|0.003<br>(0.004)|
|R<sup>2</sup>|.96|.97|.97|.95|.97|.97|.96|
|No. of donors|24|24|24|24|23|22|24|
|No. of observations|682|614|575|575|675|654|657|

Dependent variable is log of net ODA disbursements. Note *p< .1, **p<0.05, ***p<0.01. All regressions control or country and year effects, not shown for space reasons. Standard errors are adjusted for clustering at the country level. Banking crises dates are from L&V. Turkey is missing inflation data (equation 5), and Turkey and Korea are missing real exchange rate data (equation 6).

29
This study contributes to three strands of literature. First, it expands the knowledge of the health consequences of environmental hazards in the tropics. Second, it examines the potential role of household resources and practices in mitigating these impacts. Third, the analysis evaluates the past and future role of sanitation infrastructure, particularly toilet ownership, in reducing climate-related health vulnerabilities.

Regarding our first contribution, we add to the environmental health literature by examining the link between weather extremes and the prevalence of diarrhea, respiratory symptoms, and the spread of various diseases. The correlation between diarrhea and temperature has been studied in diverse contexts, finding that an increase in temperature, or prolonged dry seasons, is associated with a higher number of reported diarrhea cases (Amqam et al., 2019; Mertens et al., 2019; Xu et al., 2014; Zhou et al., 2013). At the other extreme, heavy rainfall has demonstrated similar effects. In countries such as India, Mozambique, Indonesia, and Peru, extended periods of intense rainfall have been associated with a higher incidence of diarrhea (Mertens et al., 2019; Horn et al., 2018).

We expand this literature by examining child morbidity in Cambodia, an understudied country. The literature assessing the health impacts of weather shocks in Cambodia is scarce. Choi et al. (2016) conducted a study examining the influence of weather factors on dengue fever incidence in Cambodia, revealing a significant connection between higher temperatures and increased precipitation with elevated dengue fever cases. On the other hand, Saulnier et al. (2018) analyzed the impact of seasonal floods on health using six years of national health data and flood maps. Their study focused on the health consequences of flooding events. While these two studies examined separate hazards, our analysis jointly considers the effects of weather conditions, including temperature and precipitation (potentially flooding), on health outcomes in Cambodia.

We add to the literature on the impacts of fine particulate matter ( _PM_ 2 _._ 5), a common form of air pollution, which has been linked to respiratory infections, asthma, and low birth weight. Airborne particles are shown to cause inflammation and exacerbate pre-existing respiratory conditions such as asthma and chronic obstructive pulmonary disease (D’Amato et al., 2014; Tecer et al., 2008; Braun-Fahrl ̈ander et al., 1992). Additionally, elevated levels of particulate matter have been associated with a higher incidence of lung cancer and cardiovascular diseases (Li et al., 2018; Shu et al., 2016). We show that symptoms associated with particulate pollution exposure can be significant and include cough, which can deteriorate the quality of life of children in already disadvantaged contexts such as many regions of Cambodia.

4
# **2. Data**

The data used for our analysis are derived from multiple sources. We introduce a novel dataset that provides a granular perspective on poverty and inequality at the subnational level. In particular, we draw on the Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021), a collaborative effort among different teams at the World Bank over a period of time. The GSAP is built on countries’ official household income (consumption) surveys, covering over 1,594 subnational units across 134 countries, with more than 90 percent of the data ranging from 2010 to 2019. In most cases, a subnational unit refers to a province or state (i.e., firstlevel administrative boundaries – ADM1) but can also be a group of regions determined by the specific sampling strategy of household surveys.

For the main outcomes, we utilize the (headcount) poverty rate at US$1.90 a day, as estimated by the percentage of the population living on less than $1.90 a day at the 2011 purchasing power parities (PPP) prices.<sup>4</sup> For richer analysis, we also employ other poverty lines of $3.20 and $5.50 a day. As alternative sources of poverty data, we also utilize two other sources: i) country-level poverty data from the World Bank World Development Indicators (WDI), which is a widely used database for global poverty measures, and ii) subnational GDP from Kalkuhl and Wenz (2020) and Kummu _et al._ (2020), which we further convert to poverty data. Panel A of Figure 1 shows that Sub-Saharan Africa currently has the highest poverty rates and the poorest countries include Tanzania (51.3 percent), Mozambique (54.7 percent), and the Democratic Republic of Congo (72.9 percent).

