rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Most problematic factors for doing DUSINESS source: word coonome Forum, Executive Opinion Survey 2017<br>Corruption 15.1<br>Inefficient government bureaucracy 13.8<br>Access to financing 11.4<br>Tax rates 10.8<br>inadequately educated workforce 10.3<br>Insufficient capacity to innovate 8.1<br>Tax regulations 59<br>Inadequate supply of infrastructure 54<br>Poor work ethic in national labor force 5.0<br><!-- End of picture text --> <!-- Start of picture text --> Foreign currency regulations 29<br>Policy instability 20<br>Poor public health 15<br>Inflation 12<br>Crime and theft 1.2<br>Govemment instability/coups 0.9<br>1 16<br>Note: From the lst of factors, respondents to the World Economic Forum's Executive Opinion Survey were asked to select the five moat problematic factors for doing business in their country<br>and to rank them between 1 (most problematic) and 5. The score correaponds to the reeponaes weighted according to their rankings.<br><!-- End of picture text -->
|
|Sector: Other services|0.06|0.02|0.08|0.01|0.23|0.04|
|Number of employees|4.4|3.0|||||
|Employees: 1-2|0.71|0.76|||||
|Employees: 3-5|0.20|0.16|||||
|Employees: 6+|0.09|0.08|||||

_Note:_ The sample is restricted to males aged 18-59 who are employed in non-agricultural sectors. Net revenue and income are inflation-adjusted and converted to 2016 constant dollar. _Source:_ Non-migrant data is from the Household Income and Expenditure Survey 2016/17, while data on returnees is from the Bangladesh Return Migrant Survey 2018/19.

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# **3.2 Can household surveys help bridge existing climate migration data gaps?**

<mark>The conceptual framework presented here suggests that, despite existing limitations,</mark> multi-topic household surveys <mark>are an essential source of information</mark> for studying the key elements of the climate-migration nexus, and that even greater gains could be achieved if efforts are made to enhance their fitness for purpose in this domain. For example, standard household surveys are clearly disadvantaged in assessing the impact of fastonset shocks: because these appear suddenly and tend to be localized, nationally-representative surveys are unlikely the ideal tool to study their impact. However, coupling them with _ad hoc_ , swiftly implemented post-disaster surveys – perhaps in the form of (high frequency) phone surveys, light/rapid “response” surveys, or community-resident-based surveys – may improve their usefulness for addressing such shocks. Having established, well-functioning, regular national survey programs in place makes these types of data collection workable as there are higher chances that national survey data can be used as pre-shock baselines in quasi-experimental research designs (see for example McCarthy et al., 2018) and, more broadly, can facilitate the design of such instruments.<sup>22</sup> When considering slow-onset events, the framework as well as the literature more generally, implies that the migratory outcome should not be the only outcome of interest for researchers and policy makers. This suggests that broader reference statistical population for climate migration studies should be considered – one which encompasses ‘potential migrants’ and people ‘trapped’ in immobility due to the formation of local vulnerability pockets and poverty traps triggered by climate change. From a practical perspective, the study of immobility and the identification of such populations would benefit from the collection of additional information within multi-topic household surveys, such as migration aspirations and intentions, and household adaptation and coping strategies in the face of climatic shocks. Strengthening the focus on climate-induced immobility traps can also represent a complementary solution to the ‘rare
currently available in the trade facilitation literature.

The paper proceeds as follows. The next section reviews the theoretical and empirical literature on production networks, focusing on the role of trade costs and facilitation. Section 3 presents our dataset,

> 4 A number of papers use disaggregated trade data, but do not examine in detail the possibility that the sensitivity of trade flows with respect to trade facilitation performance differs systematically across sectors. Examples include: Dennis and Shepherd (2010), and Shepherd (2010).

4
markets, relying on an original survey across Kakuma and another town<sup>98</sup> to collect data on household demographics, remittances, employment opportunities and trends, wages and prices.

- The Bank with UNHCR is undertaking a regional assessment of social, economic and environmental impacts of forced displacement in the **Lake Chad Basin** [ongoing] to consider the extent, causes, character and socio-economic impact of forced displacement in Nigeria, Niger, Chad and Cameroon and possibly CAR.

