rafmacalaba/data-use-mentions-extended huggingface dataset:
To answer these questions, we deploy a nationwide phone survey that includes a new approach to ascertain the valuation of migration options among respondents, and a randomized survey experiment that identifies the effects of political or economic stabilization on migration intentions. The remainder of this paper is organized as follows: Section 2 describes the survey conducted for this study including a new approach to measuring valuations of migration, and a survey experiment designed to estimate the effects of stabilization on migration intentions. Section 3 describes the survey data and summary statistics. Section 4 discusses the identification strategy. Section 5 presents the main results as well as key heterogeneous effects. Section 6 concludes with implications for policy and future research.

# 2. Study Design

While international migration typically accompanies the development process, resulting in net gains for both sending and receiving countries especially in the medium- to long-term, a sudden and large-scale immigration of high-skilled workers can result in critical shortages of qualified workers in key sectors of economic activity in the sending country. When emigration is undertaken under duress, induced by economic and/ or political instability, such detrimental effects may be more pronounced. When migration is motivated by a need to escape duress at home, prospective emigrants may be willing to accept lower wages to move abroad, than under more normal circumstances. They may also be more willing to take up work that they are overqualified for. Both such choices represent a potential misallocation of labor, provoked by the difficult circumstances faced by potential emigrants at home.

Our study seeks to understand the extent to which (i) political instability (resulting in civil conflict) and (ii) economic uncertainty drive the willingness of high-skilled youth in Myanmar to migrate abroad, the threshold wage differential at which they become indifferent between migrating and remaining at home, and the extent to which they may be amendable to undertake work for which they are overqualified. We examine these questions in the context of the military takeover of the Government in Myanmar since 2021, followed by high levels of violent conflict and economic collapse.
estimates for _αL_ and _αK_ are 0.619 and 0.283, both statistically signicant. The input requirement for materials _vm_ is computed as a weighted average of the share of expenditure in materials on total value of production. The estimate is 0.41.

We assume that the sector-level and rm-level shocks, _bjt_ and _Aijt_ follow independent AR(1) processes with correlation coecients _ρb_ , _ρa_ , variance of the innovation terms _σb_<sup>2and</sup><sup>_σ_</sup> _a_<sup>2,constant</sup> _b_ for the aggregate shocks and, without loss of generality, a constant of zero for the idiosyncratic shocks. Since the shocks are not observable we rst infer rm-level prot shocks from data on prots and inputs, the functional form assumption of the prot function, and the estimates of the production function parameters, as in Cooper and Haltiwanger (2006). We split rm-level prot shocks into _b_ and _A_ by computing sector-year means and subtracting them from rm-level shocks. We then run two separate AR(1) regressions to estimate the correlation coecients, variance and 15
# **5 Conclusion**

Measuring willingness to pay for goods and services is an important part of many research studies. Among other things, information on WTP can provide insights about welfare (e.g., Allcott and Kessler, 2019), allow cost-benefit comparisons (e.g., Cole and Fernando, 2020), and help explain treatment effects (e.g., Guiteras and Jack, 2018). Multiple price list elicitation is attractive for many reasons. It offers a compromise between the precise information revealed by BDM and the simplicity of TIOLI, and data from repeated choices can in principle be used to learn about error from inconsistent and dominated choices. As we show in this paper, in combination with standard MPL design features this error can introduce bias in WTP estimates.

We propose a straightforward, two-part approach to revealing both idiosyncratic and systematic choice error and incorporating it into WTP estimation. First, random variation in the order of choices within MPL and within binary choice reveals bias and minimizes its net impact on the estimated WTP. Second, latent utility estimation using random effects probit accommodates multiple switching, never switching and interval data, and can be used to estimate bias terms and both individual choice error and subject-level variance in WTP. To support other researchers interested in adopting these innovations, we offer a SurveyCTO package and a Stata ado file, along with the necessary instructions.

We focus on a single case study to demonstrate both the challenges with alternative approaches and the strengths of our approach. This case is intended to be illustrative and we are well aware of the limited general conclusions we can draw. For example, WTP estimation in populations that exhibit less error and bias will benefit less from both the randomized choice implementation and the estimation approach. Note, however, that only by randomizing the order of choices in the MPL and options within each binary choice researchers can test for any framing or anchoring effects in new samples.

A second limitation of our approach is that the correction of order bias
46 The expenditure share of LPG for all households increased in both urban and rural quintile, while the share for urban user households remained almost constant. The share for rural user households actually increased with quintile level (Table A2.14).

