rafmacalaba/data-use-mentions-extended huggingface dataset:
the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is much greater among women than men.

Moreover, the cluster definitions are robust to feeding into the algorithm a larger or a smaller set of ownership, rights, and decision-making variables. The profiles of owners across the clusters indicate that owners who have transfer rights tend to have other decision-making and rights. These findings, together with the MCA results, imply that household survey questions on land rights can be trimmed down to only include those on transfer rights, without any loss in our ability to cluster landowners. Documented ownership (whose incidence is very low in the three countries) or economic ownership do not appear to be influential in determining rights and decision-making in our sample. This finding is also consistent with the findings of Doss et al. (2014) who have shown that having a land title in Uganda did not guarantee other land rights.

Furthermore, the differentiation of exclusive versus joint ownership and rights variables as part of the hierarchical clustering exercise creates more variation in clusters relative to simpler approaches (as expected) but results in clusters that are much more consistent across countries, which eases cross-country comparisons. This finding reveals not only the importance of eliciting self-reported data on exclusive versus joint ownership and rights arrangements, but also the utility of machine learning techniques in accommodating the high-dimensional survey data and efficiently creating clusters of landowners that facilitate cross-country comparable profiling, particularly across contexts with significant variation in institutions and norms governing land ownership.

The profiling of landowners based on the clustering results provides an entryway to explore further gaps in our knowledge of landownership.<sup>9</sup> In later sections, we analyze gender differences in ownership bundles; the rigidity of bundles (defined as the variation in bundles within a cluster) across countries; how ownership bundles interact with permission structures and decision making; and whether individuals with land use rights are different from landowners in terms of their rights bundles (in a subset of
26|9|0.18|26|157|18|
|25|Greece|24%|54|24|90|24|345|28|9|0.16|28|58|30|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. Institutions were then assigned a country. An _h_ - index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2).
Second, the impact of these trade agreements on production sectors are somewhat more significant, and generally similar across agreements. Under either agreement, there is some flow of labor out of import-competing (primarily textile and machinery) and non-traded goods (services and transportation) and into exportable (mining and food products) sectors. The major difference between the two agreements seems to be that NAFTA has a more favorable impact on the agricultural sector, but a more unfavorable effect on the other manufacturing sector (primarily chemicals). This reflects the differences in comparative advantage of the NAFTA and MERCOSUR countries.

Third, the sectoral effects of either of these agreements is significantly smaller than the one that would result form a complete elimination of trade barriers. The negative impact on the textile and machinery sectors of free trade would be several times larger than that of either preferential agreement (even under high assumptions regarding the degree of trade diversion), probably because of the presence of important suppliers in Europe and Asia that would not be part of either preferential arrangement.

It should be emphasized that the results reported above do not include the gains to Chile generated by improvements in the terms of trade resulting from tariff cuts in partner countries. For the case of NAFTA, suppose we view the US domestic price as being the world price of exports. A reduction in the US tariff will allow Chile to sell its exports at the US domestic price, which result in a terms of trade improvement for Chile. Incorporation of this effect is likely to increase the attractiveness of NAFTA relative to MERCOSUR, given the greater size of the US market. Effects of this type could be incorporated by utilizing data on the tariffs faced by Chile in the partner market. In the NAFTA case, it would seem reasonable to model the terms of trade improvement for Chile as the amount of the reduction in the US tariff. For Mercosur, where Chile is likely to have a significant impact on prices in partner countries, it would be preferable to endogenize the prices in the partner countries
Table 8 presents a simple dynamic model of investment. Here investment is regressed on lagged financial savings and cash stocks. We find that one year lags of financial savings have negative impact on investment. However, the second lag is positive. This is true for both savings and cash stocks. These results suggest that it takes about 2 years for firms between the time they increase their financial savings and the time they undertake new investment. The first year after the firms increase financial savings and cash stocks they are less likely to invest. This confirms that firms are accumulating their internal savings before they can undertake new investment. This finding indirectly suggests that external finance is limited or too costly for these firms and hence they prefer to accumulate their own internal funds before undertaking new investment.

