rafmacalaba/datause-encoder-data huggingface dataset:
# Roads and the Geography of Economic Activities in Mexico<sup>*</sup>

Brian Blankespoor<sup>†</sup> Théophile Bougna<sup>‡</sup> Rafael Garduno-Rivera<sup>§</sup> Harris Selod<sup>¶</sup>

**Keywords:** Mexico, Roads, Market Access, Market Potential, Specialization, Industrial Localization.

## **JEL classification:** R12; R30; C23.

> *We thank Luis Rodrigo Gonzalez Vizuet, Shiyan Zhang, and Lucie Letrouit for excellent research assistance. We are indebted to Mike Bess for the many inputs he provided regarding the history of roads in Mexico, and to Matt Turner, Uwe Deichmann, Maria Marta Ferreyra and Mark Roberts, who provided very useful comments on the preliminary versions of this paper. We also thank participants to the authors’ workshop for the World Bank study on ‘Cities, Productivity and Growth in Latin America and the Caribbean’, and at the Toulouse Development, Labor and Public Policy Seminar. Funding from the World Bank and from dfid under the World Bank Strategic Research Program (srp) ‘ _Transport for Sustainable and Inclusive Growth_ ’ is gratefully acknowledged.

> †Development Data Group, The World Bank, E-mail: `bblankespoor@worldbank.org`

> ‡Development Research Group, The World Bank, E-mail: `tbougna@worldbank.org`

> §Centro de Investigación y Docencia Económicas, Mexico. E-mail: `rafael.garduno@cide.edu`

> ¶ _Corresponding author_ . Development Research Group, The World Bank. E-mail: `hselod@worldbank.org`
Sudanese refugee population comes from households where the head has completed at least a primary education level. In terms of social capital, the majority of Sudanese refugees (about 65 percent) originate from households with no connection outside the camp. This underrepresentation of the factors associated with a substantial reduction in the poverty gap could offer an explanation for the current high level of the cost of basic needs for Sudanese refugees in Chad.

# 6. Estimating the participation scenario

So far, the analysis focused on identifying those factors which promote refugee income generation. But what might be the upper limit? Estimating this involves two things: controlling for the fact that refugees’ personal characteristics are different from those of hosts (see Table 2) while ensuring that refugees are comparable to hosts in terms of labor market access and other critical resources like land. Hence, the evaluation of the cost of hosting refugees under the participation scenario essentially requires constructing a counterfactual cost, which consists of calculating the fiscal cost of aid for refugees under the hypothetical situation where they are fully integrated into the home country economy, have similar levels of assets, and are not discriminated against, for example, in the labor market.

**Table 2. Descriptive statistics**

||**Sudanese**<br>**Refugees**|**Nearby hosts**|**Nearby hosts and**<br>**rural Chadians**|
|---|---|---|---|
|Age of household head|47|47|43|
|Female-headed household|52%|43%|24%|
|Household head is married|75%|81%|79%|
|Household head is widowed/divorced/separated|22%|18%|18%|
|Share of dependents (young and elderly)|50%|61%|52%|
|Household size|5.0|4.8|5.2|
|Head has completed at least primary education|11%|3%|16%|
|Household has access to land<sup>**11**</sup>|50%|92%|88%|
|Number of large cattle owned by households|0.2|1.9|4.1|
|Number of small ruminants owned by households|0.7|8.6|8.1|
|Household head works in agriculture|47%|80%|80%|
|Household head works in industry|11%|3%|5%|
|Household head works in services|23%|7%|11%|
|Household head is self-employed|51%|82%|90%|

Source: EHCVM 2018, Authors’ calculations Categorizing this assessment task as a counterfactual analysis rules out several alternative frameworks commonly used in other contexts, such as a standard Ordinary Least Squares (OLS) regression based on the characteristics of refugees and their host communities. The OLS regression is not suitable in this context for at least two reasons. Firstly, OLS estimates are known to be biased in the presence of selection, 11 Access to land encompasses both landowners and those who, while not owning land, rent at least one agricultural plot from others. As such, involvement in agricultural activities is the primary focus when referring to access to land.

15
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crossovers and other groups. Moreover, we assess if the observed differences are associated with suboptimal scale of operations for a specific group.

