rafmacalaba/data-use-mentions-extended huggingface dataset:
both their business start-up and day-to-day operations. In return, private entrepreneurs may provide quid pro quo benefits to cadres or their family members.

Given the mechanisms through which village cadres in rural China may enhance their income, and the ways that markets and other institutions may have undermined these opportunities, we turn to the empirical question of whether or not cadres earn a return on their positions. Further, in an effort to understand whether any returns are excessive and may thus reflect rent-seeking, or simply sufficient to attracting talent to China‘s pool of rural cadres, we next estimate the source and magnitude of these returns. We address these analytical questions with the use of a unique panel of household survey data which we describe below.

# **III. Data**

The analysis of the paper makes use of a large rural household panel data set that comes from annual household surveys conducted by the Survey Department of the Research Center on the Rural Economy (RCRE) at the Ministry of Agriculture in Beijing. To sample households, RCRE first selected counties in the upper, middle and lower income terciles in each of the 31 provinces and administrative regions in China. A village in each county was then randomly selected. Depending on the village population, between 40 and 120 households were randomly chosen and surveyed in each village. RCRE started the household survey in 1986 and intended a longitudinal survey, 11
# **1. Introduction**

Natural disasters are frequent and hugely costly in terms of human life and household welfare. Just in 2015, the year of the massive Gorkha earthquakes in Nepal, 396 disasters were recorded worldwide with poorer nations accounting for 95 of the 119 countries affected (EM-DAT database, accessed in May 2016). Historically, developing countries have experienced 80 percent of the disasters recorded (Noy 2009). With a greater share of their population living in poverty, and with more limited fiscal space to cope, poorer nations are bound to experience greater hardship (Fomby, Ikeda & Loayza 2013). How much greater, however, is difficult to tell (Kliesen and Stuart Mill 1994).

The most obvious cost of a disaster stems from the human deaths and injuries it causes. Estimating this cost requires some measure of the statistical value of life, an issue with its own methodological challenges that this paper does not attempt to address.

Beyond these direct human impacts, disasters often result in a major fall in household welfare. Part of the fall stems from the destruction of dwellings caused by the disaster, and part of it is from its indirect consequences on economic activity and access to services. Sometimes these two components are called _damage_ and _loss_ , respectively. Boundaries between them are not always easy to delineate. Intuitively, however, mangled assets fall in the first category, whereas business disruptions belong in the second one (Hallegatte and Przyluski 2010).

The actual cost of a disaster could in principle be assessed through private insurance claims and government-provided emergency support, with the former yielding an estimate of damages and the latter of losses. However, formal insurance coverage is partial in developing countries, and government support in the aftermath of a disaster is most often insufficient. This kind of assessment would therefore result in gross underestimates. In the absence of direct information of the sort available in advanced economies, estimates of the cost of natural disasters in developing countries need to be based on other data sources, combined
# **Supplementary Material**

# **A1 Data Preparation**

## **A1.1 World Bank Enterprise Surveys**

We compile our data set from the World Bank Enterprise Surveys (World Bank, 2020a), a firm-level survey of a representative sample of an economy’s private sector, including both manufacturing and services firms. We used the 2019 surveys, which were conducted in 31 countries in Europe, the Middle East and North Africa, and the Russian Federation and Central Asia. In each country, firms responded to either the Manufacturing survey or the Services survey based on their self-identified sectors. The survey includes productive inputs, performance measures, business indicators, management practice scores, and a green economy module. This module covers energy and environmental impacts as well as energyand climate-related management practices. We use the energy practices in the green economy module to compute the energy management score. This score is designed to capture the extent to which a firm has implemented structured practices aimed at reducing or raising the efficiency of energy use. The green economy module also covers the physical quantities of various forms of energy consumption such as electricity, petroleum, coal, and natural gas. We use these energy data to compute energy intensity in physical terms. To the best of our knowledge, this paper is the first to analyze the impact of general and energy management practices on physical and value measures of energy intensity.

Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

## **A1.2 Merging**

We merge the firm-level responses from all the 31 countries into a combined dataset. For categorical variables such as region that are country-specific and thus inconsistent across surveys, we tagged the possible responses by country throughout the merge to ensure that the responses remained
important in determining volatility. Specifically,<sup>_BANK_is a measure of the importance</sup> of banks residing in that country. One possible<sup>interpretation of this result is that if</sup> international capital flows exacerbate investment<sup>volatility, a stronger banking sector</sup> reduces their relative importance.

