rafmacalaba/datause-encoder-data huggingface dataset:
# _The spatial dimension_

The question is then whether these convergence clubs translate into geographical “regions” as well. Quah identified a similar “twin peaks” phenomenon in the kernel derived from a international cross section of per capita incomes and found it to be geographically driven- regional clusters of poor countries were getting poorer, the agglomerations of rich countries were getting richer. To use the kernel density plots to see if the same geographical patterns are emerging in Mexico, we regenerate the kernels replacing _t_ +5 with the income of the state relative to the average income of its contiguous neighbors (“neighbor-relative”) in _t_ If the local and economy wide distributions of income are similar, that is, there are no clusters of states with similar incomes, we would find a concentration of probabilities along the main diagonal. If, on the other hand, poor states are found with poor states and rich with rich, we should expect a rotation toward a vertical line at unity- a country-relative poor state will have the same income as its neighborhood.

Figure 4a plots the spatially conditioned kernel density plots. Several points merit note. First, geography does not determine destiny in Mexico to the degree that it appears to globally. Had the income clusters identified previously been totally determined by space, the three observed peaks would have been aligned along the vertical line at unity on the Y axis. However, the post 85 figure is fairly similar to the multiple peaked unconditional kernel of figure 3b and there is a large group of states (for example Zacatecas, Tlaxacala, Michoacan, Nuevo Leon) whose mass lies largely on the 45 degree line. In the post -85 period the intermediate peak mainly consists of the northern states of Baja California Norte, Baja California Sur, Coahila and some successful central states such as Queretaro, Jalisco and, Aguascalientes (recently arrived) who show negative spatial correlation with neighbors. Quintana Roo and Mexico similarly constitute unusually high income areas, the former joining Mexico and Nuevo Leon as especially rich states in the third cluster identified on figure 3b. Even post liberalization, the richest states could not be more independent spatially being, as they are, from three different regions of the country. We do not find Quah’s dramatic convergence clubs of rich and poor states.

However, there is evidence of spatial dependence that would suggest regional effects. Prior to 1985 there is some rotation and compression of the upper mass that suggests that, particularly among northern states of Baja California Norte, Baja California Sur, Chihuahua 11
Failing to fully capture the consumption of the middle and upper classes—in SSA they occupy the top two deciles (African Development Bank 2011; Shimeles and Ncube 2015; Corral Rodas et al. 2019)— may lead to an underestimation of the welfare of this group. While this does not pose a problem for poverty measurement, it does mean that consumption is less well suited to measuring inequality. Furthermore, this problem is exacerbated when attempts are made to measure inequality changes over time, since the creation or consolidation of national middle classes in SSA pushes a greater proportion of households into that part of the distribution that is poorly captured by consumption measures (Kharas 2010; Ravallion 2010; Ncube and Lufumpa 2014; Schotte et al. 2018)—potentially leading to an underestimation of the rise of inequality. This, we argue, calls for adjusting the way we measure welfare in the region.

Our proposal to overcome this problem is to adjust the top of the consumption distribution by using insample information from a corresponding income distribution (Clementi et al. 2020). In a nutshell, our approach is to recalibrate the consumption figures for the middle-class segments by imputing information from the shape of the income distribution of the same households. This recalibration of the consumption distribution for a sample of selected African countries is made possible by constructing an ad hoc database that combines information on consumption with information obtained from household-based surveys maintained by the Food and Agriculture Organization (FAO) of the United Nations, which provides several important indicators on rural livelihoods but also constructs income estimates at the household level.

The paper is organized as follows. Section 2 details the data and the methodology used to produce our results. Section 3 presents unequivocal evidence of inequality underestimation by means of multivariate statistics and econometric analysis. Section 4 suggests original solutions to overcome the problem, also providing the empirical results. Section 5 concludes.

# 2 Data

Various data sources are used for this paper. The general overview on consumption versus income (section 3) is conducted using grouped data from PovcalNet, the global database of budget surveys that are conducted by national statistical offices under the supervision of government or international agencies 4
suggests that there are, at least, two important qualifications to be made.

First, our event study illustrates that weak political institutions precede crises and positive institutional change occurs during and in the immediate aftermath of slumps. Our interpretation of this stylized fact is that, while good institutions may sustain growth, growth collapses can in turn contribute to endogenous institutional change. Severe economic crises seem to raise the pressure for institutional reform in a very broad sense.