We acknowledge limitations of this analytical approach, namely the potential problems of endogeneity, including omitted variable bias, simultaneity and self‐selection issues. Nevertheless, such an analysis is helpful in identifying the initial factors that might be relevant in explaining the unconditional differences in performance.

Table 5 shows the estimates of production functions for female‐owned firms, with and without controls for capital, labor and material inputs.

**Table 5: Production Function Estimates for female‐owned enterprises Dependent Variable: log Sales, restricted sample of women‐only** **<u>(Kassida data set)</u>**

||est1<br>B|se|est2<br>B|Se|
|---|---|---|---|---|
|Crossover|1.027***|0.193|2.817*|1.552|
|log Capital|||0.086**|0.038|
|log Labor|||0.558***|0.073|
|Log Material Inputs|||0.103***|0.035|
|cross*lnK|||‐0.121|0.140|
|cross*lnL|||0.481|0.329|
|cross*lnM|||‐0.091|0.159|
|_cons|13.089***|0.072|10.388***|0.559|
|N|309||309||
|r2|0.065||0.256||
|Non‐crossovers CRS (p‐value of F‐test)|||0.0014***||
|Crossovers CRS (p‐value of F‐test)|||0.9519||
|Crossovers RS = Non‐crossovers RS(p‐value of F‐test)|||0.3266||

note: *** p<0.01, ** p<0.05, * p<0.1. CRS‐ Constant Returns to Scale; RS = Returns to Scale.

The first conclusion that can be drawn from this table is that crossovers have higher sales than non‐crossovers, even after accounting for the capital, labor and material inputs invested in the business as the crossover variable remains significant in the second set of estimates. A second conclusion is that scale does not appear to be driving the enterprise outcome differences (here measured in terms of sales) between crossover and non‐crossover female entrepreneurs. As the last three rows of the table show, 13
- 16 -

Table **8**

# Effects of VERs a Stool

(billions of 1964 dollars)

|||Exogenous|----Endogon|ous<br>Wage Dist|ortion*/---|
|---|---|---|---|---|---|
||NoWage<br>Distortion|Wage<br>Distortion|Union Wage<br>Weight *.2|Union Wage<br> <br>Weight =.C|Union Wage<br>Weight a.$|
|1. Distortionary<br>Costs of<br>VERxk/||||||
|a.CRTS|0.128|0.0|0.0 6|0.069|0.113|
|b. Contestablo<br>Markets|0.038|-0.046|-0.046|-0.015|0.027|
|2. Capturo Rentsb/||||||
|a. CRTS|0.786|0.73C|0.740|0.803|0.879|
|b. Contestable<br>Markots|0.716|0.715|0.721|0.784|0.862|
|3. Total Costs of VERx b/||||||
|a. CRTS|0.664|0.768|0.776|0.872|0.992|
|b. Contestable<br>Markets|0.764|0.667|0.676|0.769|0.889|
|4. Wage DistortionInSteel c/||||||
|a. CRTS|0.0|.260|.263|.293|.330|
|b. ContestableMorkots|0.0|.260|.268|.298|.3B1|
|5. Change EmploymentinSteel d/||||||
|a. CRTS|20.8|20.6|20.2|18.7|S.9|
|b. ContestableMarkots|18.9|18.8|18.8|12.5|6.4|

Notes: All simulations are with *ndogonous economywide labor supply.

- */ Weight on wages is given by 7 and in all casoo 7 + _6 a_ 1.O.

- _E/_ EV mesure In billions of 1984 dollars. The distortionary cost Is calculated **as** the difforonce between the total costs of VERc and the ronts.

- _SI_ Percentage wago distortions In steeI.

- d/ Change in employment In steel in thousands of work-years.
Gregorio Impavido, Alberto R. Musalem, and Thierry Tressel

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_-15 -_
Importantly, in our system approach there will be constraints in the number of parameters to be estimated simultaneously due to the degrees of freedom reflected in the behavioral relationships and in the limited amount of data. The estimates are therefore linked intrinsically. For example, the posterior estimates of the elasticities will tend to match their prior values if many of the other parameters are set free and/or are capturing much of the structural change story in the economy. The trade-offs can be exploited in the following ways. If there are good prior estimates of the elasticities, then the entropy method can be used to gain better estimates of the productivity and other parameters. If new estimates of the elasticities are important, then no many of the other parameters can be set free. This is illustrated with the estimated elasticities via the model variations on the CES and CET functions of the CGE; from table 4 through table 11 we present model results that are consistent with what is being allowed to be estimated with respect to the other parameters in the CGE model.

