rafmacalaba/datause-encoder-data huggingface dataset:
|Work is harmful or<br>dangerous<br>Constant|4.219***|4.217***|5.173***|5.172***|4.840***|4.840***|0.121***<br>(0.0233)<br>4.769***|
|---|---|---|---|---|---|---|---|
||(0.0222)|(0.0222)|(0.0828)|(0.0824)|(0.0643)|(0.0644)|(0.0649)|
|Observations|60318|60318|60318|60318|4318|4318|3881|
|Year FE|Y|Y|Y|Y|-|-|-|
|Region FE|Y|Y|Y|Y|Y|Y|Y|
|Individual FE|N|N|Y|Y|N|N|N|

Source: Authors’ estimation using RLMS 2004-2016. Note: Columns (1) to (6) include all observations, column (7) exclude self-employed informals.

This table shows the estimated coefficients from OLS regressions of hourly wages on informal employment and other individual and job-related characteristics. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Sample used in these wage regressions exclude outliers (1% from both tails). All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

54 Informal Employment and Worker’s Wellbeing in Russia / June 2019
market activity, unpaid work burdens, informality, constraints to accessing markets, and inequalities in access to skills and productivity-enhancing technologies and inputs. Along with an overview of the policy and research landscape, this paper presents data from recent LSMS-ISA surveys (Ethiopia, Malawi, Nigeria, and Uganda), as well as recent country pilots conducted by the ILO, to highlight priority areas on measuring rural women’s employment. The recommendations in this paper outlined at the end of each section can broadly be categorized into two areas: first, what we already know and should currently be incorporated in surveys; and second, where survey methods still need to be explored through research and testing.

Under the first set of recommendations , ensuring the accurate counting of rural women’s employment is important through boundary questions in agriculture, asking about different forms of casual labor, and framing/recovery questions to ensure key areas of self-employment are not underreported. Adding questions on desire to work can also be explored in the short term. On decision making, ensuring the same respondents are reporting across labor/non-farm enterprise modules is important (or that both men and women are asked). A question on constraints to seeking better economic opportunities is also needed, across safety, market access, and time-related constraints.

The second category of recommendations requires further research, including how unpaid work burdens can be better elicited in surveys, why proxy reporting tends to under-report time in paid and unpaid work, and how community data could complement individual data on hard-to-measure topics such as wages and access to child care. It also includes developing a survey research agenda around measuring work amid rural economic transition, including individual-level data on work-related migration across surveys, skills and access to technology.

Overall, this paper provides an initial overview of specific issues that should be highlighted in surveys to ensure more accurate counting of rural women’s employment, and in turn improving links to the design of rural employment policies. When collected over time, additional data in these areas can also help provide insights on the dynamics of women’s employment during economic cycles and rural economic shifts, and help in broader economic planning as well.

# References

Anderson, C. Leigh, Travis W. Reynolds and Mary Kay Gugerty. 2017. “Husband and Wife Perspectives on Farm Household Decision-making Authority and Evidence on Intra-household Accord in Rural Tanzania.” _World Development_ 90: 169–183.

Beegle, Kathleen, Emanuela Galasso and Jessica Goldberg. 2017. "Direct and Indirect Effects of Malawi’s Public Works Program on Food Security.” _Journal of Development Economics_ 128: 1-23.

Benes, Elisa, and Kieran Walsh. 2018a. “Measuring Employment In Labour Force Surveys: Main findings from the ILO LFS pilot studies.” International Labour Organization.

Benes, Elisa, and Kieran Walsh. 2018b. “ILO LFS Pilot Studies Cognitive Interviewing Tests: Methodology, Process and Outcomes.” International Labour Organization.

23
puts us in a position to understand what it takes to actually create demand for public action and participation, and how long it can be sustained.

