rafmacalaba/data-use-mentions-extended huggingface dataset:
a real benefit in data quality) should these shorter modules be considered.

Apart from the recall modules rooted in the recent past (7 or 14 days), the usual month approach seeks to encourage respondents to abstract away from a concrete time period and report on consumption for an idealized construct. We have demonstrated that this approach results in a different error pattern whereby consumption incidence is significantly higher than the benchmark. Whereas forgetting any consumption appears to be a problem in standard recall modules, the usual month exercise yields a crowding in of consumption to a large degree, especially for food items that are not commonly consumed in the benchmark. While consumption incidence is biased upward in the usual month approach, the consumption value appears to be significantly underreported for all food groups except Cereals. Compounding these problems, the error magnitudes in both incidence and value are substantially worse among disadvantaged households. Finally, this module took an average of 76 minutes to complete, far longer than the 49 minutes for the 7- day recall. The longer span to completion likely derives from the large cognitive burden placed on respondents by the usual month approach. Because this large cognitive burden also results in a stark divergence from the benchmark in a variety of dimensions, the usual month is not a survey design we can recommend.

The interpretation of the error patterns in the household diaries is somewhat more straightforward because the point of divergence from the benchmark is the reliance on one household member to record the consumption of all members, and, hence, the influence of personal leave out likely becomes more prominent (along with differences in the frequency of supervision in the less frequently supervised module). The household diaries do record 7 percent – 10 percent lower consumption incidence for food goods such as Fruits, Beverages, and Outside Meals, which are more likely to be consumed by individual members alone or outside the household. The reported values are also lower for many of the same commodities. For some food groups, the negative error in consumption value is smaller in magnitude in
2012 2015 2018<br><!-- End of picture text --> D. Countries with increasing central bank independence and transparency <!-- Start of picture text --> Percent of countries<br>100<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text --> F. Central bank independence and pass-through from exchange rate changes to inflation <!-- Start of picture text --> Number of branches and subsidaries<br>600 Within region<br>Other EMDEs<br>400<br>200<br>0<br>Before After Before After Before After<br>GFC GFC GFC GFC GFC GFC<br>SSA ECA EAP<br><!-- End of picture text --> Source: Dincer and Eichengreen (2014); Ha, Kose, and Ohnsorge (2019); International Monetary Fund; World Bank. A. Based on data for 155 EMDEs. B. Dincer/Eigengreen Transparency Index. The index ranges from 0 (least independent and transparent) to 15 (most independent and transparent). C. Inflation expectations are long-term (five-year-ahead) expectations of annual inflation, measured at a biannual frequency. Based on a sample of 23 EMDEs during 1995H1-2018H1. D. Based on data from 1998 to 2014. Figures shows percent of countries with a higher index (by at least 0.1) in 2014 than 1987.
It is also worth noting that, beyond any direct link between laws and formality, legal reform may also exert influence on the social norms that may have historically blocked women out of the private sector. Aldashev et al. (2012) discuss how changes to formal laws can have a “magnet” effect, thereby, drawing informal laws in the same positive direction as legal reforms. Laws facilitating female entrepreneurship may also provide a useful backstop mechanism to women who wish to establish formal enterprises in the face of informal laws that would prevent them from doing so.

In the second part of our analysis, we consider the relationship between starting informally and subsequent firm performance. The results displayed in tables 6 to 8 suggest that, holding other observable characteristics fixed, firms that began operations informally have lower sales, lower labor productivity and, for firms with female owners are less likely to be engaged in exporting activity. We find no evidence that firms that began informally and survived through to the time of the survey possess a higher level of productivity that allowed them to formalize and survive. Overall, it seems that the negative relationship between beginning informally and subsequent performance is more robust for firms that are fully owned by men; however, the coefficients between this group of firms and those firms with female participation in ownership is not generally statistically significant. The one exception is firms in South Asia; in this region, it appears that beginning informally is only associated with lower performance for firms with female owners. Informality is notoriously high in South Asia—over 90 percent of businesses in the region are informal (Bussolo et al., 2020). In this region, the Enterprise Survey data a show much lower prevalence of firms that began informally with female owners relative to firms that are fully owned by men. We know from ILO data that women are more likely than men to be engaged in the informal economy in the region; the fact that we see fewer of them formalizing (and, thus, represented in the Enterprise Survey data
per year attached to the compensation scheme which is widely used by the Chinese government-that is, minimum income support'<sup>8</sup> t of approximately 250 Yuan plus 50 Yuan for medical expenses per month per worker, and considering to illustrate an average unemployment spell of one year, the program would cost 0.96% of 1996 GDP for 43% of SOE industrial labor transfer (about 22.49 million workers'<sup>9</sup> ). This one short cost is close to the benefits of reform that would accrue year after year. It thus seems as a very good deal from a pure financial/fiscal perspective, both on cash flow (small loss in year 1, pure gains after), and from a stock perspective (net present value gains in the order of 10 percent of GDP).

