rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9451

# **Abstract**

Entrepreneurs in Myanmar face many challenges to starting and operating a business. As is the experience globally, women often experience these challenges to a greater extent and face additional sociocultural barriers, limiting their equal participation in the economy. To develop a better understanding of the dynamics holding back private sector development, especially for women, this paper uses data from the first-of-a-kind, firm-level data set available in Myanmar. The analysis explores the variance of experience female-owned micro, small, and medium-size enterprises face compared with their male-owned counterparts. The paper assesses the barriers imposed on entrepreneurs and their businesses and identifies firm-level characteristics leading to the use of good business practices. Further, the analysis investigates the adoption of gender and family-friendly policies, as an outcome and as a determinant of business success. The purpose of the study is to gain a better understanding of the barriers to gender-inclusive private sector development in Myanmar and provide tangible recommendations to private- and government-level actors. Overall, the analysis finds the major constraints for women entrepreneurs are access to finance and sociocultural factors, such as family responsibilities and household work.

This paper is a product of the Macroeconomics, Trade and Investment 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 authors may be contacted at fhayati@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
002]|[0.002]|[0.684]|[0.607]|[0.642]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, gender, education, marital status, number of children, dummies for selfemployment, dummy for working and an interaction between self-employment and lagged (2015) dependent variable and dummy for “willingness to remain in the city permanently if policy permits.”), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. 32
TABLE 9 Regression results of labor force participation rates, robustness check: linear-quadratic trend

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 y**|**ears old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Fertility|0.697***||0.059*||0.000||||||
||(0.000)||(0.011)||(0.922)||||||
|Secondary enrollment|0.202***|0.125***|||||||||
||(0.000)|(0.000)|||||||||
|Tertiary enrollment|||-0.113***|-0.180***|||||||
||||(0.000)|(0.000)|||||||
|Completion of secondary<br>dti|||0.040|-0.013|0.236***|0.1340***|0.403*
the review.

Almost all studies are described as _natural experiments_ by the authors but face major measurement and identification challenges. Availability of micro data is one of the challenges and the first explanation of why these types of studies have emerged only very recently.<sup>7</sup> All studies have been undertaken _ex-post_ , after the displacement crisis has taken place. The unexpected nature of the crisis and the randomness of the allocation of displaced persons are two elements used to defend the natural experiment assumption. However, all papers address the central question of endogeneity and unobserved heterogeneity. How random is the decision to leave, the choice of destination or the type of people who flee? What other

> 7 National household surveys do not normally cover displaced populations and humanitarian agencies in charge of displaced populations do not normally cover host populations in their surveys. These latter surveys also rarely contain socio-economic information of sufficient quality to be used in econometric studies, not least because issues such as sampling and questionnaire design are extremely difficult with mobile populations such as refugees and IDPs. Registration data do not always capture the whole displaced population, might be outdated and focus on the displaced rather than host communities. Displaced people are usually hosted in marginal areas where data are scarce or of poor quality.

4
Prevalence, Economic Contribution, and Determinants of Trees on Farms across Sub‐Saharan Africa Daniel C. Miller<sup>†</sup> , Juan Carlos Muñoz-Mora<sup>‡</sup> , and Luc Christiaensen<sup>*1</sup>

**JEL Codes:** O13, Q23, Q20 Q57. **Keywords:** Trees on Farms, Agroforestry, Forests, Poverty, Livelihoods, Sub-Saharan Africa.

1 †Corresponding Author: Daniel Miller (dcmiller@illinois.edu), Assistant Professor, Natural Resources and Environmental Sciences, University of Illinois, Urbana-Champaign.<sup>‡</sup> Juan Carlos Muñoz-Mora (juancarlos.munoz@upf.edu), Post-Doctoral Fellow, Department of Economics and Management, Pompeu Fabra University.<sup>*</sup> Luc Christiaensen (lchristiaensen@worldbank.org), Lead Economist, Jobs Group, World Bank. This paper has been produced under the “Agriculture in Africa - Telling Facts from Myths” project, which revisits common wisdom about African agriculture and farmer livelihoods using household survey data collected under the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative. Funding from the Program on Forests (PROFOR) is gratefully acknowledged. The authors thank Karen Brooks, Frank Place, Laura Vang Rasmussen, Cristy Watkins, and participants at the Myths and Facts workshop at IFPRI (2015) and the Forests & Livelihoods: Assessment, Research, and Engagement (FLARE) Network Conference in Paris (2015) for helpful comments on an earlier draft.
(in particular, inflation) does not have a significant effect. The institutional measures do not seem to be able to capture any effect beyond what was already reflected by the development indicator.