**Table 6: Summary of Intervention Design**

||**Intervention 1:**<br>**LOCAL AGENCY**|**Intervention 2:**<br>**[1] + LEARNING**|**Intervention 3:**<br>**[2] + ACTION**|
|---|---|---|---|
|**Provides**<br>**information**<br>**about**|Village Education<br>Committees (VECs)|VECs + hamlet-by-<br>hamlet information on<br>children’s enrollment<br>and learning status|VECs + Learning outcomes +<br>Pedagogical tool with rapid<br>impact on reading|
|**Mode of**<br>**information**<br>|Facilitating local genera<br>meetings where local ag|tion and public sharing of i<br>ents share information with|nformation—organizing village<br>the village|
|**transmission**|||Volunteers invited and trained in<br>pedagogical tool|
|**Element of**<br>**advocacy**|People urged to come to<br>explore what they can d|gether as a village to discu<br>o about it|ss problems in education and|
|||Using the testing tool to<br>on learning outcomes|mobilize people; encouraging focus|
||||Volunteers invited to make their<br>village a “Reading Village”|
|**Immediate Goal**|Local participation in e|ducation services|Direct improvement in learning<br>outcomes|

# 5. **Intervention implementation and evaluation plans**

The three interventions were simultaneously launched on September 5, 2005. The interventions were separately implemented in randomly selected 65 villages each, with the remaining 85 villages being the “controls” where no campaigns take place. There were 10 intervention teams, consisting of 3 people each, and 2 additional teams for the training of local volunteers in the third intervention. The implementation was completed toward the end of December 2005.

We plan to evaluate how the interventions described above impact local participation and learning outcomes, and what local conditions it depends upon, by comparing changes within “treated” villages with changes within “control” villages before and after the interventions were implemented. Follow-up surveys will collect information on the same basic variables as the baseline survey—child learning, school functioning (funds available, facilities available, teacher performance), and local participation (VEC activity, parent engagement). New data will also be collected to examine more carefully the mechanisms of change that might be brought about by the interventions. Following is a 27
<mark>transformative Bandebereho couples’ intervention to promote male engagement in reproductive and maternal health and violence prevention in Rwanda: findings from a randomized controlled trial.</mark> _<mark>PloS one</mark>_ <mark>,</mark> _<mark>13</mark>_ <mark>(4), e0192756.</mark> Freire, P. (1990). Pedagogy of the oppressed. New York, NY: Continuum.

<mark>Hatlebakk, M., & Gurung, Y. B. (2016). Female empowerment and the education of children in Nepal.</mark> _<mark>The Journal of Developing Areas</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(2), 1-19.</mark> International Labor Organization (ILO). (2017). Empowering Women in the Rural Economy.

Article.

<mark>Kabeer, N. (1994).</mark> _<mark>Reversed realities: Gender hierarchies in development thought</mark>_ <mark>. Verso.</mark> <mark>Kabeer, N. (1999). Resources, agency, achievements: Reflections on the measurement of women's empowerment.</mark> _<mark>Development and change</mark>_ <mark>,</mark> _<mark>30</mark>_ <mark>(3), 435-464.</mark> <mark>Kim, J., & Motsei, M. (2002). “Women enjoy punishment”: attitudes and experiences of genderbased violence among PHC nurses in rural South Africa.</mark> _<mark>Social Science & Medicine</mark>_ <mark>,</mark> _<mark>54</mark>_ <mark>(8), 12431254.</mark> Kishor, S. (2000). Empowerment of women in Egypt and links to the survival and health of their infants. in H.B. Presser, G. Sen (Eds.), _Women’s Empowerment and Demographic Processes_ , Oxford University Press, Oxford (2000), 119–156.

33
- Tarozzi, Alessandro et al. (2014). Micro-loans, insecticide-treated bednets, and malaria: evidence from a randomized controlled trial in Orissa, India. American Economic Review 104.7, pp. 190941.

- Tatem, Andrew J et al. (2009). The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents. Malar J 8, p. 287.

- Thomas, Christopher J, Dónall E Cross, and Claus Bøgh (2013). Landscape movements of anopheles gambiae malaria vector mosquitoes in Rural Gambia. PloS one 8.7, e68679.

- Torres-Sorando, Lourdes and Diego J Rodriguez (1997). Models of spatio-temporal dynamics in malaria. Ecological modelling 104.2, pp. 231240.

