rafmacalaba/datause-encoder-data huggingface dataset:
work and practice. In addition, we asked four questions about locus of control. Internal locus of control measures the degree to which people believe they have power over the outcome of events in their lives, as opposed to external forces beyond their control. Finally, we administered the Strengths and Difficulties Questionnaire (SDQ)—a brief behavioral screening questionnaire aimed at measuring two main constructs: behavioral difficulties and pro-social behavior.

The reliability of, and validity evidence for, these measures are lower than in Pakistan. After correcting for acquiescence bias, Cronbach’s α’s of the different constructs range between 0.08 and 0.71, with only one of 11 measures passing the 0.7 threshold (Table 2). Moreover, as in Pakistan, the test-retest correlations are low, ranging from 0.14 to 0.43. Finally, the skills factor structure is not reproduced when conducting a factor analysis. Only three factors are retained, and one single factor encompasses a wide range of items that aim to measure different concepts.

Keeping these limitations around reliability and construct validity in mind, Tables A15 to A19 examine the correlation of these different skill measures with years of schooling and labor earnings. As in Pakistan, we document low levels of cognitive skills in these youth populations. For instance, only 34% of respondents correctly answered the following test question: “Three ox carts carry 1,000 kg of rice. How many kg of rice can nine ox carts carry?”. We also find strikingly similar associations between years of schooling and cognitive skills. <mark>Every additional year of schooling is associated with a 0.17sd increase in cognitive skills in Pakistan and a 0.18sd increase in Cambodia (Table A15). The results from Cambodia also c</mark> onfirm that the correlation between SEM skills and years of schooling is weaker. An additional year of schooling is associated with a 0.05sd increase in SEM skills (and in related experimental work on the value of additional years of schooling, Barrera-Osorio, de Barros, and Filmer (2018) show no causal link between schooling and SEM skills in their sample). Thus, the results replicate across two very different rural regions of the world, with an association between schooling and cognitive skills but weaker associations between schooling and SEM skills.

Turning to the correlation between skills and earnings, we first highlight that the Cambodian sample is very different in its occupational structure, with 84% of respondents reporting agriculture as their primary occupation and LFP rates of 95% for both men and women. The earnings regressions lack precision in many specifications, but if there are any associations between earnings and skills, they are entirely for SEM skills, with zero or even negative associations between earnings and years of 27
Table E.2: Descriptive Statistics for Application-Level Attributes

||Mean|Std Dev.|# Obs|
|---|---|---|---|
|_Panel A: Characteristics of applications submitted_||||
|Had one report in a vacancy with one report|0.12|0.33|3992|
|Had one report in a vacancy with three reports|0.38|0.48|3992|
|Had no report in a vacancy with one report|0.37|0.48|3992|
|Had no report in a vacancy with three reports|0.13|0.33|3992|
|_Panel B: Responses to applications submitted_||||
|Any response received|0.15|0.35|3992|
|Interview request received|0.09|0.29|3992|

acknowledgements of receipt. If any application sent to a vacancy receives a type (3) or (4) response, we drop the vacancy from the sample. We define two outcome variables for analysis. First, any application that receives a type (1) response is coded as an ‘interview invitation.’ Second, any response that receives a type (1), (2), or (5) response is classified as ‘any response’. We forward all responses to the relevant candidate so they can contact the firm.

The final sample consists of 3,992 applications sent to 998 vacancies, after dropping 70 vacancies with bounce or scam responses. Of these applications, 15% receive any response, including 9% that receive interview invitations (Table E.2, panel B).

60
the fight against poverty. Without research, failed policy orthodoxies would often persist without the critical inspection needed to show that they have failed and successful policies would be dropped for the wrong reasons.

In recent years, the World Bank research program has increasingly been involved in two broad cross-cutting types of research: evaluative research and methodological research.<sup>36</sup> Evaluative research rigorously assesses whether development policies are effective, and under what circumstances they tend to be more effective. This embraces both “micro” interventions in specific sectors and macro policies. The second type of cross-cutting research that has increasingly occupied Bank research in the past decade can be termed methodological research. The Bank has become a major producer of development data, and a number of the Bank’s most successful data initiatives started as research projects. The best example is probably the _Living Standards Measurement Study_ (LSMS), which started as a research project in the mid-1980s with the aim of greatly improving household survey data in developing countries. The LSMS database now houses more than 75 LSMS datasets with all the relevant documentation. The World Bank is also a leader in governance indicators: enterprise surveys (also known as Investment Climate surveys), and Purchasing Power Parity measurement through the International Price Comparison Project.

