metadata: authors: 0: author: Selod,Harris
1: author: Steinbuks,Jevgenijs
2: author: Trotter,Ian Michael
3: author: Blankespoor,Brian
count: Brazil
docna: 0: docna: Infrastructure Complementarities and Local
            Economic Growth : Evidence from Electrification and Highway
            Construction in Brazil
docty: Policy Research Working Paper
abstracts: cdata!: This paper uses four decades of data on
            Brazilian municipalities to study the separate and joint
            impacts of highway and electricity infrastructure access on
            local economic outcomes. The identification strategy employs
            difference-in-difference estimators with staggered adoption
            design. The results show strong contemporaneous effects of
            electrifying municipalities that already have access to a
            highway, whereas electrification or highway provision alone
            may, at best, have no effect. Infrastructure investments
            also facilitated long-lasting structural transformation
            effects, with both types of infrastructure access spurring
            growth of the industrial output share.
display_title: Infrastructure Complementarities and
            Local Economic Growth : Evidence from Electrification and
            Highway Construction in Brazil
listing_relative_url: /research/2024/05/34323885/infrastructure-complementarities-local-economic-growth-evidence-electrification-highway-construction-brazil
topicv3: Infrastructure and Growth m1326244 1083
publishtoextweb_dt: 2024-05-23T00:00:00Z
dois: 10.1596/1813-9450-10785
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 46
author_keys: 0: author_key: 750638
1: author_key: 666646
2: author_key: 1524436
3: author_key: 957135
model_extractions: Policy Research Working Paper 10785 Infrastructure Complementarities and Local Economic Growth Evidence from Electrification and Highway Construction in Brazil _Harris Selod Jevgenijs Steinbuks Ian Trotter Brian Blankespoor_ Infrastructure Chief Economist Office Development Research Group & Development Data Group June 2024
Policy Research Working Paper 10785

# **Abstract**

This paper uses four decades of data on Brazilian municipalities to study the separate and joint impacts of highway and electricity infrastructure access on local economic outcomes. The identification strategy employs difference-in-difference estimators with staggered adoption design. The results show strong contemporaneous effects of electrifying municipalities that already have access to a highway, whereas electrification or highway provision alone may, at best, have no effect. Infrastructure investments also facilitated long-lasting structural transformation effects, with both types of infrastructure access spurring growth of the industrial output share.

This paper is a product of the Infrastructure Chief Economist Office, the Development Research Group and the Development Data Group, Development Economics. 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 hselod@worldbank.org, jsteinbuks@worldbank.org and bblankespoor@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
Infrastructure Complementarities and Local Economic Growth: Evidence from Electrification and Highway Construction in Brazil<sup>∗</sup> Harris Selod<sup>1</sup> , Jevgenijs Steinbuks<sup>1</sup> , Ian Trotter<sup>2</sup> , and Brian Blankespoor<sup>1</sup>

> 2Universidade Federal de Vi ̧cosa, Brazil

Originally published in the Policy Research Working Paper Series on _May 2024._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL: O13, O18, O47, O54 Keywords: Brazil, electricity, highways, infrastructure complementarities, local economic development

> ∗The authors thank Cl ́ement de Chaisemartin, David McKenzie, Niclas Moneke and St ́ephane Straub as well as attendees of the NCID workshop on Energy and Environmental Issues in Developing Countries, the European Meeting of the Urban Economics Association, the annual workshop on the Empirical Methods in Energy Economics conference, and the Urbanization and Poverty Reduction Research conference for useful advice, comments and suggestions.
# **1 Introduction**

It is well established in the economic literature that infrastructure investment is a major driver of economic development, particularly through its impact on structural transformation (Berg et al., 2017). Infrastructure investments can have sizeable multiplier effects on local economies by boosting productivity through better factor allocation (Vagliasindi and Gorgulu, 2021), facilitating trade through improved access to markets (Jedwab and Storeygard, 2021), or removing barriers to entry that prevent the development of local economic activities (Perez-Sebastian and Steinbuks, 2017). As various types of infrastructure may differently contribute to growth, it is important to disentangle their effects and how these effects may vary when provided in isolation or in combination. In particular, are different infrastructure types complements or substitutes in driving local economic effects? The answer to this question bears much policy relevance as policymakers need to optimize costly infrastructure investments and their distribution over localities. It is also important for policy makers to understand how these effects may be nuanced and vary according to location and sector.

In this paper, we study the joint impact of access to transportation (highways)<sup>1</sup> and electricity infrastructure on local gross value added (GVA) using panel data over four decades and more than 3,000 Brazilian municipalities. The study fills an important knowledge gap by focusing on infrastructure complementarities. It estimates the causal effect of infrastructure complementarities using the difference-in-differences model with potentially heterogeneous treatments (De Chaisemartin and d’Haultfoeuille, 2020), and an identification strategy that relies on the plausible exogeneity of the timing and common trends across municipalities in the absence of treatment. It then establishes a causal mechanism of infrastructure investment impacts, distinguishing between productivity and relative input use channels.

We find that neither the provision of highways nor electricity alone impacts GVA per capita (our main variable of interest) or, separately, GVA or population. At the same time, we find a strong complementarity associated with the joint provision of highways and electricity, which increases GVA per capita at the time the infrastructure investment is made. The effect, however, disappears after one

> 1We focus on highways because they are Brazil’s main transportation mode for both people and freight/cargo. Brazil has the fourth largest highway system in the world. In 2014, road cargo transport accounted for over 60% of Brazil’s transportation matrix (Sandoval, 2014).

2
or two periods. It percolates through increased output thanks to productivity gains, whereas reallocation of labor and capital has a small, if any, impact. We also find evidence of structural transformation associated with providing single and joint infrastructure types. Specifically, the provision of highways and electricity leads to a higher GVA share of industry in the local economy, and this effect persists over time. These results withstand robustness and sensitivity checks and are confirmed by an alternative identification strategy using two-way with instrumental variables.

Our paper is closely related to several recent papers in the literature exploring the interactions between different types of infrastructure and economic development in low- and lower-middle-income countries. Moneke (2020) studies how big push infrastructure investments affect welfare through a positive TFP shock in Ethiopia in a spatial general equilibrium model. P ́erez-Sebasti ́an, Serrano Quintero, and Steinbuks (2023) endogenize the government’s decisions to invest in the transport and electricity networks in a multi-sector quantitative spatial equilibrium model that incorporates the quality of the infrastructure networks, which determines sectoral productivities and trade costs. Using a reduced-form econometric approach, Vanden Eynde and Wren-Lewis (2021) and Abbasi et al. (2022) find evidence of strong economic complementarities for roads and electricity in the context of rural India and the Sub-Saharan Africa region, respectively. Our paper extends the approaches of the latter two studies using robust econometric approaches and focusing on the long-term effects in a middle-income country.