# **3.2. Sensitivity Analysis**

The previous section establishes that higher<sup>values of</sup><sup>_PRIVATE_</sup> are associated with lower values of consumption and GDP volatility,<sup>that higher values of</sup><sup>_BANK_</sup> are associated with lower values of output,<sup>consumption and investment volatility, and that</sup> higher values of _LLY_ are associated with higher<sup>values of consumption volatility. In this</sup> section we examine how robust these results<sup>are to different data selection and estimation</sup> techniques.

As explained in Section 2, our main data set<sup>contains a measure of financial devel-</sup> opment at the beginning of a time period<sup>and a measure of subsequent macroeconomic</sup> variability. However, the years we use to<sup>calculate initial financial development imme-</sup> diately precede those years which we use to calculate<sup>subsequent volatility. In order to</sup> determine if our results are sensitive to the<sup>time period in which we measure financial</sup> development, in Table 4 we repeat the estimations<sup>presented in Table 2, but this time we</sup> 'measure financial development with a five year<sup>lag between the last year we use in the</sup> financial development measure and the first<sup>year we use in the variability measure (i.e.,</sup> financial
We conclude by mentioning limitations of this study. First, relying on the Facebook mobility data has drawbacks. The data may not represent the mobility trend of urban workers and households, as it only captures the movement of Facebook users who keep the smartphone app and location information on— they are probably younger and wealthier. Moreover, the Facebook mobility data track the mobility trend only at a geographically aggregated level. Thus, we cannot differentiate within-city locations or individual characteristics. Second, the HFPS data do not capture the initial shock period between midMarch and May in Kinshasa. People who were relatively resilient against the COVID-19 shock might have already recovered by the first round of the HFPS. If the share of those workers is correlated with town population or density, our estimation would be biased. Third, the HFPS data record employment conditions of only one respondent, typically the household head. As a result, we cannot analyze other workers within the household. This limits, for example, the possibility of differentiating the estimated impacts for youth and female workers. Finally, the HFPS data do not record the exact amount of labor income of individuals and households, let alone their consumption levels. As a result, we cannot quantify the welfare impacts of the COVID-19 pandemic in our research design.

20
1500|1126|0|5552|0.370|
|Paddy|247000|61000|74000|0|382000|0.294|
|Peanuts|1203|1850|1388|0|4441|0.240|
|Pepper|14562||10922|3640|29124|0.710|
|Potatoes|2310|1890|1750|0|5950|0.850|
|Rubber|6767||7151|4391|18309|0.146|
|Sesame|1595|1740|870|0|4205|0.290|
|Soybeans|.|.|.|.|.|0.067|
|Tea|96050||36864|59206|192120|0.959|
|Undu|1755|2700|2025|0|6480|0.240|
|Vegetables|22744|21297|14920|0|58961|0.640|

_Notes_ : for each crop, the table displays total fertilizer use in Sri Lanka (in MT) for four types of fertilizer: urea, triple superphosphate (TSP), muriate of potash (MOP), and other fertilizers. The last column shows fertilizer intensity (in MT/ha), which is computed by dividing the crop’s total fertilizer use (in MT) by its cultivated area (in ha). The data comes from Sri Lanka’s NFS, except for soybeans, whose fertilizer intensity data comes from the International Fertilizer Development Center and the International Fertilizer Association (specifically, from their ‘Asia’ estimates).

Producer Prices). We proxy the national prices of maize, onions, potato, rice, and tea with their (national) unit costs of production.<sup>102</sup> Since unit costs of production are not available for cinnamon and cloves, we use their (national) FOB export prices instead.

# **A.E Fertilizer Use and Subsidies**

To compute chemical fertilizer use in the production
believed to range from 20,000 to 80,000 (Lomo and Hovil 2004; Pham et al _._ 2007; Blattman and Annan 2010).

The prolonged violence resulted in several types of migration. Wealthier households were able to flee toward urban areas. Poorer groups tended to move to internally displaced persons (IDP) camps located within Northern Uganda (Fiala 2009). Additionally, since the LRA often attacked at night, a large number of children commuted nightly to the relative safety of urban areas or to the centers of IDP camps. At its peak, there were an estimated 30,000 “night commuters” (Amnesty International 2005).

Beginning in 2002, the government displaced large numbers of individuals, primarily from conflict prone areas, to IDP camps. Between voluntary and involuntary movements, certain districts virtually emptied. For instance, by 2004, approximately 90 percent of the original populations of Gulu and Kitgum districts were no longer in their original districts (Pham et al. 2005).<sup>8</sup> Although the reasons for the government’s choices of particular districts are not known, it is reasonable to assume that these areas were among those with the highest risk of future attacks. Consequently, _ex ante_ losses from the risk of conflict may be underestimated as the individuals in the high risk communities were moved to lower risk communities.