# **Shifts in Country Allocations, 1961 ­ 2010**

Before the early 1990s, with a few exceptions (such as P.T. Bauer), the economic literature on the effectiveness of aid in general, and IDA in particular, was inconclusive and consisted mostly of general studies and cross-country regressions.<sup>37</sup> Initial crosscountry results, which emphasized growth to reduce poverty based on the Washington consensus, sound fiscal and monetary policies, openness, liberalization of markets and deregulation. However, as it has been difficult to find robust evidence from cross-country data that the policy prescriptions of the Washington consensus – hence, aid based on those - generate growth, subsequent empirical work by researchers from the World Bank

> 36 See Martin Ravallion, “Research in Practice at the World Bank,” DECRG, September 2007.

> 37 The most well known was probably the 1986 volume by Robert Cassen (who had been the main author of the 1981 World Development report), _Does Aid Work?_ Oxford: Clarendon Press

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Figure 3 MARGINAL "FFECTS OF EDUCATION -- SELF-EMPLOYED WORKERS

# Lima

<!-- Start of picture text --> Earnings relative to no education<br>1.82<br>1.65 Slgnificant at 0% In 2-talled toat<br>r[<br>1.49 UnierIt<br>msu<br>1.35<br>Seseoidary Welinleal<br>1.22<br>1.11 -----<br>Sooondary goneral<br>1.00<br>primary<br>-0.90 i a I I a I I I I a I a<br>o Pl P2 P3 P4 P6 81 S2 S3 S4 S6 Tl T2 T3 T4 T6<br>Highest education completed<br><!-- End of picture text -->

# Other Urban Areas

<!-- Start of picture text --> 3.3 Earnings relative to no education<br><!-- End of picture text --> <!-- Start of picture text --> 2.7 Significant at 6% In 2-talled test<br>2.2 |.Siniltlcant at 10% in 2-talled test Univer|l<br>1.8<br>1.5<br>1.2<br>Primary Se.ondary technical<br>1.0<br>O PI P2 P3 P4 Pb Si S2 S3 S4 S6 Ti 12 13 14 15<br>Highest education completed<br><!-- End of picture text -->
Table 1: Coverage Ratio of Non-Tari¤ Measures by Importing Countries

|Importing Country|Percent|Importing Country|Percent|
|---|---|---|---|
|ARE|99.22|JPN|67.08|
|ARG<br>|51.38|KAZ<br>|62.80|
|AUS<br>|79.32|KOR<br>|94.39|
|BGD|98.20|LKA|89.87|
|BOL|90.11|MAR|37.30|
|BRA|88.27|MEX|49.14|
|CAN|56.54|MYS|72.48|
|CHE|60.62|NGA|99.00|
|CHL|57.43|NZL|86.21|
|CHN|71.86|PAK|56.47|
|CIV|94.78|PER|89.13|
|CMR|71.90|PHL|99.01|
|COL|97.25|PRY|82.37|
|CRI|59.45|RUS|69.53|
|CUB|98.03|SAU|38.77|
|DZA|64.74|SEN|98.36|
|ECU|75.31|SGP|84.98|
|ETH|97.87|THA|90.05|
|EUN|60.98|TUN|46.83|
|GHA|97.07|TUR|86.34|
|GTM|86.63|URY|73.84|
|HKG|84.33|USA|85.44|
|HND|96.19|VEN|92.22|
|IDN|78.95|VNM|84.25|
|IND|79.19|||

Data Source: UNCTAD TRAINS. Coverage Ratio measures the percentage of HS 6 digit products that are subjected to at least one border NTMs in the importing country.

40
migrant rural workers and _Lr_ is the laobor force in rural area. δ and γ are elasticity and shift parameters respectively.

The transactions cost function in (1) has a simple, constant elasticity functional form, which begins at the origin and reaches the observed cost-of-living adjusted indirect tax rate of 81% of rural wages at the current level of temporary migration (about 70 million workers in our baseline scenario). We assume that further increases in temporary migration have only a modest impact on these transactions costs.<sup>2</sup> In addition to these indirect costs, the temporary migrant also incurs direct costs associated with the higher urban cost of living. Based on Shi, Sicular and Zhao (2002), these are estimated to be 10% of the urban-rural wage gap and about 17% of the rural wage rate.

In our subsequent analysis, the direct and indirect transactions costs associated with the temporary migration of unskilled and semi-skilled labor will play an important role.3 Any labor market reforms that reduce the transactions costs imposed on rural labor will increase the flow of workers to the city, thereby depressing urban wages and increasing rural wages. This will have the effect of reducing inequality. These transactions costs will also play an important role in the case of trade liberalization. Here, increases in the demand for unskilled labor in the urban areas will cause urban wages to rise, thereby drawing in more rural labor. However, this supply of rural labor will come at some cost – both in terms of higher transport and living costs for the worker, as well as the indirect transactions costs – since the additional workers are presumably being drawn from a greater distance or from less favorable family/social circumstances.

> 2 We assume that a doubling of temporary migration would only increase the marginal cost of migration by 10%.