# **2 Background and Data**

## **2.1 Policy Context**

Brazil sporadically started building roads and hydropower plants in the late nineteenth century, with the first highways constructed in the 1920s. This trend slowly continued in the first half of the twentieth century and significantly accelerated in the 1960s and 1970s. The impetus came from President Kubitschek’s prioritization of public infrastructure to support industrialization and fulfill his electoral platform of “fifty years of progress in five.” During his presidency (1956–1961), Kubitschek launched a major national development plan ( _Piano de Metas_ or “Targets Plan”) focusing on the transportation and energy sectors. This led to a major expansion of both electricity and transportation systems 3
along with the creation of the new capital city, Bras ́ılia (Ayres et al., 2019). This impetus was continued under subsequent governments with a series of national development plans in 1967, 1973, and 1976 emphasizing the construction of hydroelectric dams and roads improvement and highway construction (Leturcq, 2018).<sup>2</sup> The plans required a large public investment effort toward infrastructure. In the initial Targets Plan, 71 percent of public investments targeted energy, transportation, and communications (Obhara,<sup> ̄</sup> 1974). These investments were sustained in the national development plans that followed. They were undertaken at a time when electricity demand increased due to population growth and urbanization. New investments then slowed down – starting with transport followed by electricity – during the “lost decade” of the 1980s and 1990s as Brazil’s governments focused on addressing the macroeconomic crisis (Raiser et al., 2017). This led to a progressive deterioration of the transport network as investments did not cover maintenance needs (Correa and Ramos, 2010).

All in all, infrastructure investments resulted in a significant expansion of the electricity and highway networks. Lipscomb, Mobarak, and Barham (2013) report that the transmission network in Brazil grew at an average rate of 8.9 percent per year between 1950 and 2000, resulting in a 72-fold increase from 2,359 kilometers to 167,443 kilometers over the period. As a consequence, household access to electricity rose from 48% in 1970 to 93% in 2000. The total length of highways increased from 41,189 km in 1970 (of which 3,488 km of radial highways originating from Bras ́ılia) to 90,788 km in 2000 (of which 7,440 km were radial highways originating from Bras ́ılia–calculation by authors based on DNIT data).

# **2.2 Data**

We start with creating a longitudinal dataset with four points in time (1970, 1980, 1996, and 2000) across 3,102 Brazilian municipalities for which economic data are available.<sup>3</sup> It covers more than 93 percent of the Brazilian population throughout the study period.<sup>4</sup> Our data comprise the municipal Gross Valued Added (GVA),

> 2The 1973 plan aimed to create 8 radial roads, 17 longitudinal highways, 24 transverse roads, 27 diagonal roads, and 62 connecting highways (Pereira and Lessa, 2011).

> 3Note that the data is unequally spaced in time, which will not affect the scope and magnitude of our results as shown in Section 4.4.

> 4We work with administrative boundaries at the municipality level from the Brazilian Institute of Geography and Statistics ( _Instituto Brasileiro de Geografia e Estat ́ıstica, IBGE_ ) using the 1970 data as a baseline reference. The 568 missing municipalities for which economic data are not available only represent 6 percent of the country’s population in 1970 and 7 percent in 2000.

4
population, and labor and capital by sector of activity from Brazil’s national accounts data disseminated by Brazil’s Institute for Applied Economic Research ( _Instituto de Pesquisa Econˆomica Aplicada, IPEA_ ).<sup>5</sup> Unfortunately, the data for labor and capital is available only for a subset of municipalities and years.

We use georeferenced data on roads for each decade from 1970 to 2000 obtained from Brazil’s National Department of Transport Infrastructure ( _Departamento Nacional de Infraestrutura de Transportes_ , DNIT). From the road data, we subset the road segments that are paved, duplicated, or in the progress of duplication.<sup>6</sup> For the purpose of identification (see next section), we separate out radial roads extending from Brazilia towards the 25 state capitals<sup>7</sup> along with two coastal highways based on DNIT classification.<sup>8</sup> For each time period, we construct the start and end points of the DNIT radial and coastal highway road segments that existed at the time. Our road access measure is a dummy variable equal to 1 for municipalities that have a radial road within 50 km of its centroid and 0 otherwise.

Electricity access data over the period 1970 to 2000 come from various sources including the Geographic Information System of the Electric Sector ( _Sistema de Informa ̧c ̃oes Geogr ́aficas do Setor El ́etrico, SIGEL_ ) of the Brazil National Electricity Regulatory Authority ( _Agˆencia Nacional de Energia El ́etrica, ANEEL_ ), the Business Information System for the Electricity Sector ( _Sistema de Informa ̧c ̃oes Empresariais do Setor de Energia El ́etrica, SIESE_ ) of a major Brazilian electric utility, _Eletrobras_ , and Brazil’s and Cadastral Geographic Information System of the National Interconnected System ( _Sistema De Informa ̧c ̃oes Geogr ́aficas Cadastrais Do Sistema Interligado Nacional, SINDAT_ ) of the Brazil’s Operator of the National Electricity System ( _Operador Nacional do Sistema El ́etrico, ONS_ ). As in Lipscomb, Mobarak, and Barham (2013), we define electricity access in a given year as the share of municipality area located within 50 kilometers of a transmission line, distribution substation, or autonomous power plant.

Table 1 shows variable means for population, gross-valued added, and capitallabor ratio across municipalities, overall and for the industry sector, in the study’s four periods.

> 5See `www.ipeadata.gov.br` .

> 6More precisely, the road segment is defined as one of the following: ‘Pavimentada’,‘Duplicada’, and ‘Em obra de Duplica ̧c ̃ao’.

> 7This includes the state of Tocantins and its capital, Palmas, even though Tocantins had not not yet been created in 1970.

> 8Radial highways include road segments with labels ‘010’, ‘020’, ‘030’, ‘040’, ‘050’, ‘060’, ‘070’, ‘080’, ‘090’. Coastal highways include segments with labels ‘101’ and ‘116’.