The household and community data are drawn from the Northern Uganda Survey (NUS) that was administered by the Uganda Bureau of Statistics in 2004. It contains representative data for 386 georeferenced rural communities and 3,867 households.<sup>9</sup>

> 8. Although the most affected districts were almost completely displaced to IDP camps, the data is representative of a broader area (only 17 percent of the sample currently resides in an IDP camp).

> 9. While representative for the population in rural Northern Uganda, the sample cannot account for any migration to urban areas in Northern Uganda or to areas outside of Northern
## **_Box 1 Is reported ownership of HEs biased by social norms?_**

Social norms in highly patriarchal (or matriarchal) societies could lead to a tendency for men (or women) being reported as the owner of HEs. For instance, despite a woman being the main person responsible for a HE, her husband could be reported as the owner while the woman is reported to be a family helper in the enterprise. Is this a problem in our household survey data? Is there a bias toward one gender?

The data do not allow an analysis of this question, but it does indicate that if such a bias exists, it does not affect our analysis very much. While it is true that the majority of those who report their employment status as family helpers are females, there simply are not very many contributing family workers in HEs. Depending on the country, between 77 and 93 percent of HEs are single person operated, hence only between 7 and 23 percent of the observations at most could have some misreporting (assuming that all people working in the HE were reported). Even if half of the observations that could have misreporting did so, only 7 percent of observations on average would be biased, leaving little room for a significant impact on the averages shown here.

# **3. Recent employment trends in a growing Sub-Saharan Africa – Why is employment in household enterprises on the rise?**

The past decade has seen a resurgence of economic growth in low-income SSA countries, including the countries in our study. Mozambique, Ghana, Rwanda, Tanzania, and Uganda all reported GDP per capita growth greater than 3 percent per capita per annum 2000-2008, and the others reported per capita growth between 1-3 percent per annum (World Development Indicators, 2012). During this growth period, in all of the study countries non-farm private wage and salary employment grew faster than the labor force, as did employment in non-farm enterprise activity, including owners and family members in both HEs and
model for thinking about IOE and a unified empirical approach to decompose the variance within a two-way fixed-effects context. In the remainder of the paper we implement and compare results from three main choices for the fixed-effects initialization parameter, π, and validate our results by considering both alternative empirical methods and a wider range of choices for π. Based on our preferred results, we also go on to investigate heterogeneity in the variance contributions.

# **3.1 Data**

Our application of these methods uses test score data from East Africa. Since 2010, the Uwezo initiative has undertaken large-scale household surveys in Kenya, mainland Tanzania and Uganda (for further details and comparison to other regional assessments see Uwezo,

> 12 Note that extensions to more than two variables are possible in theory but add substantially to the number of covariance terms to be estimated, as well as the dimensionality of the choice space for the initialization process.

10
|44.0|49.7|77.4|28.3|
|2015|41.5|46.3|77.0|25.0|41.6|46.3|77.1|25.2|
|2016|44.1|53.6|78.1|26.4|44.2|53.7|78.1|26.3|
|2017|43.1|50.9|78.0|24.6|43.0|51.1|78.2|24.8|
|2018|43.0|50.3|76.4|25.9|43.0|50.2|76.3|25.7|
|2019|44.2|48.3|76.5|25.6|44.2|48.4|76.6|25.6|
|2020|43.7|46.6|-|26.4|43.7|46.6|-|26.2|
|2021|42.5|44.7|-|22.4|42.5|44.7|-|22.4|

_Notes:_ Based on data from EH 2007-2021. We define low-skilled workers for people with incomplete secondary education or below and informality for employees with some kind of written contract and employers, self-employed with a Tax Identification Number (TIN).

Table 2 provides summary statistics that offer an overview of the labor market in Bolivia over the years. The first two rows indicate that the aggregate participation and employment rates have remained relatively stable over the past decade. However, Row (3) reveals a 10-percentage point increase in the share of the population aged 15 to 64 years old who were self-employed between 2007 and 2021. Moreover, the labor income of this population ceased to increase in 2014,
We concluded earlier from indirect evidence on the relative changes in real income of different groups that at turning point in the fortunes of the Ghanaian economy in 1983, rural income levels were probably higher than the urban. It is seen that although recovery had improved incomes in both sectors, rural incomes were still higher after four years of the ERP.