98 Recognizing that there are other factors impacting on the welfare of host communities over the 20-year lifespan of the Kakuma refugee camp, the analysis aims to construct a counterfactual to isolate the changes caused by the presence of refugees.
Figure C14: Heterogeneity in poverty gradient across countries, DOU <!-- Start of picture text --> MWI<br>NER<br>UGA<br>AGO<br>LSO<br>BFA<br>GHA<br>TCD<br>GNB<br>SEN<br>TZA<br>GIN<br>CIV<br>ETH<br>BGD<br>MRT<br>COL<br>Urban center<br>VNM<br>Urban cluster<br>GAB<br>−.6 −.5 −.4 −.3 −.2 −.1 0 .1<br>Regression coefficients and confidence intervals<br><!-- End of picture text --> Source: International Urban Poverty Database. Each dot represents the regression coefficients _DOUj_ ( _i_ ) of regression 5 for each country _c_ , excluding control variables. Confidence intervals are represented with a line. The dependent variable _POVi_ is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. _DOUj_ ( _i_ ) is a vector of dummy variables: "Urban cluster" ("Urban area") takes the value 1 if household _i_ lives in an "Urban cluster" ("Urban area") as defined by the DOU method. The baseline category is "Rural". WorldPop 250m is used.

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to 513 (Hurun Report, 2015). Finally, account holdings leaked from a number of tax havens show considerable wealth in developing countries (ICIJ, 2016).

According to our analysis, global inequality as measured by the Gini index has declined very slightly between 1988 and 2008. As the solid line in Figure 1 shows, the global Gini index fell from 72.2% in 1988 to 70.5% in 2008, a fall of almost 2 percentage points which was particularly strong since 2003. Not surprisingly, global inequality is much higher than what is found within individual countries. For example, in 2008 the Gini index of South Africa, one of the most unequal countries, was 63%. The results are robust to using the same countries throughout, as shown by the dashed line in Figure 1.<sup>5</sup> But given the numerous margins of errors involved in these calculations, both sampling and non-sampling (e.g. PPP exchange rates) (Anand and Segal, 2015), it would be premature to claim that global inequality declined robustly. Furthermore, once we attempt to impute top incomes (Figure 1, dotted line), global inequality has remained almost unchanged over these 20 years, although the decline in the last five years remains.<sup>6</sup> Taken together this suggests that at the very least, there is no evidence pointing toward _increasing_ inequality at the global level. Newer data for 2011 finds that the downward trend is accelerating (Milanovic, 2016, p. 120). Although different methods and inequality measures disagree over the timing and size of the decline, the fall since the mid-2000s is robust across a number of sources.<sup>7</sup> Viewed over the long run, this is a remarkable development. Bourguignon and Morrisson (2002) find that global inequality rose steadily between 1820 and the 1990s, by some 15 percentage points. Therefore, as Bourguignon (2015) and Milanovic (2016
poverty attainments and income distribution changes to underline the heterogeneity of countries in our sample. The first classification includes countries with historically substantial natural resource rents, as a share of gross domestic product.<sup>23</sup> ROC economies are those above the 90<sup>th</sup> percentile of country observations of resource rents in the period 1970–2015. In contrast, NROC— or more-diversified—economies are those with smaller shares of natural resources. The second typology comprises economies in three sub-categories based on rates of growth of output per capita in recent decades. We define thresholds for GDP per capita growth rates using cross-country sample statistics between 1970 and 2014. These selected thresholds are the 30<sup>th</sup> and 70<sup>th</sup> percentiles of pooling all our country GDP per capita growth rates in the period 1970–2014.<sup>24</sup> TABLE 1. COUNTRY CATEGORIES