5
Table 1: Variable Means (3,102 municipalities)

||1970|1980|1996|2000|
|---|---|---|---|---|
|Population|28.16|35.92|47.17|50.93|
|Gross Value Added (GVA)|93.59|230.59|287.65|307.75|
|GVA per capita|1.56|3.42|3.08|3.77|
|GVA, Industry|32.48|92.35|93.33|84.66|
|Capital-Labor Ratio|11,023|34,279|N/A|N/A|
|Capital-Labor Ratio, Industry|33,272|77,434|N/A|N/A|

Note: Population and labor are in million people; GVA and capital are in million R$ (constant 2010 prices). The capital-labor ratio is available for 1231 municipalities.

# **2.3 Infrastructure Investments in the Data**

Figure 1 shows the spatial evolution of radial and coastal highways and electricity access in Brazil by comparing infrastructure access for 1970 and 2000, which are the starting and end dates for our study. The maps show that electrification and radial and coastal highway expansion mostly occurred towards the Northeast and Center-West regions, whereas investments in other regions were modest. Figure 2 further details the number of municipalities by their degree of electrification in 1970 and 2000. We see that the bulk of electrification investment consisted of full electrification of formerly non-electrified municipalities. Only a small share of municipalities received partial electrification. As we construct a binary measure of electrification for the empirical analysis, we focus on the case of full electrification (i.e., municipalities with 100 percent of their population having electricity access).

Figure 3 shows the roll-out of both radial and coastal highway investment and full electrification. Consistent with the roll-out of the above investment plans, infrastructure access increased significantly. In 1970, 1,901 municipalities (i.e., 61 percent of all municipalities) did not have access to power or highway infrastructure, 975 municipalities only had electricity access, 117 municipalities only had highway access, and 109 municipalities had both infrastructure types.

6
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<!-- Start of picture text --> 1400<br>1200<br>w« 1000<br>=rH<br>Tt<br>a<br>€<br>a<br>E<br>8 eo<br>a<br>4<br>E<br>a<br>= 400<br>200<br>0<br>No electricity <20% 20%-40% 280%. Fully electrified<br>1970 = 2000<br><!-- End of picture text --> <!-- Start of picture text --> 3000 - | [8 [8 [B<br>”<br>2<br>= 2000-<br>Qa<br>=<br>fo}<br>©<br>2<br>5 1000 1<br>2<br>o-<br>1970 1980 1996 2000<br><!-- End of picture text -->
424 of the municipalities that lacked any infrastructure and of 32 municipalities that already had highway access. Highways were provided to 93 municipalities that had no infrastructure and to 6 municipalities that were already electrified.<sup>9</sup> Only 9 municipalities were simultaneously granted highway and electricity access.

# **3 Identification Strategy and Estimation Sample**

We are interested in estimating the average causal effect of infrastructure provision on local economic outcomes at the municipality level (i.e., output per capita, output, input use, and productivity measures). As infrastructure is not necessarily placed independently of local economic outcomes, a potential reverse causality problem arises. Also, given that some variables correlated with both infrastructure and economic outcomes might not be observed, an omitted variable bias might also be present. We address both of these issues, leading to endogeneity problems, using two different approaches.

Our main approach estimates a difference-in-differences model with potentially heterogeneous treatments. Similar to Vanden Eynde and Wren-Lewis (2021), our identification strategy relies on the plausible exogeneity of the timing and common trends across municipalities in the absence of treatment. Following Bird and Straub (2020) and Morten and Oliveira (2023), we specifically focus on the radial network originating from Bras ́ılia, which was planned in the 1950s to become the new capital of Brazil. Because the creation of Bras ́ılia motivated the development of the current radial highway system linking it to other state capitals (Morten and Oliveira, 2023), we can obtain a sample of exogenously treated municipalities by removing these state capitals along with Bras ́ılia. To address the potential contamination effects of road placement, we restrict the analysis to “inconsequential areas”, excluding urban nodes that might have motivated highway construction. We also exclude municipalities that have received non-radial highways. Finally, to ensure the comparability of context, we only keep municipalities within 150 km of treated municipalities in the control group. This leaves us with 384 municipalities, either without any infrastructure or endowed with a radial highway in 1970.<sup>10</sup> Between 1970 and 2000, we observe

> 9This small number appears reasonable because electrification requires some means of transport. Moneke (2020) makes a similar observation for Ethiopia.

> 10Appendix Table A1 replicates the descriptive statistics from Table 1 for these 384 municipalities.

9
193 instances of infrastructure investments either by electrification or provision of a radial highway. In section 4.4, we explore the sensitivity of these sample construction criteria by expanding the control group to municipalities within 200 km of treated municipalities. To address the possibility of spatial spillovers (that could lead to potential contamination effects), we also perform a robustness check that excludes municipalities within 50 km of treated municipalities from the control group.

We denote with index _i_ individual municipalities, and with index _s ∈ {N, E, H, B}_ the groups of municipalities with similar initial infrastructure endowment, where _N_ stands for no infrastructure, _E_ stands for electricity, _H_ stands for highway, and _B_ for both infrastructure types. We also use index _t_ to refer to the periods defined in section 2.2. Adopting the notation of Freedman et al. (2023), we focus on treatment exposures that occur at the group × time level (with infrastructure staying in place once provided). In this perspective, we denote _As,P_ the period during which infrastructure type _P ∈{E, H, B}_ is first provided to group _s_ . We set _As,P_ = _∞_ for groups _s_ that are not provided with infrastructure _P_ during the study period. We can then define the binary treatment variable for group _s_ with infrastructure _P_ in period _t_ as _Ds,t_ = I( _t ≥ As,P_ ).

We are interested in estimating the average effect of treatment on the treated (ATT) in period _t ≥ a_ for treatment _P_ provided in period _a_ , which can be written as where _βi,s,P,t_ ( _a_ ) = _Yi,s,P,t_ ( _a_ ) _− Yi,s,P,t_ (0) is the causal effect at time _t_ of provision in period _a_ of infrastructure _P_ to municipality _i_ in group _s_ on outcome variable _Y_ compared to a hypothetical scenario in which group _s_ would not have received the infrastructure by period _t_ .