Second, the length and depth of economic slumps is negatively correlated with constraints on the executive but the favorable effects of strengthening constraints are greater in ethnically heterogeneous societies. We take this as an indication that effective coordination and responses to slumps are hampered by the existence of many identity groups with whom the ruling group needs to coordinate. However, the coordination problems implied by ethnic heterogeneity are not immutable and can be overcome by strong impersonalized rules. We also provide evidence that these effects run primarily through the duration until the recovery starts and not through the pace of decline.

This interplay between weakly constrained leaders and group diversity is not well captured by current theories of policy reform and delay, which typically focus on information asymmetries or uncertainty about the benefits of reform. Our results are more in line with recent historical accounts which stress the emergence of impersonal rules, though without recognizing the role of ethnic or interest group diversity. Hence, a key reason to strengthen political institutions is the stabilization of growth, particularly in diverse countries and perhaps more so than the literature has emphasized so far.

33
ties. The field appraisal team worked to ensure that the land was suitable on a variety of dimensions that could affect future outcomes (for example: agriculture, environment, social welfare, and so forth). The findings of the field appraisal were written up as a formal proposal to purchase the land and were then presented to the project Committee. The proposal had to be approved by the Project Manager (located in the urban area of Blantyre) and approvals for beneficiary relocation were done by the National Technical Advisory Committee. Once all approvals were completed, money was disbursed and public notices of the land sale were issued to do a final verification of the estate owner’s title.

Only six BGs were moved in the first year of the program (2005), and 123 were moved in the latter part of 2006 until early 2007. Subsequent groups were moved in the following years under a slightly changed implementation scheme. This evaluation focuses on beneficiaries who moved in the second year, and uses baseline and follow up data that were collected for these beneficiaries as well as for comparison households living in neighboring districts. For a map of the areas affected by the program, see Annex Figure I.

Each beneficiary household received approximately two hectares of land, a cash grant held in a group bank account, and title to their land through a group-level title deed. The total amount per household was $1050, from which 30 percent could be spent on the purchase of land, 8 percent was given as a relocation allowance prior to resettlement, and the rest of the money was supposed to be applied toward farm development<sup>ii</sup> . Cash was released in tranches to the BGs upon request. Each tranche of money was released to the group as a whole, and then allocated among households. Project administrators often requested a report of how the money had been spent before releasing subsequent tranches. Beneficiaries could use the money to pay themselves for their own labor inputs, which was very common in the first two years of the 9
B7, are used to impute a malaria risk for each patient. Appendix Figure B2 shows that the distribution of predicted malaria risk by treatment group is very similar, and Table 2 finds no significant difference in average predicted risk across treatment groups.

The probit is estimated off the selected sample of individuals who consented to a home-based test. If this selection is partly based on unobservables that are informative about malaria status, the malaria risk of tested patients may be different, conditional on recorded symptoms, from that of the average patient. However, our most important tests do not require estimated risk to be unbiased; rather we require a proxy that is strongly correlated with true risk. The regression assigns positive weight to symptoms known in the medical literature to indicate malaria, like fever, chills, nausea, and headache (CDC, 2015), which suggests that imputed malaria risk is correlated with true risk. Consistent with this, predicted risk is highly correlated with malaria treatment outcomes. Figure 3 uses data from the Control group to plot local linear regressions, where predicted malaria risk is the running variable. The share of individuals prescribed an antimalarial steadily increases with risk; lower risk patients are more likely to receive simple treatment, while higher risk patients are more likely to receive severe. As a proportion of prescriptions, the share of patients who decline the prescribed treatment is decreasing in predicted malaria risk for both simple and severe treatment. Appendix Figure B3 shows that these patterns hold for those with valid home-based RDT tests and those without.

# **4 Main Results**

## **4.1 Empirical Approach**

We use equation 1 as our core specification for estimating treatment effects. To improve precision and address any concerns of patient selection on PV and DV days, we augment equation 1 to control for additional clinic and patient-level characteristics. To avoid specification searching, we select these additional covariates using double lasso (Belloni, Chernozhukov and Hansen, 2014).<sup>27</sup> Appendix Tables B8 and B9 show that results with no additional omit the basic demographic characteristics from the probit.

> 27Candidate controls include a set of clinic fixed effects, as well as all individual characteristics included in Table 2, except those only measured at the home survey: symptom

24
is determined by At the steady state, the condition for a self-selection equilibrium to emerge (i.e., skilled workers only emigrate) is : In this case, migration prospects impact on the critical ability level required for investing in education; the condition for investing in education becomes: which is higher than cn = h ¡ 1 providing that the self-selection condition holds.

