rafmacalaba/data-use-mentions-extended huggingface dataset:
To economically justify the need for better SWM services in the developing countries, good valuation studies on the potential benefits of such services are necessary. Several techniques for assigning economic values to SWM services have been used in the literature, including travel cost (Anex, 1995), hedonic housing price (Arimah, 1996), choice modeling or experiments (Huhtala, 1999; Othman, 2002; Naz and Nazm 2005 and Boyer, 2006 and Jin et al. 2006). But the method that is used the most is the method of contingent valuation or ranking; a non-exhaustive research in the literature gives a list of 19 contingent valuation studies in this area<sup>4</sup> . Evidence from the existing research suggests that the estimates of environmental and public good benefits from well-designed and properly executed contingent valuation surveys appear to be at least as good as estimates obtained with other valuation techniques (OECD, 1994, Mitchell and Carson 1989, Whittington et al., 1990).

Two advantages can further explain the widespread use of the contingent valuation method in environment-related public service valuations. The first is its flexibility. Based on hypothetical markets that can be flexibly defined by researchers according to the specific characteristics of the public services in question, the respondents of valuation surveys are invited to directly state their preferences and to reveal their willingness-to-pay for the specified qualities or quantities of improvement. This is very different from the hedonic price method, whose applicability to a public service valuation depends closely on the existence of compatible real-market data. The second advantage is the capacity of contingent valuation method to measure not only the use value but also other intangible values from the improved public service, such as the non-use value and especially the existence value (Krutilla, 1967). This is very different from the market-based valuation methods, such as the travel cost method, which can only measure (partial) use values of a public service.

Though the contingent valuation method can be a feasible and valid technique to measure the levels of payment for SWM
>40</sup> We estimate a probit model to assess the likelihood of consistent answers between the original and the back-check interviews, controlling for firmlevel fixed-effect. Reporting the use of a technology in the back-check interview is associated with 80.6% of likelihood of reporting the use of the same technology in the original interview. Conversely, reporting that a technology is not used in the back-check interview, is associated with a 29.3% likelihood of being reported in the original survey.

# **Additional validation exercise with employer-employee census (RAIS) in Brazil**

Some final ex-post checks were conducted with the Brazil data and takes advantage of the fact that we have access to the RAIS administrative data, which is a matched employeremployee dataset that covers the universe of firms in the sampling frame. This allows us to

> 39The pilot coincided with the beginning of the data collection for phase two which includes new countries, and Kenya is one of them. Despite the fact that Kenya is not in the sample, the pilot is informative about the significance of response bias. The re-interviews produce 1,661 answers (106 interviews times 3 business functions times an average of 5.2 technologies per function).

> 40The consistency ranges from 65% in business administration to 77% in sales across business functions, and from 85% among the most basic technologies to around 61% in intermediate, and 77% at the most advanced technologies across functions.

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# **3. Theoretical Considerations**

Since Stark and Bloom’s work on the “new economics of migration” (1985), migration has been recognized as part of a household’s strategy for maximizing its welfare. Migration from Kerala for work is appropriately viewed as a household decision rather than an individual choice. The sending household finances migration expenses upfront—particularly for a move overseas—in return for a future stream of remittances from the migrant family member. Migration expenses to the Gulf are costly for low-skilled jobholders from Kerala, averaging US$1,018 as computed from the 1998 KMS, which was 2.7 times Kerala’s per-capita domestic product for that year.<sup>10</sup> An expected utility model would see a household that receives such remittances choosing between consuming the income and investing it in assets during the early period of migration. The availability of remittance income would then be expected to raise the recipient household’s investments in assets in subsequent periods, to the extent that the household were to treat that income as a positive transitory income shock.<sup>11</sup> Remittances from temporary migrants working in the Arabian Gulf can be readily regarded as transitory income for the household back home. There is limited opportunity to settle permanently in these countries or acquire citizenship, and foreign workers earning below a certain threshold are prohibited from bringing their dependents.<sup>12</sup> Existing evidence shows that these “single” migrants transfer the bulk of their earnings back home and for the most part, spend in the host country only on what they require for subsistence.<sup>13</sup> Hence, the origin household’s marginal propensity to invest using international remittances is potentially high, given the impermanent nature of overseas migration to the Gulf. A mitigating factor, however, is that a

> 10 Migrant households must typically cover the costs of an airline ticket, visa, commission to the recruitment agency, and even
|11.7|10.5|12.8|11.0|14.1|15.1|8.7|7.9|
|(std dev)|(3.4)|(3.7)|(2.9)|(4.4)|(3.4)|(3.3)|(4.1)|(4.3)|
|Proportion workingforpay|0.58|0.37|0.63|0.55|0.68|0.54|0.60|0.59|

# Notes:

new arrivals are classified as those arriving in the past 2 years. Source: Tongans from Pacific-Island New Zealand Migration Survey Migrants in the United States are from 5% public use sample of the 2000 Census, obtained through the Integrated Public Use Microdata Series (IPUMS) website (Ruggles et al. 2004).