In our staggered adoption design, the average treatment effect on the treated in period _t_ = _a_ + _k_ (with _k ≥_ 0) for treatment in period _a_ is given by 10
To estimate equation (2) we use the difference-in-differences estimator (DIDM) robust to heterogeneous effects proposed by De Chaisemartin and d’Haultfoeuille (2020). As noted by De Chaisemartin and d’Haultfoeuille (2020), the DIDM estimator could fail if municipalities that received infrastructure investments experienced different trends than municipalities that did not receive any infrastructure. To test the parallel trends assumption, we use the placebo estimator proposed by De Chaisemartin and d’Haultfoeuille (2020) that compares the outcomes between periods _a −_ 2 and _a −_ 1 in municipalities that became treated in period _a_ to those of the control group. If the parallel trends assumption is violated, the placebo estimator will be significantly different from 0.

# **4 Results, Robustness Checks, and Discussion**

## **4.1 Aggregate Output and Population**

In this section, we present the DIDM estimator (De Chaisemartin and D’Haultfoeuille, 2022b; De Chaisemartin and d’Haultfoeuille, 2020) of the effect of the separate and joint provision of electricity and highways on various municipality level outcomes. Our main focus is on gross value added per capita, which is a normalized measure of output (allowing comparisons of impact across municipalities of different sizes). It also provides a measure of the apparent productivity of municipalities. The DIDM estimator estimates the average treatment effect at the time when a municipality receives the infrastructure across all municipalities that become treated at some point. All outcomes are expressed in natural logarithms and have semi-elasticity interpretations.

We use binary measures of infrastructure access and consider whether a municipality has access to a highway or is fully electrified. We distinguish across three switching scenarios depending on whether a municipality without any infrastructure type receives electricity access only (denoted N2E), highway access only (denoted N2H), or whether a municipality with highway access becomes electrified (H2B). As we have seen in Figure 3, there are only a few transitions where an electrified municipality is provided with a highway (E2B), or a municipality is provided with both highway and electricity (N2B). We exclude those rare switching cases from the analysis as results for them would not have meaningful interpretations.<sup>11</sup>

> 11The standard errors cannot be accurately estimated when the number of switching municipalities is too small.

11
Tables 2-5 present the estimates of the impact of infrastructure provision on the logarithm of gross value added (GVA) per capita, the logarithm of GVA, the logarithm of population, and GVA shares. The first row presents the estimates for the entire period of the study (1970-2000), whereas the next three rows show estimates for the sub-periods 1970-1980, 1980-1996, and 1996-2000 respectively. The penultimate row presents the dynamic estimator of De Chaisemartin and D’Haultfoeuille (2022a), which measures effects one period after the infrastructure provision. The last row shows the placebo estimator (see section 3) of the effect of the separate and joint provision of electricity and highway access. To preserve space, we only report the number of observations and treated municipalities (switchers) in Table 2 as these numbers are the same for all tables.

Table 2 focuses on municipal gross value added per capita, our main variable of interest. We see from the first row that neither the provision of highways nor electricity alone has a statistically significant impact on value-added per capita. This contrasts with previous research on the impact of electricity and highway provision on output per capita and productivity (Bird and Straub, 2020; Fernald, 1999; Perez-Sebastian, Steinbuks, et al., 2020). These previous studies, however, did not consider whether municipalities receiving a type of infrastructure investment already had received the other type. At the same time, the electrification of municipalities already endowed with a highway increases the value added per capita by 29 percent.<sup>12</sup> This result highlights the strong impact of providing the two types of infrastructure. This is in line with the findings regarding the complementarity of roads and electricity provision by Moneke (2020) for Ethiopia and by Vanden Eynde and Wren-Lewis (2021) for rural India, but contrasts with the study of Lewis and Severnini (2020) for rural areas in the United States. We do not find statistically significant dynamic estimates (row 5), which suggests that the effect of infrastructure placement is temporary. Placebo estimates of the provision of electricity, whether alone or following highway provision, are much smaller and statistically insignificant. This indicates that the parallel trends assumption is unlikely to be violated for electricity provision. For highway provision, the placebo effect is positive and significant, preventing us from concluding whether the provision of highways alone impacted GVA per capita.

> 12As variables are measured in logs, the percentage change in the outcome is given by the formula exp( _β_ )-1, where _β_ is the reported regression coefficient.

12
Table 2: DIDM estimates of infrastructure provision (GVA per Capita)

||_Dependent varia_|_ble: Gross V_|_alue Added _|_per Capita (log)_|
|---|---|---|---|---|
|||N2E|N2H|H2B|
|1970-2000 (E)|coef.|0.036<br>|0.021<br>|0.252<sup>_∗∗_</sup><br>|
||s.e.|(0.046)|(0.088)|(0.101)|
||Obs./Switchers|390 / 102|338 / 55|274 / 36|
|1970-1980|coef.|-0.030|0.273|0.081|
||s.e.|(0.057)|(0.174)|(0.175)|
||Obs./Switchers|168 / 35|134 / 21|89 / 4|
|1980-1996|coef.|0.139<sup>_∗∗_</sup>|-0.035|0.276<sup>_∗∗_</sup>|
||s.e.|(0.069)|(0.121)|(0.132)|
||Obs./Switchers|133 / 44|113 / 22|104 / 23|
|1996-2000|coef.|-0.063|-0.319<sup>_∗∗∗_</sup>|0.269<sup>_∗∗_</sup>|
||s.e.|(0.105)|(0.081)|(0.129)|
||Obs./Switchers|89 / 23|91 / 12|81 / 9|
|1970-2000 (D)|coef.|0.074|0.170<sup>_∗_</sup>|0.268|
||s.e.|(0.061)|(0.100)|(0.170)|
||Obs./Switchers|234 / 79|213 / 43|165 / 27|
|Placebo|coef.|-0.110|0.135|0.089|
||s.e.|(0.067)|(0.080)|(0.087)|
||Obs.|222|204|166|

_Notes: Bootstrapped standard errors (200 replications) in parentheses. ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision. (D): estimated effect one period after the infrastructure provision._ The results by period (rows 2-4) show a more contrasted picture. There were positive, albeit marginally significant, impacts of highways on output per capita in the 1970-1980. The provision of electricity alone positively affected output per capita in 1980-1996. By contrast, the period 1996-2000 shows negative impacts on gross value added per capita of the separate provision of highways. This could be due to the sequencing of investments targeting lagging regions in the later period, as discussed in Section 2.1. We see suggestive complementarity effects for the last period, as the point estimate of the electrification effect for municipalities already endowed with a highway (column H2B) is greater than that for electrification only (column N2E).

