metadata: authors: 0: author: Jedwab,Remi Camille
1: author: Ianchovichina,Elena
2: author: Haslop,Federico
count: World
docna: 0: docna: Consumption Cities versus Production Cities
            : New Considerations and Evidence
docty: Policy Research Working Paper
bdmdt: 2022-06-24T00:00:00Z
abstracts: cdata!: Cities dramatically vary in their
            sectoral composition across the world, possibly lending
            credence to the theory that some cities are production
            cities with high employment shares of urban tradables while
            others are consumption cities with high employment shares of
            urban non-tradables. A model of structural change highlights
            three paths leading to the rise of consumption cities:
            resource rents from exporting fuels and mining products,
            agricultural exports, and premature deindustrialization.
            These findings appear to be corroborated using both country-
            and city-level data. Compared to cities in industrialized
            countries, cities of similar sizes in resource-rich and
            deindustrializing countries have lower shares of employment
            in manufacturing, tradable services, and the formal sector,
            and higher shares of employment in non-tradables and the
            informal sector. Results on the construction of “vanitous”
            tall buildings provide additional evidence on the
            relationship between resource exports and consumption
            cities. Finally, the evidence suggests that having mostly
            consumption cities might have economic implications for a country.
display_title: Consumption Cities versus Production
            Cities : New Considerations and Evidence
closedt: 2022-06-24T00:00:00Z
listing_relative_url: /research/2022/06/33851490/consumption-cities-versus-production-cities-new-considerations-evidence
topicv3: Natural Resources m1326446 1979,Macroeconomic Analysis of Economic
            Development m1327386 1066,Urbanization and Jobs m1503883 2875,Trade Policies and Jobs m1504146 2826,Commercial Property and Housing m1504599 3779
publishtoextweb_dt: 2022-06-24T00:00:00Z
dois: 10.1596/1813-9450-10105
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1140935
1: author_key: 453205
2: author_key: NA
model_extractions: Policy Research Working Paper 10105 Consumption Cities versus Production Cities New Considerations and Evidence _Remi Jedwab Elena Ianchovichina Federico Haslop_ Latin America and the Caribbean Region Office of the Chief Economist June 2022
Policy Research Working Paper 10105

# **Abstract**

Cities dramatically vary in their sectoral composition across the world, possibly lending credence to the theory that some cities are production cities with high employment shares of urban tradables while others are consumption cities with high employment shares of urban non-tradables. A model of structural change highlights three paths leading to the rise of consumption cities: resource rents from exporting fuels and mining products, agricultural exports, and premature deindustrialization. These findings appear to be corroborated using both country- and city-level data.

Compared to cities in industrialized countries, cities of similar sizes in resource-rich and deindustrializing countries have lower shares of employment in manufacturing, tradable services, and the formal sector, and higher shares of employment in non-tradables and the informal sector. Results on the construction of “vanitous” tall buildings provide additional evidence on the relationship between resource exports and consumption cities. Finally, the evidence suggests that having mostly consumption cities might have economic implications for a country.

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 jedwab@email.gwu.edu, eianchovichina@worldbank.org, or fhaslop@gwmail.gwu.edu.

_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
**_Consumption Cities_ versus** **_Production Cities:_ New Considerations and Evidence**

Remi Jedwab, Elena Ianchovichina and Federico Haslop<sup>_∗_</sup> JEL Codes: O11; E24; E26; O13; O14; O18; R1; R12 Keywords: Structural Change; Urbanization; Consumption Cities; Macro-Development Economics; Industrialization; Natural Resources; Deindustrialization; Construction

> _∗_ Corresponding author: Remi Jedwab: Department of Economics, George Washington University. Elena Ianchovichina: Office of the Chief Economist for Latin America and the Caribbean, The World Bank. Federico Haslop: Department of Economics, George Washington University. We thank Martina Kirchberger, Somik Lall, Justin Yifu Lin, William Maloney, Tommaso Porzio, Luis Quintero, Martin Rama, Xiaodong Zhu and seminar audiences at George Washington, the International Annual Workshop of New Structural Economics (Beijing), the Urban Economics Association meetings, and the World Bank for helpful comments. We are grateful to Andrew Durrer for his research assistance. 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 The period since the early 1960s has been one of rapid urbanization in developing countries (World Bank, 2009; United Nations, 2018). This process has been linked to a virtuous circle between economic development and urbanization (Henderson, 2010; Duranton, 2015). However, urbanization can also proceed without growth (Bairoch, 1988; Fay and Opal, 2000; Glaeser, 2014; Jedwab and Vollrath, 2015; Castells-Quintana, 2017).

In the macro-development literature, urbanization is often a by-product of structural change. As countries develop, people move out of agriculture and engage in urbanbased manufacturing and service activities (Herrendorf et al., 2014). Structural change occurs due to _Green Revolutions_ , which increase food productivity and push labor out of agriculture (Matsuyama, 1992; Gollin, Parente and Rogerson, 2002; Restuccia, Yang and Zhu, 2008; Yang and Zhu, 2013; Lagakos and Waugh, 2013; Gollin, Hansen and Wingender, 2018). In addition, _Industrial Revolutions_ or _Service Revolutions_ increase manufacturing or service productivity and pull labor out of agriculture (Hansen and Prescott, 2002; Lucas, 2004; Alvarez-Cuadrado and Poschke, 2011).<sup>1</sup> Gollin et al. (2016) (henceforth GJV16) show that only in some developing countries rapid urbanization has been accompanied by industrialization, following the historical patterns observed in richer nations. Manufacturing and tradable services agglomerated in _production cities_ whose growth was driven by the countries’ increased _production_ capacity. In others, the spending of resource rents on urban goods and services led to _consumption cities_ whose growth was driven by increased _consumption_ capacity. Since manufactured and tradable services – _urban tradables_ – are often non- goods imported, tradable services – _urban non-tradables_ – dominate their sectoral composition.

This new evidence on the of cities. Whether paper provides “origin” consumption a country has production cities or consumption cities could then matter for economic development. Productivity in manufacturing and non-traditional services varies little across countries, and productivity catch-up in these sectors may explain some of the large gains in aggregate productivity observed across countries (Duarte and Restuccia, 2010, 2020). Hence, low productivity in urban non-tradables in developing economies, and thus the possible lack of catch-up in this sector, might contribute to explaining the lack of

> 1Other mechanisms include quality-of-life amenities (Jedwab and Vollrath, 2019; Gollin, Kirchberger and Lagakos, 2021), demographic growth (Jedwab et al., 2017; Castells-Quintana and Wenban-Smith, 2020; Jedwab et al., 2021a), urban-biased policies (Lipton, 1977; Ades and Glaeser, 1995; Davis and Henderson, 2003; Castells-Quintana, 2017), trade (Glaeser, 2014; Venables, 2017), technology (Jedwab et al., 2021c), and disasters (Barrios et al., 2006; Henderson et al., 2017; Jedwab et al., 2021b; Castells-Quintana et al., 2021).
2 international convergence. In addition, returns to education and work experience, i.e. the rates of human capital accumulation at work, may vary across sectors (Islam et al., 2019). Likewise, agglomeration economies may also vary by sectors (Venables, 2017; Burger et al., 2022). That may also be the case for congestion externalities. Finally, international trade affects endogenous innovation and growth (Melitz and Redding, 2021).

We first establish that, for a given population size and a given level of urban economic development, cities dramatically vary in their sectoral composition across the world. Using census data for about 65 countries, we obtain the sectoral composition of 7,000 agglomerations comprising three-fourths of the world’s urban population. We classify them as “production cities”, “consumption cities”, or “neutral cities”, depending on their employment share of manufacturing and tradable services. We also use our classification to highlight novel stylized facts related to the global distribution of urban employment and show that cities, not just countries, experience different patterns of structural change.

