rafmacalaba/data-use-mentions-extended huggingface dataset:
the corrupt dealings of subordinate officers are likely to be uniformly popular with top politicians. In the Gambia, before the 1994 change in regime, foregone customs and tax revenues amounted to 8 to 9 per cent of gross domestic product, and losses from income tax evasion alone were 70 percent of revenues due. Experienced African observers believe that this case is not exceptional. A survey of Gambians indicated that corruption was one of the major causes of the shortfall, and the regime then in power supported a reform program (Dia 1996, pp.46-47, 94100). The current military regime also appears to be following an anticorruption strategy. Tax and customs reform have been supported by such autocratic rules as Ferdinand Marcos in the Philippines (Klitgaard 1988) and Suharto in Indonesia. Suharto contracted with a Swiss company to take over its customs service, thus cutting out corrupt Indonesian officials, at least for a time (GATT 1991, 1995). Given the World Bank's concern that lending programs be "owned" by borrowers, tax and customs reform look promising if the Bank seeks a trouble free relationship with top political leaders. However, a policy of following the line-of-least-resistance raises the question of whether economic growth will be furthered by permitting rulers operating with little popular accountability to develop more efficient revenue collection systems. A military regime may simply purchase more equipment for the armed forces. An autocrat may search for expensive capital projects that can be a source of kickbacks.

Our analysis suggests that in helping borrowers set priorities, governance reform cannot be carried out without an overall view of the relationship between the public and the private sectors. Simply encouraging a ruler to develop institutions that permit him to engage in more efficient predatory behavior ought not be a high priority for the World Bank. This means that even if the Bank has developed "best practices" for tax and customs reform, it should not finance 42
both sides. Since 2001, 60,000 people have been killed in clashes with the Fulani militias in Nigeria.

Table A1 in the appendix shows descriptive statistics of the displacement data that are available for the northeastern states. In January 2018, the IOM’s Displacement Tracking Matrix (DTM) registered over 1.7 million IDPs, of which 80.8 percent are female or under 17. The main reason listed for displacement is the Boko Haram insurgency (96 percent), followed by communal clashes (3.8 percent) and natural disasters (0.2 percent).<sup>6</sup> Figure 3 shows the location of IDP sites—i.e., locations where IDPs reside, including both camps and host communities. The size of the bubbles is larger the greater the number of IDPs residing in the site. The figure shows that the majority of the IDPs reside in Borno, followed by Adamawa, Yobe, Taraba, Bauchi, and Gombe.

# **3 Data and Sample**

We first describe our data sources and then present descriptive statistics.

## **3.1 Data Sources**

We combine data from different sources to identify the impact of distant violent events on economic and non-economic indicators for men and women. All databases are georeferenced.

_Household survey data._ We use data from the Nigeria Demographic and Health Survey (DHS). The DHS is conducted on a nationally representative sample of households. For the main analysis, we focus on the female roster and restrict our sample to married women between the ages of 15 and 49 who are not residing in Borno. We restrict the sample to married women because many questions related to violence and fertility are only asked of this subsample.<sup>7</sup> We have data on 25,451 married women for the 2013 wave and 27,043

> 6The main reasons listed for not returning are lack of safety (68.7 percent) and property damage (17
P 2 1 1) (6) ii2 ii1 = PP(yy ≤zz |zz < yy < vv

In this study we specify the vulnerability index P to be 20% and we calculate the corresponding vulnerability line based on the 2004/5-2011/12 synthetic panel interval. This is associated with a vulnerability line of Rs. 770 in 2004/5 prices and can be compared to the all-India rural poverty line in 2004/5 of Rs.447. Note that P is bounded below by the percentage of the nonpoor population in period 1 falling into poverty in the second period. Note further that 1 provides the solution to expression (6), which in turn can be used to obtain 2 with an appropriate price deflator. Given vv , we can extend expression (2) to analyze the dynamics for vv ii these three categories: poor, vulnerable, and secure. For example, the percentage of poor vv households in the first period that escape poverty but still remain vulnerable in the second period is<sup>11</sup>