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Table 3: DIDM estimates of infrastructure provision (Gross Value Added)

||_Depen_|_dent variab_|_le: Gross Value Added (log)_|
|---|---|---|---|
|||N2E|N2H<br>H2B|
|1970-2000 (E)|coef.|0.035|-0.004<br>0.325<sup>_∗∗∗_</sup>|
||s.e.|(0.047)|(0.081)<br>(0.125)|
|1970-1980|coef.|0.067|0.190<br>0.469|
||s.e.|(0.071)|(0.184)<br>(0.309)|
|1980-1996|coef.|0.063|-0.018<br>0.284<sup>_∗_</sup>|
||s.e.|(0.071)|(0.112)<br>(0.167)|
|1996-2000|coef.|-0.069|-0.317<sup>_∗∗∗_</sup><br>0.367<sup>_∗∗_</sup>|
||s.e.|(0.103)|(0.103)<br>(0.156)|
|1970-2000 (D)|coef.|0.101|0.112<br>0.390<sup>_∗_</sup>|
||s.e.|(0.075)|(0.121)<br>(0.220)|
|Placebo|coef.|-0.183<sup>_∗∗∗_</sup>|0.138<br>0.046|
||s.e.|(0.070)|(0.094)<br>(0.109)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision._ _(D): estimated effect one period after the infrastructure provision._ Tables 3 and 4 decompose the previous findings by showing how municipality gross value added and population responded to infrastructure investment. The first row of Table 3 shows a strong complementarity effect as the gross value added in municipalities endowed with a highway that was subsequently electrified increases by 38 percent compared to untreated municipalities. The provision of electricity alone or highways alone did not have a statistically significant impact on GVA. The first row of Table 4 does not show any statistically significant impact on the population in municipalities due to any type of infrastructure provision. Taken together, these results suggest that the increase in gross value added per capita (Table 2) is solely driven by the effects of gross value added resulting from the electrification of municipalities already endowed with a highway. We do not find any significant dynamic effects except for a positive and marginally significant effect on GVA for the electrification of municipalities already endowed with a highway (row 5). The placebo estimates of highway provision and electrification in municipalities already endowed with highways are not statistically significant, lending credibility to the parallel trend assumption.

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However, this is not the case for the provision of electricity alone, preventing us from concluding whether the provision of electricity alone had an impact on either GVA or population.

A closer examination by the period of the electrification of municipalities already endowed with a highway (rows 2, 3, and 4 of Tables 3 and 4) indicates that the complementarity effect mainly occurred in the 1996-2000 period. In that period, providing electricity in places already supplied with a highway positively increased gross value added by 44 percent and population by 10 percent.

Table 4: DIDM estimates of infrastructure provision (Population)

||_Dep_|_endent va_|_riable: Pop_|_ulation (log)_|
|---|---|---|---|---|
|||N2E|N2H|H2B|
|1970-2000 (E)|coef.|-0.001|-0.025|0.073|
||s.e.|(0.025)|(0.033)|(0.066)|
|1970-1980|coef.|0.097<sup>_∗∗_</sup>|-0.083|0.388|
||s.e.|(0.041)|(0.063)|(0.342)|
|1980-1996|coef.|-0.076<sup>_∗_</sup>|0.016|0.008|
||s.e.|(0.041)|(0.044)|(0.073)|
|1996-2000|coef.|-0.006|0.002|0.098<sup>_∗∗∗_</sup>|
||s.e.|(0.023)|(0.031)|(0.022)|
|1970-2000 (D)|coef.|0.027|-0.058|0.121|
||s.e.|(0.055)|(0.062)|(0.116)|
|Placebo|coef.|-0.073<sup>_∗_</sup>|0.003|-0.044|
||s.e.|(0.041)|(0.057)|(0.067)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision. (D): estimated effect one period after the infrastructure provision._

# **4.2 Structural Transformation**

We now turn to the impact of infrastructure provision on the industry share in Gross Value Added. The findings (summarized in Table 5) point towards evidence of structural transformation. There are also noticeable heterogeneous effects associated with the separate and joint provision of electricity and highways.

Providing electricity (columns N2E) increases, on average, the share of industry by 5 percent as compared to municipalities that were not electrified.

15
The impact is consistently positive and significant only in the 1970-1980 period. The placebo test validates the impact of electrification. For electricity access, the size of dynamic effects is comparable to the average effect. This indicates that the impact of electrification on the industry GVA share persists over time, indicating a long-lasting effect on the economy’s structure.

We find little average effect of highway connection (columns N2H) on the industry GVA share but a positive dynamic impact (6 percentage points), which is three times as large as the average effect and is statistically significant. Contrary to electricity provision, this indicates that highway provision has a long-lasting impact on the GVA share of the industry sector. The placebo test for highway provision does not reject this finding.

Both electricity and highway effects indicate structural transformation through an increase in the industry share. This is consistent with the previous literature on the impact of electrification and structural transformation (Gaggl et al., 2021; Perez-Sebastian, Steinbuks, et al., 2020) and previous studies that found that highways facilitate input use for industry and spur the development of industries trading heavier goods that can be transported by roads (Duranton, Morrow, and Turner, 2014; Jaimovich, 2019). Finally, we look at the impact on the industry GVA share following the electrification of municipalities that were already connected to the highway network (columns H2B). We find no statistically significant effect. This suggests no complementarity in the sense that the share of industry does not change when both types of infrastructure are present. The placebo test does not reject the validity of these results.

16
Table 5: DIDM estimates of infrastructure provision (Industry GVA share)

||_Depe_|_ndent varia_|_ble: Industry GVA share_|
|---|---|---|---|
|||N2E|N2H<br>H2B|
|1970-2000 (E)|coef.|0.049<sup>_∗∗∗_</sup>|0.020<br>0.027|
||s.e.|(0.016)|(0.020)<br>(0.029)|
|1970-1980|coef.|0.088<sup>_∗∗∗_</sup>|0.030<br>0.042|
||s.e.|(0.025)|(0.042)<br>(0.069)|
|1980-1996|coef.|0.027|0.026<br>-0.000|
||s.e.|(0.023)|(0.028)<br>(0.040)|
|1996-2000|coef.|0.034|-0.009<br>0.090|
||s.e.|(0.033)|(0.020)<br>(0.059)|
|1970-2000 (D)|coef.|0.051<sup>_∗∗_</sup>|0.058<sup>_∗∗_</sup><br>0.025|
||s.e.|(0.020)|(0.027)<br>(0.053)|
|Placebo|coef.|0.023|0.022<br>-0.009|
||s.e.|(0.022)|(0.015)<br>(0.023)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision._ _(D): estimated effect one period after the infrastructure provision._