# **9. Summary and Conclusions**

This paper investigated measures related to the corporate savings and investment behavior of firms in Egypt using two different datasets of firm level information. Both datasets have advantages and disadvantages described in the text. Despite the limitations, this is a first step in studying saving and investment behavior in Egypt using data at the microeconomic level. The main findings are summarized below.

Egypt falls in the middle of the cross-country distribution of financial savings and physical savings (i.e. investment), but below average on debt levels.

Main results of the ICA approach:

- a) The usage of financial products is associated with increased incidence and amount of investment.

- b) Small firms are more constrained in access to finance and they invest less in fixed assets (i.e. less physical savings) and use more retained earnings to finance their investment.

- c) TFP is associated with more investment and larger cash flows.

- d) Exporting firms use more external finance and invest more (plausibly to stay more competitive).

Main results of the listed companies exercise:

- a) Large firms make more physical savings (i.e. investment into fixed assets);

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of 1.026 and an intercept of 0.003 (R ~~-~~ squared of 0.918), which is almost identical to the 45° line. This shows that the imputation strategy used to construct wage rate proxies (which involves a constant age premium for all countries, and a country ~~-s~~ pecific immigrant premium for all skil ~~l~~ -experience cells) is very consistent with labor force survey data.

# 4 Equilibrium wage effects

In this section, we use the benchmark labor market model to quantify the relative and aggregate wage effects of the soci ~~o-~~ demographic changes (education and ageing) and mi ~~-~~ gration that have taken place between 2000 and 2010, for non ~~-~~ migrant natives by skill and age group. Remember the model is calibrated to match the wage structure observed in the year 2010. We simulate counterfactual wage structures in 2010 as if soci ~~o-~~ demographic changes had not happened, and express the wage effect as a percentage of the counter ~~-~~ factual level. In other words, the wage effects are computed as (wsAM — welAu) /we,M> where we, vr is the equilibrium wage rate obtained in the counterfactual without ageing, <u>~ 10This group includes Austria, Belgium,</u> Switzerland, Spain, Ireland, Hungary, Greece, the United Kingdom, Italy, Norway, Poland, Portugal and the United States.

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._ 2017) together with attempts at private provision of key public goods by the affluent leading to residential segregation and social polarization.

Impersonal land markets and the institutional infrastructure to support them (Arrunada 2012) are critical to building vibrant and productive cities in several respects (Lall 2017). First, they are needed to bring land to its best use rather than having it locked up in slums or uses that, while consistent with political objectives, may have hugely negative welfare impacts.<sup>1</sup> Second, by facilitating use of land as collateral (Besley and Ghatak 2010), land markets can facilitate credit access to build durable housing stock that can support dense settlement to lower the cost of service delivery (including for transport) and cities’ carbon footprint. Third, property registries and cadasters provide the data needed for property taxation to encourage efficient land use and generate local revenue, thereby encouraging a virtuous cycle where tax collection, service provision, and higher property values reinforce each other and in doing so have the potential to strengthen

> 1 For example, in Nairobi, overcoming frictions to land reallocation would yield gains amounting to about thirty times annual rental payments per slumdweller even after all relevant parties have been compensated (Henderson _et al._ 2020).

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# **3. Data and descriptive statistics**

The main data set we used for this study is the Tunisian Statistical Business Register, namely the _Répertoire National des Entreprises_ (RNE), collected by the National Institute of Statistics in Tunisia (Institut National de la Statistique). The RNE is an exhaustive data set, compiled from merging different administrative folders from the social security fund (Caisse Nationale de la Sécurité Sociale – CNSS), which is the source for the employment data, the Tunisian Customs, the Tunisian Ministry of Finance, and the Tunisian Investment Promotion Agency (l’Agence de Promotion de l’Industrie et de l’Innovation – APII). A major and unique advantage of the Répertoire is that it accounts for all enterprises and covers a relatively long period.

This database contains information on the number of employees, age, trade activities (exports and imports), foreign ownership, as well as trade regime categorization (i.e. offshore) of all registered private firms. The RNE database allows us to track the exit and entry of firms over time. We also exclude firms that report less than one employee to consider only active firms.<sup>10</sup> A detailed description of the RNE is presented in Rijkers et al (2014). Our analysis considers the 1996-2015 period. The data cover approximately 60,000 registered firms per year, for which about 1.2 million firm-year observations were available.

