rafmacalaba/data-use-mentions-extended huggingface dataset:
We run five different panel regressions for each dependent variable. The first regression _uses_ pooled data _for_ the seven emerging econonies in the sample. **A** second _and_ third regressions analyze capital structure for the Asian and Latin American economies separately. The last two regressions focus on the contrast between bank-based and marketbased economies.

The results are displayed in Tables 1-VI. We report results from pooled ordinary least squares and _within_ estimators (or fixed effects), with robust standard errors. In this way, we are able to compare our results with those from the existing literature in corporate finance. Since _within_ estimations control for firm specific effects, these models give us intra-firm information. For example, _within_ estimates tell how deviations from each firm's average net assets affect deviations from the average debt-equity ratio. On the other hand, **OLS** estimations combine both inter-firm and intra-firm effects. Pooled **OLS** estimates do not contain firm specific effects. Then, we are able to include country specific effects and the variable that captures the production mix (whether firms produce tradable goods). These variables cannot be included in the _within_ estimations because they are perfectly collinear with firn specific effects." The **OLS** models estimated are:

> such that f=1..N _c_ = **,.,C,** and t **_= 1,..,_** _T._

Y,,,,, represents the five variables defined above,<sup>which measurethe firms'</sup> financing choices. The sub-indexes _i, c,_ and t stand for firm,, country, and time respectively.

**13**
to all respondents at the beginning of enumeration. We refer to the remainder of variables, which amount to 37 items in our Indonesian dataset, as optional. In the subsequent discussion and simulations, we take the core questions as given and refer to optional questions when discussing items such as questionnaire length.

Having been selected for administrative reasons, the short core questionnaire has limited predictive power for household consumption. At the other extreme, a full socio-economic population sample survey contains rich household information that confers greater predictive accuracy, but is unsuitable for administration to a large section of the population. The following three subsections describe methods that provide a menu of choices in between, and expose the relationship between survey cost and accuracy.

# **3.1 Truncated questionnaires**

A simple approach is to order the optional features from most to least predictive. A truncated questionnaire, i.e. one that is limited to a certain number of questions, arises from adding the desired number of most predictive features to the core set. Computation of the predictive accuracy for each of the iteratively growing variable sets provides the cost-accuracy trade-off. In addition to a predictive model, this approach requires an ordering of features by their predictive power for household consumption. We test several variable selection methods to perform such an ordering and thus generate a sequence that can be truncated.

- _Stepwise selection._ The classic mechanism of PMT modelling<sup>6</sup> . To generate a complete variable sequence instead of a particular set of regressors, we adjust algorithm 1 by eliminating the stopping criterion and recording the order in which variables are added to the set of predictors.

- _Variable importance._ Tree-based predictive models perform variable selection during the construction of their tree structure. Our gradient boosting model, which is based on decision trees, generates two measures of variable importance. One is the proportion of trees in which each variable is used, the other is the sum of increases in the trees’ objective function produced by the splits of each variable
, which delivered all interventions under the PVP project; and they were verified through a multi-stage process. These data were collected through standardized reporting formats and informed a range of project functions from the identification of target and beneficiary populations to the tracking of expenditures and process-related outcomes. All monitoring data was also validated through a 3-step process which involved local social audit committees, PVP project staff, and international thirdparty monitoring and audit firms. The third-party verification included visits to a rotating random sample of GPs so that every GP was reviewed at least once in a calendar year, and audits of financial statements on an annual basis. Any data that related to private transfers was also required to be publicly disclosed through regular Gram Sabha meetings and public notice boards. Appendix B provides more details on the types of monitoring data collected and the data verification procedures 8
have significant impact on both income and the explanatory variables; _vi_ is a vector of time dummies capturing universal time trends; and finally, _εit_ represents the error term. The original analysis is based on data for 126 countries spanning the period 19702010. Variable definitions and data sources are shown in Table 1 (Annex). To smooth out potential business cycle effects and reduce measurement error, the data are organized into five-year periods.<sup>9</sup> The baseline model in Equation (2) is estimated using the System GMM. Endogeneity problems are circumvented by deploying suitably lagged values of the independent and dependent variables as instruments (Blundell and Bond, 1998). Araujo _et al_ . (2014) and Moller and Wacker (2017) show that the baseline results are quite robust to a wide array of robustness checks, which include using: alternative instruments, alternative samples, alternative specifications, alternative variables, 10-year averaged data, balanced panel data, time-varying coefficients, unconditional models (to mitigate weak instruments), and more control variables. The key findings also stand up well to parameter heterogeneity (See Section 5).