# **IV. Validating the Synthetic Panel Method**

As described above, the methodology we follow to produce synthetic panels with the NSS data is predicated on various assumptions. While these assumptions have been validated against actual panels in other contexts, they cannot all be readily tested empirically using crosssectional data alone. Hence we attempt to gauge the likely reliability of our mobility estimates by validating the methodology with the IHDS data. Despite some limitations with the IHDS data themselves, our strategy here is to consider this data source as the benchmark to validate the synthetic panels.<sup>12</sup> To the extent that we find that the method appears to work well, we then

> 11 See Dang and Lanjouw (2017) for further discussion, including another definition of the vulnerability line, as well their full derivations and properties.

> 12 As noted above, the IHDS data have far fewer consumption items than the NSS, which can result in
: the frequency and coverage ratio of NTMs. The frequency ratio accounts only for the presence or absence of an NTM, and summarizes the percentage of products to which one or more NTMs are applied. In more formal terms, the index for country _j_ is calculated as follows: where _D_ is a dummy variable reflecting the presence of one or more NTMs affecting product _i_ , and _M_ indicates whether country j imports good _i_ (also a dummy variable). To capture the relative value of the affected products, we use the coverage ratio. In formal terms, the measure is given by: where _D_ is defined as before, and Vi is the value of country j ’s imports of product _i_ . Table 6 presents the results.

Table 6. NTMs: Frequency and Coverage Ratio

||Frequency|Coverage Ratio|
|---|---|---|
|China|100%|100%|
|European Union|90.1%|86.2%|
|Indonesia|43.5%|36.8%|
|Japan|39.4%|59.5%|

Source: Gourdon and Nicita (2012) and authors’ calculations for China using data from UNCTAD accessed through WITS (2012).

It is important to acknowledge that this analysis assumes that all NTMs are applied on a nondiscriminatory basis. This implies that if a country applied a particular NTM to the imports of a product from a single country, it will do the same with the imports of the same product from all countries.

10
# **Figure 14. Required skills by industries**

<!-- Start of picture text --> Manufacturing 17 29 5 6 12 11 5 7 5 3<br>Construction 9 31 6 6 13 11 7 6 8 3<br>Wholesale, retail, hotel, restau 24 23 9 10 8 10 4 6 4 2<br>Transport, storage, postal 20 34 4 5 11 9 6 4 5 2<br>Information, communication 82 5 6 1 112111<br>Finance, real estate 22 28 10 5 8 3 5 8 4 7<br>Education, health 26 19 5 12 5 9 8 7 6 2<br>Professional, scientific, techni 44 16 8 7 6 6 3 4 3 3<br>Admin, social, personal services 37 18 6 14 7 7 3 5 3 2<br>Others 19 24 6 4 17 7 5 6 7 4<br>0 20 40 60 80 100<br>Programming Sales<br>Designing Communication<br>Finance Microsoft<br>Analytical/ Research Other soft skills<br>Leadership/ Management Others<br><!-- End of picture text --> _Source_ : Authors using the data from Rozee.pk.

Preferred genders are explicitly indicated in some job postings as discussed in Section 3.4. Figure 15 compares required skills between jobs that prefer different genders. Programming skills are the most frequently required in male-preferred jobs (50%) and gender-neutral jobs (69%), but programming skills are much less frequently required in female-preferred jobs (13%). Compared to male-preferred and gender-neutral jobs, female-preferred jobs are more likely to require sales, communication, Microsoft, and other soft skills.

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economies versus one that covers only developing economies. Our aggregation methodology discussed in Section 3 below addresses these differences.

