metadata: authors: 0: author: Conte,Bruno
1: author: Ianchovichina,Elena
count: Latin America
docna: 0: docna: Spatial Development and Mobility Frictions
            in Latin America : Theory-Based Empirical Evidence
docty: Policy Research Working Paper
abstracts: cdata!: Using fine-grained spatial data and a
            dynamic spatial general equilibrium model, this paper
            assesses the magnitude of mobility frictions in Latin
            America as well as the effects of their reduction on spatial
            development in the region. The results suggest that in most
            Latin American countries, migration frictions calibrated
            based on spatially differentiated initial utility are on
            average smaller and less dispersed than those obtained
            assuming uniform within-country initial utility. A reduction
            in trade costs due to optimal investments in road
            infrastructure in most Latin American countries increases
            the present discounted value of real per capita income on
            average in the region by 15.1 percent. This effect is larger
            than the effects obtained with static quantitative trade
            models because of substantial dynamic gains. By contrast, a
            reduction in migration entry costs in the most productive
            and more populous locations in the Latin American countries
            has a negligible effect on the present discounted value of
            the region’s real per capita income, reflecting the
            relatively small dispersion in domestic migration frictions
            and their relatively low levels in top locations. In both
            counterfactuals, the welfare increases are significantly
            larger than the increases in real per capita output because
            the reductions in mobility frictions allow people to
            relocate to areas with better amenities and therefore derive
            higher utility. These results suggest that trade costs, not
            migration barriers, represent a major constraint to the
            efficient spatial distribution of economic activity and
            growth in Latin America.
display_title: Spatial Development and Mobility
            Frictions in Latin America : Theory-Based Empirical Evidence
listing_relative_url: /research/2022/05/33833730/spatial-development-mobility-frictions-latin-america-theory-based-empirical-evidence
topicv3: Transport and Economic Geography m1326235 1813,Economic Growth m1326241 471,Dynamic Computable General Equilibrium
            m1439225 3111,Dynamic Agglomeration m1503886 2878
publishtoextweb_dt: 2022-05-31T00:00:00Z
dois: 10.1596/1813-9450-10071
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 453205
model_extractions: Policy Research Working Paper 10071 Spatial Development and Mobility Frictions in Latin America Theory-Based Empirical Evidence _Bruno Conte Elena Ianchovichina_ Latin America and the Caribbean Region Office of the Chief Economist May 2022
Policy Research Working Paper 10071

# **Abstract**

Using fine-grained spatial data and a dynamic spatial general equilibrium model, this paper assesses the magnitude of mobility frictions in Latin America as well as the effects of their reduction on spatial development in the region. The results suggest that in most Latin American countries, migration frictions calibrated based on spatially differentiated initial utility are on average smaller and less dispersed than those obtained assuming uniform within-country initial utility. A reduction in trade costs due to optimal investments in road infrastructure in most Latin American countries increases the present discounted value of real per capita income on average in the region by 15.1 percent. This effect is larger than the effects obtained with static quantitative trade models because of substantial dynamic gains. By contrast, a reduction in migration entry costs in the most productive and more populous locations in the Latin American countries has a negligible effect on the present discounted value of the region’s real per capita income, reflecting the relatively small dispersion in domestic migration frictions and their relatively low levels in top locations. In both counterfactuals, the welfare increases are significantly larger than the increases in real per capita output because the reductions in mobility frictions allow people to relocate to areas with better amenities and therefore derive higher utility. These results suggest that trade costs, not migration barriers, represent a major constraint to the efficient spatial distribution of economic activity and growth in Latin America.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at b.conte@unibo.it or eianchovichina@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Spatial Development and Mobility Frictions in Latin America: Theory-Based Empirical Evidence**

Bruno Conte<sup>∗</sup> and Elena Ianchovichina<sup>♦</sup>

**Keywords** : economic geography, Latin America, mobility frictions, trade costs, migration frictions, quantitative trade models

**JEL Codes** : F11, F22, F43, O10, O40, O54, R42

> ∗ Bruno Conte is assistant professor in the Department of Economics, University of Bologna, Piazza Scaravilli 2, 40126, Italy. E-mail: b.conte@unibo.it.

> ♦ Elena Ianchovichina is a lead economist and the deputy chief economist for the Latin America and the Caribbean Region of the World Bank, 1818 H Street NW, Washington, DC 20433, USA, Tel: +1 202 2803576. E-mail: <u>eianchovichina@worldbank.org.</u>

We would like to thank Nicole Gorton for sharing the data on trade cost reductions due to optimal road infrastructure investment in Latin American countries. We are grateful to Adam Storeygard and Martin Rama for their comments on earlier drafts of the paper. The findings, interpretations, and conclusions expressed in this paper are entirely ours. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors or the governments they represent.
# **1. Introduction**

What is the effect of mobility frictions in the form of transportation costs and barriers to migration on the long-term spatial development in Latin America? Which type of mobility frictions – trade costs or migration entry barriers – represent a bigger obstacle to the efficient spatial allocation of economic activity and growth in the region? Mobility frictions play an important role in understanding spatial differences in income because they affect the distribution of economic activity and people and therefore the productivity differences within and across countries and their changes over time. Trade costs stand for transportation costs, which reflect the geography of locations, the condition and availability of infrastructure networks for different modes of transportation, and the cost of different modes of transportation.<sup>1</sup> Migration entry costs include information and psychological costs, the cost of getting to a location and settling there, and any legal migration restrictions. A mix of policies and external shocks influence mobility frictions. Trade costs respond to changes in infrastructure and transportation policies, technological improvements in transportation services, telecommunications shocks, as well as shocks to commodity prices and other sectors of the economy. Migration entry costs are influenced by immigration policies, information campaigns, urban planning, public service provision, credit markets, and shocks related to natural and man-made disasters.