The regression results show that movements in GDP per capita across the 126 countries in the sample were mainly due to changes in structural features and, to a limited extent, stabilization and external factors. See Table 1. Column 1 shows the baseline results while columns 2-4 report results from sensitivity analyses.<sup>10</sup> The results are generally consistent with theoretical expectations. Structural

> 7Some WAEMU countries, including Togo, experienced banking crises in the 1980s.

> 8See Brueckner (2014) and Araujo et al. (2015) for more details on model specification.

> 9The dynamics of our model allows for longer-run effects beyond the five-year interval. The model formulation accommodates

> both contemporaneous impacts of changes in _Xi,t_ �1 (captured by _β1_ ) and lagged impacts on future income per capita (captured by the lagged dependent variable). Thus, the overall long-run
WPS3814

# **A Normal Relationship?**

## **Poverty, Growth, and Inequality**

### **J. Humberto Lopez and Luis Servén* The World Bank**

### **Abstract**

Using a large cross-country income distribution dataset spanning close to 800 countryyear observations from industrial and developing countries, this paper shows that the size distribution of per capita income is very well approximated empirically by a lognormal density. Indeed, the null hypothesis that per capita income follows a lognormal distribution cannot be rejected -- although the same hypothesis is unambiguously rejected when applied to per capita consumption. The paper shows that lognormality of per capita income has important implications for the relative roles of income growth and inequality changes in poverty reduction. When poverty reduction is the overriding policy objective, poorer and relatively equal countries may be willing to tolerate modest increases in income inequality in exchange for faster growth -- more so than richer and highly unequal countries.

JEL classification codes: C12, C23, D31, I32.

World Bank Policy Research Working Paper 3814, January 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* This paper was prepared for the World Bank’s Latin America Regional Studies program. We thank Lant Pritchett for comments and Patricia Macchi for excellent research assistance.
<mark>single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant’s for these households remain</mark> after humanitarian assistance is received.

<mark>To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD‐ HV). The ProGres database for Jordan includes information on refugees’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘proof of registration’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016). This assistance is crucial and has been reported to support a meal a day, a better</mark> roof, and dignity for Syrians who have fled to Jordan.<sup>8</sup> <mark>The JD‐HV database has detailed information on expenditure, sources of income, and indicators of household‐level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD‐HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the
et al., 2008). We complement these papers by providing consistent stylized facts on transport expenditures and its composition across and within countries for most countries in the world.

Second it provides estimates of Engel curves that have been widely used including in the assessment of policies related to taxation, housing, trade, but relatively less for transport. We estimate transport expenditures as a linear-quadratic function of total expenditures and other controls. Vehicle purchase is a main part of household expenditure, for which we also document an S-shaped relationship between income and durable asset ownership. Gertler et al. (2016) have rationalized the non-linearity of asset purchase in developing countries because of the presence of credit constraints. They show, in the context of rural Mexico, above a certain threshold increases in income are much more likely to lead to asset purchases. In terms of identification of the elasticity estimates, there are many unobservables that affect both total expenditures and transport expenditures that might bias the Engel curve estimates. AIDS/QAIDS type of models that overcome this problem cannot be used as price data are not available in standard household surveys. Additional works have focused on non-unitary (bargaining and collective) models to include intra-household consumption patterns and inequality (Chiappori and Meghir, 2015; Dunbar et al., 2013; Chavas et al., 2018) and on the identification of potentially confounding sources of heterogeneity in standard household Engel curve estimations due to intra-household interactions (Vreyer et al., 2020). We use standard household-level crosssectional surveys and therefore cannot identify that effect. However, Vreyer et al. (2020) find that the null hypothesis that the household-level estimation of the Engel curves gives the same parameter estimates as one would obtain by consistent aggregation of the individual-level Engel curves for household members cannot be rejected for transport. This can be explained by the fact that vehicle purchases, a large part of transport expenditures, are common goods for the household.

Among the studies that examine the determinants of transport demand, Gandelman et al. (2019) show
the city or the country. This point notwithstanding, the informal surveys we use have been used extensively in the literature to study the sector (see Amin and Islam 2015, Islam 2019, La Porta and Shleifer 2014).