There is a great deal of evidence that migration prospects indeed impact on people’s decisions to invest in higher education. For example, in their survey on medical doctors working in the UK, Kangasniemi et al. (2004) evaluate that the migration premium in the medical profession lies between 2 and 4 (in PPP values); about 30% of Indian doctors surveyed acknowledge that the prospect of emigration a¤ected their e¤ort put into studies; furthermore, the doctors surveyed estimate that migration prospects a¤ect the e¤ort of about 40% of current medical students in India. Focusing on the software industry, Commander et al. (2004) estimate that the migration premium for Indian IT workers contemplating emigration to the US lies between 3 to 5 (depending on the type of job) in PPP values.<sup>10</sup> According to the IOM (2003), the prospects of working abroad have increased the expected return to additional years of education and led many people to invest in more schooling, especially in occupations in high demand overseas.

Migration prospects stimulate domestic enrollment in education but actual emigration deprives the country of its educated citizens, the proportion of educated in the remaining population falls to zero, and the average level of human capital of remaining members falls to 1. In the presence of a minimal threshold for consumption, migration can be limited by an additional liquidity constraint. Liquidity constraints are due to the monetary fraction of the migration costs only (as psychological costs of leaving and assimilation costs are incurred only once migration has occured). Let us denote by k0w¤ < kw¤

> 10In current $, the migration premium is much larger (higher than 10 for many countries). Many migrants confess that they were unable to compare earnings on a PPP basis. The expected migration premium is likely to lie between the PPP and the current $ values.

16
may grow on average but poor households become poorer is less desirable than another where the economy can slightly contract but poor households are better off.

Finally, the typology provided in Table 2.1 is general enough to be employed with different (absolute or relative) definitions of welfare categories, as well as different welfare outcomes including objective measures and subjective measures. As proposed in Dang and Lanjouw (2016), the cutoff points delineating the different income groups can also be obtained using a variety of approaches, such as employing a range of fixed percentiles of the income distribution (say, between the 40<sup>th</sup> and 80<sup>th</sup> percentiles as in Alesina and Perotti, 1996) or some absolute cutoff thresholds such as between $2 and $10 PPP dollars (Banerjee and Duflo, 2008).

We also show for supplementary analysis estimates that employ the World Bank’s definition of shared prosperity, which is growth in mean consumption for the bottom 40 percent of the income distribution (see, e.g., Basu, 2013; Jolliffe et al., 2015).<sup>10</sup> But note that this definition is perhaps more relevant for anonymous growth analysis, where the consumption level for the bottom 40 percent, rather than for the poor population, in each period is tracked. Our typology is more explicitly related to pro-poor growth analysis, where we track welfare of the different population groups over time.

# **II.3. Data**

Construction of the synthetic panel requires a country to have at least two cross sectional surveys. These two surveys should preferably be comparable: that is, they are nationally representative, are conducted around the same time in the calendar year (e.g., to avoid seasonality), and the reporting period and instruments (diary or recalled consumption) are consistent in both surveys. This follows from Equations (1), where it is assumed that to obtain the counterfactual

> 10 In a slight abuse of notation, we use the pairs of terms “income” and “consumption” interchangeably in this paper.

11
deeper analysis on the individualization of public goods by the middle class in LAC. Most of the countries in Latin America and the Caribbean seem to present similar problems to those of the Dominican Republic in what regards lack of quality of public goods and privatization by the middle classes (see, for instance, Ferroni et al 2008 and OECD 2010).

We argue that enhancing transparency and accountability in public sector service delivery, building capacity at the civil society and aligning the interests of the poorer and middle classes are some of the ways that could help the DR and other countries in Latin America to get out of this sub-optimal equilibrium and provide citizens with efficient and improved public services.

[31]
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and lower regime (defined by output growth being below the threshold value). We use the WaldStatistic with three separate tests: Sup-Wald, Avg-Wald and Exp-Wald.<sup>1</sup> Based on this procedure, the value of the threshold (� in equation (1)) that is found to maximize the log determinant of the structural residuals is estimated at 0.191. The second technique we rely upon to determine the value of the threshold value is the multivariate procedure as developed by Tsay (1998). This procedure assumes a null hypothesis of linearity in the data throughout the entire period. Table 1 presents the results of this analysis for various lags of the VAR model and the threshold. For our model, we select a threshold lag of 1 and lag VAR of 2.<sup>2</sup> The threshold value we use to estimate our model as determined by the Tsay procedure is equal to 0.1603, which is close to the threshold determined by the Balke (2000) procedure as mentioned above.