Table 2 describes the main variables used in the analysis. On average, Tunisian firms are about 14 years old, while a median firm is 11 years old. The main variable used in the analysis, to define HGF status, is employment. On average, Tunisian firms have about 15 employees, while a median firm has only 2 employees. Less than 10 percent of the firms have more than 20 employees.

Table 2 – Descriptive statistics of firm-level data for Tunisia (1996-2015)

|**Variable**|**N**<
how to best tackle energy poverty by taking an integrated approach at the local level. It involves setting up a detailed strategy and defining short- and long-term goals. The last step is the implementation of the Local Climate Plan. 22 In this context, the World Bank is exploring the development of a methodology for small-area estimates of energy poverty, under a DG Regio Trust Fund. The project aims to establish a methodology to measure energy poverty at the sub-national level using small-area estimation techniques and assess data availability across selected member states. The methodology will use a specific indicator of energy poverty (e.g., utility arrears, the share of energy expenditures within the household budget) and will then develop and apply an appropriate empirical methodology to project estimates of energy poverty from survey data into census data.

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Policy Research Working Paper 9510

# **Abstract**

The economic crisis caused by the COVID-19 pandemic has sharply reduced mobility and economic activity, disrupting the lives of people around the globe. This paper presents estimates on the early impact of the crisis on labor markets in 39 countries based on high-frequency phone survey data collected between April and July 2020. Workers in these countries experienced severe labor market disruptions following the COVID-19 outbreak. Based on simple averages across countries, 34 percent of the respondents reported stopping work, 20 percent of wage workers reported lack of payment for work performed, 9 percent reported job changes due to the pandemic, and 62 percent reported income loss in their household. Stopping work was more prevalent in the industrial and service sectors than in agriculture. Measures of work stoppage and income loss in the high-frequency phone survey are generally consistent with gross domestic product growth projections in Latin America and the Caribbean but not in Sub-Saharan Africa. This suggests that the survey data contribute new and important information on economic impacts in low-income countries.

This paper is a joint product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. 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 dnewhouse@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
2002) whereas others find that the effects depend on the measure of trade openness used (Rodriguez and Rodrik 2001). This may, in part, reflect omitted variables. For example, some authors find a large positive impact of trade on growth only when this is accompanied by high levels of education, well-developed financial systems, and institutional reforms (Chang, Kaltani, and Loayza 2009). Likewise, regulatory reforms have been found to enhance the impact of trade on growth (Bolaky and Freund 2004).

Trade and productivity. A number of cross-country and firm-level studies find a positive link between trade and labor or total factor productivity.<sup>3</sup> A cross-country study of 138 countries for 1985 finds that a 1 percentage point increase in trade openness is associated with 1.2 percent higher labor productivity (Alcala and Ciccone 2004). A more recent study of a large number of advanced economies and EMDEs finds that around 15 percent of the increase in total factor productivity growth during 1994-2003 was accounted for by rising trade openness, while for developing countries alone, the proportion was larger, at 32 percent (Broda, Greenfield, and Weinstein 2017). Studies that address firm heterogeneity also point to trade-induced productivity gains. For example, one study finds that firms facing international competition enjoy 3-10 percent higher productivity than those that sell only in domestic markets (Pavcnik 2002). A study for Brazil finds evidence of reductions in inefficiencies in firms that engaged in international trade (Muendler 2004).

Trade and capital accumulation. Several studies find evidence of a positive relationship between trade openness and capital accumulation (Alvarez 2017; Sposi, Yi, and Zhang 2019). A study covering the period 1950-98 indicates that countries that liberalized their trade regimes subsequently experienced 1.5 percentage points higher annual investment growth than before liberalization, on average (Wacziarg and Welch 2008). The literature also points to a close association between trade openness and FDI inflows, which are a source of funding for investment in addition to domestic
not only consistent with telescoping effects but also with more general cognitive errors by respondents when they compute the market value of their consumption. Moreover, we find that the quality of recall data improves with the actual value of usual consumption, which suggests that errors may be comparatively less important for households with higher permanent income and human capital.