But how does it compare to the likely losses born by labor? The 1997 figures that are available to us show that private sector wage income is 9092 Yuan per worker, versus 9128 Yuan per worker in state sector. Since these are about equal, the main loss experienced by workers is the drop in their income during their search for a new job, which is large given the ongoing levels of compensation if job search lasts long. The minimum support for example is about a third of average wages only. In addition, while housing is almost always provided for SOE employees, it is far from clear that laid-off workers would continue to be able to use the same facilities, is The national "minimum living income" was set at 164 Yuan in 1996, 155 Yuan in 1995, and 136 Yuan in 1994 and 114 Yuan in 1993. However, layoff allowances are set in different regions. For example, allowance in Beijing is 260 yuan every month, relatively high, the National average standard is 200 Yuan in 1993. A national survey in 1998 shows that 93.2% of the Xiagang workers have received the monthly allowance, ranging from 140
food insecure or worse using the FIES and zero otherwise as the dependent variable. Approximately 56.0 percent of the non-poor population is moderately food insecure or worse, which is more than double the figure in Column (1); and the share for poor Nigerians was 3.2 percentage points larger. Although the difference between poor and non-poor populations is statistically different from zero, the magnitude of the difference is a small share of the population and is very low relative to the large share of the population that is moderately food insecure or worse according to the FIES. Furthermore, we can still rule out the possibility of the share being much larger for the poor population, where one can reject the hypothesis that the share is more than 5.5 percentage points larger at standard significance levels.

The results for the FIES are in stark contrast to the strong alignment between the FCS and monetary poverty. Column (3) re-estimates Specification (1) using an indicator equaling one if the household had a poor or borderline FCS and zero otherwise as the dependent variable. Only a small share of the non-poor population had poor food access according to the FCS, and the difference in the share between the poor and non-poor populations was large. Specifically, only 7.2 percent of the non-poor population had poor food access according to the FCS; and the share among the poor population was 19.4 percentage points larger than among the non-poor population.<sup>28</sup> Not only is the magnitude of the simple percentage point difference larger for the FCS than for the FIES (19.4 versus 1.4 and 3.2), the resulting share of the poor population with poor food access is very large relative to the share of the non-poor population. Specifically, using the FCS, the prevalence of poor food access for the poor population is 3.7 times that of the non-poor population. Alternatively, the share of the population that is either moderately food insecure or severely food insecure using the
# **Figure 1. The Gorkha Earthquakes hit areas that were not among the poorest in Nepal**

- _a. Dwellings destroyed (percent of the total)_

<!-- Start of picture text --> (50,100]<br>(10,50]<br>(5,10]<br>(1,5]<br>[0,1]<br><!-- End of picture text -->

- _b. Poverty rate (percent of the population)_

<!-- Start of picture text --> (50,100]<br>(20,50]<br>(10,20]<br>(5,10]<br>[0,5]<br>No data<br><!-- End of picture text --> _Note:_ The triangles in panel a represent the earthquakes’ epicenters.

- _Source:_ Central Bureau of Statistics of Nepal (2012b) and Government of Nepal (2015) for panel a and Central Bureau of Statistics of Nepal (2012a) for panel b.

Reportedly, 776,895 private houses – 12 percent of the total housing stock recorded by the 2011 population census – had been destroyed (Government of Nepal 2015). Given that before the earthquakes imputed rent for housing accounted for about 12 percent of overall household consumption expenditure, the likely impact on household wellbeing was massive. The PDNA evaluated the average loss per capita at USD 171, large enough to have pushed between 700,000 and 980,000 additional people into poverty (Tiwari and Uematsu 2015). Not surprisingly, the destruction of dwellings accounted for 25 percent of the total estimated losses from the Gorkha earthquakes, and for 60 percent of the total damages (National Planning Commission 2015).