|**Category**|**Descriptor**|**Definition**<br>|**Country examples**|
|---|---|---|---|
|**ROCs**|Resource output-<br>oriented countries|R<sup>�</sup>≥pR70−15<br>90<br>|Algeria, Cameroon, Myanmar, and Venezuela.|
|**NROCs**|Non-resource output-<br>oriented countries|R<sup>�</sup>< pR70−15<br>90<br><br>|Argentina, Finland, Japan, and Vietnam|
|**LOGCs**|Low-output growth<br>countries|g�y≤py70−15<br>30<br>|Haiti, Madagascar, Niger, and Zimbabwe|
|**MOGCs**|Middle-output growth<br>countries|py70−15<br
Considering the various data issues, there appear to be two ways forward: higher quality cross-country datasets (Bourguignon 2004) or country specific micro data (Banerjee and Duflo 2003). A problem with the latter is that only surveys for very large countries provide sufficient data points to meaningfully include inequality indicators in a regression while census data typically do not provide the income or wealth variables and covariates needed in a growth regression. As a result, there is only a very small number of inequality-growth studies that use micro or regional data. Ravallion (1998) estimates a household level growth model with local externalities and finds a significant negative effect of asset inequality for rural China. Balisacan and Fuwa (2003) find a positive effect of land inequality on provincial level growth for the Philippines. An important advantage of regional or household data, apart from the usually large number of observations, is that comparability problems are much less severe than in cross-country datasets: the definitions of variables or phrasing of survey questions are generally uniform across regions for a given dataset.

The unavailability of inequality data has long precluded the study of the inequalitygrowth relation for smaller countries. However, application of techniques of welfare estimation for small area target populations (see Elbers et al., 2003) has recently provided expenditure estimates for all households in Uganda for 1992 and 1999 (Hoogeveen et al., 2003). It is the availability of these estimates that allows us to estimate the effect of income inequality on growth for a smaller country such as Uganda.

Estimates of expenditure and inequality are available for 719 rural sub-counties. Based on comparable household expenditure data, they represent one of the first data 7
and services, in the case of Ethiopia, while representativeness is captured only at the two digit level in Ghana and Kenya. Adequate sectoral representativeness is important for our calculation because aggregate sufficient statistics of misallocation and the aggregate counterfactual gains in productivity are all constructed based on 4-digit weighted averages. Thus, ensuring that an industry is properly represented in the aggregate is essential for the validity of the results.

To illustrate the importance of the adequate weighting of sectors, we perform two versions of our calculations. In the first one we weight 4-digit industries according to the value added shares implied by the firms sampled in the surveys. In the second one we adopt the value added shares from the Census. Under the assumption that Census-based weights are closer to the true ones, the experiments allow us to assess the quantitative significance of the weak sectoral representativeness of the enterprise surveys.

A second challenge that we face, which is related to the same limitations in the construction of the ES that we highlighted above, is that some 4-digit industries may receive no coverage at all in the surveys. To gain comparability in face of this difficulty, we restrict the sample of firms in the Census to those belonging to industries that are covered both in the Census and the ES, re-weighting valued added shares accordingly.

> 20Obviously, we do not expect the sample to resemble our census due to the structure of the sampling procedure. Large establishments are in typically oversampled in the ES relative to the random sampling of firms in each country. To further explore the comparability of the two datasets, we compare the size distribution in our census data with the sampling frames used to draw samples in the ES. We find that the sampling frame significantly underestimates small firms in Ethiopia and Ghana.

> 21Note that Cˆote d’Ivoire and Ethiopia are excluded in this exercise due to the large number of missing values of capital in the ES.

24
informal firms, 42% of which are in the lowest quartile of predicted formality). These are followed by a cluster we term “smaller professional firms” (4% of informal firms, 39% of which are in the lowest quartile of predicted formality).<sup>23</sup> All three of these clusters skew toward young firms, with the proportion of zero-year-old firms being twice the overall average in the first and third clusters and the proportion of one and two year-old firms being 2.5 times higher than average in the second cluster.

The analysis using EcC data, shown in Figure 15, highlights clusters that we term “smaller professional firms” (5% of informal firms in EcC), “smaller service firms” (7%), “smaller manufacturing and food service firms” (6%) and “one-person retail firms” (27%). From 65% to 95% of these firms are in the lowest quartile of predicted formality. Firm age does not appear to be as much of a distinguishing feature of a low probability of formality in the EcC cluster results as it was in the grouping analysis and the ELMPS cluster results.

We now move to the other end of spectrum to characterize the profiles of informal firms that have a relatively high probability of formality and can presumably be targeted in formalization efforts. According to the ELMPS grouping analysis, these include older firms with above median revenue per worker, irrespective of size or owner education (21% of informal firms in ELMPS), older firms with more educated

> 23 We assigned names to the clusters after examining the predominant characteristics that define them. These characteristics are summarized in Table 13 and Table 14 in the Appendix for the 10 clusters we identify in the data from each data set.