In addition, under models 1, 2, 3 and 4, we allow for the estimation of the labor augmenting productivity. In the first year, one reference sector, manufacturing in Korea and SSA region, is set to 0.67<sup>16</sup> and to 1.0, respectively, allowing other sectors to vary relative to it so that there are relative differences in the sector productivity to start with. In subsequent years, labor augmenting productivity in all sectors will vary relative to their initial values.

value in the agriculture, mining, utilities, construction and services sectors, while manufacturing remains at the same level during the period 1990-2011. On the other side, considering the assumptions of model 2 (dynamics on the delta-share parameters of the CES/CET functions and static elasticities of substitution), the delta-share index defined as the ratio of the posterior to the prior mean times one hundred shows that the agriculture, mining and utilities sectors experience a concave behavior in the period 1990-2011, the construction sector presents a convex index path. In contrast, manufacturing declines its ratio delta-share index while services does the opposite from 1990 to 2011.

In the same value added – factors CES bundle, two more parameters are estimated: total factor productivity and labor augmenting productivity. Under model specifications 1, 2, 3 and 4 we present posterior and prior TFP values in table 12 and table 13. For the Korean case, in model 1 and 2, mining and construction present a downward slope linear behavior, whereas agriculture, manufacturing, utilities and services describe a positive linear trend. Our results suggest that under this setup 1, manufacturing is the

> 16 Based on the World Bank CGE LINKAGE model sources.

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beneficiary villages as well as those of equation (4) with fully discretized controls for the numbers of experimental treatment, evaluation, and non-evaluation beneficiary localities.<sup>15</sup> While the former provides an average spillover effect, the later specification allows us to check for the presence of non-linearities in the marginal effects of neighboring evaluation localities assigned to treatment.

Finally, several other features of the empirical specifications depicted above should be noted. First, the parameter _α_ 2 in equation (1) is estimated out of the subset of eligible households of the controlled experiment that have other evaluation villages in the neighborhood of radius _d_ . For a radius of five kilometers, we have such identifying variation for 42 percent of the evaluation villages.

_X_ Second, the inclusion of the vector of sociodemographic variables _i_ , _j_ , _d_ in equations (1), (3), and (4) is meant to increase the precision of the estimates. The control variables are all measured at baseline using the 1997 data in order to avoid any endogeneity concern, and, taking advantage of the panel dimension of the data, include, in particular, baseline school enrollment. The remaining control variables are as follows: child’s gender and age (both in levels and squares), parental education, distance to the nearest city, the share of eligible households, the presence of a secondary school in the locality, total population in the locality, the number of localities, total population, and the mean degree of marginalization in the neighborhood. We also include state- and year-fixed effects.

Lastly, in order to account for the fact that evaluation villages may belong to multiple neighborhoods, we cluster standard errors for groups of partially overlapping neighborhoods. These groups are defined as sets of evaluation villages such that each village lies within the radius-based neighborhood of another village of the set. Intuitively, as soon as an evaluation village belongs to two radius-based neighborhoods, those two neighborhoods will belong to the same cluster. This allows for correlations beyond single radiuses. In the empirical analysis, our preferred specification uses a five-kilometer radius, but we also use concentric radiuses of ten and 20 kilometers. Considering a larger radius 15. Given the small number of experimental treatment localities within the neighborhoods in our sample (see table 1), we group them into two binary categorical variables according to the presence of one or two or more such localities (vis-a-vis zero) in the neighborhood.

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Figure 7. Government debt after recessions <!-- Start of picture text --> A. Global government debt B. Government debt in advanced economies<br>Index, t = 100 Index, t = 100<br>140 140<br>Previous global recessions 2020 Previous global recessions 2020<br>120<br>120<br>100<br>100<br>80<br>80 60<br>t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t-2 t-1 t t+1 t+2 t+3 t+4 t+5<br>C. Government debt in EMDEs D. Countries with higher government debt five<br>years after global recession<br>Index, t = 100 Percent of countries<br>190 100<br>Previous global recessions 2020 World Advanced economies EMDEs<br>160 75<br>130 50<br>100 25<br>70 0<br>t-2 t-1 t t+1 t+2 t+3 t+4 t+5 1975 1982 1991 2009<br><!-- End of picture text --> Source: Kose et al. (2021).