For inequality, we mostly employ the Gini index and Theil index, which are the most commonly used measures of income inequality. For robustness checks, we also use the distribution of income (consumption) shares held by each decile and calculate different

> 4 The data are accessible on the Harvard Dataverse depository at <u>https://doi.org/10.7910/DVN/MLHFAF.</u>

7
32

# Table 2. Selected Indicators of Growth of the TVP Sector in China 1978-83 and 1983-87

(percentages)

||Average Annu|al Change||
|---|---|---|---|
|Indicator|1978-83|1983-87|1978-87|
|Rural industrial output|19.7|41.5|26.4|
|Rural industrial labor force|3.1|15.9|7.4|
|Fixed assets*|15.7|26.7|20.5|
|Bank loans*|34.7|55.4|43.8|
|Profits before tax*|7.4|14.1|10.3|
|Employment|2.7|9.8|5.8|
|Wage bill|15.2|24.9|19.4|
|Average wage|12.1|13.7|12.9|
|Memorandum items||||
|Average wage, state firms|6.1|15.6|10.2|
|Inflation (CPI)|3.1|7.6|5.1|

* TVCEs only.

Sources: State Statistical Bureau of China (various years); China Rural Statistical Yearbook (various years).
on gender. As such, the resulting analyses are a prerequisite for understanding determinants of individual-level participation in off-farm employment, which is concealed by using householdlevel data.

We find that a significant share of the rural and urban population in these countries participates in off-farm employment with a cross-country weighted participation rate of 44 percent, and the national rates ranging from 34 percent in Ethiopia to 58 percent in Malawi. Yet, women are consistently less likely to participate, with a cross-country weighted gender gap of 9 percentage points, and the national estimates of the gender gap ranging from 4 percentage points in Nigeria to 19 percentage points in Uganda. In addition, women are less likely to access wage employment; to work in higher-productivity sectors; and to be full-year/full-time employed. Gender differences are highest in Malawi, Tanzania and Uganda but low in Nigeria, where female non-farm entrepreneurship is more common. The gender gap in job quality is more pronounced in urban areas than in rural areas. However, in most countries the gender inequality in accessing (qualitative) off-farm employment is observed to have declined between 2010 and 2016.

We document that a substantial share of the population, amounting to about 39 million individuals across the countries of interest, switched employment status between 2010 and 2016, pointing to the dynamic (and unstable) nature of off-farm employment. Women are less likely to continue employment than men in all countries except in Nigeria. Drivers of entry into employment and continued employment are mostly country- and gender-specific, with occurrence of shocks and demographic factors as most important determinants. Differences between men and women relate mostly to household composition, with a positive effect of child dependency ratio on women’s entry into employment, and wealth, which is negatively correlated with women’s entry into employment and positively with men’s continued employment in rural areas. Part-year work in the agricultural sector leads to a higher exit rate while self-employment is more conducive for stability in rural Ethiopia and Nigeria, but not in urban areas or in other countries.

Our findings imply that off-farm employment is not a negligible activity and that development policies and programs should pay more attention to these jobs, especially in rural areas. To improve the inclusiveness of employment towards women, it is paramount that gender-specific employment policies and programs are designed and implemented. For participation rates to increase steadily over time, these policies and programs should focus on keeping people employed as well. From a 21
# **Table A33: OLS; Effects of refugee presence on household total Fruits production by change in main source of income, with analysis at cluster level and the distance weighted refugee index at -** **<u>50km from the clusters (Panel 2009 2012)</u>**