We extend the theoretical analysis of GJV16, who show that countries can urbanize because of _industrialization_ broadly defined (which includes tradable, or “industrialized”, services) or because they export _natural resources_ . An increase in resource export earnings raises incomes. The resource export earnings are used to import food and other tradable goods whereas the higher demand for urban non-tradables is met by an increase in labor in that sector. Since the rural sector contracts, the country urbanizes. However, urbanization generated by resource rents differs from industrialization-led urbanization in that cities will have different employment shares of tradables and non-tradables.

We consider two other paths leading to consumption cities. In GJV16, resources include fuels, mineral products, and a few high-rent cash crops, which require little labor. Yet, many countries export _agricultural products_ , including food crops. Their production generates rents. However, it also requires rural labor, leading to ambiguous urbanization effects. We show that, under a reasonable set of assumptions, the urban share should increase. The agricultural export earnings can indeed be used to import additional food and other tradable goods. Much like what can be observed for non-agricultural resources, cities in these countries will have high employment shares of non-tradables.<sup>2</sup>

> 2 Any significant source of foreign income could also promote urbanization through the rise of consumption cities. For our sample countries, during the period 1960-2020, remittances and foreign aid averaged just 1.7% and 1.8% of GDP per year, respectively, compared to 7.8% earned from natural resource and agricultural exports (source: World Bank (2021)).
3 Next, many countries have experienced “premature” _deindustrialization_ (Rodrik, 2016), due to the removal of import substitution industrialization (ISI) policies adopted by nations to the 1980s or increased trade due to trade liberalization many up competition or rising productivity related to advances in automation in some nations. We discuss why “premature” _deindustrialization_ may not lead to de-urbanization. However, since the country experiences deindustrialization, its cities do so as well.

Using GJV16’s sample of 116 developing countries (as of 1960) and long-difference and panel regressions for the period 1960-2020, we first show that: (i) higher urban shares are found in countries with higher GDP shares of manufacturing & services, a proxy for industrialization broadly defined (including tradable services); (ii) countries exporting natural resources, whether fuel & mining products _or_ agricultural products, are also more urbanized; and (iii) urban shares are unchanged in deindustrializing countries.

Second, we take advantage of newly available data, including IPUMS census microdata for about 60 countries over time and I2D2 household and labor force survey data for about 90 countries over time, to examine the correlations between the sectoral structure of urban areas and industrialization, resource exports, and deindustrialization.<sup>3</sup> We study sectors not covered in GJV16 and informality, use panel regressions, and identify which parts of the city size distribution are affected by the structural change mechanisms mentioned above. Cities in industrialized countries have more in tradables and more while cities in resource- employment wage employment, rich and deindustrializing countries have higher shares of non-tradables and selfemployment. Differences between industrialized, resource-rich, and deindustrializing countries are stable across city sizes. Thus, the origin of the urbanization process also impacts the largest cities, hence countries’ “engines of growth” (World Bank, 1999, 2009).

Third, focusing on the mechanisms, we take advantage of novel data on urban construction across countries to shed light on the “quality” of spending in cities. For example, we explore whether the spending of resource rents in cities might have led to “white elephant” projects. We make use of a remarkable data set that inventories all the world’s tall buildings – whose height is above 80 meters – with information on their year of construction and height. Long-difference and panel regressions suggest that exporting

> 3The _International Income Distribution Database_ (I2D2) of the World Bank’s _World Development Report_ unit consists of 1,500 individual-level household/labor force surveys. Details will be provided in Section 4..
4 natural resources correlates with the construction of tall buildings whose economic rationale is questionable, for example very tall buildings that are more expensive to construct than other types of buildings. Since resource-rich countries do not have a larger construction sector, as measured by cement use or construction GDP, but use more cement in their tall construction sector than industrialized economies, the expansion of the sector might have occurred at the expense of non-tall construction.<sup>4</sup> Fourth, an important theme in the macro-urban literature is the fact that urban-biased policies cause urban primacy (Ades and Glaeser, 1995). In particular, governments use resource rents to implement policies that disproportionately favor the largest city (Bates, 1981; World Bank, 2020). However, using both country- and city-level data, we do not find that resource-rich countries have relatively higher urban primacy rates. Indeed, resourcerichness appears to have the same consequences for _all_ population size categories of cities.

we various that that more Finally, perform analyses suggest having consumption cities might have economic implications for a country. Using evidence on the number of years of schooling, education quality, and the returns to education in urban areas globally, we actually do _not_ find that consumption cities have significantly less human capital than production cities. Yet, for a given level of human capital, consumption cities have more informality and higher employment in urban non-tradables than production cities. This implies that the human capital in consumption cities is employed in less productive sectors and jobs, at least in the short to medium run. Indeed, controlling for selection and sorting, we find large wage gaps between urban tradables and urban nontradables. In addition, we find that returns to experience in urban areas are overall lower in countries with more consumption cities. Finally, we discuss various studies showing that agglomeration economies are possibly weaker in urban non-tradables.

In addition to the structural change literature, we contribute to the macroliterature on the determinants and characteristics of urbanization across development countries (Gollin, Lagakos and Waugh, 2014; Gollin, Jedwab and Vollrath, 2016; Lagakos, 2016; Jedwab and Vollrath, 2019; Lagakos, Marshall, Mobarak, Vernot and Waugh, 2020; Lagakos, 2020; Porzio, Rossi and Santangelo, 2020; Gollin, Kirchberger and Lagakos, 2021;

> 4These results on possibly “inefficient” construction echo the work of Collier, Kirchberger and S ̈oderbom (2016) for roads and link this work to recent studies on the global role of market constraints (Kirchberger and Beirne, 2021) and geographical constraints (Jedwab et al., 2020b; Ahlfeldt and Jedwab, 2022) in construction. See Kirchberger (2020) for a survey of the literature on construction in developing countries.
5 Gai, Guo, Li, Shi and Zhu, 2021; Lagakos, Mobarak and Waugh, 2022). We study the links between changes in economic and export structures and the nature of the urbanization process, as well as urban structural change at the city level, not just the country level.

Much like the macro-development literature, this paper offers a model of structural change and data to establish new stylized facts regarding the development process. We consider the potential effects of various factors on country- and city-level variables rather than focusing on identifying a clean effect for one of them, which would not be credible anyway since we compare countries. Our results are not causal and instead, our goal is to generate a number of theoretical predictions and empirical results that collectively contribute to corroborating, but not proving, our main messages.

_Consumption cities_ differ from the _consumer cities_ in the urban literature. In our case, resource-rich countries experience the rise of consumption cities due to their increased consumption capacity. Deindustrializing countries see their consumption cities grow because their cities lose their on consumer production production capacity. Analyses cities rely on the Rosen-Roback model to show that, _within_ a country, cities with better amenities attract residents that accept lower wages and/or higher rents to live there (Rosen, 1979; Roback, 1982; Glaeser et al., 2001; Glaeser and Gottlieb, 2009). Relatedly, Gollin et al. (2021) find that the quality of amenities is at least as high in cities as in rural areas in Africa, showing that the observed urban wage premia in developing economies (Gollin et al., 2014) do not represent a compensation for lower amenities.

We do not claim that consumption cities are harmful for economic development. Urbanization through the rise of consumption cities may generate agglomeration economies and thus boost growth. Furthermore, even if consumption cities offer smaller economic benefits than production cities, they may still be economically significant. Lastly, consumption cities could evolve into production cities, especially considering that urban areas in resource-rich and urbanized economies have similar human capital levels.<sup>5</sup> The is structured as follows. Section 1 discusses the data and paper methodology used to classify world cities into production or consumption cities. Section 2 presents the model and four propositions that guide our empirical analysis in Section 3 on

> 5Porzio et al. (2020) show that human capital explains structural change out of agriculture. Countries with more human capital should thus be more urbanized. Yet, the study does not discuss how human capital impacts urban sectoral composition. Since we do not find large urban human capital differences between resource-rich and industrialized economies, urban sectoral structure does not appear to be related to urban human capital. However, we need better data to study this question and capture “omitted” factors.
6 the respective roles of natural resources, industrialization, and de-industrialization in urbanization. Sections 4, 5, and 6 study the role of these factors for urban employment, construction, and urban primacy, respectively. Section 7 focuses on urban human capital.