Migrants in Canada are from the 2.7% public use file on individuals from the 2001 Census. Mexicans in Mexico are from the 10.6% public use sample of the 2000 Mexican Census, obtained through the IPUMS-International website (Sobek et al. 2002).

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respectively), while utilities and energy score very poorly, with a score of 1. Industry, trade and construction are all on the low side.

So far, out of 9 different measures considered, some sectors score well on some measures, while they score poorly on other measures. To aggregate the results in a simple index, we add the number of times the sector is noted as having high vulnerability and subtract the number of times that sector is noted as low vulnerability. The overall vulnerability index then shows the net effect of different measures. Positive overall index suggest that the sector is more often noted as having higher vulnerability, while negative overall index shows overall lower vulnerability.

Figure 1 plots the values of the overall vulnerability index. The industrial sector turns out to have the highest vulnerability, followed by trade, transportation and construction. Other sectors present lower overall vulnerability. Note that these results are static, for the end of 2006.

**Figure 1.  Overall Vulnerability Index**

<!-- Start of picture text --> Overall Vulnerability Index<br>6.00<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br>-1.00<br>-2.00<br>-3.00<br>-4.00<br>-5.00<br>5.<br>1. Mining<br>Hotels Energy 8. Financial 3. General 2. Industry<br>6. Tourism and 7. Utilities and 4. Construction Transportation Trade/Services<br><!-- End of picture text --> Source: ASE data An important caveat to consider is that some of these sectors are not homogeneous. Thus the sector referred to as industry contains 48 firms, out of which 11 are in the chemical industry, 11 are in food processing,
**Table 2: Percent share of inequality in per capita expenditures within and between provinces**

|Inequality index|||||Ye|ars|||||
|---|---|---|---|---|---|---|---|---|---|---|
||2002|2004|2006|2008|2010|2012|2014|2016|2018|2020|
|Theil L|||||||||||
|Within|65.8|68.8|73.8|76.4|72.1|77.9|78.4|72.3|77.4|73.2|
|Between|34.2|31.2|26.2|23.6|27.9|22.1|21.6|27.7|22.6|26.8|
|Theil T|||||||||||
|Within|64.6|67.4|73.7|76.2|73.5|78.6|79.1|72.9|78.1|74.1|
|Between|35.4|32.6|26.3|23.8|26.5|21.4|20.9|27.1|21.9|25.9|

Note: i) Authors’ estimation from VHLSSs 27
# **6. Conclusion**

We use data from three rounds of ES COVID-19 Follow-up Surveys for 15 emerging markets and developing economies in ECA to assess the effect of government support during the COVID-19 crisis on firm performance by mid-2021, over a year after support measures were first enacted. We first document that 42 percent of firms received government support by Round 1 of the survey and 23 percent received support between Rounds 1 and 2, for a cumulative 50 percent of firms that received government support.

We find that the effect of government support on firm performance varies with the timing of the support. Firms that received support in Round 1 performed better in terms of Round 3 sales, but only if they did not have continued support in Round 2. Firms that received support in Round 2 had a similar sales performance in Round 3 and were more likely to decrease employment than those that received no support.

Overall, these results suggest that during economic crises such as the COVID-19 pandemic, government support should be provided to firms quickly, but that it should also be phased out quickly. Doing so ensures that support measures help firms weather the crisis but do not delay insolvencies for firms that may no longer be competitive. This conclusion is consistent with the notion that fiscal stimulus should be timely and temporary (Elmendorf and Furman 2008). In practice, responses to previous crises have often been implemented too slowly and were not rolled back fully, leading to increasingly higher government spending (Taylor and Castillo O'Sullivan 2015).

By mid-2021 the COVID-19 crisis was still ongoing. An avenue for future research is to investigate the effects of government support measures during the COVID-19 crisis on long-run firm performance once such data becomes available. To help inform the government response to future crises, researchers could also try to examine the relative merit of different firm support measures such as wage subsidies compared to cash subsidies.

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It would be desirable to analyze whether gifting is more common among female heads of the household (i.e. widows) but the sample size does not allow a more fine-grained analysis.