Table A2.14 : Shares of total household expenditure on LPG for all households and for purchasing households in Pakistan (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|<br>**Rural**|**User households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.1|0.1|0.1|2.5|4.4|2.9|
|**2**|0.1|0.2|0.1|2.6|4.4|3.0|
|**3**|0.2|0.4|0.2|2.8|4.9|3.4|
|**4**|0.3|0.3|0.3|3.3|4.4|3.6|
|**5**|0.6|0.4|0.5|3.3|4.6|3.7|
|**All**|0.3|0.3|0.3|3.1|4.6|3.5|

_Source:_ Authors’ calculations.

# **Sri Lanka (HIES 2006–2007)**

The data are taken from the 2006–2007 Household Income and Expenditure Survey (
estimators in terms of bias and efficiency also relies on normality. Note that the sample size of household surveys is typically very large and therefore normality tests are all expected to reject the simple null hypothesis of normality due to the strong evidence against it (which is never exactly true in real applications). Hence, usual residual plots for checking the normality assumption using both predicted area effects and conditional unit-level residuals should be an important part of any application with real data.

In fact, log transformation of welfares is indeed conventionally taken because practically all welfare variables show strong right-skewness and heteroscedasticity. First of all, it is important to note that, even after log transformation, residuals in some applications still show a skewed distribution, where the skewness is transferred to the left tail. This occurs often in the presence of very small welfare values, which the log function actually shifts to minus infinity. A simple way to solve this left skewness problem caused by the log function is to add a shift _c >_ 0 to the original survey welfares, shifting them to values further apart from zero, where the slope of the log function is less pronounced. As Molina, Nandram and Rao (2014) recommends, this shift _c_ can be obtained by fitting the nested error model to a grid of values of _c_ in the range of the welfare values, and selecting the value for which the Fisher skewness of unit level residuals is closest to zero. Another possibility is to select a different transformation from the Box-Cox or power families of transformations (which contain the log), as implemented in the sae R package of Molina and Marhuenda (2015). In fact, Box and Cox (1964) included also the shift in their original family of transformations.

If one wishes to avoid finding the correct transformation to achieve normality<sup>29</sup> or if residuals still show skewness, even after selecting the best possible transformation of welfare, approaches for EB estimation have been recently developed for skewed distributions, see Graf, Marín
# **2.3 The “Domestic Firms” Spreadsheets – AD-CTY-Domestic-Firms**

For many user countries, we also have a spreadsheet presenting a common set of information on domestic firms, trade associations and/or labor unions that are part of the filing of the antidumping investigation petition. The AD-CTY-Domestic-Firms spreadsheet has the same 2 initial column headings (Columns A and B) for each of the using countries, described in table 2.3.1.

**Table 2.3.1 : Variables in the AD-CTY-Domestic-Firms Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**CASE_ID**|Case identifier used to link observations across different elements of the database<br>[generated by us]|
|**B.**|**D_FIRM**|Domestic firm, trade association, industry group, labor union, etc. that is part of the<br>antidumping petition requesting the investigation|

# **2.4 The “Foreign Firms” Spreadsheets – AD-CTY-Foreign-Firms**

For many user countries, we also have a spreadsheet presenting a common set of information on foreign firms subject to the antidumping investigation, and, where available, the foreign-firm specific outcomes (e.g., level of duties imposed) at the resolution of the investigation. The AD-CTY-Foreign-Firms spreadsheet has the same 3 initial column headings (Columns A through C) as described in table 2.4.1.

**Table 2.4.1 : Variables in the AD-CTY-Foreign-Firms Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**CASE_ID**|Case identifier used to link observations across different elements of the<br>
purchased in our robustness checks), which is our reference input for measuring relative input adjustment costs.<sup>17</sup> Descriptive statistics by year on the main ASI variables used in our analysis are reported in Table 3.2.

As regards our classification of fast- and slow-growing states, we use value added time series provided by the Institution for Transforming India Policy Commission of the Government of India (NITI Aayog) and compute the growth rate of manufacturing value added from 1999 to 2014. States are classified as fastgrowing if they have an above-median rate of growth (Annex Table A3.1).

Our main governance classification is sourced from a study carried out by Transparency International India and the Centre for Media Studies (CMS) in 2005 on the experience of the average citizen with 11 public services18 across the country. The measure combines information on aspects such as level of satisfaction in the services of the relevant State Department, or on number of households reporting paying bribes, or using influence to speed up administrative processes. States are then ranked with respect to a weighted average of the scores obtained by the various Departments, and we assign a State to a “low governance” group if its score lies in the bottom 50 percent of the ranking. Alternative governance indicators come from the Indian National Council of Applied Economic Research (NCAER), the Enterprise Survey of the World Bank, the Indian National Crime Records Bureau, and various policy reports, and are described in more detail in Section 6.

> 14 The main central government’s law on conditions of work. See Section 5 for details.

> 15 The cross-sectional data sets are available for the years 1983-84, 1984-85, 1989-90, 1993-94, 1994-95, and more recently from 1996-2014, whereas the panel data is available from 1998 onwards.