Since cross-border migration frictions are greater than those within countries, productivity convergence is expected to be faster within rather than across countries. However, even within countries, productivity differences across regions may persist or increase with time. In a fully integrated economy, divergence in regional productivity may occur due to the concentration of economic activity and people in high-productivity agglomerations, where firms benefit from sharing, matching, and learning and larger markets, and workers earn higher real wages. From an economic efficiency perspective, such a market-driven productivity divergence is not a cause for concern as it corresponds to an efficient spatial allocation of resources within the economy. Yet, when persistently high mobility frictions slow down progress toward convergence or adjustment to shocks (e.g. trade liberalization, economic crises), economic growth suffers, political instability rises, and policy interventions that can speed up economic adjustment and growth may be warranted.

> 1 As in Desmet et al. (2018) trade costs do not include cross-border trade policy measures such as tariff and non-tariff barriers.

2
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 multiple factors, including institutional restrictions, imperfect information, moving costs, and social norms or idiosyncratic preferences for a given location. Desmet et al. (2018) estimate the cost of moving in and out of each location in 2000 using initial technology, amenities, and utility as well as the change in population between 2000 and 2005. Then, they identify migration costs between two locations as the product of an origin- and a destination-specific cost. They proxy initial utility in all locations in each country with the average Cantril Ladder score available for the country from the Gallup World Poll.<sup>2</sup> Thus, if productivity increases due to innovation in a high initial utility location,

> 2 Desmet et al. (2018) assume uniform utility within countries only in the initial period. After the initial period, utility varies both across and within countries.

3
but population does not increase much in the same location, the location must be a hard place to move to. By contrast, if there is only a small decline in population in a low initial utility location, the place may be difficult to leave for any of the reasons mentioned above. By proxying initial utility in all locations within a country with the average country-wide subjective well-being (SWB) score, Desmet et al. (2018) assume that population changes in the location can be attributed either to differences in initial technology or fundamental amenities. In such a model, a situation of lower real wages in a location with similar population density to other parts of the country will be explained with much better fundamental amenities, when in fact people may have lower initial welfare and may not be able to leave the location due to institutional restrictions on their mobility or other reasons, including idiosyncratic preferences.

The assumption of uniform initial utility within countries is appropriate in smaller or advanced economies, where spatial convergence of welfare might hold. However, in many developing countries this may not be the case. Regional differences in welfare may be substantial, when proxied by differences in self-reported well-being (Burger, Hendriks, and Ianchovichina, 2021) or when proxied with differences in per capita consumption (Gollin, Kirchberger, and Lagakos, 2017). These differences in welfare might reflect higher mobility frictions in these developing economies (Bryan and Morten, 2019), differences in non-monetary amenities (Gollin, Kirchberger, and Lagakos, 2017), or idiosyncratic preferences for living in a certain place (Chauvin, Glaeser, Ma, and Tobio, 2017). In line with this evidence, one of the contributions of this paper is that we relax the assumption of uniform initial utility in the case of the Latin American countries. Using geo-coded data on subjective well-being, which we obtain from multiple survey rounds of the Gallup World Poll and Latinobarómetro, we compute the average subnational (level-1 administrative region) subjective wellbeing scores in each large Latin American country and assign the same initial utility only to locations within the same subnational region. This way domestic migration costs between any two locations in different subnational level-1 administrative regions could be different not only due to differences in initial technology or amenities, but also to different initial welfare levels. Then, a small change in population in a location with low initial utility relative to other parts of the country can be correctly attributed to migration frictions restricting the movement of people out of this location rather than with good fundamental amenities in the location.

This modification allows us to assess more precisely the size of domestic migration frictions circa 2000. Our results suggest that in most Latin American countries the adjusted migration entry costs are 4
substantially lower and less dispersed than the ones calibrated using uniform initial utility; this is particularly the case in Bolivia, Guatemala, the República Bolivariana de Venezuela, and Chile (Desmet et al. 2018). International comparisons suggest that the adjusted migration frictions are also smaller on average in Latin America than those in advanced economies, but this is not the case for trade frictions.

We also perform two counterfactuals which allow us to investigate the relative importance of trade and migration frictions for economic growth in Latin America. We show that a reduction in trade costs due to the optimal expansions in the domestic road networks in most Latin American countries (obtained from Gorten and Ianchovichina (2021)) increases the present discounted value of real per capita income in the region on average by 15.1%, but the results vary by country. The magnitude of this effect is much larger than the one obtained with static quantitative trade models, including the one reported in Gorton and Ianchovichina (2021), because of substantial dynamic gains. The results from this counterfactual also indicate that the optimal reductions in trade costs tend to stimulate the economic development of the relatively more populous and productive locations in the Latin American economies in the long run. By contrast, a reduction in migration entry costs in such top locations has no effect on the present discounted value of the region’s real per capita income, reflecting the relatively low migration entry costs in these locations. In both counterfactuals, the welfare increases are significantly larger than the increases in real per capita output because the reductions in both types of mobility frictions allow people to relocate to locations with better amenities and therefore derive higher utility. These results suggest that trade costs, not migration frictions, represent a major constraint to the efficient distribution of economic activity and growth in Latin America.