Given the gender discriminatory bequest allocation among siblings in Islamic inheritance law, individuals who have a sibling of the opposite sex would be expected to be less willing to embrace legal reform, as it would change the inheritance allocation within their family. The effect should be stronger for men because they would themselves materially suffer from reform as their sister(s) gain equal inheritance right. Consistent with the theory, I find that respondents who have a sibling of the opposite sex are indeed less likely to favor reform (not statistically significant) and 14 percentage points more likely to favor gifting over reform (marginally statistically significant at the 10 percent level). The limited sample size does not allow for analysis by gender.

Higher levels of education are generally associated with more gender equal preferences, as the most recent survey round of the Tunisian Arab Barometer confirms (Arab Barometer; 28
32 the reader can easily translate them to the length of the period that he or she thinks is appropriate.

The compensated labor elasticity is a key parameter in the calculations of the deadweight loss of income tax. Browning (1987) uses the compensated labor elasticity of 0.2-0.4. Feldstein (1999) uses an elasticity of 1.04. Here we use a compensated elasticity of 1.

Income distribution matters for our calculations. The Congressional Budget Office provides data about income distribution in the US.<sup>5</sup> We use the income distribution for 2011 described in Table 1. The share of income of the top 1 percent is 14.6 percent while the share of income of the lowest quintile is only 5.3 percent. We use these data to create 100 individuals indexed 0.01, 0.02,...,1. The income of the highest earning individual (indexed 1) is 14.6. The income of each of the four individuals indexed 0.96-0.99 is 12.7/4=0.03175 and so on. In the absence of distortions the income of each individual is <sup>2</sup> . We use 14.6 as a proxy for the <sup>2</sup> of the individual indexed 1 and 0.03175 as a proxy for each of the individuals indexed 0.96-0.99 and so on. The last row in Table 2 is the imputed <sup>2</sup> per individual in each income bracket. Note the difference from Figure 6 that assumes a uniform distribution.

Table 2: The <u>distribution of income in the US (2011)</u>

|Percentile|1-20|21-40|41-60|61-80|81-90|91-95|96-99|100|
|---|---|---|---|---|---|--
liberalizations in developing countries (Goldberg and Pavcnik, 2004; 2007) and for Latin America in particular.<sup>12</sup> The literature for the region indicates, in general, that trade liberalization had a significant but relatively small impact on skill premia, widening the wage skill gap and increasing inequality in the income distribution as a whole, although these effects typically explain only a relatively small fraction of the total increase in the skill premium. An important issue that needs to be established on a case by case basis is the factor of production that represents each country’s comparative advantage with respect to its trade partners, and will thus benefit from trade liberalization – either non-renewable resources or unskilled labor, for instance.<sup>13</sup> Besides these direct effects of trade openness, trade can also be a factor in the incorporation of capital goods complementary to skilled labor and thus introduce skill biased technical change (Acemoglu, 2003; Yeaple, 2005), which is also compatible with the evidence on the relationship between wage skills gaps and imports of machinery and equipment in the region (Acosta and Gasparini, 2007), as well as with exports (Brambilla _et al_ ., 2011). Because of the relevance of direct and indirect effects of trade on the wage skill premium, an index of average tariff levels is included in the regressions.<sup>14</sup> While trade reforms where important in the 1990s in Latin America, there were relatively less policy changes in this area during the decade of 2000s. But a more salient feature of the decade of 2000s in Latin America was the growing importance of exports in the midst of a boom in commodity prices (Baffes and Haniotis, 2011) and in the context of the emergence of new trade partners for the countries in the region, most notably China (De La Torre _et al_ ., 2011). Changes in terms of trade, were not homogeneous across the region – while they increased on average during the
the results they produce are informative, as they capture distinct dynamics regarding active versus passive job search and engagement in subsistence activities, which are particularly relevant for women in Egypt. Female unemployment rates have actually fallen between 2012 and 2018, as opposed to those of men. Striking differences, however, emerge when examining female unemployment rates in Egypt relying on these various definitions (Figure 3). When relying on the market definition of employment, female unemployment rates are higher, as a large proportion of Egyptian women engage in subsistence work. While female unemployment rates seem overall to have fallen between 2012 and 2018, women’s unemployment rates according to the broad unemployment (market definition) have increased. The latter suggests that the share of female discouraged workers, who are not actively searching for employment, actually increased. This pattern is consistent with the overall fall in female unemployment rates between 2012 and 2018.