> 16 Our final sample includes Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha,
which predicts that a country will specialize in the production and export of goods more intensive in the country’s abundant factor endowments (Kletzer and Bardhan, 1987). This paper considers a country’s levels of financial sector development (FSD) and governance as among the country’s endowment basket. The paper then uses product and firm level data from the BACI International Trade Database<sup>4</sup> and the World Bank’s Export Dynamics Database (EDD)<sup>5</sup> to assess how FSD and governance matter for export levels and specialization profiles.

Delving deeper into the existing literature, we find that export shares of sectors more dependent on external financing—funding external to the firm, i.e., from sources other than cash flow from operations—are higher in countries with more developed financial sectors (Beck, 2003; Svaleryd and Vlachos, 2005, Rajan and Zingales, 2001). Another strand of research finds that export shares

> 2 Andrew Bernard and Bradford Jensen “Exporters, Jobs and Wages in U.S. Manufacturing...

> 3 See Beck, 2003; Braun, 2003; Braun and Larrain, 2005; Claessens, Stilin, and Laeven, 2003; Demirguc-Kunt and Maksimovic, 1998; Fisman and Love, 2003a; Fisman and Love, 2003b; Hur, Raj, and Riyanto, 2006; Raddatz, 2006; Rajan and Zingales, 2001; Svaleryd and Vlachos, 2005.

> 4 BACI: International Trade Database at the Product Level, Gaulier and Zignago (2010).

> 5 EDD website: http://econ.worldbank.org/exporter-dynamics-database

2
cognitively burdensome than their weekly alternatives and to exacerbate recall bias in the reporting of family farm labor.

# **5. CONCLUSION**

How accurate are data on household farm labor? Our survey experiment finds that recall data collected in the postharvest period leads to overestimates of the time household members spend on specific plots over the course of the season, in some cases by a factor of 3.7. Yet, this overreporting is counterbalanced by considerable underreporting—by up to 50 percent each—in the number of plots worked by household members engaged in family farming. Accordingly, at the household level of aggregation, the total number of seasonal hours worked vary little across recall periods. Recall bias appears to result both from forgetting and from the extrapolation of seasonwide labor from erroneous inferences about past labor. Both of these distortions are rooted in the irregular nature of farmwork schedules and practices in our study region. In the absence of a typical work schedule or a typical and consistent level of engagement among workers and on plots, traditional end-of-season recall surveys force respondents into cognitively taxing calculations. These calculations result in labor inferences that appear to be based on recent rather than representative experiences, the omission of members only intermittently engaged in family farm labor, and the exclusion of plots further from the house and, thus, less salient in memory.

This paper makes two contributions to the literature. The first contribution is to the literature on measurement. If our results hold in other settings, then agriculture-based low-income countries, asking about farm activities 6–12 months after they have ended will tend to exaggerate estimates of the total days and hours household members spend working on their plots and farms. These findings may even hold outside the context of agriculture, for instance, in settings in which some but not other components of the labor calculation face considerable variability (for example, see Dupas, Robinson, and Saavedra 2015).

Clearly, survey designers should tread lightly when asking questions about the frequency of nonsalient, irregular events. But what
This change substantially alters key labor market indicators such as the employment and unemployment rates, particularly in countries with high levels of subsistence farming (Global Strategy, 2018). In Rwanda, for instance, according to the 2018 LFS, the ‘new’ employment rate equals 44.8%, while the employment rate equals 75.5% if the ‘old’ definition of employment is used (NISR, 2018, p. 34). The LSMS‐ISA surveys in Malawi (2016/17) and Nigeria (2014/15) ‐ the only LSMS‐ISA surveys which allow distinguishing own‐use producers from market‐producers ‐ also reveal a large effect of the new definition on the employment rate. In Nigeria, for instance, the employment rate of the working‐age population (15 to 65 years old) changes from 65% to 40% if own‐use producers are not classified as employed. Young people are even more affected (from 39% to 18%). The overall effect is mainly driven by the reclassification of subsistence farmers. While 43% of Nigeria’s working‐age population engages in household farming, only 20% produces primarily for the market (see Figure B in the Appendix for detailed results, broken down by sex and age). The new definition may disproportionally affect the female employment rate, as women are more likely to engage in own‐use production (Gaddis & Palacios‐Lopez, 2018; Walsh, 2018). However, analyses for Malawi (Figure B in the Appendix) and Nigeria (Koolwal, 2019) do not confirm this hypothesis. In Malawi and Nigeria, women are only slightly more likely to be own‐use producers than men.