- Venkataramani, Atheendar S (2012). Early life exposure to malaria and cognition in adulthood: evidence from Mexico. Journal of health economics 31.5, pp. 767780.

- Vercruysse, J, M Jancloes, and L Van de Velden (1983). Epidemiology of seasonal falciparum malaria in an urban area of Senegal. Bulletin of the WHO 61.5, p. 821.

- Wesolowski, Amy, Nathan Eagle, et al. (2012). Quantifying the impact of human mobility on malaria. Science 338.6104, pp. 267270.

- Wesolowski, Amy, Elisabeth zu Erbach-Schoenberg, et al. (2017). Multinational patterns of seasonal asymmetry in human movement inuence infectious disease dynamics. Nature communications 8.1, p. 2069.

- Wesolowski, Amy, CJE Metcalf, et al. (2015). Quantifying seasonal population uxes driving rubella transmission dynamics using mobile phone data. Proceedings of the National Academy of Sciences 112.35, pp. 1111411119.

- Wesolowski, Amy, Taimur Qureshi, et al. (2015). Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences 112.38, pp. 1188711892.

- White, NJ (1997). Assessment of the pharmacodynamic properties of antimalarial drugs in vivo. Antimicrobial agents and chemotherapy 41.7, p. 1413.

46
This note is organized as follows. Section II describes the data used in the note, Section III summarizes aggregate impacts of the financial crisis, Section IV describes the methodology used to measure distributional impacts of the crisis, Section V presents results, and Section VI concludes.

# **II. Data**

The main data source for this effort was the Latvian EU-SILC 2006 database, which was the most recent survey available in 2009 when the simulations were conducted. The survey is annual with a four-year rotational panel.<sup>4</sup> In 2006, the fieldwork was carried out March through November during which time 4,315 households (9,391 individuals aged 16 and over) were interviewed. Sampled households also included 1,594 children under 16 years of age. The reference population comprises private households with members residing in the national territory. The survey includes modules of income, labor market activity, demographics, education, health, housing, social programs, access to some durable consumption goods, and subjective welfare.

# **III. Aggregate Impact of the Financial Crisis**

Latvia’s GDP contracted by 18 percent in 2009 relative to its GDP in 2008.<sup>5</sup> The Ministry of Economics forecast the sectoral breakdown of the GDP contraction (Figure 1). The three hardest hit sectors were trade hotels and restaurants (projected to contract by 24 percent); construction (projected to contract by 19.5 percent); and manufacturing (projected to contract by 18.8 percent). The forecasts suggested that the sectors likely to be least affected were transport, expected to decline by 4.2 percent, and communication and the primary sectors, expected to decline by 7.2 percent.

> 4  The Latvian EU-SILC survey uses a stratified two-stage sampling design. In the first stage, systematic sampling of the primary sampling units was selected. In the second stage, simple random sampling was used to select secondary sampling units. The survey was stratified by the degree of urbanization. 5  Ministry of Economics, Government of Latvia.