The between-country results reflect the determinants of cross-country variability at a given point in time, where countries implement different technologies, but presumably operate under the same set of available technology. Schooling is positively significant, suggesting that education was conducive to the techniques used by the more productive countries. Again, peak yield and the institutional measures are not significant. Development has changed to being significantly negative.

> 30 The early literature on cross-country studies on agricultural production was reviewed in Mundlak, Larson, and Butzer (1999).

16
employment impact of further deregulation is thus not negligible, although it appears to be modest compared to other, non-CFA African countries (particularly, Kenya and Zimbabwe). Unfortunately, these surveys only allow to identify the microeconomic distortions created by labor market policies. They do not allow to assess whether these policies. are a source of wage misalignment.

# 3. The Extent of Wage Misalignment

a) Compared to wages in other countries One way to assess the extent of wage misalignment is to compare average wages in the CFA zone to those in other countries with a similar development level. Controlling for the development level is key in this respect. Indeed, wages can be expected to be high, relative to the average productivity of labor, in poor countries. The salaried relationship is characteristic of the formal sector of the economy, and this sector is small in poor countries. Labor productivity and eamings tend to be much lower in agriculture, and for the urban self-employed. As a result, wage earners appear as genuinely privileged compared to the average worker. The issue is whether wage earners in CFA countries are more privileged than those in other developing countries.

This issue can be addressed by regressing the ratio of wages to labor productivity on an indicator of the development level, and then analyzing whether CFA countries exhibit some systematic deviation with respect to the estimated regression line. This is done in Table 2, using data from a cross-country data base of labor market indicators currently under construction at the Research Department of the World Bank. This data base includes information on annual wages in current dollars for more than one hundred countries, including six from the CFA zone. Most of the figures are from plant-level surveys collected by UNIDO. The data are from period 1985-93, but in many countries there are only a few observations over this period.

10
do not account for this biological externality because they are paid according to the tons they catch and the international price of fishmeal, not the composition of juveniles and adults they catch (Fr ́eon et al., 2014; Sindicato Univo de Pescadores de Nuevas Embarcaciones del Peru (SUPNEP), 2017; Hansman et al., 2020). A tax could achieve the optimal level of juvenile catch, but the fishing industry opposes such a policy (Grainger and Parker, 2013; Instituto Humboldt de Investigaci ́on Marina y Acu ́ıcola (Instituto Humboldt), Sociedad Nacional de Pesquer ́ıa (SNP), Centro Desarrollo y Pesca Sustentable (CeDePesca), 2018). Instead, the regulator attempts to reduce juvenile catch by implementing temporary spatial closures in areas where it believes juveniles are abundant.

I analyze the regulator’s temporary spatial closures policy in this paper, accounting for other regulations that also affect fishing. I use administrative microdata and data from fishing firms, which together contain the location, time, and number of juvenile anchoveta each vessel catches each time it sets its net in the water. These data comprise hundreds of thousands of vessel-level fishing operations. Vessels report the percentage juvenile they catch to the regulator in real-time.<sup>1</sup> When percentage juvenile values in an area are high, the regulator temporarily bans fishing in that area for three to five days. Vessels are not allowed to fish inside actively closed areas. But they are allowed to fish inside closed areas between the announcement and the beginning of closure periods, just outside closed areas during closure periods, and inside closed areas after the end of closure periods. Between 2017 and 2019, the regulator implemented 410 temporary spatial closures, each covering a different area of ocean and time period.

Estimating the causal effect of the temporary spatial closures policy requires counterfactual areas and times that could have been closed and are comparable to closures declared by the regulator. To address this challenge, I generate “potential closures” by creating
eliminate this risk. We will nonetheless discuss the robustness of our results to the addition of Kampala.<sup>5</sup>

# **3.2. LSMS-ISA household data**

We use the Living-Standards Measurement Study - Integrated Studies on Agriculture (LSMSISA) data set for Uganda provided by the World Bank. These LSMS-ISA data sets are derived from the Uganda National Panel Survey (UNPS) which comprise 5 waves of interviews (20092010, 2010-2011, 2011-2012, 2013-2014 and 2015-2016). The UNPS collects information for a sample of households which is representative at the national, urban/rural and main regional levels (North, East, West and Central regions). The LSMS-ISA data set provides household and individual level information including household welfare measured by consumption aggregate per adult equivalent, indicators of participation and performance in the workforce within the agricultural and non-agricultural sectors.