# **1. The Global Sectoral Composition of Cities**

It is not obvious how cities of the same population size and located in countries with similar levels of economic development differ globally in their employment composition. GJV16 find examples of countries where urban areas have high shares of _urban tradables_ , which include manufacturing (MFG) and FIRE (finance, insurance, real estate, and business services). They also find examples of countries where urban areas have high shares of _urban non-tradables_ , which include the (non-tradable) wholesale and retail sector.

Since the work of GVJ16, which is cross-sectional and only focuses on the urban sector as a whole, many countries have added censuses to the IPUMS repository (Minnesota Population Center, 2020) and GIS files corresponding to the second level administrative units in which a household is located.<sup>6</sup> In addition, the Global Human Settlements Layer (GHSL) database provides geocoded polygons of urban extent boundaries for the world c. 2015 (Schiavina et al., 2019). More precisely, this database uses satellite data on built-up area to identify “Functional Urban Areas” (FUAs), i.e. commuting zones of at least 50,000 residents today. Combining the two data sets allows us to obtain the sectoral composition of most world cities. Using a simple methodology presented below, we then classify each city as being a _production city_ – a city with a disproportionately high share of employment in tradables; a _consumption city_ – a city with a disproportionately low share of employment in tradables; or a _neutral city_ – a city in which the shares of employment in tradables is neither disproportionately low nor disproportionately high.<sup>7</sup>

**Data.** For 76 countries and 191 country-years (1960-2015), we have IPUMS census microdata with information on the administrative unit in which the respondent lives, the respondent’s sector, _and_ whether the respondent lives in an “urban” area.

We process the data following several steps. First, we select “urban” observations.<sup>8</sup> Second, we have information on the resident’s second level administrative unit. Such units are typically smaller than FUAs.<sup>9</sup> Third, IPUMS classifies employment into 16

> 6These units correspond to _counties_ in the U.S. and _municipalities_ in most Latin American countries. 7We interchangeably use the words “FUAs”, “agglomerations” and “cities” in the rest of the analysis. 8When the urban identifier is unavailable, we identify urban residents from information on the locality. 9In some cases, information is only available for first level administrative units, which leads to coarser
7 groups. We focus on the employment share of urban tradables (MFG & FIRE). We could have alternatively focused on urban non-tradables but it is not yet clear which sectors should be included. Having obtained employment data for each administrative unit (1960-2015), the next step is to use these data to obtain the labor shares for each FUA.

Censuses only take place every 10-15 years. To ensure that we have enough countries for our comparison, we select for each country-FUA the closest observation to the year 2000 (within the 1990-2015 period). Doing so, we are left with 6,865 FUAs in 74 countries. Lastly, we need data on their population size c. 2000. The GHSL database reports their population for the “epoch 2015”. However, population is almost always estimated for earlier years. In many cases, the last census indeed took place in the 2000s.

**Sampling.** The 6,865 FUAs include 3 billion people and account for 75% of world’s urban population. Kolmogorov–Smirnov tests confirm that the distribution of city sizes is not significantly different between our sample and the world. However, data on a few large countries are not available in IPUMS To increase our developed (e.g., Japan). sample’s representativeness, we divide all the countries in the world into ten deciles based on their log per capita GDP c. 2000 (PPP; source: World Bank (2021)). We then obtain the share of each decile in the world’s urban population and compare these shares to the shares in our sample. Based on the differences, we create weights over-sampling developed countries.

**Methodology.** Since we aim to identify which cities have high shares of MFG+FIRE relative to other cities of the same size and for a given level of urban economic development, we categorize the FUAs in 10 deciles. Based on a range of log population from 10.8 (50K) to 17.2 (30 million), the resulting thresholds are: 95K, 180K, 341K, 648K, 1,227K, 2,306K, 4.377K, 8,351K, and 15,570K. The top two bins end up having few FUAs (N = 22 and 13, respectively), so we aggregate them together (N = 35).

Then, using the sample of 6,865 FUAs, we run a regression relating the FUA’s employment share in MFG+FIRE (%) c. 2000 to 8 size category (CAT) dummies (omitting the lowest size category, hence 50-95K) and their interactions with the 2000 urban share (URB) of the FUA’s country. We also add a dummy if the FUA is the capital city of the country in 2000 (CAP). For FUA _a_ in country _c_ and population category _p_ , the model is: MFGFIRE _a,c,_ 2000 = _α_ +Σ<sup>9</sup> _p_ =2<sup>_βp_1(CAT</sup><sup>_a_=</sup><sup>_p_)+Σ9</sup> _p_ =2<sup>_γp_1(CAT</sup><sup>_a_=</sup><sup>_p_)</sup><sup>_∗_URB</sup><sup>_c_+</sup><sup>_δ_URB</sup><sup>_c_+</sup><sup>_ζ_CAP</sup><sup>_a_+</sup><sup>_μa_</sup> estimates of sectoral shares. All FUAs in the same first-level unit are then assigned the same composition.
8 Finally, we use as weights the populations of each FUA. However, to ensure sample representativeness, we modify the weights so as to oversample richer countries.

While Web Appx. Table D.1 reports the estimated coefficients, Figure 2 shows the implied relative employment share in MFG+FIRE of each population size category for a mostly unurbanized country (urban share = 20%) and a highly urbanized country (85%). More urbanized countries have higher urban shares of MFG+FIRE. The share is higher for smaller cities, consistent with MFG moving away from larger cities as countries develop. The regression residual measures to what extent the FUA has a high, or low, MFG+FIRE share (%) given its size and its country’s level of economic development. The 5th, 10th, 25th, 75th, 90th and 95th percentile values are about -15, -10, -5, 5, 10 and 15.

**Classification** . In our classification, a _production city_ is any FUA with a residual value above 5, indicating a city with a disproportionately _high_ share of employment in urban tradables. Our definition further distinguishes production cities with a “low” (5-10), “medium” (10-15) or “high” (15+) value. A _consumption city_ is any FUA with a residual value below -5, also distinguishing consumption cities with a “low” (-5;-10), “medium” (-10;-15) or “high” (-15+) value. Cities in the [-5; 5] range are classified as _neutral_ .

Figure 1 shows the distribution of production (in blue), consumption (red), and neutral cities (grey) c. 2000. Paler shades of the blue and red colors indicate lower values for the extent to which a can be as a or city classified production consumption city. Production cities are located in China and parts of Europe, the United States, Mexico, Brazil and India, while consumption cities are located in parts of Africa, the MENA, South America, and Southeast Asia.<sup>10</sup> The patterns for Asia, Africa, Europe and the Americas are shown in Web Appx. Fig. D.1-D.5. As discussed in Web Appx. Section A, they conform with our priors.

Next, Table 1 shows the classification into production, consumption, or neutral cities for those FUAs in our sample whose population exceeds 10 million people (“Residual” is the value used to classify cities). Production cities include Ho Chi Minh, Bangalore, Istanbul and Paris as well as all Chinese megacities, while consumption cities include Kolkata and Chennai, two historically important Indian cities, as well as Lagos, Jakarta, Rio and Surabaya.<sup>11</sup> Approximately half of the mega cities are classified as neutral. In

> 10China’s cities are mostly production cities whereas India has a mix of production, consumption, and neutral cities (Appx. Fig. D.1). Different configurations within a same country are possible and frequent.

> 11Web Appx. Fig. D.7 separately considers MFG and FIRE, classifying cities according to their “best”
9 these cities, the employment in tradables is neither too low nor too high, implying a relatively balanced distribution of employment between tradables and non-tradables. Unsurprisingly, 8 of the 13 neutral mega cities are capital cities, where the government sector generates substantial employment in urban non-tradables.