# **4.3 Causal Mechanisms**

We now investigate in greater detail the possible mechanisms that can explain how infrastructure provision affects output and productivity. Assuming a Cobb-Douglas aggregate production technology at the municipal level, we focus specifically on the capital/labor ratio (capital intensity) and the total factor productivity (TFP), which captures technological improvements (see derivation and estimation procedure of TFP in Appendix 1). Capital and labor data are available only for the period 1970-1980, which, fortunately, is a period when significant investment was made. However, as we explain in section 2.2, the data are only available for some municipalities, leaving us with a smaller sample of municipalities during that period. Therefore, we first apply the DIDM estimator to this restricted subsample to make sure that the main results in the second row of Table 2 are unchanged. Appendix Table A2 shows that this is indeed the case. Because the subsample does not include municipalities with highway access that were subsequently electrified during the period, we cannot estimate 17
complementarity effects. This said, our results for the provision of single infrastructure types indicate that the subsample does not seem to suffer from sample selection bias.

Table 6: DIDM estimates of infrastructure provision on TFP and capital-labor ratio: 1970-1980

|||All S<br>|ectors<br>|Ind<br>|ustry<br>|
|---|---|---|---|---|---|
|||N2E|N2H|N2E|N2H|
|K/L|coef.|-0.128|-0.103|-0.361|-0.292|
||s.e.|(0.137)|(0.180)|(0.255)|(0.492)|
|TFP|coef.|0.166<sup>_∗_</sup>|0.367<sup>_∗∗_</sup>|0.618<sup>_∗∗∗_</sup>|0.100|
||s.e.|(0.094)|(0.173)|(0.199)|(0.241)|
||Obs./Switchers|87 / 25|89 / 19|87 / 25|89 / 19|

_Notes: Bootstrapped standard errors (200 replications) in parentheses. ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01. Dependent variables are in natural logs._ Table 6 shows the impact of electrification and highway provision on capital intensity and TFP, pooling all sectors together, and separately for the industry sector. We do not observe any statistically significant effects of the provision of electricity or highways on the capital/labor ratio at the aggregate level. On the contrary, the provision of either infrastructure type has strong positive effects on TFP. Electrification increases TFP by almost 18 percent, although this effect is only marginally significant.

When focusing on the industry sector, we see that electrification significantly increases TFP, which is 86 percent larger in electrified municipalities. This is consistent with the previous literature documenting positive total factor productivity effects in the industry sector following electrification (Kassem, 2021). These findings are also consistent with our estimated changes in industry GVA share following electrification (see Table 5), with TFP being the driver of this change (for a detailed explanation of this mechanism, see Perez-Sebastian, Steinbuks, et al., 2020).

Similar to electrification, we find a positive and significant effect (a 44 percent increase) of highway provision on aggregate TFP. The effect of highway provision on the industry sector TFP is positive but not statistically significant. This is consistent with the absence of significant effects of highway provision on the industry GVA share for the 1970-1980 period in Table 5.

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Overall, the above results indicate that infrastructure-induced growth percolated through productivity and, thus, more efficient use of production inputs facilitated by better access to electricity, predominantly for the industry sector and by highways for the aggregated economy.

# **4.4 Robustness Tests**

We find that there is no significant difference in the distribution of gross valueadded and population across treatments and controls before treatment occurs, as shown in Appendix Figures A2 and A3. Consistent with Kahn-Lang and Lang (2020), this gives us further confidence that our difference-in-differences estimates are plausible.

Next, we re-estimate our specifications from Tables 2, 3 and 4 using the doubly robust difference-in-differences estimator proposed by Callaway and Sant’Anna (2021). Figures 4 and 5 show the corresponding event study graphs for our key results (the impact of electrification of municipalities previously endowed with highways on GVA per capita and the impact of electrification of municipalities without highways on the industry GVA share). Appendix Figure A4 and Table A3 show event study graphs and the estimated coefficients for all results. The event study graphs and estimation results confirm our earlier findings. First, only joint infrastructure placement has a significant though contemporaneous effect on GVA per capita (due to a temporary increase in GVA without effect on population, see Appendix Table A3). Second, the electrification of municipalities without highways has a longer-lasting effect on structural transformation, increasing the industrial GVA share. The estimated coefficients also carry the same significance as in our main specification (i.e., using De Chaisemartin and d’Haultfoeuille (2020)) for all types of infrastructure investments and outcome variables. If anything, all coefficients measuring complementarity effects (i.e., electrification in the presence of highways) are larger, indicating that our main estimates are likely to be conservative.

We also replicate our main results from Table 2 using a less stringent measure of electricity access, i.e., considering municipalities that are 80 percent electrified instead of fully electrified. The results presented in Appendix Table A4 are broadly of the same magnitude and significance as those presented in Table 2. One difference is that we lose the significance of the effect of electrification of municipalities already endowed with a highway for the 1996-2000 period (which was only marginally significant in Table 2). The only other difference is that 19
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# **4.5 Alternative Identification Strategy**

Our quantitative placebo, balance, and sample sensitivity tests and the use of alternative DID estimators give us reasonable confidence that our identification approach using difference-in-differences estimation is justified. To strengthen our inference, we show in this section that our main results are also robust to an alternative identification strategy.

Specifically, we estimate an instrumental variable model for data with twoway fixed effects. As with the main specification, we exclude the state capitals, the areas covered by non-radial highways, and keep municipalities within 150 km of treated municipalities in the control group. We regress our outcomes of interest (log GVA, log population, and log GVA per capita) on a dummy variable indicating the presence of a radial highway in the municipality, a dummy variable indicating whether the municipality is electrified, and on the interaction between the two treatments (to assess complementarity effect). To address the endogeneity of electrification, we use the time-variant instrument developed by Lipscomb, Mobarak, and Barham (2013) and further refined in Szerman et al. (2022). To address road placement endogeneity, we use a time-variant instrument based on the natural least cost path between the urban nodes (see, e.g., Faber (2014), Donaldson (2018) or Bird and Straub (2020) who proposed time-invariant versions of the least-cost path instrument). Appendix A2 details the estimation and construction of our instruments.