> 5 Details of the methodology can be accessed through the following link: http://www.enterprisesurveys.org/methodology

> 6 The difficulty in accounting for time periods is one of the limitations of cross-sectional data sets.

9
are part of the publicly available survey data. More information on the steps taken to calculate the consumption aggregate can be found in the surveys’ online documentation (see Table 1). We also compute a household asset index using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household assets. A regression method is used to predict factor scores (</mark> Rencher, 2002) <mark>. We further compute the household dietary diversity index, following guidelines from the Food and Agriculture Organization (FAO). It consists of the sum of distinct food groups consumed by the household over the past 7 days (</mark> Kennedy et al., 2011) <mark>. Finally, we include information on whether the household suffered any shocks in the last 12 months prior to the interview. These variables are all drawn from self-reported data. In addition, a geospatial variable capturing population density is also included in this dataset.</mark> <mark>Finally, the individual-level dataset includes information about the household members, which is derived from the household questionnaire. A first set of demographic variables includes age, sex, educational attainment including indicators for any formal and primary education. A variable which identifies the members’ relationship to the household head has been included in text format, due to the absence of a unified set of codes. On time use and work/employment, we have indicators capturing any work done during the reference period, the number of hours worked overall, on a farm, in a small enterprise, or in a wage job over a reference seven-day period (either the last seven days, or a typical seven days in the year). Additional variables were added to identify the industry of members’ wage jobs, if applicable. The</mark> 13
0,34<br>N 40 39 31<br><!-- End of picture text -->

Note: Regression 2 excludes Taiwan because income per capita not available for Taiwan from International Financial Statistics, IMF. Observations are “clustered”, such that independence is assumed across countries but not between observations from the same country (Argentina, Indonesia, Malaysia, Turkey, and Thailand). The standard error of checks and M,/GDP is evaluated assuming the interaction term is zero.

29 The coefficients on the linear and non-linear terms actually suggest that for sufficiently high costs of favors to voters (4,/GDP), an increase in the number of veto players has a positive effect on the size of fiscal transfers. This is not predicted by the theory, but is easily explained. It is plausible that the profits earned by special interests from financial sector favors, and the size of the financial sector, are both related to the number of veto players. For example, expropriation of financial assets is more difficult when the number of veto player is larger. Given this, it is easy to show that the net effect of an increase in checks and balances can be to increase the magnitude of banking crises.
the general feeling of worsening welfare conditions. The issue of public perception highlighted earlier can also be linked to the economic performance observed in the second half of the decade, especially since 2008. Looking at the data from the 2008 household income and expenditure survey, the figures indicate that the welfare trend observed in the first half of the decade was reversed, with economic growth becoming anti-poor and unequal. Table 1 reveals that growth in welfare between 2008 and 2010 was positively correlated with richer 14
**Table A.14. Differences by ethnicity in association between social norms indices and women’s paid**

**work outcomes**

||**HH Roles**|**Public Space**|**Both themes**|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
||**Paid work**|**Paid work**|**Paid work**|
|PNB<br>Index_HH<br>Roles<br>#<br>Madheshi**(**β)|0.04<br>(0.07)||-0.04<br>(0.08)|
|PNB Index_HH Roles**(**α)|-0.09** (0.04)||-0.07* (0.04)|
|SNE<br>Index_HH<br>Roles<br>#<br>Madheshi**(**β)|-0.09<br>(0.12)||0.01<br>(0.12)|
|SNE Index_HH Roles**(**α)|-0.03<br>(0.06)||-0.04<br>(0.05)|
|PNB Index_Public Space #<br>Madheshi**(**β)||0.10* (0.05)|0.09<br>(0.06)|
|PNB Index_ Public Space**(**α)||-0.04<br>(0.03)|-0.03<br>(0.03)|
|SNE Index_ Public Space #<
and includes active and dead cross-regional and regional equity and bond funds registered in various domiciles globally.<sup>10</sup> These funds invest in over 124 developed and developing countries around the world. For each fund and month, the dataset contains the total net asset (TNA) value of the fund denominated in U.S. dollars, the percentage of fund assets allocated to each country (which we call _country weights_ or _weights_ and are non-negative), and the percentage held in cash. The dataset has both actively and passively managed funds with different investment scopes: global, emerging markets, and different regional funds (Table 1). The data also contain information on the fund domicile, the family (investment or asset management company), and main currency denomination.<sup>11</sup> We generally use the term “fund type” to refer to any of these dimensions of fund characteristics, clarifying the precise dimension when necessary.