39
2020). They also tend to be few and relatively larger relative to non-exporting firms (ES data). According to the Egyptian Enterprise Census in 2017, which contains all enterprises, only 1 percent of firms export. The average sizes of exporting and non-exporting firms are 18.9 and 3.2 employees, respectively.<sup>5</sup>

> 5 The Enterprise Survey, which targets larger firms (5 employees and larger) similarly finds that exporters are on average considerably larger than non-exporters (313 versus 65, respectively, in 2020).

6
percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). The corresponding estimated effects using the long differences model are less pronounced at 0.5, 1.2, and 2.0 percentage point increases in poverty (which correspond to 5.3 percent, 6.1 percent, and 5.9 percent increases), suggesting household adaptation to gradual warmer temperature over time. Analysis of subnational inequality data suggests that a 1<sup>◦</sup> C rise in temperature leads to 0.8 and 1.4 percent increases in the Gini and Theil indices, respectively.

3
Table 4 shows the correlations between the logarithm of per capita GDP, poverty headcounts, and income inequality (Gini) by region worldwide. Globally, all indices are positively correlated with per capita GDP and negatively with monetary poverty and income inequality. Correlations are usually stronger for per capita GDP, followed by poverty and then inequality. By dimension, the correlation is strongest between each of the income variables and process legitimacy, followed by inclusion. When correlations are estimated by region, their statistical significance frequently wanes as a result of much-reduced sample sizes (results available upon request to the authors).

# **Table 4. Correlations between per capita GDP PPP international $ 2017 (log) and poverty headcounts at national poverty lines**

||Per capita<br>GDP (log)|Monetary<br>Poverty|Income<br>Inequality<br>Gini|
|---|---|---|---|
|P1|0.77*|-0.46*|-0.05|
|P2|0.46*|-0.38*|-0.11|
|P3|0.17|-0.40*|-0.17|
|P4|0.80*|-0.47*|-0.38*|

* Significant at 95% confidence interval Source: Social Sustainability Global Database 2022

# **4.3 Robustness checks**

We first check for the best-fitting association between each social sustainability component and per capita GDP, poverty and inequality. To do that we compare our results in figures 2 to 4 (fitting a linear association between social components and incomes) with an alternative nonlinear, quadratic, specification. For monetary poverty and income inequality, the linear specification is preferred across all components (as a quadratic fit no longer estimates a statistically significant correlation). In the case of per capita GDP, the quadratic form (first decreasing, then increasing)
_Firm profits:_ these are monthly, and were asked directly of the owner as “the total income of the business during each of the last month after paying all expenses including wages of employees, but not including any income you paid yourself”. This follows the wording and recommendation of De Mel et al. (2009).<sup>20</sup> We consider several transforms of profits to deal with outliers and firm closure. This includes unconditional profits (which put zeroes in for closed firms) truncated at the 99<sup>th</sup> percentile, the inverse hyperbolic sine of profits, truncated profits conditional on the business operating, and log profits conditional on operation. Nominal values were deflated to real values using the Consumer Price Index for Colombo, gathered by the Sri Lankan Department of Census and Statistics.

_Firm sales:_ these are firm sales in the past month, deflated into real terms using the CPI. As with profits, we consider several transforms of the raw data to account for outliers and firm closure, with the variables defined analogously to profits.

# _Business Practices score:_

The _total score_ – the composite business practice score -- ranges from a minimum of -1 to a maximum of 29. The total is the sum of the following component scores: the _marketing score_ , the _stock score_ , the _records score_ , and the _financial planning score_ .

The _marketing score_ ranges from 0 to 7, and it is calculated by adding one point for each of the following that the business has done in the last 3 months:

- Visited at least one of its competitor’s businesses to see what prices its competitors are charging

- Visited at least one of its competitor’s businesses to see what products its competitors have available for sale

- Asked existing customers whether there are any other products the customers would like the business to sell or produce

- Talked with at least one former customer to find out why former customers have stopped buying from this business

- Asked
# **References:**

Adamopoulos, T. and D. Restuccia. 2020. "Land Reform and Productivity: A Quantitative Analysis with Micro Data." _American Economic Journal: Macroeconomics_ , **_12_** (3), 1-39.