The paper is structured as follows. Section 2 provides an overview of the main features of the dynamic spatial general equilibrium framework of Desmet _et al._ (2018), the data, and parameter calibration. Section 3 discusses the additional data on subjective well-being, the procedure used to relax the assumption of spatially uniform initial utility, and the adjusted migration frictions in the Latin American countries. We also compare the adjusted and non-adjusted entry costs to gauge the value added of the modification. Section 4 presents international comparisons of the migration and trade frictions in Latin America with those in select advanced and emerging economies, which we consider appropriate comparators for Latin America. In Section 5 we discuss the results from two counterfactual simulations. The first counterfactual explores the effect of reduced trade costs due to optimal expansions of domestic road infrastructure networks in the Latin American countries. The 5
(r) x m(7,r) ne (r) =a(r)L a(r)
externalities to population density, λ > 0. When choosing where to live, agents draw the stochastic i taste shock εt(r), distributed according to a Fréchet distribution such that: The parameter Ω determines the heterogeneity of agents' preference on where to live and the intensity of congestion forces in the economy. The higher Ω is, the higher are the frictions to mobility of agents across _S_ . Finally, the bilateral moving cost between two locations _s_ and _r_ , , can be decomposed into an origin (m1(s)) and a destination-specific component (m2(r)): mm(ss, rr) where m(r, r) = 1 for all _r_ ∈ _S_ since the model assumes no migration frictions within a location. These assumptions enable the decomposition of the utility penalties of leaving or entering a location in such a way that only the entry barriers m2(r) matter for the optimal location choice.<sup>5</sup> In this setup, an individual’s location choice depends only on current variables.

Given the distributional assumptions on the taste shock, the total population in location _r_ at time _t_ has the following closed-form solution: where ut(r) = at(r)yt(r). Equation (1) shows that locations with higher real income and/or amenities are preferred by consumers, while locations with higher entry barriers m2(r) are less appealing to agents. The parameter Ω drives the extent to which population choices react to changes in these two factors.

In each location, _r_ , firms produce a good ω ∈[0,1] using land and labor so that for a unit of land production of good ω in location _r_ at period _t_ takes the format:

> 5 The intuition behind it is that the 1 costs are somehow "refunded" upon the decision on where to live. It implies that Desmet et al. (2018) focus on net, rather than gross, migration flows and utility changes. See Assumption 1 of the mentioned reference for details. mm (rr)

7
ω 6 The firm's productivity depends on a local, variety-specific productivity shifter, zt (r), as well as an innovation parameter, φωt (r). The latter requires firms to employ νφωt additional units of labor, ξξ where ξ is a parameter. Thus, the production decisions of firms will implicitly contain innovation (rr) decisions, which will drive the dynamics of local and aggregate growth. Moreover, the productivity shifter takes the format of an i.i.d. (across goods and time) draw from a Fréchet distribution with cumulative distribution: where Tt(r) = τt(r)L<sup>�</sup> t(r)<sup>α</sup> , α ≥0, θ > 0, and τ0(. ) are exoneously given. Therefore, local productivity at any point in time is partially driven by agglomeration economies (determined by the parameter α) and by τt(r). The latter is an endogenous dynamic process that depends on local innovation and the diffusion of innovation to nearby locations.<sup>7</sup> In each unit of land a continuum of firms compete in prices. Since the firms are similar over small units of land, locally firms face perfect competition.

The solution for the static profit maximization of the firm<sup>8</sup> will be such that the shipping price of variety ω produced in _r_ and consumed in _s_ is proportional to the marginal cost of production at _r_ and the trade costs between the two locations: where mct(r) is the marginal cost of producing ω at _r_ and ζ(s, r) is the trade costs between _r_ and _s_ . The bilateral structure of production (existence of trade) allows us to obtain the export probabilities between two locations _r_ and _s_ , πt(s, r), in the fashion of Eaton and Kortum (2002):

> 6 The model assumes that across locations r the variety-specific productivity shifters, ztω(r), are spatially correlated, with the correlation increasing and becoming perfect as the distance between locations goes down to zero, while for sufficiently distant pairs the draws become independent. 7 In particular, 1 � 2 2. Parameters γ1 and γ2 belong to the interval [0,1]. If γ1 < 1, the distribution of productivity draws is shifted up by past innovations with decreasing returns.  If ττtt(rr) = φφtt−1ωω (rr)θθγγ �∫ηηττSS tt−1(ss)ddss 1−γγ ττtt−1(rr)γγ γ2 < 1, the dynamic evolution of a location’s technology also depends on the aggregate level of technology. Convergence is ensured when both parameters are less than one since then there are local decreasing returns to scale but economywide linear technological progress. 8 Desmet et al. (2018) show that when firms face local perfect competition, they bid the land price up to the point at which they make zero profits after paying for their investment in innovation. This implies that current innovation decisions do not affect future profits -- as they will also be zero. Therefore, the solution to the dynamic innovation problem becomes a sequence of static innovation decisions that maximizes static profits.

