rafmacalaba/data-use-mentions-extended huggingface dataset:
9 (Atkinson, 2011) might be called for. Sklar’s Theorem, from the statistics literature, tells us that any joint distribution function can be decomposed into the marginal distribution functions of each dimension (corresponding to the dashboard approach), and a **copula function** , which captures the information of the degree of interdependency between the dimensions. Copulas, which are the third approach we want to highlight, have already been used to study the relationship between health and income (Decancq 2009, Quinn 2007). Formally, the copula function is the joint distribution function of _n_ vectors whose elements are the _relative position_ of every individual in each of the _n_ dimensions of well-being. As in the case of the margins, once the copula function is constructed, one could perform a stochastic dominance analysis to assess whether the degree of interdependency between the components has changed in an unambiguous way. In addition, and in the same way that information on unidimensional inequality can be summarized by an index (such as the Gini coefficient or the mean log deviation), measures of rank correlation allow us to order distributions unambiguously in terms of their degree of interdependency (Decancq, 2009).

As an example, we reproduce from Decancq (2009) a graph showing the evolution of the Spearman correlation coefficient, a commonly used measure of rank correlation, in Russia between 1995 and 2003 (Figure 3). The author considers three dimensions: standard of living, health, and schooling, represented respectively by household income, self-assessed health, and years of schooling as primary ranking variables (another three variables are used as secondary ranking variables in case of ties). By way of context, the Russian Human Development Index increased from the beginning to end of the period, albeit with a deterioration around the 1998 financial crises.

In this figure, the dashed line represents the 95% confidence interval obtained by Monte Carlo simulations, while the dotted line represents an alternative computation of the same confidence interval, by bootstrapping. The figure illustrates
In Latin America, the process of catching up to the standards of living in advanced economies has been a slow one. In 2017, Latin America’s real per capita income was only about a quarter of the one in the US, compared to a fifth in 1960 (Cavallo and Powell, 2018). Spatial convergence of mean household incomes between the mid-1990s and mid-2000s was also slow (Modrego and Berdegue, 2015). Unsurprisingly, within-country spatial inequality remained high in most Latin American countries (Acemoglu and Dell, 2010). Using census data mostly from the 2000s, Acemoglu and Dell (2010) show that the between-municipality differences in labor income within countries were twice the size of the between-country differences within Latin America. They attribute these differences to differences in human capital and inequality in proximity to paved roads. In a series of studies on Peru, Escobal and Torrero (2005) also empirically connect poor local road infrastructure to higher transaction costs, lower market participation, and reduced household income.

This regional context elevates the importance and policy relevance of the questions posed in the opening paragraph of this paper. To answer these questions, we adopt the state-of-the-art dynamic global spatial general equilibrium framework of Desmet, Nagy, and Rossi-Hansberg (2018). It is wellsuited for the task at hand as it differentiates the effects of mobility frictions from those of other forces that shape the spatial patterns of economic activity and welfare in a dynamic, global context. It also offers a way of estimating the mobility frictions and tracing the effect of changes in these frictions on economic activity in the long run. We follow Desmet et al. (2018), who identify trade frictions with transportation costs, and Allen and Arkolakis (2014), who compute bilateral transportation costs using a least-cost route algorithm between any two cells. This way the costs reflect the geography of locations, the availability of different types of connectivity infrastructure, and the prices of different transportation service modes. Migration frictions capture moving costs into and out of a location due to
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data rate, rather than a nominal rate. Assuming a linear approximation for the unknown where t denotes all unobserved effects that are not captured by X2 but which cause S<sup>t</sup> to deviate from true sales. Equations (7) and (10) form a MIMIC model with one indicator and one latent variable. Plugging equation (7) into equation (10) we obtain a reduced form which can be estimated. However, the constants d1and a cannot be identified separately and parameter values in and b for variables which occur in both X1 and X2 are also not identified. We need at least one more indicator and measurement equation in order to identify the key coefficients for predicting the latent variable true sales.

# _C. Second Measurement Equation  for Survey Data: Measurement Error Model_

The second indicator for the latent variable of true sales is from the 2003 Mongolian Productivity and Investment Climate Survey data organized by the World Bank.<sup>21</sup> Without an obvious economic theory to explain (mis)reporting behavior in the PICS survey, we assume a standard log linear measurement error model linking reported survey and true sales:

> 21 See section 4 and Appendix A for more information on the survey.

19
more capacity to smooth expenditure or that cotton income is a smaller share of total income and therefore does not distort expenditure patterns as much as it does for poor cotton farmers. In contrast there is very little difference in the seasonality pattern for coffee/cocoa farmers, somewhat surprisingly.