Finally, we show how, for a given size, cities in Latin America and the Caribbean (LAC) have experienced sectoral employment changes over time.<sup>12</sup> We use FUA data for 8 countries to estimate the mean population-weighted urban employment share of tradables for the whole region and the periods “pre-1980” (obs. from 1962 to 1982; mean = 1978), “early 1990s” (1990-1994; 1991) and “c. 2010” (2001-2012;2009). As seen in Figure 3, employment in tradables (MFG+FIRE) has been declining over time. The evolution of LAC’s urban system was driven by its largest cities, as the decline at the “top” is 15 percentage points. Large LAC cities have thus increasingly become consumption cities.<sup>13</sup>

**Robustness.** We obtain similar results if we (Web Appx. Section B): (i) include the square, cube, and perfect fourth of the urban share, and their interactions with the population dummies, in case there are non-linearities; (ii) use log per capita GDP instead of the urban share or control for the urban definition used by each country;<sup>14</sup> (iii) compare the raw (i.e. non-residualized) employment shares; (iv) use other weights or ignore them; (v) consider other classifications for the population dummies; and (vi) study urban non-tradables or informal employment. Since our residuals are estimated _relative to_ similar cities in the world, we may worry that adding more countries to the analysis could change the results. However, our sample captures close to three-fourths of the world’s urban population, which limits such possibility. Results are also very similar if we use the raw shares.

To conclude, _ceteris paribus_ cities dramatically vary in their sectoral composition across the world. Theil decompositions suggest that half of the variation comes from differences across countries, implying an important role for aggregate structural change.

sector. While some cities are production cities because of their high MFG shares (e.g., Guangzhou and Ho Chi Minh), other production cities have high shares of FIRE (e.g., Bangalore and Paris).

> 12Indeed, only the LAC sample has good coverage over several decades in IPUMS.

> 13Additional analysis (not shown but available upon request) shows that this evolution was driven by declines in the relative urban employment shares of MFG, not FIRE.

> 14In the appendix, we discuss why results should not depend on the use of different urban definitions. Relatedly, while the urban classification is not stable across countries and years, we do not use the population density of the administrative units to select urban observations. Some rural areas of the world have particularly high density, for example in Bangladesh, Burundi, Haiti and Rwanda, which are some of the densest _and_ least urbanized countries in the world. Other areas of the world are undoubtedly urban despite their low density, in particular sprawling suburban cities (e.g., many million-plus U.S. cities).
10 Important differences are then observed for the largest cities. To better understand why consumption cities may grow, in the next section we discuss a model of structural change.

# **2. Theoretical Insights: Paths to Consumption Cities**

We consider four sectors. The urban economy has a _tradable sector_ (e.g., MFG + FIRE) and a _non-tradable sector_ (e.g., wholesale & retail trade + personal services). The rural economy has an _agricultural sector_ , which produces a tradable agricultural good (mostly crops, but also livestock). A _natural resource R_ is an endowment that is internationally traded and is a source of foreign exchange earnings. Natural resources include fuels and mining products but also, for the sake of simplicity, cash crops characterized by high rents.

The model offers several paths to consumption cities. A commodity boom due to a resource discovery or a boost to commodity prices on account of strong external demand boosts resource revenues _R_ and influences urbanization/cities through two channels: (i) an income effect, which through non-homotheticities in the domestic demand for food pulls workers into urban sectors; and (ii) export earnings increase domestic demand for non-tradable services and pull workers away from agriculture and urban tradables.

We also consider faster productivity growth in agriculture, which has an income effect and a foreign earnings effect if the country exports agricultural products, which increases demand for, and employment in, urban non-tradables. However, if the level of agricultural productivity is not high enough, this increase may pull workers back to agriculture in order to meet the food sufficiency requirement. Some crops then “behave” as a pure natural resource, in which case their effects go through _R_ . Regardless of whether the country urbanizes on account of fuels/mining or agricultural exports, its employment share of urban non-tradables increases and its cities become consumption cities.

De-industrialization can occur due to the removal of ISI policies or due to increased trade competition from industrializing nations. We discuss why such cases may not lead to de-urbanization, but “consumption” cities in already somewhat urbanized nations.

## **2.1. Set-Up**

We assume a log-linear utility function over the consumption of rural agricultural products ( _cf_ ), urban tradables ( _cm_ ), and urban non-tradables ( _cn_ ): where expenditure shares _βf_ , _βm_ , and _βn_ are between 0 and 1 and sum up to 1, and c _~~f~~_<sup>is</sup>
11 the subsistence level of agricultural consumption. With income elasticity for agriculture less than one, any income increase drives up the budget shares of urban tradables and non-tradables. For the sake of simplicity, production in each sector only requires labor: _Lj_ is the share of workers in each sector _j ∈ {f, m, n}_ , and _Aj_ is sector-specific productivity. Agricultural commodities produced mostly for export and mostly with land or capital and little labor are included in resource endowments _R_ . Thus, the agriculture sector comprises other agricultural subsectors, including subsistence food crops. The prices of urban tradables and agricultural products are assumed to be exogenous (*) and the budget constraint of the individual is: _z_ = _p_<sup>_∗_</sup> _f_<sup>_cf_+</sup><sup>_p∗_</sup> _m_<sup>_cm_+</sup><sup>_pncn_.</sup> Since the household first covers its agricultural subsistence requirement and urban non-tradables are the total on urban non- produced only domestically, expenditure tradables equals the value of their production: Assuming balanced trade, the following accounting relationship must hold, where _R_ is the revenue from exporting natural resources and both agricultural products and urban tradables can be produced domestically, imported from the rest of the world, or exported: With perfect labor mobility, wages equalize across any two sectors _j_ and _k ∈{f, m}_ : The above relationships are used to determine the implicit function for the allocation of labor in the non-tradable urban sector: <u>1 1</u> _<u>A</u>_ = ( _p_<sup>_∗_</sup> _m_<sup>_Am_)</sup> _α_ + � _p_<sup>_∗_</sup> _f_<sup>_Af_</sup> � _α_<sup>�</sup><sup>_α_</sup> is a composite measure of agricultural productivity and � productivity in urban tradables. Given _Ln_ , the rest of labor is allocated to the tradable sectors in proportion to the relative productivity in agriculture and tradable non- <u>1</u> agriculture: _∗_
12 The urbanization rate, _U_ , is then simply _U_ = _Lm_ + _Ln_ .

# **2.2. Main Predictions**

We obtain four predictions (Web Theory Appendix C provides details and proofs): **Proposition 1 (Urbanization through commodity rents and “consumption cities”)** Proposition 1 reiterates GJV16’s result that resource revenues _R_ offer a path to urbanization U and the emergence of consumption cities. Indeed, employment in urban non-tradables _Ln_ is increasing in _R_ whereas employment in manufacturing and tradable services _Lm_ is decreasing in _R_ . In other words, a positive shock to _R_ (a resource windfall) leads to the rise of consumption cities. The effect on urbanization U is also positive.

## **Proposition 2 (Productivity growth in agriculture and “consumption cities”)**

So long as _R < p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>, given</sup><sup>_y_=</sup><sup>_p∗_</sup> _f_<sup>_Af_, it follows that:</sup> Faster productivity growth in agriculture has an income effect _and_ a foreign earnings effect if the country exports agricultural products. Both result in a disproportionate increase of urban non-tradables, while the increase in foreign earnings enables the importing of urban tradables, whose share in employment decreases. If the level of is the urban share increases as the urban non- agricultural productivity high enough, tradable effect dominates the urban tradable effect. However, if the level of agricultural productivity is not high enough, an increase in agricultural productivity pulls resources back to agriculture in order to meet the subsistence requirement. Then the urban share decreases. However, we will show that urban shares almost never decrease.