Table 7 shows the estimation results of the instrumental variable model.<sup>15</sup> Consistent with our findings using our main identification strategy reported in section 4.1, we see no effect of highway provision alone on GVA per capita, GVA, or population. However, the estimated impact of electricity provision alone differs from the findings in section 4.1. Instead of finding no effect, we now see a negative and statistically significant effect of electrification on GVA and a positive and statistically significant effect on population. This translates into a negative and statistically significant effect of electricity provision on GVA per capita. We are not concerned about this difference since the placebo tests had failed for electricity provision in our main identification strategy. The new results imply that GVA is smaller in electrified places that lack highway access. This is consistent with the literature that documents how workers and activities leave unproductive peripheral areas when infrastructure is improved (Faber, 2014).

> 15See Appendix Table A11 for the first-stage estimation results.

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Table 7: IV estimates of infrastructure provision (second-stage)

|||_Dependent var_|_iable:_|
|---|---|---|---|
||GVA per<br>capita (log)|GVA (log)|Population (log)|
||(1)|(2)|(3)|
|Electricity Access|_−_2.341<sup>_∗∗∗_</sup>|_−_1.366<sup>_∗∗∗_</sup>|0.975<sup>_∗∗∗_</sup>|
||(0.386)|(0.462)|(0.214)|
|Highway Access|_−_0.554<br>(0.496)|_−_0.406<br>(0.576)|0.148<br>(0.276)|
|Electricity Access X|3.372<sup>_∗∗∗_</sup>|2.898<sup>_∗∗∗_</sup>|_−_0.475|
|Highway Access|(0.951)|(1.110)|(0.532)|
|Wald Test _χ_<sup>2</sup>|3844.2<sup>_∗∗∗_</sup>|2577.3<sup>_∗∗∗_</sup>|2501.3<sup>_∗∗∗_</sup>|
|Observations|1,536|1,536|1,536|

_Note: Robust standard errors clustered at the state level in parentheses. ∗p<0.1,∗∗p<0.05,∗∗∗p<0.01._ However, these negative effects associated with the provision of electricity alone are fully compensated when both types of infrastructure are present, confirming our main findings regarding infrastructure complementarity in section 4.1. The overall effect of joint infrastructure provision (given by the sum of the three coefficients on electricity access, highway access, and their interaction) is a twofold increase in GVA and an increase of GVA per capita by 61 percent.

# **5 Conclusions**

This paper is among the few to investigate the effects of different types of infrastructure provision on local economic development and the first to investigate the issue over a long period (3 decades). Focusing on several decades is necessary not only because investments are staggered in time, but also because effects may take time to materialize. In our case, the study period allows us to capture the bulk of infrastructure investment in Brazil, which started in the 1970s, and to assess how these large investment programs affected targeted municipalities.

In contrast with previous results from the literature, we do not find strong 23
evidence of single-type infrastructure provision on output and apparent labor productivity (which we measure with municipal GVA and GVA per capita). We do find, however, evidence of an impact of the joint provision of electricity and highway infrastructure, which we estimate to increase GVA by 38 to 100 percent and GVA per capita by 29 to 61 percent, depending on the econometric approach. Importantly, the impacts differ across decades, being highly significant in some and not significant in others. This arguably reconciles findings from the literature where short-term and long-term effects can significantly vary (Lee, Miguel, and Wolfram, 2020).

In addition, we find a positive effect of infrastructure provision (both highways and electricity separately) on TFP, though our data restricts the analysis of TFP impacts to a single decade.

Finally, we find evidence of infrastructure investment playing a role in facilitating structural transformation. The separate provision of highways and electricity increases the local GVA share of the industry by about 5 percent. These effects are found to persist over time.

These results have an important policy implication: the government should consider infrastructure provision as a package. If the objective is to promote local economic development, then failing to improve transport access along with electrification may not be effective.

# **References**

- Abbasi, Mansoureh et al. (2022). _Roads, Electricity, and Jobs: Evidence of Infrastructure Complementarity in Sub-Saharan Africa_ . Policy Research Working Paper 9976. The World Bank.

- Ali, Rubaba et al. (2015). _Highways to Success or Byways to Waste: Estimating the Economic Benefits of Roads in Africa_ . The World Bank, Washington D.C.

- Ayres, Joao, Marcio Garcia, Diogo A Guill ́en, and Patrick J Kehoe (2019). _The Monetary and Fiscal History of Brazil, 1960-2016_ . Working Paper 25421. National Bureau of Economic Research.

- Balestra, Pietro and Jayalakshmi Varadharajan-Krishnakumar (1987). “Full Information Estimations of a System of Simultaneous Equations with Error Component Structure”. _Econometric Theory_ 3.2, pp. 223–246.

24
- Bensch, Gunther, J ̈org Peters, and Colin Vance (2021). _Development Effects of Electrification in Brazil–A Comment on Lipscomb et al.(2013)_ . USAEE Working Paper.

- Berg, Claudia N, Uwe Deichmann, Yishen Liu, and Harris Selod (2017). “Transport Policies and Development”. _The Journal of Development Studies_ 53.4, pp. 465–480.

- Bird, Julia and St ́ephane Straub (2020). “The Brasilia Experiment: the Heterogeneous Impact of Road Access on Spatial Development in Brazil”. _World Development_ 127, p. 104739.

- Bun, Maurice JG and Teresa D Harrison (2019). “OLS and IV Estimation of Regression Models Including Endogenous Interaction Terms”. _Econometric Reviews_ 38.7, pp. 814–827.

- Callaway, Brantly and Pedro HC Sant’Anna (2021). “Difference-in-Differences with Multiple Time Periods”. _Journal of Econometrics_ 225.2, pp. 200–230.

- Correa, Vivian Helena Capacle and Pedro Ramos (2010). “A Precariedade do Transporte Rodovi ́ario Brasileiro para o Escoamento da Produ ̧c ̃ao de Soja do Centro-Oeste: situa ̧c ̃ao e perspectivas”. _Brazilian Journal of Rural Economy and Sociology (Revista de Economia e Sociologia Rural-RESR)_ 48.2, pp. 1–26.

- Croissant, Yves and Giovanni Millo (2008). “Panel Data Econometrics in R: the plm Package”. _Journal of Statistical Software_ 27.2.

- De Chaisemartin, Cl ́ement and Xavier D’Haultfoeuille (2022a). _Difference-indifferences Estimators of Intertemporal Treatment Effects_ . Working Paper 29873. National Bureau of Economic Research.