8
Finally, in the absence of credit markets, trade is balanced location by location, so that local nominal income equals total expenditure from all locations _s_ on goods produced in a location _r_ . Formally, that implies: where wt(r) and wt(r)Ht(r)L<sup>�</sup> t(r) stand for the nominal wages and income in location _r_ , respectively. Desmet et al. (2018) show that a dynamic equilibrium of the economy exists and is a sequence of static equilibria where, in each time period _t_ , markets for goods and labor clear. That requires equations (1) and (2) to simultaneously hold in each period _t_ .

# _2.2 Data and parameter calibration: Quantifying spatial frictions_

A path-breaking contribution of the presented framework is its ability to quantify spatial frictions in the world economy using real world data. In this application, the Earth’s surface is divided into about 17,000 1<sup>∘</sup> × 1<sup>∘</sup> grid cells. By using global data at this unit of disaggregation for several characteristics, one can use the model’s structure to back out several parameters of the model, including the two mobility parameters of key importance to our investigation – the trade frictions, ζ(s, r), and the migration entry costs, m2(r). In what follows, we explain the conceptual idea behind the estimation of the mobility frictions in Desmet et al. (2018).

Trade costs, ζ(s, r), are estimated using a rich transportation infrastructure data covering the entire Earth’s surface and capturing different modal types of transportation as well as geographical characteristics. Combined with the Fast-Marching algorithm in Allen and Arkolakis (2014), these data allow us to estimate a bilateral matrix, ζ(s, r), containing about 300 million entries for _s_ - _r_ location pairs (17,000x17,000). We estimate trade frictions only for 2000, because the sources of transportation and geographic data are only available for that year.

Migration entry costs, m2(r), are quantified for each location _r_ using a calibration strategy. It starts with the estimation of the fundamental amenities (a�(r)) to initial level of utility (u0(r)) ratio (a�(r)/u0(r)) for each location _r_ of the model in a way that allows the model-implied economic activity 9
and population to exactly match the spatial distribution of the economic activity and population worldwide for the year 2000.

**Table 1. Parameter values and trade costs**

|**Parameter**|**Value**|**Source / Comment**|
|---|---|---|
|**Internally calibrated**|||
|Discount factor|0.965||
|Relation between amenities andpopulation(λ)|0.32||
|Relation betweenpopulation distribution andgrowth(γ1)|0.319||
|Relation betweenpopulation distribution andgrowth(γ2)<br>|0.993||
|Parameter calibrated to match the elasticity of trade flows with<br>respect to distance of -0.93 (Υ)|0.393|The<br>elasticity<br>of<br>-0.93<br>calibrated by Head and Mayer<br>(2014)|
|Trade costs (ζ(s,r))||Estimated matrix for 17,000 x<br>17,000 location pairs|
|**Externally calibrated**<br>|||
|CES elasticity of substitution among differentiated varieties of<br>a represented consumptiongood(ρ)|4|Bernard et al. (2003)|
|Elasticity of migration flows with respect to income (Ω)|0.5|Monte et al. (2018)|
|Relation between subjective well-being and utility in the model<br>(ψ)|1.8|Deaton and Stone (2013)|
|Static elasticityofproductivityto density (α)|0.06|Carlino et al. (2007)|
|Trade elasticity (θ)|6.5|Eaton & Kortum (2002);<br>Simonovska & Waugh (2014)|
|Nonland share of production|0.8|Greenwood et al. (1997);<br>Desmet & Rappaport (2017)|
|Parameter (ξ)|0.125|Desmet & Rossi-Hansberg<br>(2015)|
|Modes of travel parameters: Rail|0.1434|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Rail|0.4302|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Major Road|0.5636|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Other Road|1.1272|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Road|1.9726|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Water/No Water|0.0779|<sup>Allen and Arkolakis (2014)</sup>|

The main source for these data is the G-Econ data set (Nordhaus et al., 2006), which provides gridded GDP and population data covering the entire globe. Desmet et al. (2018) use the Gallup World Poll 10
(GWP) average subjective well-being (SWB) Cantril’s Ladder scores circa 2000,<sup>9</sup> at the country level, to obtain the initial level of well-being u0(r) and separately identify the fundamental amenities a�(r). Subsequently, the model is simulated one period forward; the estimated migration frictions m2(r) are the values such that the model-implied population distribution for the subsequent year matches the one observed in the data. To convert the ordinal SWB scores into cardinal values, Desmet et al. (2018) show that for a given time period t, the following relationship holds: , where ψ is ii ii ψψS SStt(rr) the inverse of the estimated coefficient on the log of real income in a cross-country regression of SWB tt uu (rr) = ee of an individual _i_ residing in location _r_ on the log of their real income (Deaton and Stone, 2013; Kahneman and Deaton, 2010).<sup>10</sup> Table 1 presents the values of all internally and externally calibrated parameters along with their sources.

# **3. Migration frictions: Adjusting for spatial variation in initial utility in Latin America**

When calibrating the migration frictions, Desmet et al. (2018) assume that the initial level of wellbeing u0(r) is the same in all geolocations within a country. This assumption might be appropriate in the case of advanced economies, but not necessarily in the case of developing countries (Burger et al., 2021; Bryan and Morten, 2019; Chauvin, Glaeser, Ma, and Tobio, 2017). Hence, we relax the assumption of uniform initial level of utility in Latin America. Using geo-coded data on subjective well-being from several survey rounds of the Gallup World Poll (GWP) and Latinobarómetro, we first compute the average subjective well-being scores for different subnational (level-1) administrative regions in each LAC country.<sup>11</sup> We then estimate a set of adjusted fundamental amenities and migration frictions in locations across Latin America. In the next section, we provide details on the quantification methodology for the baseline period of 2000.