> 3 Skilled workers make up a very small portion of the rural labor force and typically are able to obtain hukou if they choose to move to the urban areas.

7
# **1. Introduction**

Since its first case of COVID-19,<sup>1</sup> detected on March 11, 2020, Côte d’Ivoire has experienced recurrent waves of the pandemic, with the most recent ending in January 2022. The resurgence in cases has been driven by the spread of highly transmissible COVID-19 variants, low COVID-19 vaccination coverage, increasing social interactions and the inconsistent application of public health measures (e.g., social distancing, handwashing, and mask wearing). There were 88,064 confirmed cases of COVID-19 between January 3, 2020, and February 15, 2023 (WHO., 2022). The highest incidence of COVID-19 cases was observed in December 2021 due to the Omicron variant, while the deadliest wave was associated with the spread of the Delta variant between July and October 2021. As of January 18, 2024, a total of 835 people had died as a result of COVID19. Although this number of deaths might not seem high compared to the United States (US), where COVID-19 is estimated to have caused 1,161,235 deaths as of January 18, 2024, the mortality costs in Côte d'Ivoire might be high for a nation that is still working to build a strong and resilient health care system and enhance inclusive and sustainable economic growth. Furthermore, since the pandemic began, policy makers in Côte d'Ivoire have lacked evidence of the long-term death costs associated with COVID-19. These estimates could give policy makers a clear picture of the mortality costs associated with COVID-19 over time and help them take a forward approach to better prevent, prepare for, and respond to future pandemics.

The COVID-19 pandemic has spawned a plethora of research articles that assess its costs. According to an estimate by Cutler and Summers (2020), the total cost of the pandemic in the US is estimated at more than $16 trillion, or over 90% of the country's annual gross domestic product. The premature death cost amounted to US$ 4.4 trillion. In Türkiye, Gökler & Metintaş (2022) found that the premature death cost due to COVID-19 was US$ 227,396,694. Even though these studies shed some light on the mortality costs associated with COVID-19, it is not known how these mortality costs change over time.

> 1 COVID-19 has been described in a variety of ways since its inception. It was referred to as a pandemic, health crisis, public health emergency of internal concern, and disaster.

2
In the next section, the econometric strategy for the paper is described, building on the socioeconomic, georeferenced climate data, and dietary diversity indicators.

# **III. Econometric Strategy**

In a nonseparable household model, production and consumption decisions are jointly determined (Strauss 1984, Benjamin 1992, Bardhan and Udry 1999, LaFave and Thomas 2013). Identification of the direction of causality is potentially confounded by cross sectional correlation. In our empirical strategy, a reduced form regression of climate variables on dietary diversity would also be mis-specified due to omitted production variables. Behrman et al. (1997) addressed these econometric challenges by developing a dynamic nonseparable household model which motivated using planting and harvest season data to improve the identification of calorieincome elasticity estimates. Our strategy builds on this approach and the post-planting and postharvest data structure of the Nigeria LSMS-ISA by distinguishing between the timing of seasonal production decisions to understand the effect of planting period production decisions on postharvest dietary diversity within a full agricultural season, _t_ .

In the dynamic formulation of the agricultural household model, households maximize expected utility given the production function (Qt), time endowment (E<sup>L</sup> ) and intertemporal budget constraint (equation 4) (LaFave and Thomas 2013). The household’s problem is to choose produced agricultural goods (xat), purchased market goods (xmt), agricultural inputs (Vt) and leisure (lt) to maximize utility given observed (μt) and unobserved household characteristics (εt) such that: 10
sources of omitted variable bias. Following, among others, Rajan and Zingales (1998) and Buccirossi et al. (2013), we test for the theoretical predictions of the “legalist” model of informality, which are unlikely to hold if the results suffered from reverse causality, omitted variable bias, or measurement errors.

Our results reveal that higher informal competition has a positive and statistically significant impact on the likelihood of obtaining quality certificates by the formal manufacturing SMEs. According to our baseline estimates, a one standard deviation increase in informal competition leads to a 2.9 to 3.6 percentage point increase in the probability of acquiring quality certificates by the formal manufacturing SMEs. Note that only 10.4 percent of the firms have quality certificates. This positive impact of informal competition is found to be much larger in countries with weaker rule of law and greater regulatory burden on formal firms.

# **2. Conceptual framework and literature review**

# 2.1 _Impact of informal competition_

A few studies have analyzed the impact of informal competition on the performance and functioning of formal firms. These studies find that higher informal competition leads to lower job creation by the formal firms (Amin 2023), a higher chance of being credit constrained (Distinguin et al. 2016), higher total factor productivity and more efficient energy use (Wen et al. 2021), lower output (Rozo and Winkler 2021), and higher likelihood of R&D activity and innovation (McCann and Bahl 2017, Mendi and Costamagna 2017, Amin 2024). Some studies find that informal competition has no significant effect. For example, Avenyo et al. (2021) use the World Bank Enterprise Surveys (WBES) data for five Sub-Saharan African countries collected in 2011-2014.