## **Proposition 3 (Urbanization through industrialization and “production cities”)**

<u>1 1</u> so long as _R_ - _p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0 and agricultural productivity is high enough:</sup><sup>_α_(</sup><sup>_p∗_</sup> _m_<sup>_Am_)</sup> _α <_ ( _p_<sup>_∗_</sup> _f_<sup>_Af_)</sup> _α_ . Increasing MFG-FIRE productivity leads to an expansion of urban tradable
13 employment. Thus, a MFG or FIRE revolution causes urbanization and production cities. **Proposition 4 (de-industrialization without de-urbanization and the transformation of existing production cities into consumption cities)** _When Lf is fixed, by definition U_ = 1 _− Lf is also fixed, implying that a productivity shock that decreases (increases) employment in manufacturing would lead to a corresponding increase (decrease) in employment in non-tradables._ Proposition 4 says that shocks to the manufacturing or FIRE sector can cause existing production cities to become consumption cities when de-urbanization is unlikely. In the next section, we show that urbanization rates almost never decrease and discuss why.

**Empirics.** The urban share and the employment composition of urban areas should depend on the resource windfall _R_ , (tradable) agricultural productivity ( _p_<sup>_∗_</sup> _f_<sup>_Af_), and urban</sup> tradable productivity ( _p_<sup>_∗_</sup> _m_<sup>_Am_).In our econometric analysis, we focus on the period 1960-</sup> 2020 and 116 countries that were still “developing” economies in 1960.<sup>15</sup>

We do not have reliable historical measures of _Am_ . It is also not obvious which price levels should be used for _p_<sup>_∗_</sup> _m_<sup>.FIREGDPisonlyreportedforsomecountriesandrecent</sup> years (previous ISIC classifications did not separate FIRE). MFG and FIRE employment is likewise only measured for some countries and years when there is a census or a labor force survey (surveys were rare before 1990). Productivity could then be high because employment is low and/or “selected”, for example if a country has a few MFG/FIRE firms and these belong to high-productivity subsectors or are politically connected. Given such issues, we use the GDP share of manufacturing and services (MFGSERV). Web Appx Fig. D.6 shows that countries with a high GDP share of MFG+FIRE are countries with a high GDP share of MFGSEFV today, thus validating this proxy (correlation of 0.78).

It is also unclear how to distinguish agricultural products belonging to _R_ (those generating high rents) from agricultural products belonging to _p_<sup>_∗_</sup> _f_<sup>_Af_.</sup> In addition, productivity and yields are typically poorly measured for the latter and it is not obvious which price level to use. Since we aim to measure the fact that a country is urbanizing because it is exporting fuels and mining products or agricultural products, and given the difficulty in separating agricultural products in different categories, we aggregate them together and use as a proxy the ratio of natural resource exports (NRX) to GDP.<sup>16</sup>

> 15The sample excludes a few ex-communist countries due to the lack of pre-1989 data.

> 16NRX/GDP differs from the GDP share of natural resources, for which we have no historical data for
14 Finally, for deindustrialization, we use as a proxy the decline in the GDP share of MFG over time. Indeed, if MFG productivity decreases relative to the world, MFG employment should decrease. The GDP share combines information on productivity and employment.

**3. Resources, (De-)Industrialization, and Urbanization**

**Data.** We study 116 relatively large countries, with a developing status as of 1960, and for which data on urbanization, resource exports, and the GDP shares of manufacturing (MFG) and services (SERV) are available every 5 years between 1960 and 2020.

We obtain urban shares (%) from the United Nations (2018) and the share of fuel and mining exports in total exports for the period 1960-2010 from GJV16 who rely on data from World Bank (2021) and the _Mineral Industry Surveys_ of USGS (2020). We extend their data up to 2020, modifying them when needed. The USGS data are important due to the fact that the World Bank data are incomplete or incorrect in many cases.<sup>17</sup> Measurement error, if classical, would make us under-estimate the contribution of these resources to urbanization. The export shares of all agricultural products come from FAO (2020). Using these shares and knowing from World Bank (2021) the export-to-GDP ratio of each country in each year, we calculate NRX/GDP (%) as in Sachs and Warner (1995, 1997). Next, we obtain the time series of the GDP share of MFG+SERV by relying on World Bank (2021), Central Intelligence Agency (2021) and United Nations (1960-1980, 2020c) and the GDP share of FIRE c. 2020 from United Nations (2020a).<sup>18</sup>

**Country-level results.** For countries _c_ , we estimate the following cross-sectional model:

URB _c,_ 20 = _α_ + _β_ NRXGDP _c,_ 60 _−_ 20 + _γ_ MFGSERV _c,_ 20 + _δ_ DEINDU _c,_ 80 _−_ 20 + _Xcπ_<sup>_′_</sup> + _μc_ (8) In this equation, URB is the urban share (%) in 2020, NRXGDP – the mean export-to-GDP ratio of natural resources (%) in 1960-2020, MFGSERV – the GDP share of MFG and SERV (%) in 2020 (capturing structural change due to manufacturing or tradable services), and DEINDU – the absolute decline in the GDP share of manufacturing (%) between 1980 and 2020 transformed so that a positive value indicates _more_ deindustrialization (= 0 if no

> too many countries. Furthermore, NRX/GDP has the advantage of attributing to NRX the “value” of any input used in producing the resources, as such inputs also directly contribute to urbanization. 17According to the WDI of World Bank (2021), fuel and mining exports accounted for 8% of Botswana’s exports in 2015. According to USGS: “Botswana’s exports were $6.33 billion, of which diamonds accounted for 82.9%; copper and nickel, 5.9%; and soda ash, 1.4%.” Likewise, according to the WDI, fuel and mining exports accounted for 6% of Burkina-Faso’s exports in 2013. According to USGS: “The International Monetary Fund estimated that gold production [...] accounted for about 71% of the country’s total exports.” 18See Web Data Appx. Section D for details on the data sources.
15 decline is observed). Summary statistics are provided in the notes of Table 2.<sup>19</sup> _Xc_ is a set of controls that includes: (i) controls for country size: log area and log population in 2020 and their squares and a dummy if the country is a small island country because such countries are mechanically more urbanized; (ii) controls for the urban definition used by the country c. 2010;<sup>20</sup> and (iii) controls for initial conditions, i.e. URB, NRXGDP and MFGSERV c. 1960. Our regression is thus a long-difference regression.<sup>21</sup> Col. (1) of Table 2 Panel A shows similar coefficients for NRXGDP and MFGSERV. A one standard deviation in MFGSERV is associated with a 0.57 standard deviation in urbanization (Proposition 3) vs. a 0.49 standard deviation in NRXGDP (Propositions 1-2). No correlation is observed for DEINDU (Proposition 4). Indeed, cases of de-urbanization are very rare. For 116 countries x 13 years (1960, 1965...), 94% of country-years showing an economic decline do not show a decline in their urban share. In addition, while (annualized) economic declines can be large (mean = -2.6%; 10th percentile = -5.5%), urbanization declines are tiny (mean = -0.16 percentage points; 10th percentile = -0.24).

Several factors could account for these facts. Glaeser and Gyourko (2005) first show that, due to housing being durable, city growth rates are skewed so that cities grow more quickly than they decline. Thus, negative shocks decrease housing prices more than they reduce population. In addition, workers born in urban areas may be particularly unproductive in agriculture, making it unattractive for them to move to rural areas even when faced with large negative shocks. They may also have strong urban preferences (Jedwab et al., 2017). Furthermore, urban incomes may still be high enough for workers to afford their subsistence requirement, implying they can do so without having to work in agriculture (Lagakos and Waugh, 2013). That should be the case for countries that are already somewhat urbanized, e.g. deindustrializing nations. Finally, migration costs that prevent workers from migrating from rural to urban areas (Lagakos et al., 2020; Gai et al., 2021) may also prevent workers from migrating from urban to rural areas.

In cols. (2)-(4), we examine the correlations in a panel framework with country and

> 19NRXGDP, MFGSERV and DEINDU are not correlated with each other. Countries with high NRXGDP values include Angola, Kuwait, Nigeria, Saudi Arabia, and the Rep ́ublica Bolivariana de Venezuela. Countries with high DEINDU values (conditional on MFGSERV) include Argentina, Brazil, Colombia, the Philippines, and South Africa.

> 20 We include dummies identifying whether the definition is based on a population threshold, another condition, an administrative function, or a combination of these, and the log of the threshold (U.N., 2011).