> 9 The Cantril’s Ladder is an ordinal scale indicating how an individual rates their own life. The scores vary from 0 for their worst possible life to 10 for their best possible life. Although the benchmarks for worst and/or best possible life might vary across individuals, regions, and countries, Desmet et al. (2018) abstract from such potential differences in the benchmarks based on the finding by Deaton and Stone (2013) and Stevenson and Wolfers (2013) of a relationship between subjective well-being (SWB) and the log of real income that is similar within the U.S. as well as across countries.

> 10 The inclusion of a location fixed effect in the estimation of the linear relationship between SWB and log of real income addresses endogeneity concerns that a location with a higher utility attracts more people and affects the amenity levels in the location.

> 11 The details of this procedure, as well as its geographical coverage, are provided in the Data Appendix.

11
<!-- Start of picture text --> A: Uniform SWB B: Adjust./ Normalized SWB C: Adjusted SWB<br>1 1<br>20°N ap, 20°N ap 20°N aa,<br>0° & 0° ‘ 0° &<br>20°S Initial well- Normalized Initial well-<br> 5| being 20°S wellbeing G 20°S 4| being "<br>7 09 7<br>6 0.8 6<br>40°S 5 40°S. 07 - 40°S 5<br>4 06 4 4<br>60°S 3 60°S 05 -_ 60°S 3<br>120°W 100°W 80°W 60°W 40°W 120°W 100°W 80°W 60°W 40°W ¥ 120°W 100°W 80°W 60°W 40°W<br><!-- End of picture text -->
By using this information in the subsequent step of the model calibration, we can improve the precision with which we quantify the migration frictions m2(r). With uniform initial subjective wellbeing levels, domestic migration costs between any two locations in different subnational regions can be due only to differences in initial technology or fundamental amenities. With heterogenous initial subjective well-being levels, differences in domestic migration costs can also reflect differences in initial utility. Then, a small change in the population in a location with low subjective well-being relative to other parts of the country can be attributed to domestic frictions restricting the movement of people out of a location with low fundamental amenities. With uniform initial utility, this situation would be explained with good amenities in the location, which imply relatively high entry costs for the location.

# _3.2 Calibration of adjusted migration costs and comparison with non-adjusted ones_

Armed with a more precise measure of initial utility, u0(r), we proceed by solving for m2(r) as explained in the previous section. We use u0(r) to separately identify the fundamental amenities a�(r) and then find the values of migration frictions that allow the model-predicted population numbers to match the spatial distribution of population in the next period, 2001. In Panel A of Figure 2, we show the “raw” entry costs for locations obtained based on uniform within-country initial utility levels in Desmet et al. (2018). In Panel B, we display our results for the adjusted entry costs, obtained after accounting for the heterogeneity in initial utility.

As expected, the differences between the results displayed in Panel A and Panel B are greatest for the less developed Latin American countries. Poor locations in Peru and Bolivia, but also in Central America, have much lower estimated entry costs m2(r) than suggested by the “raw” entry costs in Desmet et al. (2018). The intuition for these differences is the following. Many of the poorest locations within these countries were previously assumed to provide an observed well-being as large as in the richer locations of these same countries. Consequently, the model did not find a very high amenity difference between the poor and rich locations, and therefore few incentives for people to move out of the former. When we account for the heterogeneity of well-being within countries, the model "understands" that these locations are less pleasant to live in, and therefore assigns lower fundamental amenities (Panel D) and migration frictions (Panel B) in these locations.

13
Panel A: Raw mz(r) Panel B: Adjusted mm(r) <!-- Start of picture text -->
| !<br>20°N a 20°N ape,<br>0° & 0° &<br>ang 4 | logtmalr)) ap°¢-4 | log(malr))<br>10 10<br>40°S 40°5<br>5 5<br>120°W = s«100°W 80°W 60°W 40°w 1200°W = 100°W BO°W 60°W 40°W<br>Panel C: Raw a(r) Panel D: Adjusted a(r)<br>gas uy<br>20°N jar x, 20°N ips<br>L TM a i<br>0° . ‘ ' 0° m ie<br>20°S 100 ah ' 20°S 100 Y ee<br>75 ie 75 7 be<br>40°5 a 40°S Lele ig<br>60°S 60°S<br>120°W =: 100°W 80°W 60°W 40°W 120°W Ss 100"W 80°W 60°W 40°W<br><!-- End of picture text -->
||Mean|Raw m/(r<br>Median|)<br>St. Dev.|Ad<br>Mean|justed m><br>Median|(r)<br>St. Dev.|% Cha<br>Mean|nge mp(r)<br>Median|
|---|---|---|---|---|---|---|---|---|
|Argentina|0.084|0.043|0.199|0.037|0.018|0.083|-55.67|-57.51|
|Bolivia|0.016|0.018|0.009|0.002|0.0003|0.004|-90.86|-98.46|
|Brazil|0.095|0.067|0.082|0.052|0.036|0.047|-44.93|-46.94|
|Chile|0.228|0.092|0.710|0.021|0.006|0.102|-90.69|-93.90|
|Colombia|0.047|0.028|0.048|0.011|0.007|0.011|-75.53|-75.57|
|Costa Rica|0.071|0.058|0.043|0.020|0.011|0.016|-71.78|-80.58|
|Dominican Republic|0.001|0.0003|0.003|0.001|0.0003|0.003|-|-|
|Ecuador|0.041|0.007|0.088|0.038|0.003|0.089|-5.48|-61.30|
|Guatemala|0.012|0.011|0.010|0.001|0.001|0.001|-91.52|-94,24|
|Guyana|0.035|0.032|0.017|0.031|0.029|0.017|-10.02|-9.88|
|Honduras|0.010|0.004|0.011|0.002|0.001|0.002|-83.94|-79.24|
|Haiti<br>|0.0001<br>|0.0001<br>|0.0001<br>|0.0001<br>|0.0001<br>|0.0001<br>|-<br>||
|Mexico|0.078|0.040|0.136|0.033|0.019|0.057|-57.83|-52.43|
|Nicaragua|0.006|0.004|0.004|0.002|0.001|0.001|-69.04|-66.38|
|Panama<br>|0.394<br>|0.156<br>|0.757<br>|0.219<br>|0.024<br>|0.775<br>|-44.58<br>|-84.84<br>|
|Peru|0.012|0.006|0.030|0.004|0.001|0.021|-70.55|-85.74|
|Paraguay|0.031|0.015|0.039|0.011|0.006|0.013|-64.13|-58.87|
|El Salvador|0.008|0.008|0.002|0.003|0.003|0.004|-58.10|-58.10|
|Suriname|0.356|0.279|0.444|0.356|0.279|0.444|-|~|
|Uruguay<br>|0.015<br>|0.015<br>|0.008<br>|0.004<br>|0.002<br>|0.002<br>|-77.05<br>|-83.84<br>|
|Venezuela|0.513|0.213|0.744|0.078|0.023|0.124|-84.72|-89.40|
# **4. Mobility frictions: International comparisons**