To de-seasonalize the 1993 and 1995 expenditure estimates, we subtracted the relevant dummy variables from the log of each household expenditure for each household in the 1993 and 1995 surveys. For the 1993 survey, we used the June-November time period dummies interacted with the region, crop, and home consumption dummies, whereas for the 1995 survey we used the April -May dummies interacted with the region, crop, and home consumption dummies. No seasonal correction was made to the Abidjan observations, since only 8 households with expenditures of 100,000 CFAF or less were interviewed in March-April in the 1985-1988 surveys.<sup>25</sup> The poverty profile, corrected for the seasonal bias, is presented in table 25. The only region in which there is a reversal of trend is in East Forest, where poverty falls instead of rises. However, neither the magnitude of the difference between the 1993 and 1995 original estimates nor the difference between the seasonally corrected estimates is large for East Forest, as well as for Savannah and Other Cities. The big increase in poverty in West Forest remnains even with the seasonal correction, and is investigated below. The reason that the poverty estimates change so little is that the relatively large changes for the cash-cropping households tend to be offset in part by the regional and home consumption dummies. Among the major socioeconomic groups in each region, the seasonally adjusted poverty figures show that poverty falls slightly among export crop farmers in East Forest, and drops more significantly among export crop farmers (primarily cotton farmers) in the Savannah (table 26).

**Table 25 Headcount Index of Poverty Corrected for Seasonal Variation** _(Poverty line = 75, 000 CFAF per year in 1985 CFAF)_

||_Seasonallv_
<!-- Start of picture text --> Number Radios (HQ)<br>Average Integrity - Politician<br>Altruism - Bureaucrat<br>Risk Aversion - Bureaucrat<br>Less than College education - Bur<br>Poverty Rate 2013<br>Voter Attachment to National Party<br>UCE score 2011<br>-.8 -.6 -.4 -.2 0 .2 .4 .6 .8<br>Point estimate and 95% CI<br><!-- End of picture text --> (a) Bivariate Regressions <!-- Start of picture text --> Number Radios (HQ)<br>Average Integrity - Politician<br>Risk Aversion - Bureaucrat<br>Altruism - Bureaucrat<br>Poverty Rate 2013<br>UCE score 2011<br>Less than College education - Bur<br>Voter Attachment to National Party<br>-.6 -.4 -.2 0 .2 .4 .6<br>Point estimate and 95% CI<br><!-- End of picture text --> (b) Multivariate Regression Figure 9: Robust Correlates of Health Service Delivery - LASSO selection _Note:_ This graph reports the point estimate and 95 percent CI for the coefficient on the variable in the axis, in a bivariate regression using District level health outcomes as dependent variables. CI is constructed using robust standard errors. The variables used here are those previously selected in a LASSO model. All variables are standardized to have unit standard deviation.

53
Our paper contributes to the literature on the nexus between urbanization and poverty. Several studies show urban-rural gaps in productivity, wages, and amenities (see Lagakos 2020 for a review). Some studies find earnings and welfare gains from rural to urban migration in the developing world, such as rural to urban migration in Tanzania (Beegle, de Weerdt, and Dercon 2011), seasonal migration in Bangladesh (Bryan, Chowdhury, and Mobarak 2014), and the interplay of locations and migrant characteristics in determining gains in China (Combes et al. 2020). Hamory et al. (2021) analyzed panel datasets in Indonesia and Kenya, finding that a large part of the measured returns from migration came from the sorting of migrants. However, very few have analyzed the role of climate change as a hindrance to urban agglomeration as the urban escalator out of poverty. Therefore, we attempt to shed light on the role of the climatic and environmental shocks on the urban escalator out of poverty.

The rest of the paper is organized as follows. Section 2 explains the framework with a description of our hypotheses. In Section 3, we elaborate on the methodology with the description of the datasets and our econometric approach. Section 4 reports the results of our econometric analysis. Finally, Section 5 concludes with the summary of findings and discussion, policy implications, and limitations.

# **2. Framework**

## **Urban escalator out of poverty: Hypothesis 1**

Larger or denser urban areas potentially provide people with ample economic opportunities to escape from poverty, through better access to higher-wage jobs, infrastructure, services, and so on. As a result, more urbanized areas tend to be characterized by higher incomes and consumption, higher productivity, better access to services, and higher human capital. Earlier studies have found that nominal wages are higher in larger or denser cities due to productivity gains from agglomeration economies in developed countries (Glaeser and Mare 2001; Melo, Graham, and Noland 2009; Puga 2010;
%|35%|19%|10%|4%|12%|7%|
|Nicaragua|42%|19%|29%|19%|9%|6%|20%|12%|
|Paraguay|68%|22%|3%|2%|25%|19%|4%|3%|
|Peru|8%|6%|80%|33%|7%|5%|5%|4%|
|Panama|31%|16%|19%|10%|13%|6%|38%|23%|
|Uruguay|98%|58%|2%|2%|0%|0%|0%|0%|
|Venezuela|48%|19%|17%|6%|22%|10%|13%|4%|
|_LCR Totals_|_48%_|_20%_|_32%_|_14%_|_10%_|_6%_|_11%_|_5%_|