> 21Since the world’s urbanization depends on the urbanization of countries with large populations, we use as regression weights the total population of each country in 2020.
16 year fixed effects (FE). More precisely, for countries _c_ and years _t_ , we estimate: URB _c,t_ = _α_ + _β_ NRXGDP _c,t−_ 1 + _γ_ MFGSERV _c,t_ + _δ_ DEINDU _c,_ 1980 _−t_ + _κc_ + _θt_ + _Xc,tπ_<sup>_′_</sup> + _μc,t._ (9) We use NRXGDP in the previous period _t_ -1, as we expect rents from resource exports to have a lagged effect on urbanization; it takes time to build cities. MFGSERV is defined in _t_ since the production of MFG and SERV mostly takes place in cities, where they have a contemporaneous effect on the urban share. The deindustrialization variable – DEINDU – measures the absolute decline in the GDP share of manufacturing between 1980 and _t_ transformed so that a positive value indicates _more_ deindustrialization, while 0 is assigned if no decline is observed or if _t ≤_ 1980. Finally, we include the time-varying controls (log population and its square) and standard errors (SEs) are clustered at the country level.

In cols. (2), (3) and (4), we consider data every 20 years, 10 years, and 5 years, respectively. As seen in Panel A, the panel estimates of NRXGDP and MFGSERV are smaller than their corresponding long-difference estimates in col. (1). The point estimates of NRXGDP are lower for shorter time periods in the panel and in all cases, they are smaller in magnitude than the point estimates for MFGSERV. These results are expected given the delayed effect of increases in NRXGDP on urbanization. Lastly, DEINDU has a small positive impact on urbanization. DEINDU countries are countries that saw their MFGSERV share increase in the past, which might still generate urbanization in the shorter run as urbanization begets urbanization. Once we control for the urban share in _t_ -1, DEINDU’s coefficient is close to 0 (0.01 with the 5-year panel; not shown).

**Timing.** With country fixed effects, identification comes from _within-country_ changes in NRXGDP, MFGSERV, and DEINDU. Web Appx. Table D.3 examines how urbanization correlates with the timing of such changes. We use the 10-year panel specification (9) and include leads and lags. The table shows that leads of the variables are small and not significant. In contrast, some lags are significant. For MFGSERV, all lags have significant coefficients. For NRXGDP, the second and third lags have larger and more significant coefficients, suggesting that urban shares increase 20-30 years after resource booms. Urbanization thus seems to follow industrialization/FIRE-ization and resource export booms, not the other way around. No significant correlation is observed for DEINDU.

**Additional results** In Panel B of Table 2, we decompose NRXGDP into the export-toGDP ratio of fuel & mining (FMXGDP) and agricultural products (AGXGDP). The long-
17 difference regression of Col. (1) where we control for initial conditions suggests that urbanization correlates with both. The Beta coefficient is twice as large for FMXGDP than for AGXGDP, likely due to agricultural exports requiring rural labor. In the panel specifications (Cols. (2)-(4)), only FMXGDP survives. Likewise, lags of AGXGDP have positive but insignificant coefficients (Web Appx. Table D.3). Indeed, not all agricultural exports may be associated with urbanization. Some crops require large amounts of labor and cities may develop too slowly for the correlations to appear with panel regressions. Web Appx. Table D.2 then shows stronger correlations for MFG than for SERV, and positive correlations for SERV, and FIRE or non-FIRE services.

The results shown so far are aligned with Propositions 1-4. We further test these propositions in the next section, and in particular the fact that the mechanism of the urbanization process might also influence the employment composition of urban areas.

# **4. Resources, (De-)Industrialization, and Urban Employment**

**Model.** We rely on IPUMS census microdata for 62 sample countries accounting for almost two-thirds of the world’s total urban population, which is high considering that we ignore most developed economies. In total, we obtain 184 census samples during the period 1990-2015. For our cross-sectional country ( _c_ )-level analyses, we then select for each country the closest year to the year 2000 and estimate the following model:

EMP _c_ 00 = _α_ + _β_ MFGSERV _c_ 00+ _γ_ NRXGDP _c_ 60 _−_ 00+ _δ_ DEINDU _c_ 80 _−_ 00+ _ε_ URB _c_ 00+ _Xcκ_ + _μc_ (10) In this equation, EMP is a sector’s labor share in the urban areas of country _c_ c. 2000. Since most censuses are centered around the year 2000, we use information from 2000 for MFGSERV in country _c_ , the mean of NRXGDP in country _c_ in the period from 1960 to 2000, and DEINDU in country _c_ between 1980 and 2000 (a positive value indicates deindustrialization). To compare countries with _similar_ levels of urban economic development, we control for the urban share (URB) in 2000. We can then ignore the initial conditions in 1960 but still add the controls ( _X_ ) for area (2000), population (2000), small islands, and urban definitions. We use country populations in 2000 as weights. Finally, we examine the differences between coefficients _β_ and _γ_ , and then between _β_ and _δ_ .

**Sectors.** IPUMS has information on 16 industries from which we construct 10 sectors. As can be seen in Table 3, for a given urban share, urban areas in industrialized MFGSERV countries have more employment in tradables (MFG, FIRE and SUM = MFG + FIRE) and
18 less employment in non-tradables (NTR...) than the resource-rich NRXGDP countries or the DEINDU countries, which deindustrialized post-1980. NTR is a non-tradable sector corresponding in this analysis to domestic _Wholesale and retail trade_ . NTR2 also includes _Other services_ that sometimes correspond to domestic commerce-related activities not included for some countries in NTR. NTR3 additionally includes _Household services_ .

We find, if anything, more government workers (GOVT) in MFGSERV countries. However, differences are not significant when adding education and health (GOVT2). This result is not since structural were surprising adjustment programs implemented in NRXGDP and DEINDU countries in the 1990s. We see more natural resource (NRX) workers in both NRXGDP and DEINDU countries, but the differences are not significant. Yet, we find significantly more fuel & mining workers in NRXGDP countries (not shown). Finally, we find less construction (CONST) in NRXGDP and DEINDU countries.

**Informality.** Using the same eq. (10), we test whether urban informal employment differs depending on the mechanism of urbanization. Our sample includes 55 sample countries for which we know whether the worker earns a wage, is self-employed, or works without pay. As shown in Cols. (1)-(3) of Table 4, the share of wage workers is lower and the share of self-employed workers higher in both NRXGDP and DEINDU countries (relative to MFGSERV countries). However, due to the sample size, differences are not significant.<sup>22</sup>

We expand on this further by testing whether tradables – proxied by MFG, the largest sector of MFG+FIRE – and non-tradables – proxied by NTR (domestic wholesale and retail trade) – have higher informality rates, as measured by self-employment. As seen in Cols. (4)-(6) and (7)-(9) of Table 4, MFG is more informal in the urban areas of NRXGDP and DEINDU countries than in the urban areas of MFGSERV countries, but these differences are not significant. NTR, however, is significantly more informal in the urban areas of NRXGDP countries than in the urban areas of MFGSERV countries.