Finally, we note that some of these data sources may revise their historical data with each new data update. In addition, we have in the past added new data sources with historical data extending to previous years in the WGI. In both cases, we update the historical data in the WGI database and recalculate the historical aggregate WGI measures. For these reasons, each annual update of the WGI supersedes previous vintages of the dataset.<sup>10</sup> Typically, the revisions to the historical data between WGI vintages are very small.

2.3 How the WGI Data Sources Are Selected and Assigned to the Six Aggregate Indicators The WGI data sources are selected based on several criteria. First, the data sources must provide subjective views or perceptions of relevant dimensions of governance, as the WGI are based exclusively on this type of data. We restrict attention to subjective perceptions data in the WGI for four reasons: (1) perceptions matter, because households and firms make decisions based on their views and perceptions of the quality of governance; (2) for some dimensions of governance such as corruption that do not leave a “paper trail”, data measuring subjective perceptions of corruption or self-reported experiences with corruption can provide valuable insights; (3) data capturing respondents’ perceptions and views can provide essential information on the gap between _de jure_ rules and their _de facto_ implementation; and (4) unlike objective indicators that capture the existence of specific laws, rules and regulations, data capturing survey and expert respondents’ views are not susceptible to “gaming” where policy makers target reforms to narrowly change specific measures simply because they are included in an aggregate indicator that they wish to influence.

Of course, we do not claim that perceptions data are the _only_ relevant type of data for measuring governance. Objective data on the specific laws and regulations “on the books” that shape the regulatory environment also
# 2.1 Description of the data sets

The primary sources of information are the following: (1) the Unified Beneficiary Registry (UBR), collected in 2017; (2) a 20 percent extract of the 2018 census provided by the National Statistical Office of Malawi; (3) the Integrated Household Survey (HIS) collected in 2016; and (4) publicly available remote sensing indicators.

## Unified Beneficiary Registry (UBR)

Malawi’s Unified Beneficiary Registry contains information on the households’ socio-economic characteristics to determine their eligibility for social programs.<sup>11</sup> For the analysis, we use the data set collected during the first phase of the UBR. These data were collected in 2017 in 10 districts: Lilongwe, Ntchisi, Kasungu, Rumphi, Chiradzulu, Nkhota-Kota, Blantyre, Karonga, Ntcheu, and Dowa. During this phase, half of the households in these districts were registered based on Malawi’s average poverty rate. The UBR data set contains apporximately 595,000 households, spread across 14,986 villages in the 10 districts.

The UBR is a crucial data set for this analysis for two reasons. First, it contains the PMT scores for the poorest 50 percent of households, which allows us to evaluate the PMT scores as a targeting mechanism. Secondly, these data include the geocoordinates of sample households, which allows us to merge the satellite data with the census data.

## Census data

The analysis uses a 20 percent extract of the 2018 census data for 10 districts provided by the National Statistics Office of Malawi. The study utilizes data from the 4,500 villages that were matched, by name, with the UBR data. The census extract includes 235,600 households in 26,150 villages in the ten UBR districts. The census extract also serves two main purposes. First, it is used, along with the parameters of the census model estimated in the household survey, to generate the benchmark welfare used for evaluation. Second, it
public transportation, a mix of restaurants, a vibrant music/art/entertainment scene, and diverse residential neighborhoods. There is a variety of foot traffic, wide sidewalks, and different types of buildings (Florida 2002, Sommers and Calson 2000).<sup>27</sup>

> 24 Florida (2002) shows that the key values of creative people include individuality, meritocracy, diversity, and openness.

> 25 There is evidence that highly conservative, very stable societies are not likely to be creative places; neither will be places in which all sense of order has disappeared.  See Hall (2000).  Historically it appears that highly creative cities have been those in which an old, established order was being challenged or overthrown, such as Vienna in 1900 and Berlin in 1920.

> 26 To measure diversity, Florida (2002) has developed a composite index that adds together the Gay Index (concentration of gay people), Melting Pot Index (relative percentage of foreign-born people), and Bohemian Index (concentration of artistically creative people).