We retain only the first 3 waves as they minimize attrition and have a similar structure. In the consecutive waves, parts of the sample were replaced by new households obtained from an updated sampling frame developed from the 2012 Uganda Population and Housing Census by Uganda Bureau of Statistics (UBOS). Additionally, a new household identification system was implemented after the third wave, making it difficult to construct a balanced panel data set across all 5 waves.<sup>6</sup> Our household data is therefore a strongly balanced panel data set comprising 2,458 households distributed across 320 enumeration areas (EA) and surveyed in the first 3 rounds of the UNPS. The data cover 106 districts out of the 111 listed in 2010 (127 districts in 2017).

# **3.3. Descriptive statistics by district**

We first compare households and individual characteristics between refugees-hosting and nonhosting districts between the first wave (2009/2010) and the last wave (2011/2012) of UNPS data collection. We do so using our main variables of interest (household welfare measured by consumption per adult equivalent, individual labor market outcomes,
and the Buenos Aires Metropolitan Area (AMBA) and the associated availability of high frequency and spatial resolution mobility data to understand how mobility evolved during the pandemic. While the pandemic impacted numerous mobility indicators that matter for the system’s environmental and financial sustainability, only a subset of these can be considered to have persisted beyond the immediate lockdown period and thus not only represent more permanent changes in preferences and behaviors but are also more relevant for transport policy and planning going forward. The analysis distinguishes between short-term changes in mobility, as observed over the period between October 2019 and October 2020, and changes that also more or less persisted a full year later despite the removal of formal restrictions to mobility in both cities. The four categories of indicators tracked for the three time points are: trip generation, temporal distribution of trips, spatial distribution of trips, and modal split. In turn, indicators under each of these categories are segmented according to traveler (e.g., socioeconomic/ income group) and trip characteristics (e.g., travel purpose), in order to identify variations in their value according to different demand segments.

# **2. Review of the literature**

Since the outbreak of the pandemic, several studies have analyzed mobility changes in cities in the context of the pandemic, including in Latin America. Focusing on Colombia and India, Vallejo-Borda et al. (2023) model the travel choices to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios. They find that in both countries the utility related to active modes (more used) and public transportation (less used) changed during the pandemic, and that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. Mejia-Dorantes et al. (2021) analyzed mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak using a combination of traditional travel surveys and smartphone data. They found that after
............................|<br>6|
|||Salary Restraint<br>...................................|<br>6|
|||Employment Reforms<br>.................................|<br>7|
|||Severance and Redeployment<br>.........................|<br>9|
|||Changes in the Salary Structure ......................|<br>10|
|||Non-Wage Mechanisms for Attracting and||
|||Retaining High-Level Personnel ......<br>............|...<br>11|
||D.|OPERATIONAL TECHNIQUES ..........<br>...................|...<br>13|
|||Prerequisites to Reform:Data Collection,Computerization,<br>Civil Service Census, Functional Review .....<br>.......<br>|<br>.. 13<br>|
|||Civil Service Censuses ............. ...............
# _3.4 Limitations of the study_