||Log of|Total Fruits Product|ion (UGX)|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
|Log of refugees (50km)|-0.0596|-0.0427|-0.0534|
||(0.0592)|(0.0560)|(0.0520)|
|Log of refugees(50km)#To Commercial farming|-0.0539|-0.0532|0.0165|
||(0.0530)|(0.0581)|(0.0588)|
|Log of refugees(50km)#To Wage employment|0.0133|-0.0004|0.0175|
||(0.0453)|(0.0491)|(0.0435)|
|Log of refugees(50km)#To Non-Agric self-employment|0.0080|-0.0015|0.0440|
||(0.0451)|(0.0446)|(0.0500)|
|HH head age||0.0530***|0.0073|
|||(0.0170)|(0.0164)|
|Age squared||-0.0004***|-0.0000|
|||(0.0002)|(0.0002)|
|HH head sex (male)||0.4980***|0.0805|
|||(0.0971)|(0.1421)|
|Annual mean temperature||0.0414|-0.0703|
|||(0.1153)|(0.2210)|
|Average 12 months total rainfall||0.0008<br>|0.0062**<br>|
|||(0.0025)|(0.0030)|
|Household size|||0.0638***|
||||(0.0212)|
|Experienced shocks|||-0.0839|
||||(0.0760)|
|**_HH head marital status_**||||
|Married monogamously|||0.0230|
||||(0.2731)|
|Married polygamously|||-0.0527<br>|
||||(0.2993)|
|Separated (divorced/widowed)|||-0.2684|
||||(0.3352)|
|**_HH head education level_**||||
|Completed primary sch|||0.3707***|
||||(0.0927)|
|**_HH main source of income_**||||
|Change to Commercial farming|1.5907***|1.5266***|0.9465***|
||(0.2709)|(0.2644)|(0.2440)|
|Change to Wage employment|-0.3141**|-0.2491|-0.2371|
||(0.1474)|(0.1544)|(0.1596)|
|Change to Non-agricultural self-employment|-0.0075|0.0063|-0.0465|
||(0.1675)|(0.1607)|(0.1523)|
|**_Other source of HH income_**||||
|Property income and investments|||0.3346|
||||(0.2359)|
|Transfers and other benefits|||0.0351|
||||(0.1577)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.6417***|
||||(0.0973)|
|Observations|1,706|1,706|1,506|
|R-squared|0.5762|0.5964|0.6544|

53
affected areas experiencing up to 1.2% greater negative impacts than non-conflict areas. Similar patterns were observed in Mozambique, where cyclones led to a 1.39% reduction in economic activities in conflict-affected populations compared to their non-conflict-affected counterparts. As a potential next step, a long-term difference in difference regression analysis, as well as the use of observational data that could be more nuanced and contextual, could explore, in addition to the short-term impact identified here, the long-term impact of disasters dynamics in conflict settings.

To fully comprehend these dynamics, it is essential to conduct further research to pinpoint the causal factors that exacerbate vulnerability in conflict-stricken settings. Our findings, however, emphasize the critical need for investment in disaster risk reduction and the prioritization of recovery initiatives in conflict-affected regions. Inhabitants of these areas suffer more significant impacts and face extended recovery periods in the aftermath of disasters, underscoring the necessity for focused interventions to reduce their vulnerabilities and bolster their resilience.

# Conclusion

has been given to understanding how conflicts influence disaster impacts and recovery. By employing a difference-in-difference method alongside geospatial and satellite imagery datasets, this paper seeks to address this literature gap by estimating the impacts of a disaster in conflict settings and contrasting them with impacts in non-conflict settings.

This study represents one of the initial attempts to quantitatively compare the diverging impacts of a disaster between conflict and non-conflict affected regions. Our findings suggest that in the aftermath of the floods of interest, conflict-affected areas in Nigeria and Mozambique experienced the most severe negative impacts following the flood event, up to 1.2% and 1.39%, respectively. These results indicate that conflict-affected areas bore a heavier burden of the floods compared to non-conflict areas. Additionally, the negative impacts in conflict settings continued to be larger than in non-conflict affected regions for the six months following the event, which gives an indication that these floods prolonged the recovery period for conflict-affected populations.

In conclusion, despite existing methodological constraints, our study demonstrates that the impacts of disasters are significantly more pronounced in conflict settings compared to non-conflict areas. In the next phase of our analysis, we aim to supplement the current 'top-down' approach of assessing disaster impacts through satellite imagery with a 'bottom-up' approach using panel household survey data. By combining both approaches, we will gain insights into household-level impacts and 29
|**Table 2: Characteristics of the 100 Discovery Schools**|
|---|

|_School var_|_iable_|_Number_|
|---|---|---|
|**Gender**|Male<br>Female<br>|42<br>44|
||Co-ed school (up to 4<sup>th</sup>grade only)|14|
|**Level**|Primary: grades 1–9|24|
||Secondary: grades 10–12|76|
|**Size**|1–499|21|
|(number|500–999|43|
|of|1,000–1,499|34|
|students)|1,500–2,000|2|

Source: Authors’ calculations.