> 27 These life-style preferences also are influencing the way architects and developers design and build the next generation of offices for creative and high-tech firms.  Sidewalks, pedestrian streets, and entertainment facilities are being emphasized.  See Sommers and Calson 2000.

17
- Psychological impacts of being infected with an HAI, especially one that is AMR, and patient dissatisfaction associated with poor quality care and lack of WASH facilities.<sup>18</sup>

- Dissatisfaction of healthcare workers affecting their motivation to work, their treatment of patients, and career choices, which subsequently could affect health outcomes (see Annex 3 for a brief review of quality of care as it pertains to WASH).

This study relied on secondary data sources, including national statistics and published literature. Due to lack of data on costs and HAI rates for some countries, these were extrapolated from countries where data were available, and therefore may not reflect that country’s situation. In general, the HAI rates used were reflective of national contexts across SSA, and some countries had sufficient studies presenting HAI rates to make tailored estimates for those countries.

The study assumes that 50% of HAIs are AMR, which likely varies significantly within and across countries, and simplifies the many complexities associated with AMR. However, for the purposes of this analysis an AMR rate of 50% (based on Murray et al (2022)) may be quite conservative, and a more fine-tuned value would need to be used in future national studies based on localized rates and types of infection and resistance levels.

Only a few studies were available from SSA which estimated the direct medical costs of treating HAIs. Hence, estimates drawn from the more representative and better quality studies were extrapolated to other countries. The evidence shows that the costs associated with treating HAIs are highly dependent on the level of the healthcare facility, due to the better availability of diagnostics and more common use of third-line or patented drugs, more 18
Our core specification uses annual data. To test whether the results hold for the medium run, we use a long difference approach suggested by Acemoglu and Johnson (2007). Thus, in column [5], we use observations only for 2000 and 2008 and find that the results remain similar to the baseline, with the magnitude of the interaction coefficient increasing by a little. In columns [6] and [7], to make sure that the results are not driven by the choice of year for measuring the index of competition, we measure the index for the years 2001 and 2002, respectively. In column [8], we use an alternative measure of competition—the export similarity index due to Finger and Kreinin (1979).<sup>12</sup> Thus, for a wide range of robustness tests, the core results remain unaltered, both in the sense that the coefficients are stable and consistently significant at the 1 percent confidence level.

In Table 3, we test for robustness to alternative measures of the exchange rate variable. In our analytical framework, we assumed that the price of Chinese goods in the importing country market is determined by a simple relationship between domestic prices in China and exchange rate pass-through. Based on the framework, in our core specifications, we deflate the nominal bilateral exchange rate (between China and the importing country) by Chinese prices. The X ijgt  Share of product _g_ in total exports from _i_ to _j_ at the4 - digit level X  ijgt g X Cjgt  Share of product _g_ in total exports from China to _j_ at the4 - digit level. X  Cjgt g The results are also robust to using the alternative formulation of the Finger-Kreinin Index defined as and the weighted Finger-Krenin Index defined as .

16
which was found to be associated with firms’ lower and more volatile earnings and cash flows. These firms also tend to hold more cash and pay less cash dividends, suggesting that more exposed firms tend to hedge more against cash flow volatility and illiquidity due to higher climate risk. Increased climate uncertainty can also affect firms by increasing their insurance premiums. This can manifest through a decline in coverage agreements and, consequently, an increase in insurer exits because of limited revenue streams (Born and Viscusi, 2006), both will inevitably lead to higher insurance premiums.

Trading networks raises the possibility that a spatial shock to one area has a ripple effect that impacts non-treated areas. If a supply chain is not diversified and features a type of O-Ring such that a break in the chain shuts down production, then the whole network is sensitive to extreme disasters and weather events Carvalho et al. (2021). Access to geocoded micro data is creating new opportunities to study how firms adapt to extreme weather events.