Further, the above properties of survey errors have important consequences for the computation of aggregate welfare statistics in our study. Precisely, we find that diary errors yield poverty rates and inequality figures that are less reliable than those obtained from recall data, which yield uniformly larger probabilities of correct classification for households with low usual consumption. However, we also find that the upper tail of the distribution of usual consumption is closer to that from diary data, suggesting that deciding which survey measurement will work best ultimately depends on the policy question of interest.

What are the implications for the design of household surveys? First, there is no loss in accuracy from using a recall module despite the higher costs of using a diary: a recall survey is almost as close to the optimal design as a diary when inequality measurement is of concern. The choice of the most appropriate collection mode can be seen as the solution to a decision problem in which each household is assigned to a diary or a recall module to minimize the overall impact of survey errors. We therefore study which assignment would make the inequality and poverty measurements computed from raw data as close as possible to their true values. We show that the optimal solution to this problem is a mix of diaries and recall interviews, with relatively more well-off households assigned to diaries. Although a 4
# **Demographic and Spatial Disparities in Labor Market Outcomes within the Kinshasa Urban Landscape**

Yele Maweki Batana, World Bank Alexandra Jarotschkin, World Bank Akakpo Konou, World Bank Takaaki Masaki, World Bank Shohei Nakamura, World Bank<sup>1</sup> Mervy Ever Viboudoulou Vilpoux, World Bank Keywords: Urban poverty; jobs, unemployment, accessibility, gender JEL Codes: C18, D31, I14, I24, I31, I32 1 Corresponding Author: snakamura2@worldbank.org. We would like to thank Andrew Dabalen, Ruth Hill, Kirsten Hommann and Clarence Tsimpo for useful comments. We also thank the Japan International Cooperation Agency (JICA) and Oriental Consultants Global for sharing the travel survey data. 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.
# **Other Development Policy Applications of CDR Data**

While CDR models are not yet sufficiently accurate to supplant traditional research methods, increasingly sophisticated techniques for combining CDRs with other data sources can enable cellular data to magnify the value of censuses and household surveys. Two especially promising approaches are described below.

## **_Unit-Level Predictions Using Mobile Survey Ground-Truth Data_**

A recent study in Rwanda used targeted telephone surveys to collect data on personal wealth rather than poverty incidence.<sup>12</sup> The advantage of this approach is that telephone survey responses can be combined with CDR data at the individual level. This is typically not possible with official household surveys, which are often anonymous and do not contain a cell phone number that can be cross-referenced against CDR records.

By coordinating the collection of survey responses with CDR analysis, the study was able to develop a set of high-quality matched individual data. However, the use of telephone surveys to gather the data presents its own limitations, as it is not possible to obtain the same detailed consumption information that face-to-face household surveys usually reveal. Instead, wealth proxies must be used—in this case, questions about asset ownership. If the results are not carefully validated, this can lead to errors in the output variables. Additionally, matching the data to telephone surveys requires that the CDRs not be anonymized, which generates substantial privacy concerns.

The Rwanda study offered an encouraging proof-of-concept for the “time extrapolation” application of CDR data. It found that a CDR model for 2009 provided a more accurate poverty assessment than the outdated 2007 household survey. However, the CDR model was not uniquely suited to this task, as macroeconomic indicators such as GDP growth could be used to create a similarly accurate projection.<sup>13</sup> Moreover, the CDR model only partially reproduced the estimates of district-level average household wealth recorded by the household surveys. Although correlations of around 0.9 were reported at the district level—higher than in the Guatemala
Demographic and Health Surveys (1988 and 2006) and Multiple Indicators Survey (2000). Total cost recovery is calculated assuming a capital cost of 40 cents/m3. — = Not available.

Botswana’s progress in increasing access to improved water supply is spread across both urban and rural areas. Between 2000 and 2008 4.2 percent of the population gained access to piped water per year, while 0.2 percent stopped using surface water as their water source. In rural areas, 1.8 percent of the population gained access to piped water per year, and 0.4 percent stopped using surface water.

During the period covered by this report (2004-2008), the reform of the water sector was still under discussion. The water service came from one three entities Water Utilities Corporation (WUC), Department of Water Affairs (DWA) in the Ministry of Minerals, Energy and Water Resources or the local councils. Due to difficulties getting access to the data, the analysis of providers provided in this report only covers WUC. Since 2009, a number of important changes have taken place (box 1).

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