How do migration and trade frictions in Latin America compare to those in other parts of the world? To answer this question, we start by displaying in Table 3 the average migration entry costs by region and country. In 2000, average migration frictions in Latin America were approximately a fifth of the entry costs in the European Union and less than 4% of those in the US in 2000. Yet, entry barriers in Latin America were much higher than in Southeast Asia and China. Furthermore, entry barriers were much higher in Brazil, Suriname, and Panama than the average for the region, and lowest in the least developed Latin American countries such as Bolivia, Guatemala, and Haiti.

**Table 3. Average migration frictions by country and region in 2000, relative to the US**

|**Region/country**|**Average **m2(r)|
|---|---|
|Panel A: Comparison Region||
|European Union|0.222|
|Southeast Asia|0.007|
|Latin America|0.039|
|Panel B: Comparison Countries||
|US|1|
|China|0.003|
|Panel C: Countries in Latin America and the Caribbean||
|Argentina|0.037|
|Bolivia|0.002|
|Brazil|0.052|
|Chile|0.021|
|Colombia|0.011|
|Costa Rica|0.02|
|Dominican Republic|0.001|
|<br>Ecuador|0.038|
|Guatemala|0.001|
|Guyana|0.031|
|Honduras|0.002|
|Haiti|0.0001|
|Mexico|0.033|
|Nicaragua|0.002|
|Panama|0.219|
|Peru|0.004|
|Paraguay|0.011|
|<br>El Salvador|0.003|
|Suriname|0.356|
|Uruguay|0.004|
|Venezuela,RB|0.078|

**Notes:** Average migration frictions within countries/regions estimated internally in the model for 2000.

16
<!-- Start of picture text --> 80°N —— a aa ae ee :<br>we x ‘ae iF es ©<br>; y 5 “sel "° <<br>20°N =>. Te 7<br>40°S: fi Ww Fi<br>o's :<br>120° 6o°W o Ore 120°E<br><!-- End of picture text -->
<!-- Start of picture text --> _____A Raw bilateral trade frictions B. Controlling for bilateral distance<br>04 IX<br>02 | 03 | \<br>|<br>2 Ops fe<br>| (ie fl<br>Vy | Wy |<br>00 ~ 00 ZT<br>* ileal ade rieionse) _ Bilateral trade fiction (01), net of euclidean distance a<br><!-- End of picture text -->
<!-- Start of picture text --> os aca — -<_ — aes = SS Py<br>wee = — j . . a ad = E< ; |<br>o : * 4“ > 3 ae ;<br>20°S FALSE East g ‘ S k;<br>TRUE<br>- , »<br>:<br>60°S 2<br>now o OE 120°E<br>A. Raw bilateral trade frictions B. Controlling for bilateral distance<br>0.20<br>os<br>0.15 a [\<br>USA _ \ S > |_| usa<br>0.05 0.1 \<br>A) q<br>:<br>a 00 - UN<br>5 10 1S 5.0 -2.5 00 25: 5.0<br>Bilateral trade friction C(s,r) Bilateral trade friction €(s,r), net of euclidean distance<br><!-- End of picture text -->
US than in Brazil or China where activity in top locations is concentrated in just one-half of the country (Figure 6). However, after adjusting for distance, it appears that there are many more pairs of locations with relatively high trade costs in Brazil compared to the US and China.