Sources: 1) Population and density are from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” include GAEZ classifications marginal, very marginal and not suitable.
sup> The group of countries in Central Europe is relatively homogeneous. The second category groups countries with more diverse economic characteristics. Data for individual countries are reported in Annex 1, where we provide both absolute figures and figures relative to GDP.<sup>4</sup> Descriptive statistics for the different types of flows for all countries (means, medians, standard deviations of flows) are provided in Table 1. Table 2 describes the composition of capital flows by _source_ (private and official) and by _type_ (long-term, short-term, portfolio and FDI). Total capital flows rose from around $1 billion in 1990 to $57 billion in 1997. Pooling together all observations (by country and by year) and measuring them as a share of GDP, the largest types of flows during the 1992-96 period were official debt flows and FDI (on average, respectively 2.7 percent and 2 percent of GDP), followed by portfolio flows (0.4 percent of GDP). Of all these flows, the highest standard deviation was for official flows (standard deviation of 3.6 percent of GDP). The following other stylized facts can be observed.<sup>5</sup> First, the share of official flows has declined sharply over the period (Figure 1). At the beginning of the transition, official flows increased sharply, with bilateral and multilateral sources accounting for most of the flows. In 1992, as some of the transition economies regained access to international credit markets, private flows begun to exceed official flows and by 1997 they accounted for 73 percent of total flows. This development is not different from what has

- 3 The CEE and Baltics country group includes Albania, Bulgaria, Croatia, Czech Republic, Estonia, Latvia, Lithuania, FYR Macedonia, Hungary, Poland, Romania, Slovakia and Slovenia. The FSU country group irtcludes Armenia, Azerbaijan, Belarus, Georgia, Kazakstan, Kyrgyz Republic, Moldova, Russia, Tajikistan, Turkmenistan and Ukraine.

-
we define the integration status at the firm level.<sup>11</sup>

2. Within integrated firms, our approach allows for a heterogeneous impact of the reforms among firms with different integration status, i.e. exporters, importers or fully integrated.

3. Our econometric approach controls for possible endogeneity problems related to a firm’s decision to change integration status; and it also attacks the attrition problem present in the Mexican industrial survey (EIA).

> 11We evaluated the correlation between the definition used by Pavcnik (2002) and our definition and found out this is only about .30.

14
44 education, 1st wealth quintile (poorest), Nairobi province. Full immunization variable measures fraction of children with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see the Annex. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.

**Table 7: Oaxaca-Blinder decomposition of the difference in postneonatal** **<u>and infant mortality between 2003 and 2008 in Kenya</u>**

||<br>Neonatal|<br>Postneonatal|Infant|
|---|---|---|---|
|2003 survey|34.32***|46.62***|80.94***|
|2008 survey|38.34***|21.85***|60.19***|
|Difference between 2003 and 2008|-4.02|24.77***|20.75*|
|Explained part of the difference|2.03|12.82*|14.86*|
|Unexplainedpart of the difference|-6.05|11.94|5.89|
|Low malaria risk zone (LMRZ)|-0.07|-0.17|-0.24|
|Intermediate malaria risk zone (IMRZ)|0.11|-0.70|-0.58|
|High malaria risk zone (HMRZ)|-0.26|-1.85|-2.11|
|Interaction LMRZ and ITN ownership|-1.16|-1.73|-2.89|
|Interaction IMRZ and ITN ownership|1.83|-0.96|0.87|
|Interaction HMRZ and ITN ownership|2.34|9.73**|12.07**|
are better at solving some of society’s problems (NWW p. 139). On this account, the stability of those societies seems to rest on individual opinions, rather than on coordinated social practices.

The conceptualization of beliefs as exclusively individualistic, rather than intersubjective as well as individual, has an important analytical consequence for North, Wallis and Weingast: it obscures their explanation of constitutional consensus, which is crucial for open access orders. The control of violence – constitutionalism – depends on what they call a “consensus condition,” which entails that “citizens in open access orders share beliefs about the appropriateness of central tenets of their constitution so that they help police the rules by withdrawing support from officials who seek to violate these tenets” (NWW p. 118). Despite the importance this idea plays in their account, they provide no direct evidence that such a consensus exists in any open access order. They argue that in open access orders the control of government is contestable and subject to “clear and well-understood rules” (NWW p. 111), yet never provide an account of which constitutional rules are perceived to be clear and well-understood. Constitutions are “focal points” that “create common expectations among citizens about the behavior of others” (Weingast 2005), but constitutions contain many provisions, often in conflict. Which constitute the focal points? How would the analyst even demonstrate that such a consensus condition existed in a society? It would be difficult even for a large, expensive opinion survey to characterize the set of conditions that might constitute a fundamental breach of the constitution. One could observe that the society is at peace, but then to infer from this that a consensus exists would be to impute a value to the explanatory variable from the value of the dependent variable. This seems to be what NWW are doing. Wedeen (2002: 718) notes that this is a characteristic problem with rational choice accounts that understand culture to be the convergence of individual beliefs. As she puts it,
, among the small number of farmers who sold to paprika buyers other than CP.