Examining differences in unemployment rates between urban and rural areas by gender, Figure 4 and Figure 5 show that male unemployment rates are much higher in urban compared to rural areas, consistently across the two survey rounds. These figures and the following ones rely on the

> 4 See Open Access Micro Data Initiative, OAMDI (2016) for the data source and <mark>Assaad and Krafft (2013) for more information on the data and the sampling.</mark>

5
In the absence of longitudinal panel datasets that follow migrants over time, the most suitable surveys are those especially designed to collect detailed retrospective information on workers’ migration and employment history. One early example is the survey of Turkish temporary migrants who went to Germany conducted by the German Institute for Employment Research (IAB) (Dustmann and Kirchkamp 2002). The IAB survey interviewed Turkish temporary migrants both at the end of their stay in Germany in 1984 when detailed retrospective questions were also asked about employment history in Germany, and after return to Turkey in both 1986 and 1988. However, the survey follows temporary migrants over only four years. In addition, the data were collected in the specific context of an assisted return program targeting Turkish migrants in Germany, and only samples return migrants that had applied to the program, which may affect the external validity of studies using this data.

Some dedicated migration surveys with detailed retrospective questions on past employment and migration overseas are cross-sectional. Among these relatively scarce surveys are the Mexican Migration Project (MMP)<sup>9</sup> and the Bangladesh Return Migrant Survey 2018/19 (BRMS)<sup>10</sup> carried out by the World Bank. The MMP is the most comprehensive survey to date to be implemented in an origin country. It captures both current and return migrants, along with the full employment history of all household members both in Mexico and abroad since the time they started working. It also records all trips overseas as well as internal migration episodes of all household members. In a very different context, the Bangladesh Return Migrant Survey collects the full employment and migration history of return migrants to Bangladesh who worked overseas under formal contractual temporary migration arrangements, primarily in the Gulf countries. The information captured includes detailed socio-economic and labor market outcomes before departure, during migration and after return, full migration history and a detailed breakdown of migration costs and sources of financing, along with savings and wage expectations prior to departure. Despite their comprehensiveness on migration and
that would reflect the long term impact of the Khmer Rouge period on self-reported illnesses.

# **V. Education**

The Cambodian school system was ruined during the Khmer Rouge period. Only basic primary schools, with a curriculum centered on agricultural skills, were open and no secondary schools were in operation. Table 7, adapted from Kiljunen (1984) indicates how, right after 1979, the secondary school system was in despair, with only a few hundred teachers for the whole country. Desbarats (1995) also provide a similar account of a very low enrollment in secondary schools during the school year 1979/80 with a gradual increase over the 1980s.

Figures 14 and 15, using data from the 2001 Cambodia Labor Force Survey<sup>14</sup> , show that the individuals who were of secondary schooling age at the end of the 1970s have a lower level of educational achievement than the preceding and subsequent birth

> 14 The same result can be found using the Demographic and Health Survey of 2000 and the Cambodia Socio-Economic Survey of 1997. Results available on request.

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predictions, the RF estimation consistently performs better than the OLS prediction. While the quality and quantity of information used in both estimation techniques were identical, since the predictor in the RF model explores the hierarchical structure of features, it can more sensitively track the possibility that the effect of each attribute on price varies by context (Hong 2020).

The limited coverage of observable property features within EMDEs could be one explanation for the acceptable accuracy of the estimation. In addition, while most studies applying the RF model focus on a very narrow housing market (e.g., Čeh et al. 2018; Levantesi & Piscopo 2020) our data expands to the entire housing market of the five emerging economies covering heterogeneous properties with varying amenities including interior decorations, building age, or other features that are not captured in the model as these property features were not consistently available on listing websites. Equally, we are jointly estimating property prices for a large swathe of locations within an economy – beyond just different neighborhoods within a city but aggregating both rural and urban areas. This deviates from the use of RF models in the literature to predict property prices for a well-defined narrow set of locations, typically a city or a province/state. The limited property features and available explanatory variables pose limitations to the use of the model to predict individual property prices across the spectrum. However, the purpose of the prediction in our case is to arrive at a typical price for a standard property that can be compared across countries and contexts. The RF and OLS-based approach, notwithstanding the relatively high MAPE, can be considered improvements over the alternative of only considering one-dimensional summary statistics of price.

# 6. Conclusion

Given the difficulty of obtaining reliable private property price data in emerging economies, most analyses of house prices or affordability assessments are constrained to developed economies and limited in scope. Most cross-country analyses that assess trends in house prices are based on available indices, which often aggregate to the national level, masking important within-country dynamics and regional differences. To