In practice, not all national statistical offices have already implemented the new definition of employment and continue to classify ‘persons exclusively engaged in own‐use production’ as employed. One reason is that distinguishing agricultural households that _mainly produce for own final use_ from agricultural household that _mainly produce for
Moneke (2020) shows that road access alone causes manufacturing employment to drop largely in skilled activities, and, by contrast, causes service employment to increase, largely in informal, small-scale retail jobs.

The enabling effects of investment in infrastructure on job creation may depend on the interactions of investments. Whether ”big push,”combined investments deliver higher benefits than isolated investments of only roads or only electrification remains an open empirical question. One of the few attempts to examine the issue is the work in Ethiopia by Moneke (2020), who finds that only the interactions of infrastructure investments - not isolated investments - gave rise to large effects on economic development.

Thus, this paper aims to complement existing empirical studies and to fill a research gap, by looking at both the average and heterogeneous impacts of investment in infrastructure, and by examining the complementarity effects between two types of investments, roads and electricity.

# **3 Data**

To analyze the effects of infrastructure on employment in sub-Saharan Africa, we use two geo-referenced, household surveys: the Living Standards Measurement Study (LSMS) surveys conducted between 2005 and 2019, and the Demographic and Health Surveys (DHS) conducted between 2000 and 2018. Both surveys are merged with spatial information on roads and electricity grids.

8
past few decades, recent trade negotiations and agreements mostly focus on NTMs. From a methodological point of view, this paper presents a novel method for estimating the advalorem equivalent of NTMs. In addition, our ...nding that AVEs and tari¤s are substitutes suggests that it is important to include NTMs in trade policy analysis.

Finally, the general results of this paper indicate that NTMs at the border add important costs to trade and that traders seek to avoid these costs by resorting to misclassi...cation of products and/or misreporting quantities. Avoidance of NTMs at the border not only undermines the trade policy of the importing country, for example by misreporting country of origin, but also undermines regulations that seek to guarantee consumers’ health and environmental protection.

# References

- [1] Bagwell, K. and Staiger, R. (2011). “What Do Trade Negotiators Negotiate About? Empirical Evidence from the World Trade Organization,” American Economic Review 101: 1238–1273.

- [2] Bhagwati, J. (1964). “On the Underinvoicing of Imports,” Bulletin of the Oxford University Institute of Economics & Statistics 27(4): 389–397.

- [3] Broda, C., and Weinstein, D. (2006). “Globalization and the Gains from Variety,”Quarterly Journal of Economics 121(2): 541-585.

- [4] Cameron, C. and Trivedi, P. (2013), Regression Analysis of Count Data, 2nd edition, Econometric Society Monograph No.53, Cambridge University Press.

- [5] Cantens, T. (2015). “Mirror Analysis and Revenue Frauds,”World Customs Organization Research Paper No. 35.

34
Policy Research Working Paper 5988

# **Abstract**

The mobile revolution has transformed the lives of Kenyans, providing not just communications but also basic financial access in the form of phone-based money transfer and storage, led by the M-PESA system introduced in 2007. Currently, 93 percent of Kenyans are mobile phone users and 73 percent are mobile money customers. Additionally, 23 percent use mobile money at least once a day. New potential for mobile money has come with the rise of interest-earning bank-integrated mobile savings systems, beginning with the launch of the M-KESHO system in March 2010. The authors examine the mobile savings phenomenon, using data collected in a special survey in late 2010. They show that the usage of bank-integrated mobile savings systems like M-KESHO remains limited and largely restricted to better-off Kenyans. However, what the authors term “basic mobile savings”—the use of simple mobile money systems as a repository for funds—is widespread, including among those who are otherwise unlikely to have any savings. Holding other characteristics constant, those who are registered for M-PESA are 32 percent more likely to report having some savings.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. 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://econ.worldbank.org. The author may be contacted at gdemombynes@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
32

# <u>Figure 8: Trends in MCH inequalities from the 1990s to the 2010s</u>

<!-- Start of picture text --> Richest quintile 1990s<br>Poorest quintile 2010s<br>Richest quintile 2010s<br>Poorest quintile 1990s<br>ANC 31 45 69 77<br>Immunization 37 51 667<br>SBA 24 40 77 89<br>Treat Acute Resp. Infection 40 46 61 65<br>Treat Diarrhea 28 38 44 55<br>Met Need for Family Planning 67 7375 80<br>0 10 20 30 40 50 60 70 80 90 100<br>25 countries with data for 1990s, 2000s and 2010s<br><!-- End of picture text --> Figure 9 shows averages across country income groups in 8 service coverage indicators used in two recent studies of progress towards UHC (Wagstaff et al. 2015; Wagstaff et al. 2016). Unsurprisingly, high-income countries have higher service coverage rates than middle-income countries which in turn have higher rates than low-income countries. The gaps are especially marked for the two cancer screening variables. Also shown in Figure 9 are the values for Thailand and Zimbabwe, which have higher values on most indicators than their peers.