# **5. Effects of reduced mobility frictions on spatial development in Latin America**

This section investigates how changes in mobility frictions could affect the evolution of Latin America’s spatial development over the next century. We rely again on the spatial dynamic growth model of Desmet et al. (2018) who evaluated the model by running a simulation backwards in time from 2000 to 1870 and comparing the model’s predictions on the distribution of population levels in the data. The correlations between the simulated population levels and the actual country-level population data were high: 0.96 until 1950 and around 0.70 before 1900. They also found highly significant correlations between the calculated amenities and commonly used measures of amenities such as temperature, proximity to water, and precipitation. Finally, one of the main predictions of their model – namely that the correlation between density and productivity is higher in areas with higher per capita incomes – was confirmed in the data.

We first establish a baseline in which the model predicts the spatial evolution of Latin America over the next century, assuming no change in mobility frictions. Starting from the calibrated model with data for 2000 and adjusted migration frictions, the equilibrium of the model is obtained by simultaneously solving equations (1) and (2) for a period _t_ as in lemma 3 of Desmet et al. (2018). Within this procedure, given the trade and adjusted migration frictions in 2000, the distribution of local amenities, net of congestion disutility, and productivities reflecting innovation choices and spatial diffusion are updated and used to solve for the equilibrium of the subsequent period, _t+1_ . This procedure for solving the dynamic problem as a sequence of static problems is repeated as many periods as needed (in our case 100 times). The baseline captures the evolution of the world economy towards its balanced growth path from 2000 to 2100. The baseline results suggest that LAC’s share in global output is projected to mostly stagnate during the next century while its share in global welfare is expected to rise only modestly (Figure 7).

# _5.1 Reducing trade costs by making optimal investments in domestic road networks_

Section 4 provides evidence of relatively high bilateral trade costs in LAC. For this reason, our first counterfactual explores the implications of cuts in these bilateral costs. A reduction in transportation 20
costs could be due to several reasons including improvements in transportation technologies, reduction in fuel costs, and/or improvements in road infrastructure. When focusing on infrastructure improvements, it is unrealistic to design a counterfactual in which transportation costs are reduced in a uniform way across all locations. Most governments have limited fiscal space and implementation capacity for country-wide infrastructure programs. Therefore, we design a counterfactual that quantifies the dynamic gains from reductions in trade frictions due to _optimal_ investments in road infrastructure in the LAC countries and projects the economic adjustments to these changes over the subsequent 100 years.

We obtain the geolocations where bilateral trade costs are reduced in an optimal way and the size of these reductions from the work of Gorton and Ianchovichina (2021). They use data from multiple sources and the general equilibrium spatial framework of Fajgelbaum and Schaal (2020) to identify the endogenous change in trade costs due to optimal expansions of the domestic road networks in most Latin American countries. Gorton and Ianchovichina (2021) demonstrate that optimal investments are more likely along road segments linking more productive and populous locations, which in LAC tend to have relatively high bilateral costs, as shown in Section 4. Since these costs ζ<sup>nn</sup> (s, r) vary across goods n, we calculate the change in average trade costs from optimal investments along each link ( _s_ , _r_ ) as follows: where _N_ is the number of goods transported in the economy and is good _n_ ’s share of all goods _N_ traded along route ( _s_ , _r_ ). ππ<sup>nn</sup> (ss, rr) In Figure 7, we compare the performance of LAC relative to the global economy in terms of utility and GDP and the evolution of these metrics over the next century in the baseline (shown in red) and the counterfactual of optimally reduced trade costs (shown in blue). The results suggest that optimal reductions in domestic trade costs are likely to increase LAC’s share in the world’s utility and GDP in a sustained and re-enforcing way. The regional effect is expected to be substantial, largely because we assume that such investments are not made in the rest of the world. Latin America’s share in GDP rises relative to the baseline in the first period and is 62 percentage points higher relative to the baseline in 2100. Lower transportation costs have a direct and immediate positive effect because fewer 21
<!-- Start of picture text --> rol 5 itty<br>aeaaa — Benchmark rid gait] — cpp<br>a ae? _—— Lowerfrictionsmig. i 2h oom UtilityUtili<br>od ral __ Lower trade wv agit<br>5 od costs i rid<br>is of wfPy<br>i, v — Benchmark<br>—_ ee _— Lowerboth migration<br>and trade frictions<br>0.2 -- Utileety 0.2 _ Lowercosts trade<br>000”: i!<br>0.0 0.0<br>2000 2025 2050 2075 2100 2000 2025 2050 2075 2100<br><!-- End of picture text -->
<!-- Start of picture text --> A. Population changes, period 100 B. Real wages changes, period 100<br>20°N . 20°N L. en ons<br>.<br>oe . F Sony<br>log Tee sy " hs<br>20°S of popu- Sas log of real eee -<br>7| lation ratio Bs, “sy ste oO 20°S 7 | wage ratio E oe<br>- 0.10 b ee el - 0.10 Fy -<br>0.05 re a y 0.05 Th 7<br>40°S 0.00 LPe 40°S 0.00 ndit B<br>= -0.05-0.10 - - -0.05-0.10 & —_—,<br>60°S 60°S<br>120°W = s«100°W-ss«80°W 60°W 40°W 120°W = :100°Wss80°W 60°W 40°W<br><!-- End of picture text -->
<!-- Start of picture text --> GON} - + ae —— en er « ‘ - -<br>wn Bs F i 3 7 2 Bay<br>0 : ~ a jtNe €¢wes= : ; ;<br>20°s wa . —t<br>ws = Pd<br>60°S:<br>120°W COW 0° 60°E 120°E<br><!-- End of picture text -->
||PanelA: Optimal cu|t in trade costs|Panel B: Reduction in|migrationfrictions|
|---|---|---|---|---|
||Change (%) inPDV of real<br>GDPpc (2000-2100)|Change (%) inPDV of<br>ofwelfare (2000-2100)|Change (%) inPDV of real<br>GDP pc (2000-2100)|Change (%) inPDV of<br>ofwelfare (2000-2100)|
|World<br>|1.32<br>|7.83<br>|0.08<br>|2.48<br>|
|LAC<br>|15.05<br>|53.64<br>|-0.53<br>|19.1<br>|
|Argentina<br>|3.11<br>|6.63<br>|0<br>|6<br>|
|Bolivia|27.55|125.87|-1.21|935.97|
|Brazil<br>|2.65<br>|4.14<br>|-0.17<br>|1.13<br>|
|Chile<br>|4.7<br>|21.59<br>|-1.19<br>|38.56<br>|
|Colombia|5.2|7.43|-1.36|8.56|
|Costa Rica<br>|55.19<br>|73.86<br>|0.16<br>|97.88<br>|
|DominicanRepublic<br>|0.03<br>|-0.9<br>|0.2<br>|-0.44<br>|
|<br>Ecuador<br>|87.63<br>|208.93<br>|0.27<br>|7.64<br>|
|ElSalvador<br>|893.31<br>|1287.65<br>|1.14<br>|0.78<br>|
|Guyana<br>|-0.54<br>|-1.49<br>|0.1<br>|2.28<br>|
|Guatemala|352.51|394.6|-1.27|1539.07|
|Haiti<br>|-0.04<br>|-1.01<br>|0.17<br>|-0.45<br>|
|Honduras<br>|-2.91<br>|-4.09<br>|1.75<br>|0.73<br>|
|Mexico<br>|3.58<br>|5.44<br>|-0.82<br>|43.19<br>|
|Nicaragua<br>|126.95<br>|224.41<br>|0.5<br>|2.22<br>|
|Panama|425.16|2774.5|0.52|28|
|Paraguay<br>|6.98<br>|7.71<br>|0.02<br>|5.96<br>|
|Peru<br>|3.66<br>|2.86<br>|-0.51<br>|18.75<br>|
|Suriname<br>|-0.62<br>|-1.45<br>|-0.73<br>|4.23<br>|
|Uruguay<br>|136.57<br>|402.87<br>|-0.37<br>|17.29<br>|
|Venezuela|3.78|4.83|-1.48|30.92|
conclude that trade costs, not migration frictions, represent a major constraint to the efficient distribution of economic activity and to high sustainable economic growth in Latin America.