We use a nonlinear impulse response function (NIRF) to analyze the reaction of the model’s variables to various shocks.<sup>3</sup> The NIRF is very useful for this type of analysis, as it allows for measuring the size, the direction, and the initial condition of the shock under each of the two regimes. The impulse function is constructed by assuming that the reaction of the model to various shocks is nonlinear between the two states of the economy (as separated by the threshold variable), while full linearity is assumed within each regime.

_Table1: Tsay Threshold Test for linearity_

|**VAR lag**|**Threshold value**|**Threshold lags**|**Test Statistics**|**P-value**|
|---|---|---|---|---|
|1|0.1603|0|73.07|0.0000|
||0.1603|1|37.18|0.0000|
||0.1603|2|7.63|0.8132|
|2|0.1603|0|72.03|0.0000|
||0.1603|1|35.01|0.0282|
||0.1603|2|18.91|0.5909|

> 1 For additional explanations of this approach and the simulation method used to conduct the inference, please refer to the pioneering work of Balke (2000) and Baum and Koester (2011).

> 2 Similar to other studies, the threshold variable is defined as the two-quarter moving average of the series.

> 3 Refer to the work of Batini, Callegari and Melina (2012) for further clarifications on the construction of the NIRF.

5
the interdecadal Censuses. Furthermore, international experience (Baker and Grosh, 1994, Bigman and Fofack, 2000) indicates that greater geographical disaggregation is likely to improve the targeting of Vietnam's anti-poverty programs. With more computational effort, it should also be feasible to estimate poverty headcounts (and other poverty measures too) at the commune/ward level, although it remains to be seen how accurate these calculations will be. More regionally specific analysis of the use and combination of additional household level targeting variables, such as housing characteristics and asset ownership, would also be useful at this time. Nonetheless, it is hoped that this paper has demonstrated the feasibility and policy relevance of these tools to targeting anti-poverty interventions in Vietnam.

> 21 The need for updated Census data is greatest if changes in poverty are principally associated with changes in household characteristics, while the need for new households survey data is greatest if poverty changes are linked to changes in the coefficients of the expenditure regressions. Further research is needed into the relative importance of these two factors.

37
patterns, with some oil abundant countries spending above the mean for developing countries (5.51 percent: Brunei, Kuwait, Saudi Arabia, Trinidad, República Bolivariana de Venezuela), while other spend below that mean (3.44 percent: Bahrain, Gabon, Iraq, Libya, Oman, Qatar, Russia, United Arab Emirates).

# **_Control Variables_**

Along the lines of Fearon and Laitin (2003), our model also includes a range of control variables, capturing the variety of factors possibly explaining conflict. All time-varying variables are lagged one year. All models include the following variables:

- The logged income per capita to control for the “state’s overall financial, administrative, police, and military capabilities.” (Fearon and Laitin, 2003). This variable also captures the argument that there exist fewer grievances in a society with higher levels of income;

- The log of the country’s population to control for “the number of potential recruits to an insurgency at a given level of income.” (Fearon and Laitin, 2003);

- Noncontiguous territory, because if a country has a territorial base separated from the state’s center due to geography, rebel groups could find it easier to take up arms;

- A country’s democracy score: a more democratic country should be less likely to experience violent conflict because political rights and civil liberties are guaranteed in democracies so that people find it easier to resolve conflict through non-violent means;<sup>19</sup>

- Political regime instability: unstable regimes signal a weakened state capacity, leading to an increase in the risk of violent challenges to government power;

- Ethnic and religious fractionalization captures the notion that civil wars are more frequent in heavily heterogeneous societies. Ethnically or religiously divided societies may face strong grievances, which increases the risk of violent conflict;

- A dummy variable for ongoing war: if a country is in a violent conflict with another group, other rebel groups may find is easier to challenge the government.<sup>20</sup>

> 19 Following Vreeland (2008), we also recode Polity IV score to avoid possible endogeneity issues.

> 20 Income per capita comes from Penn World Tables and World Development Indicators, supplemented with per capita energy consumption. Population size is based on World Bank World Development Indicators. We do not control for recent independence because for some of our specifications data is only available post 1960.  Democracy is based on Polity IV (-10 to 10). For political instability, we follow Fearon and Laitin (2003) and use a dummy variable for country/years that had a three or greater change on the Polity IV regime index in any of the three years prior to the country-year in question.

13