7
education levels, work status, migration status, and residence areas. This figure suggests that upward mobility is weaker than downward mobility both for Arab Spring and other Arab countries, and migrants are more likely to be less upwardly mobile (and more downwardly mobile) in Arab Spring countries, but the opposite holds for non-Arab Spring countries. As such, in a similar spirit, other potentially useful applications of poverty imputation methods may include emerging policyrelevant topics such as vulnerability, multidimensional poverty, or gender equality.

Yet, we end this section on a cautious note that poverty imputation methods, like most other statistical models, rely heavily on the modeling techniques and their accompanying assumptions. If the model assumptions are satisfied by the data, poverty imputation can yield encouraging and low-cost results. However, the opposite holds where the model assumptions are invalid. It would thus be useful to offer careful checks on the relevant modeling assumptions as well as the variable selection process before providing imputation-based poverty estimation.

# **VIII. Conclusion**

The growing demand for more frequent and accurate poverty estimates is not satisfied by the current availability of household consumption data, at least in the short run. Imputation methods offer a promising solution against this background and have been widely in use.<sup>27</sup> These methods have also received increasingly more attention. For example, a recent and high-profile report on monitoring global poverty (Atkinson, 2017) explicitly calls for further exploration of imputation techniques for poverty measurement purposes in data-scarce contexts. Yet, there is currently a severe dearth of research that can bridge the gaps between the typical development practitioners and the latest advances in the field. We aim to help fill in this gap with this review.

> 27 See, e.g., Jolliffe _et al._ (2015) for a recent review.

25
- 8 -

where _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the conventional maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011).<sup>5</sup> For the spatial weighting matrix, inverse distances between two locations _s_ and _t_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows the Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).” Our primary data source is the SPAM 2010 update. As discussed above, the SPAM is basically a model to disaggregate the national and subnational production data into the 10 x 10 km plots (pixels) using a number of different spatial data types. Therefore, it is critical to control for spatial autocorrelation in our analysis.

The analysis focuses on eight major crops that are produced in East Africa: maize, rice, bananas (and plantains), cassava, coffee, tea, cotton, and tobacco (Figure 2). The selection aims at examining both domestic food crops, such as maize and bananas, and export crops, such as tea and coffee. These eight crops account for about 40 percent of total agricultural commodities produced in the region.<sup>6</sup> The current productivity of these crops is generally low by regional standards, though it exceeds the African average in some cases (Figure 3).

> 5 For estimation we rely on a STATA command **_sptobitsac_** developed by Shehata and Michaiel (2013).

> 6 According to FAOSTAT, the East Africa Community member countries produced about $25 billion of agricultural commodities in 2011. The eight commodities examined amount to $9.3 billion in total.
# Intra-farm Yield Variability

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estimation sample for intergenerational mobility (18-35 year old children in 2012). The average schooling is 10 years for sons and 9.36 for daughters. In the rural sample, the average education of fathers is much lower; 3.90 years (sons sub-sample) and 3.83 (daughters subsample). The gender gap in average schooling is also much more pronounced in the rural areas; 8.13 years (sons) vs. 6.24 years (daughters). The average educational expenditure in IHDS-2005 is higher for sons and it is true irrespective of geographic location and for both the estimation samples (6-22 year old) and (8-23 year old). The bias in educational expenditure against girls is also observed in the NSS-1995 data.

# **(5) Empirical Results**

The empirical estimates of the mobility and investment equations discussed below report robust standard errors. However, we also estimate the standard errors clustering at different levels: district and state levels for India, and county (only for investment) and province levels for China. We cannot cluster the standard errors at the county level for the mobility estimates for China, as the county location is not available for the non-resident members of a household. The estimated clustered standard errors are reported in the online appendix. All the main conclusions based on the robust standard errors, however, remain intact when we use clustered standard errors instead. If and when clustering makes a difference in inference, we will note that in the following discussion.

# **(5.I) Intergenerational Mobility in India: Evidence and Interpretations (5.I.1) Urban India**

The estimates of the effects of father’s schooling on children’s schooling in urban India are reported in the upper panel of Table T2.IU.<sup>20</sup> The first two columns contain the evidence from a linear CEF which provides estimates comparable to the existing literature where linearity is a maintained assumption. The IGRC estimate is higher for the daughters, suggesting that the daughters face significantly lower relative mobility in urban India.

Although almost all of the existing studies on India focus primarily (or exclusively) on relative mobility, it has been emphasized in the recent literature that inter-group analysis of

> 20The table numbers are indexed by country and location. For example, T2.IU stands for Table 2 for the Indian Urban sample, and so on.

17