# **6. Conclusions**

Transportation costs can hinder economic development with uneven effects across space within countries and regions. Barriers to the mobility of workers in the economy could have analogous results, by not allowing labor to allocate optimally across space. In this paper, we assess the magnitude of migration frictions in Latin America c. 2000 and quantify the effects of cuts in trade and migration frictions on spatial development in the region. We show that reductions in trade costs in an optimal way can be key for the long-term spatial economic development in the region and the potential to speed up its convergence to advanced countries’ standards of living.

Using the dynamic spatial general equilibrium model in Desmet et al. (2018) and fine-grained spatial data, we first calibrated migration entry costs using information on differences in initial utility across regions within the Latin American countries. We find that in most Latin American countries, migration entry costs calibrated based on spatially differential initial utility are on average substantially lower than those obtained assuming uniform within-country initial utility. Their within-country dispersion is also mostly lower. International comparisons suggest that the adjusted migration frictions are also smaller on average in Latin America than those in advanced economies, but trade frictions are considerably higher, even after adjusting for distance.

We then use two counterfactual simulations to assess the relative importance of the two types of mobility frictions. The first counterfactual explores the effects of reductions in transportation costs due to optimal expansion of the domestic road networks in most Latin American countries. We show that such a reduction increases the present discounted value of real per capita income in the region on average by 15.1% – a much larger magnitude than the one obtained with static quantitative trade models because of dynamics gains. The optimal improvements in connectivity also tend to stimulate the economic development of the relatively more populous and productive locations in the Latin American countries. By contrast, making it easier to migrate to such top locations has no effect on the present discounted value of the region’s real per capita incomes, reflecting the relatively low dispersion in migration entry costs in most Latin American countries and the relatively low entry barriers in these locations. In both counterfactuals, the welfare increases are significantly larger than the increases in 26
real per capita income because the reductions in both types of mobility frictions allow people to relocate to areas with better amenities and therefore derive higher utility. These results imply that trade costs, not migration frictions, represent a major constraint to the efficient distribution of economic activity and high, sustainable economic growth in Latin America.

The results presented in the paper are subject to the following caveats. We do not consider the cost of implementing the optimal expansions in domestic road networks in Latin America. Therefore, the gains from optimal reductions in transportation costs can be seen as gross rather than net gains. In addition, we assume that nearly all Latin American countries undertake such optimal investments at the same time. This is easier said than done as it entails substantial coordination, political, and institutional costs, which are also not taken into consideration. Our results are nonetheless very informative of the multiplicative gains of such policies over time and across space. Economic gains diffuse across space, and improvements in the integration of markets intensify these forces. By using a dynamic spatial framework where growth (through innovation and spatial diffusion) takes place, we provide evidence that making markets within LAC better connected can lead to long-run gains that far exceed any short-run (static) gains.

27