rafmacalaba/data-use-mentions-extended huggingface dataset:
_Beyond Unequal Development_

# **3.2 Empirical Evidence**

The empirics of this inequality-growth literature is largely dominated by cross section or panel regression analysis". In general, several empirical studies tend to support the hypothesis that inequality (an explanatory variable) has a negative, often statistically significant, effect on the rate of output growth (the dependent variable in the regressions) after controlling by variables such as initial per capita income, levels of education, and political participation. This result seems to hold for separate samples of developed and less developed economies (see Persson and Tabellini, 1994) and is robust to alternative functional forms of- the distribution-growth relationship and different measures of inequality (share of top quintil, Gini coefficient, Theil coefficient, see Clarke 1992). However, not all studies agree on this. For example, Fishlow (1995) shows that the negative correlation between inequality and growth fails to be detected when a dummy for Latin America, a region with high inequality, is included in the regressions. The influence of the political regime (democracies or non-democracies) on the inequality and growth nexus seems less on agreement. While Persson and Tabellini (1994) found that the negative relationship between inequality and growth holds only for democracies, Clarke (1992) and Alesina and Rodrik, (1994) found no significant impact of the political regime on the sign and significance of the distribution parameter in the growth regressions. It is worth mentioning that all the models tested including the economic and political mechanisms, are reduced forms. A structural test of the political mechanism (median voter) proposed in the theory is hard to find.

A recent World Bank study by Deininger and Squire (1995b) shows that most of the recent test of the negative relationship between initial inequality and subsequent economic growth are based on income distribution data of limited coverage and of little cross-country and It is hard to find yet, time-series, historically-oriented, studies of distribution and growth in this literature.

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years of schooling; and living standards are summarized by gross national income per capita. Because the knowledge dimension is the most relevant aspect of human capabilities for using DFS, we rely only on mean years of schooling among adults as an explanatory variable in the regressions presented below. However, the empirical findings are qualitatively similar if we use the full HDI as our explanatory variable summarizing human capabilities, or if we replace the HDI with the Human Capital Index described in World Bank (2020).

# **World Development Indicators**

The explanatory variables that we use to describe connectivity (share of population with access to electricity, that uses the internet, or has a mobile cellular subscription) are taken from _World Development Indicators_ (WDI), as are controls for growth in GDP per capita, inflation, and population. More detailed descriptions of the variables used in the analysis are found in Table 1.

# **_3. Estimation Approach_**

Our estimation approach is designed to reduce omitted variable bias and endogeneity bias. By focusing on digital payments in 2021, well after the initial shock of COVID-19, we argue that the estimated parameters for the explanatory variables will suffer less from endogeneity bias than a model in which digital payments and the explanatory variables are contemporaneous. More specifically, serial correlation is likely in a cross-country model of digital payments based on factors such as regulatory enablers, digital infrastructure and human capabilities, but explaining variation in digital payments after experiencing the unexpected COVID-19 shock should reduce concerns about this sort of bias. To address omitted variable bias, we estimate the level of digital payments in 2021 both with and without a control for the level of digital payments in 2017, the last pre-pandemic year for which we have digital payments data. In the model that controls for the level in 2017, we are essentially modelling the change in the level of digital payments during the 11
grade 9 only become positive and statistically significant for some econometric specifications in the academic year 2007-08 and 2009-10 when the expansion of the FSSP to high school was fully implemented. This may suggest that this second phase of the program has positive impacts on enrollment in high school.

The evidence on school progression based on household-level data seems to validate the findings obtained with school data as it also suggests that the program increases the chances that

> 24 Qualitative evidence based on interviews with officials in charge of implementing the program indicates that campaigns to raise the awareness of the existence of the program began only at the second year of the program.

> 25 The parameter of interest for the models discussed here is changes in the number of girls relative to number of girls enrolled at baseline. For instance, if the numbers of girls enrolled increased by five (from 16 to 21), this corresponds to a 32 percent change in enrollment. This, however, should not be confused with a change in enrollment rate in percentage points. Suppose that the universe of girls that should be enrolled in middle school is 100, the increase in enrollment rate in percentage points for the above scenario would be around five, from 16 percent to 21 percent.

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# **I. Introduction**

Maritime transport costs have a profound influence on international trade. In some cases, their trade-inhibiting effect dwarfs that of customs duties.' More generally, economic research has highlighted the role of transport costs in determining geographical patterns of trade, production, industrial structure, and income.<sup>2</sup> Interesting new work even suggests that transport costs (as an element of trade costs) help explain a variety of puzzles in the field of international macroeconomics, such as the well-known home biases in consumption and investment, and the excessive volatility of exchange rates.<sup>3</sup> These observations are interesting from a policy point of view, however, only if something can be done about these costs. Are transport costs exogenously determined by technological developments or can they be influenced by policy?