We also implemented a follow-up survey of farmers in August 2008, once crops had been sold and income received. The sample size of this follow-up survey is 1,226 in total (borrowers plus non-borrowers), among whom 520 were borrowers.<sup>15</sup> The formal loan maturity (payment)

> 11 These survey data were collected prior to the farmers’ being informed about the role of biometrics in the project and their treatment status, to ensure that farmers’ survey answers were not influenced by knowledge of the nature of the experiment.

> 12 While a natural question at this point is whether selection into borrowing was affected by treatment status, treatment and control groups did not differ in their rates of MRFC loan approval or the fraction of farmers who ended up with a loan (as will be detailed in the results section below).

> 13 All conversions of Malawi kwacha to US dollars in this paper assume an exchange rate of MK145/US$.

> 14 Proceeds from other types of crops of course cannot be seized in this way to secure loan repayment because MRFC does not have analogous garnishing arrangements with other crop buyers.

> 15 The follow-up sample is smaller than the sample of baseline borrowers because for budget reasons we could not visit each borrowing household at their place of residence. Instead, we invited study participants to come to a central location at a certain date and time to be administered the follow-up interview. Not all farmers attended the meeting

8
mobility. In the case of formal sector loans, a small percentage list paper work as a reason for not seeking loans, in particular among the banked. Interestingly, the percentage of persons who give the high cost of credit as reason for not taking loans is higher in the formal sector than in the informal sector – even though interest rates tend to be higher for informal credit.

28. **In contrast, it is worth noting that the majority of banked and unbanked consider savings and deposits as highly desirable financial services,** as evidenced by other studies such as surveys in Mexico and Brazil. Despite these findings, policy makers tend to focus on credit for the unbanked as a primary financial service, and tend to discount the importance of savings and payment services in this context. As the DANE questionnaire did not include questions about savings habits and costs, a second, more detailed survey could yield important information on these points.

**Figure 3. 14  Reasons for not requesting a loan from the formal sector**

<!-- Start of picture text --> Other 1.7%1.8%<br>Lack of collateral/guarantees 2.6%2.7%<br>High cost of credit 3.3% 7.2%<br>Too much paper w ork 5.9% 11.2%<br>No w ork/job 4.8% 11.9%<br>Prefers ow n resources 10.6% 15.1%<br>Don't like to get into debt 17.6%19.1%<br>Not enough income 8.2% 19.0%<br>Didn't need 22.9% 34.4%<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>Banked Unbanked<br><!-- End of picture text --> _Source
# **_3.2. The micro module: linking household surveys to the CGE model_**

The poverty and distributional effects of the Canal expansion are estimated using a top-down approach. The top CGE-generated prices and labor reallocation are used to ̳shock‘ the bottom micro module so that a counterfactual income distribution can be estimated. No feedback from the micro module to the macro model is explicitly accounted for in this model. The following equations represent the core of the micro module: Income of household _h_ , _Yh_ , is defined as the sum of labor remunerations, (  _h_  , _l_<sup>_w_</sup> _l_ ) , and an exogenous, non-labor income ( _Yho_ ) . Welfare effects are _l_ approximated by changes in real household incomes which, in turn, depend on: 1) changes in the prices of purchased goods (  _pg_ ) and the initial share of expenditure on each good (  _hc_ , _g_ )<sup>11</sup> ; 2) changes in the returns to skilled and unskilled labor in the different labor market segments ( <sup>_w_</sup> _l_ ) ; and 3) changes in the allocation of workers in the different labor market segments, i.e. agricultural and non-agricultural sectors as well as formal and informal activities within urban areas (  _h_  , _l_ ) . A new household welfare aggregate is computed based on the sum of the exogenous income plus the _simulated_ labor income for each member of the household given his or her skill endowments and sector of employment. Based on the simulated welfare aggregate, a counterfactual distribution of income is generated and compared with the initial distribution.

A key issue in this modeling framework is the connection between the macro CGE part and the micro module and therefore a major difficulty consists of satisfactorily mapping the sources of incomes from the CGE model to the micro model. For example, in the CGE model it is possible to clearly distinguish labor remunerations from capital earnings; however