This assessment of the impact of the JCLIS relies on simple comparisons of the average outcomes of interest between participants of the lift irrigation system and their neighbors in the same village or in a neighboring village who do not participate. We use self-reported recall data collected from farmers through in-person interviews with them. JCLIS sites and their beneficiaries were not selected randomly. Site selection was guided by hydrological and topological characteristics. Farmers with land closer to favorable sites may differ from their neighbors in skills, motivation, social capital, and other qualities, any of which can also affect agricultural outcomes. Farmers were moreover required to pay a small one-time Rs. 1,100 fee and join the local water user group to irrigate their land from the JCLIS, and their decision to do so may also differentiate them from others who chose to stay out or wait longer before joining the group. The comparisons made are therefore qualified as suffering from omitted variables bias. Nor did the study benefit from baseline data on variables of interest prior to the intervention. Attempts to collect recall data from farmers with questions about cropping areas, patterns, and yields before the JOHAR irrigation systems were introduced led to unreliable data, particularly for earlier years, and were discontinued. We also considered revisiting farmers who were interviewed in the baseline survey carried out by Oxford Policy Management (OPM), but at the time of our survey (August-September 2021), very few villages in the baseline survey had a functioning JCLIS. We compare beneficiary and nonbeneficiary farmers across economic variables that presumably do not change over time or as a result of the JCLIS irrigation. These include average land holding size and asset holdings. We find that the two are not significantly different across the two groups, providing some evidence of baseline comparability. In an alternate specification, we drop the control farmers from the neighboring villages and compare JCLIS beneficiaries only with their geographical neighbors while controlling for village fixed effects and find results similar to our main specification. Results from this alternate
that each country produces. Intuitively, the global input-output table enables one to trace backwards through the production process to assess the value and identify the national origin of the intermediate inputs used (both directly and indirectly) to produce each country’s final goods. With this information, one can (for example) compute the amount of Canadian value added embodied in US-produced autos. We describe the exact calculations in Appendix B. We construct value-added contents

> 34Alternatively, by protecting domestic producers and raising the level of domestic production, high tariffs could mechanically raise the total amount of foreign value added used by domestic industry. This is not a concern with the log specification we implement, since ln � _FV A_<sup>_i_</sup> _xt_<sup>_/IM_</sup> _xjt_<sup>_i_</sup> � is purged of ln � _FG_<sup>_i_</sup> _xt_<sup>_/IM i_</sup> _xjt_ �. To be explict, let us write _FV A_<sup>_i_</sup> _xt_<sup>=</sup><sup>_fvai_</sup> _xt_<sup>_FGi_</sup> _xt_<sup>,where</sup><sup>_fvai_</sup> _xt_<sup>istheshareofforeignvalueaddedindomestic</sup> production. Then, ln( _FV A_<sup>_i_</sup> _xt_<sup>_/IM_</sup> _xjt_<sup>_i_) = ln(</sup><sup>_fva_</sup> _xt_<sup>_i_) + ln(</sup><sup>_FGi_</sup> _xt_<sup>_/IM i_</sup> _xjt_<sup>).Sincewecontrolforln(</sup><sup>_FGi_</sup> _xt_<sup>_/IM i_</sup> _xjt_<sup>)</sup> directly
**-** **<u>Table 5: Comparing PSG participants and non participants based on SAR & remote sensing data</u>**

||<br>**Total**|<br>**By s**|<br>**ize in hect**|<br>**ares**|<br>**By type**|<br>**of organi**|**zation**|
|---|---|---|---|---|---|---|---|
|||**< 20**|**20-50**|**> 50**|**Legal Entity**|**FOP**|**Individual**|
|**Part A. Land program successful**|**applicants**|||||||
|Agric. Land (ha)|19.92|6.64|35.76|79.75|49.46|34.29|8.78|
|Number of parcels|7.16|2.98|11.03|27.37|18.63|12.11|2.95|
|Cult. in 2019 (ha)|19.47|6.47|34.96|78.04|48.37|33.43|8.59|
|Cult. in 2020 (ha)|19.66|6.54|35.30|78.86|48.90|33.76|8.67|
|Cult. in 2021 (ha)|19.52|6.50|35.05|78.24|48.52|33.49|8.62|
|Cult. in 2022 (
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to use a full time series of hazard data prior to regression analysis to generate a standardized anomaly.<sup>14</sup> 13 Some hazard measures such as the WRSI cannot be expressed as a standardized anomaly because by construction they are top coded (for example, the top value of WRSI is 100 percent). However, WRSI can be expressed as an anomaly (i.e., the ratio of current value with the mean or median).

14 Consider, for example, a cross-sectional household data set with three survey rounds and an identifier for the same districts (or clusters, though that is less likely) in each round. Using fixed effects for districts (clusters) transforms all the variables as deviations from the sample district (cluster) mean over all the household observations across the years in the same district (cluster) within the sample. With three survey rounds over the 20-year period analyzed, the district-specific sample mean of any given hazard measured is likely to differ significantly from the district mean estimated over a 20-year period. As a consequence, estimates of the welfare impacts of shocks are likely to differ significantly depending on whether the analysis uses the specification of the 15