It has been argued that maritime transport costs are kept high by restrictive trade policies, notably the cargo reservation schemes and monopoly rights granted to providers of port and auxiliary services.4 It has also been argued that private anti-competitive practices, primarily but not exclusively of the maritime conferences, are responsible for keeping costs high.<sup>5</sup> However, most observers also argue that both public and private trade-restrictive policies are becoming less and less important.<sup>6</sup> Yet the available evidence suggests that transport costs, especially for liner trade, are not falling-despite dramatic improvements in technology, especially in the form of containerization (Hummel, 1999). Could it be that the disappearance of restrictions, like the demise of Mark Twain, has been prematurely announced?

This paper seeks to assess the relative importance of public and private trade-restrictive actions in explaining the price of maritime transport services. To measure these prices, we used newly published data on US waterborne transport from the United States Department of Transportation. A major advantage with this data was that it is broken down by type of service-liner, bulk and

> ' This has been demonstrated
where **0** is a block of zeroes; the number of columns equals the _V_<sup>ˆ</sup> _c_ from above, and the number of rows to the _V_<sup>ˆ</sup> _c_ next to it.

The _V_<sup>ˆ</sup> matrix is similar to the Ωmatrix from the ELL methodology.<sup>ˆ</sup> In the most current version of PovMap users no longer have the option to use the _GLS_ estimator originally offered by ELL (2003), and independently of how users choose to model the location effect, the manner in which the _GLS_ estimators are obtained is using the estimators from equations 16 and 17.

# **4 The Second Stage: Simulation**

The final goal of the process described up to this point is to simulate values for the variable of interest. In the Elbers, Lanjouw, and Lanjouw (2003) context, this entailed log welfare and poverty rates for specific locations using the census data. Monte Carlo simulation is used to obtain the expected welfare measures given the first stage model (ELL, 2003). This is done by applying the parameter and error estimates from the survey to the census data. The goal is to obtain a sufficient number of simulations in order to obtain reliable levels of welfare. The section begins by introducing Empirical Bayes prediction as is done by Van der Weide (2014).

## **4.1 Empirical Bayes Prediction Assuming Normality**

Along with the _GLS_ and Henderson’s Method III additions to the ELL approach mentioned before, Empirical Bayes (EB) prediction was also added. EB prediction makes use of the survey data in order to improve predictions on the location effect. Since EB makes explicit use of data from the survey, its use only improves predictions for areas that are included in the survey.

If we assume that _ηc_ and _ech_ (from equation 1) are normally distributed, then the distribution of _ηc_ conditional on
**Figure 7 The district poverty incidence in 1999 and 2006**

<!-- Start of picture text --> 0 200 400 600<br>Districts<br>0 20 40 60 80 100<br>1999 2006 The poverty incidence in 1999<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>Figure 8 presents the relation between poverty reduction in the period 1999-2006<br>and the inequality level in 2006. It shows that provinces and districts with a larger<br>poverty reduction in the period 1999-2006 tend to have a lower level of inequality in<br>2006. It means that poverty reduction can be associated with inequality reduction.<br>Figure 8 Poverty reduction and inequality during 1999-2006<br>0 10 20 30 40 -20 0 20 40<br>Poverty reduction during 1999-2006 Poverty reduction during 1999-2006<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>100<br>40<br>80<br>60 20<br>40<br>The poverty incidence 20 0<br>Poverty reduction during 1999-2006<br>0<br>-20<br>.35 .5<br>.4<br>.3<br>.3<br>Gini coefficient Gini coefficient<br>.25<br>.2<br>.2 .1<br><!-- End of picture text --> Figure 8 presents the relation between poverty reduction in the period 1999-2006 and the inequality level in 2006. It shows that provinces and districts with a larger poverty reduction in the period 1999-2006 tend to have a lower level of inequality in 2006. It means that poverty reduction can be associated with inequality reduction.

# **V. Estimates of Income Poverty and Inequality**

Since the government of Vietnam is using the income poverty line, we also estimate income poverty and inequality measures for provinces and districts.
income if income increases. We can use this “wisdom” as our prior until we have evidence that it is not the case in certain circumstances. Some human behavior is universal and imprinted in the genes of all humanity but other types of behavior are culturally determined. Therefore some behavioral responses to a particular program or policy will not be the same. For instance, it is not clear that one can draw lessons from the Latin American experience with conditional cash transfers (there are CCT programs in almost every country) that are relevant for, say, rural Africa.