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# 2. Related literature

Prior research has documented numerous challenges with respect to aggregate productivity and the private sector in Zambia. World Bank (2019b) and the first chapter of World Bank (2024)<sup>2</sup> detail Zambia’s recent economic performance. They document how Zambia’s real GDP growth of 7.7 percent per year between 2001 and 2010 was mostly driven by mining. However, growth slowed significantly in the 2010s, with GDP growth falling despite increased public investment. On an economy-wide basis, labor productivity growth averaged 3.7 per year in the 2000s but reversed in the 2010s, remaining negative for most years over the 2011-2020 period. Since 2000, within-sector productivity gains were negligible in most sectors, with any productivity gains that did occur mostly coming from inter-sectoral reallocation of resources from less to more productive sectors (World Bank 2024). At the firm level, Hoy, et al. (2022) leverage firm-level tax data to analyze the impact of the COVID-19 pandemic on formal firms in Zambia, documenting large declines in sales and employment in formal firms at the outset of the pandemic, followed by a rapid recovery in sales but less robust recovery in employment.

Merotto (2017) analyzes trends in labor supply in Zambia through Labour Force Survey data, finding evidence of growing underemployment in Zambia. He shows how, despite the share of working-age workers employed in waged jobs increasing from 17 to 29 percent between 2008 and 2014, the bulk of new job creation continues to be informal, with wide wage gaps between the formal and informal sectors, between rural and urban workers, between men and women, and between unskilled and skilled workers. He further leverages data from Zambia’s 2012 Economic Census to analyze labor demand, finding low rates of micro firm survival and low productivity in manufacturing sectors.

This paper seeks to fill gaps in the literature with respect to recent firm-level productivity dynamics in Zambia. Research on productivity in Zambia tends to focus on macroeconomic data (World Bank
standard deviations) when comparing one variable to another. <mark>To modify this formula,</mark> we <mark>first calculate the sample size required to achieve the desired level of precision for a simple random sample (SRS). We then adjust this formula by the design effect (DEFF), which in this application measures the efficiency of a stratified clustered sample compared to SRS. We also derive two different formulas for estimating the sample size based on whether the targeted variable being studied is “continuous” or “dichotomic”.</mark> To determine optimal allocation of the sample across sampling strata, we also modify Neyman’s (1939) original allocation rule, which incorporates the survey design effect and can therefore optimally allocate for multi-stage stratified clustering. The derivation of this formula follows Neyman’s (1939) original closely and consists in solving an optimization problem that minimizes the stratified clustered sampling variance of the targeted variable of interest subject to a fixed pre-established sample size that is determined using the modified Cochran (1977) formula described above. The solution to this optimization problem gives us a more general optimal rule for the allocation of the sample across strata. This rule can be used both for multistage stratified clustered samples and for stratified simple random samples.

# **3. Sampling strategy**

# **3.1. Objective of the survey**

As mentioned before, the main objective of the HIES 2016/17 is to produce reliable poverty statistics, measured by the poverty headcount ratio, at three different levels: (i) annual poverty estimates at the district level; (ii) annual poverty estimates at the division level for urban and rural areas; and (iii) quarterly poverty estimates at the national level.<sup>6</sup> A secondary objective of the HIES 2016/17 is to understand the rapid urbanization occurring in Bangladesh, and to

> 6 The poverty headcount ratio is defined as the proportion of people living below the official national
17

# **Table 1. Bank Performance and Regulation in 1999, Survey I**

|`------------------------------------`<br>`------------------------------------`|`---------------`<br>`Bank`<br>`Development`<br>`---------------`|`------------`<br>`Interest`<br>`Margin`<br>`------------`|`--------------`<br>`Overhead`<br>`Costs`<br>`--------------`|`---------------------`<br>`Nonperforming`<br> <br>`Loans`<br>`---------------------`|
|---|---|---|---|---|
|<br>`Capital Regulatory Index`|<br>`-0.0333`<br>`(0.0252)`|<br>`0.00194`<br>`(0.00323)`|<br>`0.000398`