# **5. Data description**

The dataset used to illustrate the proposed approach to estimating the cost of disasters combines information from
contributors to total employment (54.6%). However, if we were to consider both small and medium firms (SMEs) together, we find the median employment share across our sample to be 45.45%, so comparable to large firms. Previous studies such as Ayyagari, Beck, and DemirgucKunt (2007) have also found SMEs to be significant contributors to aggregate employment although they define SMEs as firms with less than 250 employees and use data from a different database.<sup>11</sup> The median summary statistics across the 104 countries are also plotted in Figures 1 and 2. Figure 1 shows that it is the large firms (>99 employees) that are the largest contributors to total employment (54.6%). Small firms contribute 16.5% and Medium firms contribute 27%. We get similar patterns if we were to use mean values in each size bin rather than median shares across the 104 countries.

Table 1 also shows that we find similar results when we look across income groups<sup>12</sup> or geographic regions. Figure 2 plots the median statistics by income groups. Across income groups, establishments that employ more than 99 people have the largest employment shares, ranging from 44.2% in low income countries to 60.6% in upper-middle income countries. This is consistent with the US evidence in Haltiwanger, Jarmin, and Miranda (2010a) who find that large (and mature firms) in the US have the largest share of employment though they also find relatively little employment in large young firms. We do not focus on size-age breakdowns since our surveys are not stratified by firm age.

> 11 The SME database in Ayyagari, Beck, and Demirguc-Kunt (2007) only covered the formal labor force in manufacturing.  The sample of 54 countries in Ayyagari, Beck, and Demirguc-Kunt (2007) were mostly rich developed nations and thus differ greatly from the developing country sample in the Enterprise Surveys (ES).
private operators as part of concession agreements; and the vast numbers of transfers resulting from mass or “voucher” privatizations across Eastern Europe as these methods did not generate revenues for government.

3. Data were amalgamated from a variety of sources. For the earlier period, 1990 to 1999, data are drawn from the World Bank Privatization Database which provides the sale price of a privatization transaction and the year in which the privatization took place. As the sale price is recorded on an “announcement” basis rather than on the basis of actual receipts, proceeds do not necessarily reflect receipts in a particular year since transactions may be paid for over several years. Data for the more recent years, 2000 to 2003, are aggregated from the following sources: (i) the World Bank’s Private Participation in Infrastructure (PPI) database; (ii) the World Bank Africa region’s privatization database; (iii) OECD’s database on privatization in
Policy Research Working Paper 9909

# **Abstract**

Using administrative criminal records from Texas, we show that heat increases crime in a heterogeneous way across neighborhoods with different housing and economic characteristics. The heterogeneity allows us to predict how effective certain forms of adaptation will be at reducing the impacts of climate change on criminal activity. The simulations show adaptation reducing, but not completely offsetting, these impacts. Differential rates of adaptation across neighborhoods will likely exacerbate the consequences of already unequal exposure to climate change across society.

This paper is a product of the Development Research Group, Development Economics. 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 abehrer@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
pay to enterprises, either on the basis of<br>the levels of their production activities or on the basis of the quantities or values of the goods or<br>services that they produce, sell, or import. Included are transfers to private enterprises that are<br>intended to compensate for operating losses.|
|**Grants and transfers**|This category captures transfers of conditional grants for financing current spending to lower<br>levels of local government.|

_Source: Adapted from IMF (2001)_ _Note_ : See annex 3 for a more detailed description of the content of each category.

In terms of the benefits of monitoring infrastructure spending, the GFSM 2001 economic classification (COFOG) allows—at the very least—for a rough distinction between current expenditures and capital expenditures. This distinction, even if basic, is extremely important when analyzing infrastructure costs and planning infrastructure needs. The systematic lack of reporting of the capital/maintenance split may well be a cover used by governments when not properly spending and/or allocating resources on needed maintenance requirements.

It is also critical to try to capture external financing that can be traced to specific activities. In this regard, two additional categories have been added to the capital expenditure categories so as to single out infrastructure capital spending that has been financed through the budget using external funding.

The GFSM 2001‘s economic classification, when cross-referenced with the functional classification, becomes an enormously powerful tool. Nevertheless, the GFSM 2001 has drawbacks in recording expenditures at the microeconomic levels needed for sector level analysis and decision making by sector specialists.

In particular, the IMF economic classification does not provide a definition of rehabilitation, although a country might have its own. If the country does not have a definition, the user should flag this and be guided by the following principles in deciding whether an expenditure item should be classified as rehabilitation. Line ministries could also help identify rehabilitation activities by applying these principles: 39
PMT irrespective of the approach we adopt – PPMT or Full PMT. 2) the CLCI-PMTs that are more highly correlated with the MOLSA-PMT will likely show higher cross-eligibility.