The political dimension of policy is also important. An evaluation should be able, at the very least, to distinguish between gainers and losers. For example, Lanjouw and Ravallion (1998) estimate the marginal odds for individuals of different income categories to participate in school and anti-poverty programs, using 1993-94 National Sample Survey data from rural India. Their results suggest that the benefits of these programs are captured early on by the non-poor. The lack of fiscal discipline (called the “soft budget constraint” by János Kornai) is an economic issue with political roots that is prevalent around the world. It makes public expenditure management and macroeconomic control particularly difficult, because the state must be able to resist the pressure of interest groups, and greatly complicates the process of reforming governance. For example, there are enormous pressures to increase local expenditures to counteract growing unemployment and the closure of loss-making enterprises, not to mention other kinds of subsidies to friends and cronies. Such pressures can create problems in restoring fiscal discipline at the macroeconomic level.

It is important to understand the heterogeneity of the impact of the program on various population groups. This is very relevant for the political economy of anti-poverty policies, or for privatization and restructuring of enterprises, since it may indicate the need for supplementary policies for better protecting the losers. For example, the foregone labor earnings incurred by participants in workfare or conditional cash transfer schemes (via the loss of earnings from child labor) will vary
In addition to the SAM, there are two types of parameters that should be estimated before the CGE model can be used for policy analysis: one is a share parameter, such as consumer expenditure share, and the other is an elasticity parameter, such as the elasticity of substitution in the production function. The share parameters can be estimated from the SAM using a calibration method that assumes the benchmark year represented by the SAM is an equilibrium solution for the CGE model, but the elasticity parameters must be obtained based on the relevant literature<sup>[10-12]</sup> . The CO2 emission factors from fossil fuels are calculated based on IEA statistical data<sup>[13]</sup> .

38
# **FDI and Trade Outcomes at the Industry Level – A Data-Driven Approach**<sup>*****</sup>

Jean-Christophe Maur<sup>†</sup> , Milan Nedeljkovic<sup>‡</sup> and Erik von Uexkull<sup>§</sup> Keywords: trade, investment, export competitiveness, dimensionality reduction, machine learning JEL classification codes: F14, F21, C55

* The authors would like to thank Mary Hallward-Driemeier, Jean-François Arvis, Yan Liu, Samuel Rosenow, and Victor Steenbergen for very useful comments and suggestions on earlier versions of this paper, as well as Samuel Fraiberger and Melise Jaud for productive discussions. We are deeply grateful to Miodrag Petkovic and Maria Reinholdt Andersen for their assistance in preparing respectively the policy dataset and the benchmarking excel tool. This research benefitted from support provided by the Umbrella Facility for Trade trust fund that receives contributions from the governments of the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom. All errors remain the authors’ sole.

† World Bank, jmaur@worldbank.org

> ‡ FEFA, Metropolitan University, Belgrade, mnedeljkovic@fefa.edu.rs

> § World Bank, jvonuexkull@worldbank.org
# 4. Consumption aggregate

The main welfare measure used in this and other analyses using SHFS data is per‐capita consumption, rather than income (Deaton and Zaidi, 2002). The SHFS collected data on realized consumption rather than the total money spent on consumption items, as this measured actual realized welfare in a utility‐ consistent way (Ravallion, 1994). This section discusses the various adjustments made to the SHFS data to construct the consumption aggregate using the Rapid Consumption Methodology. Section 6 discusses the use of the international poverty line and various aggregate poverty measures to perform poverty analysis on the SHFS data.

## 4.1. Cleaning of consumption data

Before deriving the consumption aggregate, the components of consumption –data on food consumption, nonfood consumption, and durable assets– must undergo a cleaning process to correct outliers and other mistakes (Deaton and Zaidi, 2002).

Food expenditure data are cleaned in a four‐step process. First, units for reported quantities of consumption and purchase are corrected. Typical mistakes include recorded consumption of 100 kg of a product (like salt) where the correct quantity is grams. These mistakes are corrected using generic rules (Table A.4). Then, a conversion factor to kg for all units is introduced. For example, a small piece of bread will likely have a different weight than a small piece of garlic. To avoid mistakes, enumerator trainings focused on units and introduced a common understanding of what each unit means for each food item. In addition, the conversion to kilograms was made explicit on the enumerators’ tablets (Table A.5). The third step consisted of correcting issues with the exchange rate selected (Table A.6). Finally, outliers in each component of consumption are detected using a set of cleaning rules to correct quantities and prices (see Appendix Cleaning rules for food consumption data). The non‐food data set only contains values without quantities and units. First, the same cleaning rules for currencies are applied
This demographic shift in DFS usage was furthered by government programs to transfer cash to support struggling households during the pandemic. For example, those dependent on India’s Direct Benefit Transfer (DBT)-based income support programs were compelled to use DFS to access their entitlements (Saroy et al., 2022). More generally, Gentilini et al. (2020) report that nearly 17% of the world’s population was covered by at least one government COVID-19-related digital cash transfer scheme in 2020-2021. Because governments needed to deposit these transfers into a formal account, increased use of digital payments was also associated with the presence of formal financial institutions. In the Indian context, for example, proximity to brick-and-mortar banking establishments was significantly linked to digital payment adoption during the pandemic (Saroy, 2022). And the Findex 2021 data reveal that 39 percent of adults in developing economies opened their first financial account for the purpose of receiving a direct government payment (such as a wage or benefit payment) or a wage payment from a private-sector employer, and that most of those accounts were with brick-and-mortar financial institutions (Demirguc-Kunt et al., 2022). Finally, there is also evidence that the pandemic-induced increase in use of digital payments persisted after the initial waves of COVID-19 had subsided (Jonker et al., 2022; Ardizzi et al., 2020).