**City-level results.** We use the city-level residualized measure of _Production / Consumption City-ness_ (PCC) obtained in Section 1 to examine if the patterns obtained at the countryurban sector level are driven by larger and/or smaller cities. For 6,211 FUAs, indexed with _a_ , belonging to 59 sample countries, indexed with _c_ , we regress PCC c. 2000 on the country-level variables of interest – MFGSERV (2000), NRXGDP (mean 1960-2000) and

22Self-employed workers are almost always self-employed without employees.
19 DEINDU (change 1980-2000), their interactions with the 8 city size categories, _p_ , and the controls _X_ of model (10) (except urbanization, which was used as a control in the first-step residualization procedure). We use the FUAs’ populations in 2000 as weights: PCC _a,c_ = _α_ + _β_ MFGSERV _c_ + _γ_ NRXGDP _c_ + _δ_ DEINDU _c_ +Σ<sup>9</sup> _p_ =2<sup>_ζp_MFGSERV</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_)+</sup> Σ<sup>9</sup> _p_ =2<sup>_θp_NRXGDP</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_) + Σ9</sup> _p_ =2<sup>_λp_DEINDU</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_) +</sup><sup>_Xcκ_+</sup><sup>_μa_</sup> (11) We consider two PCC measures capturing urban tradables – MFG+FIRE – and urban non-tradables – NTR2, which includes _domestic wholesale & retail trade_ and _other services_ . We plot the obtained correlations for each PCC measure-size category in Figures 4(a)4(b). Figure 4(a) shows that MFGSERV countries have higher shares of MFG+FIRE employment in their cities than NRXGDP and DEINDU countries for _all_ population size categories of cities. The patterns for the employment share of non-tradables, NTR2, are different. MFGSERV countries have the lowest shares of NTR2 across all employment city sizes (Figure 4(b)). By contrast, DEINDUSTR countries have the highest employment shares of NTR2 across _all_ city sizes. In NRXGDP countries, the employment share of . NTR2 increases with city size and is largest in the case of the _largest cities_ This analysis suggests that the whole urban system in the deindustrializing countries has experienced a shift from jobs in manufacturing to jobs in nontradable services, while in resource-rich countries only larger cities have substantially higher shares of nontradable services. In both cases, larger cities have much higher shares of urban nontradables than smaller cities, but for all city sizes these shares are much larger in the deindustrializing countries than in the resource-rich countries.

**Robustness: Panel.** We obtain the employment shares for 184 country-years and 62 countries from IPUMS and run panel regressions with country and year fixed effects (FEs). We regress the selected sectoral labor share in urban areas in time period _t_ on MFGSERV in _t_ , NRXGDP in _t_ -10, DEINDU between 1980 and _t_ (= 0 before 1980), the urban share in _t_ , and log population in _t_ and its square. We include countries with at least three years of data and cluster the standard errors at the country level. Since the sample size reduces to 124 observations, we cannot include more lags.

Most panel results are consistent with the long-difference results. We find significantly lower shares of employment in MFG and MFG+FIRE in the urban areas of DEINDU countries relative to MFGSERV countries (Web Appx. Table D.4). While these shares
20 are also lower in the NRXGDP countries, the difference with MFGSERV countries is not significant. Thus, any significant impact of resource exports on the employment composition in urban areas may take place over more than one decade. Next, the shares of non-tradables (e.g., NTR3) in the urban areas of NRXGDP and DEINDU countries are higher than those in MFGSERV countries. Informality is also higher in the urban areas of NRXGDP countries than in MFGSERV countries, but the difference is not significant.

**Robustness: I2D2.** Another data source for employment information by country is the _International Income Distribution Database_ (I2D2) of the World Bank. The full database consists of about 1,500 individual-level household and labor force surveys for more than 150 countries. Detailed information on sectoral and can employment self-employment be found in about 1,000 survey samples for almost 100 countries. The data was initially compiled by the World Bank’s _World Development Report_ (WDR) unit between 2005 and 2011. The database has expanded since then allowing us to include more countries than IPUMS. We use its December 2017 vintage to replicate the IPUMS-based results with the expanded country sample. The surveys are nationally representative and sufficiently large for our purpose (i.e., they have more than 10,000 observations).<sup>23</sup>

We obtain for each country the _mean_ labor shares of various sectors and types of employment in urban areas in 1990-2015.<sup>24</sup> We then use the same cross-sectional model as for IPUMS (eq. (10)). Since 2005 is the mean population-weighted year in I2D2, the variables of interest are defined relative to 2000 (as in Panel A of Web Appx. Table D.5) or 2010 (as in Panel B of the same table). While we observe a strong correlation (about 0.9) between I2D2 and IPUMS for MFGFIRE, the correlation is weaker (0.55) for urban non-tradables NTR, which has to do with the way I2D2 was harmonized.<sup>25</sup> NRXGDP and DEINDU countries have lower employment shares of MFG in their urban areas than MFGSERV countries (Web Appx. Table D.5). For MFG+FIRE, the differences are mostly not significant. No correlations are observed for FIRE and NTRI (the version of “NTR” in I2D2). However, DEINDU countries have significantly higher

> 23See for example Jedwab et al. (2020a, 2022) for more details.

> 24Most countries had several surveys during the period. Since the surveys vary in sample size across time periods, we use as weights the size of each survey, thus giving more weight to better measured years.

> 25During the harmonization process, sectors had to be aggregated together due to the different classifications in different surveys. _Wholesale & retail trade_ and _Hotels & restaurants_ were merged together by the I2D2 team, while we ignored the latter in IPUMS due to the lack of correlations with our variables of interest. “NTR” in I2D2 (NTRI) might also include other sectors depending on the country.
21 shares of NTRI2, which includes NTRI as well as household services and personal services (using I2D2’s classification). Finally, the results indicate significantly higher self/unpaid employment in DEINDU countries compared to MFGSERV countries. While I2D2 allows us to consider more countries, the database consists of surveys, not censuses, and classical measurement error in the dependent variable should affect precision.

Notwithstanding, relying on various measures, specifications, and databases, the evidence presented here suggests that cities in NRXGDP and DEINDU countries may be disproportionately consumption cities relative to the cities in MFGSERV countries. **5. Mechanisms: Urban Construction and Consumption Cities** We now explore some of the mechanisms behind the rise of consumption cities. In particular, cross-country differences in urban construction may stem from “white elephant” projects financed by resource rents. In the absence of a global historical data base on “white elephants”, we examine whether exports of natural resources correlate with the over-construction of tall whose economic rationale be very buildings, might questionable as discussed below. As shown by Ahlfeldt and Barr (2020), construction costs increase with tall are also more as non-linearly height. Very buildings inefficient they require more occupiable square feet to be dedicated to essential building functions (e.g., due to elevators).

We use a novel data set that inventories all the world’s “tall buildings”, with information on their of construction and The database is maintained year height.<sup>26</sup> by the _Council on Tall Buildings and Urban Habitat_ (CTBUH) and is publicly available online.<sup>27</sup> The database mostly captures buildings above 80 meters (Jedwab et al., 2020b). Since some countries have no such buildings, to avoid having their stock of heights equal to 0 when using logs, we consider for each country buildings above 80 meters as well as their 10 tallest buildings, even if some of them are below 80 meters. In the end, we use 16,369 tall buildings.<sup>28</sup> We use as our dependent variable log _urban height density_ , which is the

> 26To our knowledge, the only other econometric study on white elephant projects and tall building construction is a non-economics study on autocracies and skyscrapers by Gjerlow and Knutsen (2019). The study finds that autocratic regimes are more likely to build super-tall and possibly “vanitous” buildings.

> 27The full online database can be found here: http://www.skyscrapercenter.com/. As one example, the webpage for the Burj Khalifa in Dubai is: http://www.skyscrapercenter.com/building/burj-khalifa. According to their website, the data have been “collected by the Council for more than 40 years [...] The Council relies on its extensive member network [of academics, land developers, architectural firms, builders, city administrations, and banks] to maintain” the database with the help of “an Editorial Board”.

> 28As discussed in Jedwab et al. (2020b, 2021c), measurement error is more likely for smaller buildings
22 sum of tall building heights + 1 divided by urban population.<sup>29</sup> As previously shown by Jedwab et al. (2020b, 2021c), there is a strong relationship between log urban height density and log per capita GDP (PPP; see Figure 5). Next, we want to examine whether resource-rich countries have more tall than buildings equally developed non-resource-rich countries. And if they have more tall buildings, are these more likely to be “vanitous” in ways that are quantifiable in the data?

**Cross-sectional model.** We first examine the long-difference correlations between log urban height density (LUHT) c. 2020 in country _c_ , and the three mechanisms of urbanization: MFGSERV (2020), NRXGDP (mean in 1960-2020) and DEINDU (change in 1980-2020; positive values imply deindustrialization):

LUHT _c,_ 20 = _α_ + _β_ MFGSERV _c,_ 20 + _γ_ NRXGDP _c,_ 60 _−_ 20 + _δ_ DEINDU _c,_ 80 _−_ 20 + _Xcκ_ + _μc._ (12) In the above equation, X _c_ includes the controls for initial conditions (including log urban height density in 1960, which makes the regression a long-difference regression) and the controls for area and population in 2020, small islands and urban definitions.