_Figure 1: Comparing the variables included in the targeting models for the Northern region._ <!-- Start of picture text --> 14<br>7 7<br>4 4 4<br>3 3<br>2 2<br>1 1 1<br>0 0 0 0 0<br>Housing Vulnerability Household Employment Location Assets<br>charactersitics<br>MOLSA-PMT Legacy-PMT New-PMT<br><!-- End of picture text --> The PPMT model was developed because of the incomplete overlap between the CLCI-PMTs and the MOLSA-PMT. The surveys from which the Legacy-PMT and SEVAT were produced do not have the complete set of MOLSA-PMT variables. The PPMT weights are calculated by running an ordinary least squares (OLS) regression of household per capita consumption per month on the common indicators of the humanitarian and government models. The referral accuracy of the PPMT is calculated using a probabilistic model. As with the MOLSA-PMT, the nationally representative SWIFT survey (2018) data was used to develop the PPMT and their referral cutoffs. Two sets of regional PPMT were formulated, one for the common variables between MOLSA-PMT and Legacy-PMT, and the other for the MOLSA-PMT and SEVAT.

# 3.2 The Field-Test Survey

The field test survey implemented a complete set of questions to construct full the three targeting models (MOLSA-PMT, Legacy-VA, and SEVAT) and, hence, the two PPMT models (PPMT-Legacy-VA and PPMTSEVAT). The inclusion of the full set of variables helps to compare the PPMT performance with the original full PMT models. The CLCI conducted the field survey during the new round of MPCA vulnerability assessments in 2020 in areas of intervention in the Northern region of Iraq in Anbar, Diyala, Kirkuk, Ninewa, and Salah al-Din governorates. The sampling procedure mirrored the CLCI survey 10
less flexible. We consider in turn three possible sets of such firms, i.e. smaller firms, firms that import inputs less frequently on average, and firms that produce higher quality goods, as proxied by a higher unit value.

# **Firms participating in global value chains**

To test whether the negative effects of NTMs are driven mostly by firms participating in global value chains, we exploit an unique advantage of the Indonesian customs data which includes information on whether the firm is a producer or not.<sup>15</sup> We re-run our main specification 1 but separately for firms that participate in global value chains and firms that are not producers. The results are reported in Table 4. Most of the negative effects of NTMs on exports to Japan are driven by firms that participate in global value chains as those firms import goods for production that is then exported. The non-producers are barely affected by the NTMs, with the exception of SNI certification requirements, whose effect is negative and significant also for non-producers.

As Chinese exchange rate depreciates, Chinese exports become cheaper, and firms that participate in global value chains that face NTMs on their imported inputs, have a harder time adjusting production to the new competitive environment. Their revenue thus falls, as reflected in the negative effect of NTMs on export revenues for firms participating in global value chains. Take the 10% devaluation example, using pre-shipment inspections. For two firms participating in global value chains selling products exposed to average Chinese competition (.10), the firm at the median level of PSI exposure faces a 0.32% decline in its export value and a firm above the 90<sup>_th_</sup> percentile faces a 1.99% decline in its export value. On the other hand, for two firms who are pure traders selling products exposed to average Chinese competition (.37), these numbers are near zero and 0.64% respectively.

Table 5 reports the magnitudes of the elasticity of
# **(1) Introduction**

Intergenerational persistence in economic status in developing countries has attracted the attention of policymakers and researchers in recent years, partly in response to growing evidence that economic liberalization increased income inequality in many countries, despite a significant reduction in poverty.<sup>2</sup> In the absence of reliable income data over the life-cycle of parents and children, the focus of the recent literature on developing countries has been on intergenerational educational mobility.<sup>3</sup> However, most of the recent studies are devoted to urban households, and intergenerational mobility in rural areas remains particularly under-researched, even though the bulk of the poor live in villages in developing countries. This paper provides a comparative analysis of intergenerational educational mobility in the rural areas of the two most populous countries in the world: China and India, with more than 1.5 billion people living in villages in 2000/2001.<sup>4</sup> While understanding the role of family background in the educational opportunities of 1.5 billion people is an important research agenda in itself, the policy differences between China and India make such a comparative study especially interesting.<sup>5</sup> Perhaps, the most important difference in the 1970s to 1990s, the period during which the children in our empirical analysis went to school, was the restrictions on rural-urban migration in China because of the Hukou registration system. In contrast, there were no policy restrictions on rural-urban migration in India. Returns to education in farm vs. nonfarm occupations are also likely to be different in rural China because of policies such as the household responsibility system, rural industrialization (TVEs), and a lack of a well-functioning labor market in the 1970s to 1990s. Another important aspect in which rural India and rural China differed in this period is their schooling systems, with private schools playing a

> 2For recent contributions on China, see, among others, Fan et al. (2020), Park