A.-C. Swathe indicates the evolution of government debt in percent of GDP, indexed at 100 in the year of the global recession (year “t”). The previous global recessions include four global recessions: 1975, 1982, 1991, and 2009. The solid blue line is for the 2020 global recession. Data are available for up to 192 countries, including 39 advanced economies and 153 EMDEs.

D. Chart shows the share of countries with government debt higher by 1 percentage point of GDP or more in five years after a recession (e.g., 1975 refers to the share of countries with higher government debt in 1980 than in 1975).

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|||well as distinguishes between public and private<br>transport.||
|---|---|---|---|
|**IMACLIM**|Residential, transport,<br>industrial|In static equilibrium, the transport of passengers and<br>freight are characterized by the number and<br>characteristics of household vehicles, the efficiency of<br>the fleet of vehicles, the capacities of the different<br>transport mode and the coefficient of the<br>intermediary energy use andconsumption|Endogenous|
|**POLES**|Buildings, transport|Several modes are distinguished in the model (road,<br>rail, water, air), for both passenger and goods<br>transport. The competition across vehicle types (6<br>types of vehicles in cars and trucks: conventional ICE,<br>electric, plugin hybrid,H2 fuel cell,H2 thermal, gas)|Partly Endogenous|
|**TEA**|transport, industrial,<br>residential and commercial<br>sectors|The activity of the transportation sector is usually<br>separated into passenger and freight. The energy<br>consumed in this sector is used to achieve a<br>necessary level of activity. In this sense, within the<br>model, the exogenous demand will be expressed in<br>pkm,forpassengers,and tkm,for freight.|Exogenous|
|**GRACE**|transport, industrial,<br>residential and commercial<br>sectors|Domestic transport in a region is divided into air, sea,<br>and other transport activities, modeled as nested|Exogenous|
|**IFs**|Energy demand is estimated as<br>a function of the energy<br>demand per unit GDP and<br>total GDP, with adjustments<br>related to energy prices and<br>energy efficiency|Aggregate|Exogenous|
|**PHOENIX**||Ground transportation differs from air and water<br>transport in that we allow for substitution away from<br>conventional transport to hybrid electric (HEV)<br>and/or biofuel backstop technologies when they are<br>cost competitive. The elasticityof substitution is 1.2|Exogenous|
|**WorldScan2**|Production sectors using<br>energy as intermediate input<br>and households consuming<br>energydirectly.|Aggregated|Exogenous|

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comparison to members of households with lottery losers. Average consumption is approximately 17 percent higher in per adult-equivalent terms and income is approximately 23 percent higher (although neither is statistically significant). Since the change in income and consumption are similar, it suggests that these changes associated with emigration and remittances are being viewed as shocks to permanent income by the left behind households. There is some weak evidence that these gains become smaller over time, both because household size is rising and because the income gains are declining. However, these results are also consistent with the impacts being independent of how long the emigrants have been gone.

# **4.4 The Impact on Poverty**

Finally, Table 5 examines the impact of emigration on poverty. Again, we weight the estimates by household size and hence the results indicate the change in poverty for the average individual. The poverty rate amongst individuals living in households that sent Samoan Quota emigrants is 23 percentage points lower using the basic needs poverty line. Since the poverty rate amongst individuals in households with lottery losers is 37 percent, this represents a 62 percent reduction in headcount poverty. However, there is no measured effect of emigration on the food poverty rate which captures deeper poverty (with only 12 percent of the lottery loser households below this line), nor is there any effect on the poverty gap ratio at either poverty line. Moreover, the impact on the subjective poverty reported by an individual adult respondent in each household is statistically insignificant. Again, there is some weak evidence that any possible poverty reduction declines over time, but the years since migration term is neither significant nor the same sign across different poverty measures.

# **5. Conclusions**

The main difficulty in estimating the impacts of emigration on household members left behind is posed by selectivity issues. A common research strategy in this literature is to use household survey data to compare outcomes for households where some members have emigrated to outcomes for those where all members are currently in the particular sending country being examined. Such comparisons are complicated by a triple-selectivity problem: first, households self-select into emigration; second, in some emigrant households everyone moves (and thus are almost never included in survey data on the sending country); third, some 18