4
# **Contents**

|**1**|**Intr**|**oduction**|**4**|
|---|---|---|---|
|**2**|**Con**|**ceptual Outline of the Proposed Stress Testing Approach**|**8**|
|**3**|**Con**|**struction of Macroeconomic Scenarios**|**12**|
||3.1|TTC and PIT Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|14|
||3.2|Country-Specifc Stress Scenario. . . . . . . . . . . . . . . . . . . . . . . . . .|16|
||3.3|Stress Scenario Based on International Experience<br>. . . . . . . . . . . . . . .|19|
|**4**|**Lin**|**king Macroeconomic Scenarios to Credit Risk Factors**|**22**|
||4.1|TTC Macroeconomic Scenarios, PDs and LGDs . . . . . . . . . . . . . . . . .|23|
||4.2|Estimating the NPL Elasticities<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|25|
||4.3|Macroeconomic Mapping in Normal versus Crisis Times . . . . . . . . . . .|29|
||4.4|Linking Macroeconomic Scenarios to Changes in NPLs . . . . . . . . . . . .|30|
||4.5|Accounting for Macroeconomic Risks in Aggregate PDs: The Systemic Com-<br>ponent of Credit Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
||4.6|Incorporating Bank Specifc Characteristics: The Idiosyncratic Component<br>of Credit Risk<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|33|
|**5**|**Exp**|**osures at Default**|**36**|
|**6**|**Con**|**struction of Outcome Indicators**|**38**|
|**7**|**Em**|**pirical Application**|**49**|
||7.1|Data Requirements and Calibration of Parameters . . . . . . . . . . . . . . .|49|
|||7.1.1<br>Macroeconomic Scenarios . . . . . . . . . . . . . . . . . . . . . . . . .|49|
|||7.1.2<br>Bank Level Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|51|
|||7.1.3<br>Estimated Bank-Level PDs and LGDs<br>. . . . . . . . . . . . . . . . . .|53|
|||7.1.4<br>Credit Risk Exposures, Loan Concentration and Residual Maturity .|53|
||7.2|Results of the Stress Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|56|
|||7.2.1<br>Aggregate Results for the Banking System. . . . . . . . . . . . . . . .|56|
|||7.2.2<br>Bank Level Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|60|
|||7.2.3<br>Sensitivity of Outcome Indicators to Changes in Calibrated Parameters|63|
|||<br>7.2.4<br>Application of the Approach When No Bank-by-Bank Supervisory<br>Data are Available. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|64|
|**8**|**Con**|**clusion**|**67**|
|**Re**|**fere**|**nces**|**69**|

2
|**_Table 7c: C_**|**_ontributio_**|**_n rates ne_**|**_eded to_**|**_gener_**|**_a arep_**|**_laceme_**|**_nt_**_rate _|_of_**_50%_**|_of_**_ave_**|**_rage ma_**|**_le_**_adult_|_earnin_|_gs_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|I|||dY|=0%||dY =|1%||dY =|1.5%||dY =|2%|
|~~~<br>Contribution|~~<br> <br>e|18|21|25|18|21|25|18|21|-_<br>25|-<br>18|_<br>21|25|
||Deaith_|=|||||||||||_|
|Real i=2%||||||||||||||
|Retire at 55|80|22|24|27|26|28-|<br>31|29|30|33|31|33|36|
||81|23|25|28|27|29|32|30|31|34|32|34|37|
|___________|83|24|26|29|29|30|34|31|33|36|34|35|39|
||85|25|27|_31_|30|12|35|33|34|38|35|37|41|
|Retire at**60**|80|16|17|19|19|20|22|21|22|24|23|24|26|
|____________|81|16|17|19|20|21|23|22|23|25|24|25|27|
||83|17|18|21|21|22|24|23|24|26|25|26|29|
||85|18|20|22|22|24|26|25|26|28|27|28|30|
|Retire at 65|80|11|<br>11|13|13|13|15|15|16|17|17|17|18|
||81|11|<br>12|13|14|15|16|16|16|18|17|18|19|
||83|12|13|14|15|16|18|17|18|19|19|20|21|
||85|13|14|16|17|is|19|19|19|21|21|21|23|
|Retire at 70|80|7|7|8|9|9|10|10|10|<br>11|11|11|12|
||81|7|8|8|9|10|11|11|11|12|12|12|13|
||83|8|9|10|11|11|11|12|13|13|14|14|15|
||_85|9|10|11|<br>12|12|1|13|14|15|15|16|17|
|Real i = 3%|||____|___||||||||||
|Retire at 55|80|17|18|21|20|22|24|22|23|26|24|25|28|
|___________|L81|17|19|22|21|22|25|23|24|27|24|26|29|
|____________|83|18|20|22|22|23|26|24|25|28|26|27|30|
||85<br>|119|211|24|23|24|27|125|26|29|27|28|31|
|Retire at 60|80|12|13|14|14|15|17|16|17|19|13|18|20|
||81|12|13|15|15|16|18|16|17|19|18|19|21|
||83|13|14|16|16|17|19|17|19|20|19|20|22|
||85|14|15|17|17|18|20|19|20|22|21|22|24|
|Retire at 65|80|8|9|10|10|11|12|1<br>1|12|13|13|13|14|
||81|8|9|10|11|11|12|12|2|14|13|14|15|
||83|9|10|11|11|12|13|13|14|131|14|15|16|
||85|10|10|12|12|13|14|14|15|l6|15|16|17|
|Retire<br>at<br>70|80|5|5|6|7|7|8|7|8|8|8|9|9|
||81|5|6|6|7|7|8|8|8|Q|9<br>_|9|10|
||83|6|6|7|8|8|9|9|9|10|10|1 11|<br>11|
|_|85|7 1|<br>7|8|9|9|10|10|10|11|111|12|13|