Table 6.4 presents the summary statistics of income levels in 1992 from the GLSS 3. It is seen that the situation is changed at this date. All the indices show much higher incomes in urban areas-and higher in Accra than in other towns. The income per earner in Accra is a third higher, and income per capita nearly 50 per cent higher than in rural areas. This is a finding of major importance, and to see its robustness we compare the income relatives with the statistics on expenditures from the same survey. Researchers have sometimes maintained that expenditure figures are more reliable than income data in household surveys as they give a better measure of permanent income. The indices presented in Table 6.3 corroborate the conclusions from the income relatives given earlier.

42
share of transfers from the government to household h associated with tariff revenue in good g on total income of household h.

Because we are interested on the impact of tariff reforms on the welfare of poor households, to apply equation (6) we need the changes in _pg_ , _w_ and π _h_ that follow a trade reform and these are given by the general equilibrium prices obtained in the CGE. Notice also that the estimates obtained by using equation (6) take into account only first order effects. Indeed household substitute goods in their consumption bundle, enter and exit the factor markets, and, therefore, full effects should also consider changes in the shares θ _hkap_ θ _hc_ , _g_ , θ _h_ l (but these require a fully blown micro-simulation model).

In order to express the change in welfare in monetary units, one simply needs to multiply the expression in (6) by household income, then usual estimates of poverty effects are straightforward.

54
America) are now forced to reduce domestic spending.

The current level of total public investment is 3.4 percent. On average, about a third of this public investment goes to infrastructure. As for public-private partnerships (PPPs), the best figures available, from the World Bank PPI database, puts them at about 40 percent of Latin America’s infrastructure investments. However, an often-disregarded fact is that they depend heavily on government support. New figures that have been gathered starting in 2015 show that about a third of the recorded investments actually come from public sources, and about half are 17
.047|0.792|0.760|
|Endline control mean|0.4|68|0.5|13|0.|541|0.5|81|0.4|01|0.|446|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.011|0.134|0.144|0.260|0.005|0.103|0.069|0.166|0.014|0.128|0.195|0.305|
|Observations|1,915|1,915|1,324|1,324|1,915|1,915|1,324|1,324|1,915|1,915|1,324|1,324|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and control observations weighed with Kernel matching weights according to their similarity with FREGs and non-FREGs, respectively. Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

16
- 22 -

VTE is an important determinant of modern sector employment, as Table 8 indicates. In Abidjan, 57% of secondary level VTE graduates are employees for the government or for modern sector private enterprises. This figure rises to 81% for graduates of post-secondary VTE.<sup>9</sup> / In contrast, 21% of the people with only general education past the primary level hold such positions. A similar situation exists in other cities and rural areas. The percentage of economically inactive people among VTE graduates is also much below that among people with only general education. Nevertheless, the unemployment rates of VTE graduates are not low. In Abidjan, unemployment among VTE graduates is less than among people with post-primary general education, but it is much higher than unemployment among people with primary school or less. Private sector employment in Abidjan has indeeds been partticulatly hard hit by the recession of the eighties, and this may have had a proportionally larger impact on the employment opportunities of VTE graduates. This has induced a number of VTE graduates to opt for own account work instead.

9/ The small sample size of the CILSS clearly is a disadvantage for disaggregated analysis at this level: there are only 33 individuals in the sample with post-secondary VTE.
(see table A1.2 in annex 1)—is almost 40 percent.<sup>21</sup> It is worth highlighting, however, that these top decile firms also operate in some knowledge-intensive services sectors, though the probability is much smaller: 3.87 percent for computer programming and 2.68 percent for security and investigating activities.<sup>22</sup>

**When taken together, these results suggest that the small group of firms leading the overall increase in markups in Romania does not seem to follow the typical superstar firms’ profile** . As discussed by Van Reenen (2018) and Autor et al. (2017), superstar firms— typically with higher markups, larger, and more productive – would be rewarded with larger market shares due to their innovative nature. This hypothesis does not seem to be supported by data in Romania. As results showed, the group of firms who are driving the aggregate rise in markups—the top decile firms of the markup distribution—are typically smaller, less productive, less likely to invest in intangible assets, and are mostly active in services sectors where use and diffusion of knowledge is less important. This may suggest that the increase in markups in Romania might reflect a less competition-intensive environment rather than changes in the nature of competition, where superstar firms would be rewarded with greater market share because they are more efficient and innovative.

**Table 4. Probability of a top decile firm (in the markup distribution) being in the following sectors**

|**2-digit sector**|**Probability**|
|---|---|
|**Wholesale trade**|24.36|
|**Retail trade**|14.77|
|**Computer programming**|3.87|
|**Manufacturing of wearing apparel**|3.21|
|**Real estate activities**|3.14|
|*