To perform the empirical analyses, we cleaned the original data in standard ways, reducing the sample in about 15% and the total of funds to 1,076.<sup>12</sup> The final dataset on country allocations contains 7,429,000 observations on the investments of the included mutual funds across countries and time. There is substantially more data (cross sectional and time series) and variety of funds for equity funds than for bond funds.<sup>13</sup> We complement the analysis by collecting additional data from other sources to compute inflows and outflows to funds and countries. To calculate monthly injections into each fund, we collect data on fund prices (Net Asset Values, NAVs) from Bloomberg and Datastream (DS) that we match to the corresponding funds from EPFR Global by name and family. We are able to match about 90% of the funds in our cleaned sample, ending up with 896 and 106 equity

> 10 Over the years, several papers have used the different versions of
. Given the availability of data and the global scope, our efforts varied on adjusting official statistics and creating priors for different components. Below we discuss the construction of each component, AgGDP statistics and the allocation model followed by the global natural hazards data. Given the spatial resolution and year of reference of the input data for the crop value of production, we estimate AgGDP for the year 2010 into 5 arc-minute grids (10x10 km) across the world.

> 1Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production

5
Do Locals Know Better? A Comparison of the Performance of Local and Foreign Institutional Investors,” _A Comparison of the Performance of Local and Foreign Institutional Investors (May 19, 2015)_ , 2015.

- **French, Kenneth R** , “Presidential address: The cost of active investing,” _The Journal of Finance_ , 2008, _63_ (4), 1537–1573.

- **Froot, Kenneth A and Tarun Ramadorai** , “Institutional portfolio flows and international investments,” _Review of Financial Studies_ , 2008, _21_ (2), 937–971.

- **Greene, Jason T and Charles W Hodges** , “The dilution impact of daily fund flows on open-end mutual funds,” _Journal of Financial Economics_ , 2002, _65_ (1), 131–158.

- **Griffin, John M, Federico Nardari, and Rene M Stulz** , “Are daily cross-border equity flows pushed or pulled?,” _Review of Economics and Statistics_ , 2004, _86_ (3), 641–657.

- **Grinblatt, Mark and Matti Keloharju** , “The investment behavior and performance of various investor types: a study of Finland’s unique data set,” _Journal of financial economics_ , 2000, _55_ (1), 43–67.

- **, Sheridan Titman, and Russ Wermers** , “Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior,” _The American economic review_ , 1995, pp. 1088–1105.

- **Hau, Harald** , “Location matters: An examination of trading profits,” _The Journal of Finance_ , 2001, _56_ (5), 1959–1983.

- **IMF** , “Coordinated Portfolio Investment Survey,” _http://cpis.imf.org_ , 2016.

25
Chifunde districts of Mozambique. Within each community, a random sample of 20 female farmers were surveyed along with their husbands. The survey is the baseline of an ongoing randomized controlled trial to assess the effectiveness of a human capital program targeting female farmers. The survey elicited information on both spouses’ cognitive and socio‐emotional skills, as well as their time and risk preferences. The spouses were asked these modules separately and privately.

Second, we use data from a survey conducted in June‐July 2016 across 180 villages in the Bas‐ Sassandra, Comoé, Lacs and Lagunes districts of Côte d’Ivoire. The survey is the baseline of an ongoing randomized controlled trial to assess the impact of improving rubber farmers’ access to technologies and markets. The survey elicited information on the socio‐emotional skills, and time and risk preferences of the farmers that applied to the program, as well as of their spouses. The spouses were asked these modules separately and privately.

Third, we use data from the second wave of the Malawi Third Integrated Household Panel Survey (IHPS), conducted in 2013. This is a publicly available longitudinal survey that is representative of the Malawian population. For the rural sub‐sample, the survey elicited information on the cognitive and socio‐emotional skills of up to two plot managers. These are typically the husband and the wife, which are the focus of our analysis.

In each country, we restrict the sample to couples for whom we observe the corresponding skills and preferences of both spouses, as well as the other covariates used in the analysis. This results in working samples of 2,280 couples in Mozambique, 1,407 couples in Côte d’Ivoire, and 531 couples in Malawi.

# **2.2. Measuring Skills and Preferences**

Socio‐emotional skills were measured in the three countries by asking respondents to rate their level of agreement with different attitudinal statements (see Appendix A1). In Mozambique and Malawi, these items elicit abilities commonly associated with