Agyei-Holmes, A., et al. 2020. _The Effects of Land Title Registration on Tenure Security, Investment and the Allocation of Productive Resources._ The World Bank, Washington, DC.

Albertus, M., A. Diaz-Cayeros, B. Magaloni and B. R. Weingast. 2016. "Authoritarian Survival and Poverty Traps: Land Reform in Mexico." _World Development_ , **_77_** , 154-170.

Albertus, M. 2019. "The Effect of Commodity Price Shocks on Public Lands Distribution: Evidence from Colombia." _World Development_ , **_113_** , 294-308.

Albertus, M. and B. G. Popescu. 2020. "Does Equalizing Assets Spur Development? Evidence from Large-Scale Land Reform in Peru." _Quarterly Journal of Political Science_ , **_15_** (2), 255-295.

Ali, D. A., K. Deininger and M. Goldstein. 2014. "Environmental and Gender Impacts of Land Tenure Regularization in Africa: Pilot Evidence from Rwanda." _Journal of Development Economics_ , **_110_** (0), 262-275.

Ali, D. A., et al. 2016. "Small Price Incentives Increase Women's Access to Land Titles in Tanzania." _Journal of Development Economics_ , **_123_** , 107-122.

Ali, D. A., K. Deininger and M. Duponchel. 2017. "New Ways to Assess and Enhance Land Registry Sustainability: Evidence from Rwanda." _World Development_ , **_99_** , 377-394.

Anderson, S. and G. Genicot. 2015. "Suicide and Property Rights in India." _Journal of Development Economics_ , **_114_** , 64-78.

Aragón, F.
One set centers on a multidimensional energy access matrix recently developed by the Sustainable Energy for All (SE4ALL) program, i.e. the Multi‐tier Framework (MTF) of energy access. The Energy Sector Management Assistance Program (ESMAP) at the World Bank is using this framework to measure all the essential attributes of quality in electricity access around the world. And the World Bank is conducting a number of household surveys based on this framework to evaluate energy access in developing countries. Once such data become available, further analysis can be carried out to estimate how different aspects of the quality of service affect the impact of rural electrification.

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# Acknowledgments

We thank the Ministry of Education, in particular Honorable Minister Madalitso Kambauwa Wirima, Secretary of Education Dr Mangani Katundu, and Director of Basic Education Grace Milner; former Directors of Education Planning Rodwell Mzonde and Francis Zhuwao, and former Directors of Basic Education Joseph Chimombo and Gossam Mafuta, for support in data collection; Muna Salih Meky, Safaa El-Kogali, Hugh Riddell, Greg Toulmin, Inam Ul Haq, Sajitha Bashir, Marina Bassi, and other World Bank colleagues in Malawi and elsewhere for encouragement, support and advice; Martin Moreno, Chris Burningham, Xu Wang, Miranda Lambert, Radhika Kapoor, Stefan Nippes, Gift Banda, Linda Matumbi, Leah Mziya, and Archit Singal, for invaluable research assistance; Vigdis Aaslund Cristofoli and Elin Ruud, Royal Norwegian Embassy, and Arianna Zanolini and Sabina Morley, UK Foreign, Commonwealth and Development Office, for comments and suggestions, and funding for the preparation of this paper. The findings, interpretations, and conclusions expressed herein are our own and do not necessarily represent the views of the World Bank, its Board of Directors, or any of its member countries. All remaining errors are our own.

# Administrative information

## Ethics approval

The Malawi Longitudinal Schools Survey has been subject to ethics review and clearance by the National Commission of Science and Technology for Malawi. The Principal Investigator, Salman Asim, has completed the training “Protecting Human Research Participants” provided by the National Institutes of Health (Certification Number: 921497; Date of completion: 05/16/2012)

## Funding

Principal financing for data collection was provided by the Royal Norwegian Embasssy, Malawi, and the United Kingdom Foreign, Commonwealth and Development Office (FCDO). The interventions were financed by the Global Partnership of Education through the MESIP program and the Royal Norwegian Embassy through MESIP-Extended.

# Tables

26