36
decomposition, having access to more land confers a large earnings advantage to male farmers (18.5%). Agricultural inputs are the main contributor to a greater earnings advantage for male farmers (29.2%).

# 5. CONCLUSIONS AND POLICY IMPLICATIONS

While the previous literature overwhelmingly found an agricultural gender gap in favor of male farmers, the case of Sri Lanka is unique as agricultural productivity, measured by gross output per acre, is 25.4% higher among female farmers than male farmers. Using the Oaxaca-Blinder decomposition technique, we find that the leading factor contributing to this unconditional female productivity advantage is a smaller cultivated area among female farmers, combined with the presence of an inverse relationship between cultivated area and productivity. This female productivity advantage does not translate into higher income among female farmers, however, as smaller plots are associated with lower crop income. Thus, policies designed to promote equitable land rights for women and men could help address this gap. With regard to land ownership, there are some long-standing issues that may warrant attention. For example, Sri Lanka’s land laws may discriminate against women who opt to be governed by personal laws. Women who marry under the Thesawalami law cannot gain control of property without their husband’s consent (Zainudeen 2016). Under the Land Development Ordinance of 1935 and its subsequent amendments, the grant of state land in agricultural settlement schemes continues to favor men over women because grants are generally made to the male head of the household (Ranaraja 2020).

Another important source of Sri Lanka’s female productivity advantage is the different pattern of crop mix cultivated by male and female farmers. While the crop mix of female farmers contains a relatively large portion of export-oriented, high-value crops such as tea, the typical crop mix among male farmers includes a large share of paddy, a domestically oriented crop which yields low returns. Thus, promoting export-oriented, high-value crops can help raise productivity and incomes. However, the Sri Lankan government’s import substitution strategy, which favors import competing commodities such as paddy, along with a fertilizer subsidy program mainly focused on paddy cultivation, is likely to discourage a shift toward high-value agriculture (World Bank 2013, 2015). Improving productivity of paddy can contribute to food security while releasing land and labor for the cultivation of higher-value crops, within agroclimatic constraints.

25
Policy Research Working Paper 10276

# **Abstract**

Although many countries have established investment promotion agencies over the past two decades, there is little evidence on what characteristics make them effective in attracting foreign direct investment into their home country. To provide new insight into this question, this paper brings together sectoral foreign direct investment data with survey data on investment promotion agency characteristics. Using a structural gravity model framework, it explores the effect of investment promotion agencies’ sectoral targeting on inward foreign direct investment stocks over 2013 to 2018, across a sample of 36 middle- and high-income countries. The study finds that investment promotion agency sectoral targeting provides a significant positive effect on the sector’s foreign direct investment stock in that country. Yet, a gravity model with country-interaction effects suggests that not all countries are equally effective at promoting investment. The results from the model are used to define two groups: high-performing investment promotion agencies (those with positive, significant effects in attracting foreign direct investment) and other investment promotion agencies (those with insignificant or negative significant effects). Using t-tests, the study considers which investment promotion agency characteristics significantly differ between the two groups. The findings suggest that effective investment promotion agencies are more likely to be private or semi-private agencies. Their mandate tends to be focused narrowly on foreign investment and exclude responsibilities for domestic investment promotion. Such investment promotion agencies are more likely to have a board of directors, and their staff tends to be better compensated. Finally, high-performing investment promotion agencies tend to provide more investor services, partly by engaging smart, sectoral analytics and adopting systems for identifying investor complaints or disputes.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at vsteenbergen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team