- (2022b). _Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments_ . Working Paper 30564. National Bureau of Economic Research.

- De Chaisemartin, Cl ́ement and Xavier d’Haultfoeuille (2020). “Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects”. _American Economic Review_ 110, pp. 2964–96.

- Donaldson, Dave (2018). “Railroads of the Raj: Estimating the impact of transportation infrastructure”. _American Economic Review_ 108.4-5, pp. 899–934.

- Duranton, Gilles, Peter M Morrow, and Matthew A Turner (2014). “Roads and Trade: Evidence from the US”. _Review of Economic Studies_ 81.2, pp. 681–724.

- Faber, Benjamin (2014). “Trade Integration, Market Size, and Industrialization: Evidence from China’s National Trunk Highway System”. _Review of Economic Studies_ 81.3, pp. 1046–1070.

25
- Fernald, John G (1999). “Roads to Prosperity? Assessing the Link between Public Capital and Productivity”. _American Economic Review_ 89.3, pp. 619–638.

- Freedman, Seth M et al. (2023). _Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines_ . Working Paper 31842. National Bureau of Economic Research.

- Gaggl, Paul, Rowena Gray, Ioana Marinescu, and Miguel Morin (2021). “Does Electricity Drive Structural Transformation? Evidence from the United States”. _Labour Economics_ 68, p. 101944.

- Jaimovich, Esteban (2019). “Roadways, Input Sourcing, and Patterns of Specialization”. _European Economic Review_ 120, p. 103319.

- Jedwab, Remi and Adam Storeygard (2021). “The Average and Heterogeneous Effects of Transportation Investments: Evidence from sub-Saharan Africa 1960–2010”. _Journal of the European Economic Association_ jvab027.

- Kahn-Lang, Ariella and Kevin Lang (2020). “The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications”. _Journal of Business & Economic Statistics_ 38.3, pp. 613–620.

- Kassem, Dana (2021). _Does Electrification Cause Industrial Development_ . Working Paper.

- Klein Goldewijk, Kees, Arthur Beusen, Gerard Van Drecht, and Martine De Vos (2011). “The HYDE 3.1 Spatially Explicit Database of Human-Induced Global Land-Use Change over the Past 12,000 Years”. _Global Ecology and Biogeography_ 20.1, pp. 73–86.

- Lee, Kenneth, Edward Miguel, and Catherine Wolfram (2020). “Does Household Electrification Supercharge Economic Development?” _Journal of Economic Perspectives_ 34.1, pp. 122–44.

- Leturcq, Guillaume (2018). _Dams in Brazil: Social and Demographical Impacts_ . Springer.

- Lewis, Joshua and Edson Severnini (2020). “Short-and Long-Run Impacts of Rural Electrification: Evidence from the Historical Rollout of the US Power Grid”. _Journal of Development Economics_ 143, p. 102412.

- Lipscomb, Molly, A Mushfiq Mobarak, and Tania Barham (2013). “Development Effects of Electrification: Evidence from the Topographic Placement of Hydropower Plants in Brazil”. _American Economic Journal: Applied Economics_ 5.2, pp. 200–231.

- Moneke, Niclas (2020). “Infrastructure and Structural Transformation: Evidence from Ethiopia”. PhD thesis. The London School of Economics and Political Science (LSE).

26
- Morten, Melanie and Jaqueline Oliveira (2023). “The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City”. _American Economic Journal: Applied Economics_ forthcoming.

- Obhara, Yoshinori (1974). “Brazilian Economic Development Since 1956: A Study ̄ on Policies Reflecting the Stages of Growth”. _The Developing Economies_ 12.2, pp. 133–154.

- Pereira, Luiz AG and Simone Narciso Lessa (2011). “O Processo de Planejamento e Desenvolvimento do Transporte Rodovi ́ario no Brasil”. _Caminhos de Geografia_ 12.40, pp. 26–46.

- Perez-Sebastian, Fidel and Jevgenijs Steinbuks (2017). _Public Infrastructure and Structural Transformation_ . Policy Research Working Paper 8285. World Bank.

- Perez-Sebastian, Fidel, Jevgenijs Steinbuks, Jos ́e F ́eres, and Ian Trotter (2020). _Electricity Access and Structural Transformation: Evidence from Brazil’s Electrification_ . Policy Research Working Paper 9182. World Bank.

- P ́erez-Sebasti ́an, Fidel, Rafael Serrano Quintero, and Jevgenijs Steinbuks (2023). _Spatial Misallocation of Complementary Infrastructure Investment: Evidence from Brazil_ . Policy Research Working Paper 10650. The World Bank.

- Raiser, Martin et al. (2017). _Back to Planning: How to Close Brazil’s Infrastructure Gap in Times of Austerity_ . Technical Report 117392-BR. World Bank.

- Sandoval, Daylyne Maerla Gomes Lima (2014). “An ́alise das Implica ̧c ̃oes Log ́ısticas, Con ́abeis e Financeiras da Escolha do Servi ̧co de Transporte”. PhD thesis. Universidade de Bras ́ılia, Faculdade de Tecnologia.

- Stock, James H and Jonathan H Wright (2000). “GMM with Weak Identification”. _Econometrica_ 68.5, pp. 1055–1096.

- Szerman, Dimitri, Juliano Assun ̧cao, Molly Lipscomb, and Ahmed Mushfiq Mobarak (2022). _Agricultural Productivity and Deforestation: Evidence from Brazil_ . Working Paper.

- Tobler, Waldo (1993). _Three Presentations on Geographical Analysis and Modeling_ . Technical Report.

- Vagliasindi, Maria and Nisan Gorgulu (2021). _What Have We Learned about the Effectiveness of Infrastructure Investment as a Fiscal Stimulus? A Literature Review_ . World Bank Policy Research Paper 9796. The World Bank.

- Van Beveren, Ilke (2012). “Total Factor Productivity Estimation: A Practical Review”. _Journal of Economic Surveys_ 26.1, pp. 98–128.

27
- Vanden Eynde, Oliver and Liam Wren-Lewis (2021). _Complementarities in Infrastructure: Evidence from Rural India_ . CEPR Discussion Paper 16139.

- Verdin, Kristine L et al. (2007). _Development of a Global Slope Dataset for Estimation of Landslide Occurrence Resulting from Earthquakes_ . Technical Report.

- Wooldridge, Jeffrey M (2010). _Econometric Analysis of Cross Section and Panel Data_ . MIT press.

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