Despite the proliferation of studies on the role of digital payments during COVID-19, what has been lacking is systematic global data on drivers of DFS usage and, until the release of the 2021 Global Findex data, on DFS usage itself during the pandemic, and accompanying empirical analysis to measure the relative impacts of those drivers.<sup>7</sup> This paper is an attempt to begin filling this research gap. We first establish the wide variation across countries during the COVID-19 period in the increase in digital payments and usage of digital financial services. Our

> 7 Sahay et al. (2020) note that, “to the best of our knowledge
ers in terms of developing their irrigation sub-sector (Riddell et al., 2006). At the same time, the use of drought-resistant crop varieties among smallholder farmers also remains low (Frenken, 2005).

All these factors heighten the risks of chronic food insecurity in drought-prone countries of Sub-Saharan Africa and raise the need for risk-management mechanisms, as drought shocks become common occurrences due to climate change. Yet, despite the strong correlation between drought risks and the prevalence of polygyny, as shown in Figure 2, there has been limited interest in analyzing this relationship more closely. This paper is the first to examine this issue formally.

# **3 Context, Data, and Descriptive Statistics**

We use Mali’s data to test our hypothesis that polygyny enhances households’ resilience after drought. Our data come from various sources, including the _Fourth General Census of Population and Housing (2009)_ , the _Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages (EAC-I)_ , and climatological data.

## **3.1 Context**

Mali is a landlocked, West African country located in the Sahel region, and along the polygyny belt that stretches from Senegal to Tanzania (Jacoby, 1995). It is bordered on the north by Algeria, on the east by Niger and Burkina Faso, on the south by Côte d’Ivoire and Guinea, and on the west by Senegal and Mauritania. Mali is among the hottest countries in the world. Its two main geographic areas, North and South, each face different climatic conditions for agricultural production. The northern area is more drought-prone and is in constant threat of desertification and population migration. The southern area, in contrast, is where the vast majority of its population is concentrated, deriving its livelihoods from rainfed subsistence agriculture and pastoralism.<sup>5</sup> About 65% of Mali’s land

> 5FAO, 2017.Socio-economic context and role of agriculture. Mali Country Fact Sheet. Available online at http:
credit card data from the Federal Reserve for credit cards up to August 2020, Horvath, Kay, and Wix (2021) show increases in the interest rates of new credit cards to less creditworthy consumers, consistent with a tightening of credit supply and a flight-to-safety response of banks to the COVID19 shock.

We also review some research results for the relative effects of the COVID-19 on relationship versus non-relationship borrowers for both conventional commercial credit and credit card debt for both consumers and small businesses. Berger, Bouwman, Norden, Roman, Udell, and Wang (2021a) find that conventional commercial relationship borrowers fare significantly worse than non-relationship borrowers in their loan contract terms (spread, collateral, maturity, amount) in response to COVID-19 shocks, contrary to findings for normal times (e.g., Kysucky and Norden, 2016) and prior crises (e.g., Bolton, Freixas, Gambacorta, and Mistrulli, 2016). The results are more pronounced for larger firms and larger banks. Berger, Bouwman, Norden, Roman, Udell, and Wang (2021b) also find different effects for credit cards for consumers and small businesses. During COVID-19, both groups benefit with better contract terms, with more benefits flowing to safer relationship customers.

It is difficult to draw strong conclusions about credit supply from the relative credit terms of relationship versus non-relationship borrowers. The conventional commercial credit results of relatively worse loan contract terms for relationship borrowers may reflect that banks reduced credit supply to relationship borrowers to “hold up” these borrowers when the value of soft information from relationships is relatively high (e.g., Sharpe, 1990; Rajan, 1992). Alternatively, these banks may try to bolster their capital in the crisis with earnings from the higher spreads and other harsher credit terms that they cannot charge non-relationship borrowers. The findings may also reflect increases in supply to non-relationship borrowers to try to gain market shares in 60