The program has succeeded in introducing ICT equipment and software into 100 schools, providing, on average, 6 to 8 laptop computers to each school and creating at least one (in some cases, up to six) computer lab per school. The initiative has also trained 3,000 teachers and promoted the development of six packages of educational software and corresponding teacher training modules (Table 3). In fact, initial investments made during the first phase of operations resulted in products ready for export. JEI also introduced the concept of ―in-classroom‖ technology and advocated the teaching of IT to students based on practical, hands-on use of technology as a tool for learning.

**Table** **<u>3: Core Data on Discovery School Beneficiaries</u>**

|_Beneficiary _|_Number_|_Comments_|
|---|---|---|
|Schools|100|Discovery Schools (DS)|
|Administrators|200|Principals and assistant principals|
|Teachers|3,000|Users of e-content in DS|
|Students|80,000|Users of e-content in DS|
|Ministry experts|120|Writers and reviewers|
|Lab technicians|1000|Jobs created as an outcome of JEI|
|Youth|150|New college graduates offered internship opportunities|
|Local companies|5|Worked on developinge-content for the first time|

Source: Authors’ calculations

# **Performance**

Consistent with its objectives, JEI has been effective in: (i) establishing a test bed for innovation in the realm of ICT and education in Jordan; (ii) helping develop the ICT and education industry in Jordan; and (iii) developing and testing a model of education reform based on partnerships that can be applied by other countries.

6
Table 10: Eect of a direct SLM training intervention on other farmers' access to contact farmers, adoption, and knowledge

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Access to CF|||||
|Has access to any contact farmer in the last half year|0.170|0.032<br>[0.027]|10,955|0.046|
|Other Farmer Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.004<br>[0.012]|10,955|0.055|
|Number of techniques known by name|1.654<br>[1.538]|0.000<br>[0.120]|10,955|0.022|
|Number of techniques adopted|0.845<br>[0.891]|-0.034<br>[0.071]|10,955|0.060|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a dummy for male, a single dummy, a widow dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

CF=contact farmer; ITT=intent-to-treat eect; SLM=sustainable land management.

41
We also analyze whether firms of different size differ in the degree to which their issuance of new equity responds to increased funding by foreign equity investors.

Ours is the first study, of which we are aware, that examines the links between capital inflows and investment using issuance-level data. To do so, we assemble a large granular dataset containing information on 20,306 seasoned equity issuances (SEOs) and financial statements for 12,723 firms in 25 emerging market countries, in addition to capital inflows during the 26-year period 1991-2016. The total number of firm-year observations is 330,798.

Figure 1 presents the evolution of the aggregate amount of equity investing by foreign investors into our sample countries alongside the total value of seasoned equity raised by firms in those countries. The figure shows that periods of large capital inflows coincide with periods of active equity issuance activity. This shows that inflows are associated with more than a simple transfer of equity ownership from domestic to foreign investors, and suggests that issuances are not purchased solely by domestic investors. In terms of causality, this correlation could reflect supply-side variation. For example, foreign equity inflows resulting from greater global liquidity increase the supply of funds available to firms.<sup>2</sup> Such an increase in the supply of foreign participation in equity markets implies a higher investor demand for equity and lower required equity returns, which in turn encourage destination countries’ firms to issue shares. Alternatively, the positive correlation between inflows and issuances could reflect shifts in the demand for capital by firms (resulting, for example, from improvements in investment opportunities at the country level). According to that view, better firms’ prospects induce foreign investors to send more funds to the country.

> 2 Chari et el. (2017) document significant effects of U.S. monetary policy surprises around FOMC meetings on capital flows from the U.S. to a range of emerging markets as well as on the associated emerging market valuations. They find that equity positions and valuations are more sensitive to monetary policy shocks than debt positions and valuations.

2