Col. (1) of Panel A of Table 5 shows a strong correlation between tall building construction per capita and MFGSERV. The point estimate for NRXGDP is not negligible either, but it is not significant. In cols. (2) and (3), we consider residential and nonresidential buildings only, with the latter including office towers ((4)), hotels ((5)) and retail towers ((6)). Col. (7) shows the correlations for government buildings. We observe positive and significant correlations for most types of buildings and MFGSERV. We also see positive and significant correlations for residential and non-residential (office) towers and NRXGDP. Thus, MFGSERV or NRXGDP countries urbanize as they develop (Table 2) and build _more_ and _taller_ buildings to accommodate urban growth.

In Panel B of Table 5, we aim to capture “excesses” in tall building construction per capita for a _given_ level of urban economic development. We therefore add controls for urban economic development. These include the urban share and log per capita GDP (PPP; constant 2005 international dollars) in 2020 and the mean log per capita GDP in 1960-2020 (since past economic development also matters and since GDP can fluctuate).

> than tall buildings above 80 m. Classical measurement error in tall buildings then only affects precision.

> 29The sum of heights is above 0 for almost all sample countries in 2020. For the full sample covering the period from 1960 to 2020, it is equal to 0 for half of the observations. It is 0 for most countries in 1960. Indeed, the U.S. contained almost all of the world’s tall buildings up to the 1980s (Jedwab et al., 2020b). Thus, few of the sample countries (with a developing status as of 1960) had tall buildings before the 1980s.
23 The results suggest that NRXGDP countries have higher stocks of non-residential towers than MFGSERV countries ((3)), with this correlation being driven by office ((4)) and retail towers ((6), not significant though). No differences are observed for government towers.<sup>30</sup> Panel C examines the same correlations in a 10-year panel framework. The dependent variable is log urban height density in _t_ and we include three extra lags of MFGSERV, NRXGDP and DEINDU.<sup>31</sup> We control for the urban share, log per capita GDP and log population in _t_ . Lastly, the panels show the total correlations estimated when summing the across the The correlations are not as as in the up coefficients lags. strong longdifference specification (panel B). Indeed, construction does not respond instantaneously to economic changes. However, by adding lags, we lose observations and degrees of freedom, making our standard errors larger. Abstracting from the lack of significance, one can see that the overall correlation for NRXGDP is always higher than for MFGSERV.

We also use data on the characteristics of the buildings to explore their “vanity”. In Panel A of Table 6, we use the long-difference specification, control for urban economic development, and consider the dependent variable to be the log urban height density constructed using only non-residential buildings above a certain threshold. In our data, 125, 140, 160, 200 and 240 meters correspond to the median, the mean, the 75th percentile, the 90th percentile and the 95th percentile value in total height. The results indicate that NRXGDP countries have more _very tall_ buildings, and the difference with respect to MFGSERV countries tends to be largest and significant for the tallest buildings in the data set. When studying the same relationships in our 10-year panel framework, considering four lags, and summing up the coefficients across the lags (Panel B), the point estimates are broadly consistent with the long-difference results for NRXGDP countries.

We perform additional tests in Panel C. We only consider the long-difference specification and include the urban economic development controls. First, in col. (1), the dependent variable is an index of _construction vanity_ , more precisely the sum of the differences (in meters) between height at the tip and the height of the top occupied floor. Buildings constructed to project power are more likely to include space at the top. However, the height of the top occupied floor is only available for enough buildings in 38

> 30These results suggest that private companies or individuals directly or indirectly affiliated with the government and/or fuel, mining or agricultural firms might be using such buildings.

> 31The lag structure is as follows: MFGSERV _t_ , _t_ -10, _t_ -20, _t_ -30; NRXGDP _t_ -10, _t_ -20, _t_ -30, _t_ -40; DEINDU _t_ , _t_ -10, _t_ -20, _t_ -30 (for DEINDU, all lags are defined relative to 1980).
24 countries. Nonetheless, we find stronger correlations for NRXGDP than for MFGSERV.

We also know the main structural material used to construct the tall buildings in the data set. Steel is more durable, resistant and safe than concrete but also more expensive. Cols. (2)-(3) show the results with the dependent variable being log urban height density for buildings whose structural material is steel vs. concrete, respectively. Interestingly, we find strong correlations between steel use and MFGSERV (but steel could be cheaper in MFGSERV countries) and between concrete use and NRXGDP.

Third, we examine if the correlations are stronger for larger cities. In col. (4), the dependent variable is log height density considering only buildings in the capital city. We find a stronger correlation with NRXGDP than MFGSERV, although the differences with respect to MFGSERV is not significant. If we consider both the capital city and the largest city, we find a weaker correlation ((5)). For other cities, we also find a possible gap, indicating higher height density in NRXGDP than MFGSERV countries ((6)).

The excess of tall buildings in NRXGDP countries may be accompanied by lower occupancy rates. Since we do not have information on occupancy rates, we instead examine how the overall construction sector is potentially affected. For 80 countries, we know the annual cement production from 1970 to date.<sup>32</sup> Cement – the main ingredient of concrete – is typically not traded (World Cement, 2013). Using UN COMTRADE data we find that the world trade of cement (tons) only accounted for 3.5% of world cement production (tons) during the period of study, and for just 2.5% today. For this reason, cement production should be a very good proxy for cement use.

We on the same model as for tall but use as our rely long-difference buildings dependent variable the log sum of cement production over the period 1970-2020 while controlling for log urban population in both 1970 and 2020, thus capturing _cement consumption per urban capita_ . The results in Cols. (1)-(2) of Table 7 show that cement production is positively correlated with MFGSERV and NRXGDP (significant for MFGSERV), consistent with the tall construction results. However, once we control for urban economic development, no differences are observed.

If we use the COMTRADE data to reconstruct cement consumption, we find that the log sums of cement consumption and production over the period 1970-2020 are highly correlated (corr. = 0.98). Likewise, using the log sum of cement consumption produces the

> 32We obtained the data from the _Minerals Yearbooks_ of the U.S. Geological Survey (USGS).
25 same results as when relying on production ((3)-(4)). Lastly, for both cement production and consumption, if we use 10-year panel regressions with several lags of the variables of interest, we do not find significant differences between the types of countries.<sup>33</sup> Therefore, although NRXGDP countries have more tall buildings, in particular concrete towers, they do not use more cement, which might imply eviction effects within the construction sector. Of course, with economic development and rising land values, tall buildings replace short structures (Jedwab et al., 2020b). However, we here compare NRXGDP and MFGSERV countries with _similar_ levels of urban economic development, thus focusing on “excesses” that might lead to “gaps” in the rest of the urban sector.

To study urban construction more generally, we turn to information on the value added of the construction sector (in PPP; constant 2005 international dollars) from 1970 to date.<sup>34</sup> We use the long-difference model and consider as the dependent variable the log sum of construction GDP over the period 1970-2020 while simultaneously controlling for log urban population in both 1970 and 2020, thus capturing _construction GDP per urban capita_ . Again, we observe no significant difference ((5)-(6)). If we use 10-year panel regressions with several lags of the variables of interest, we also do not find significant differences between the types of countries (not shown). However, one caveat with these analyses is that both cement use and construction include infrastructure.

Finally, if residential construction is indeed crowded out in NRXGDP countries, one might see higher slum shares there. However, to project prestige NRXGDP may more willingly adopt slum clearance policies. Data on slum shares are only available for the most recent years (United Nations, 2020b). If we use the cross-sectional version of the long-difference model (thus not controlling for slums c. 1960), we find smaller slum shares in MFGSERV and NRXGDP countries when not controlling for urban economic development ((7)). Consistent with the results on tall building construction, when MFGSERV or NRXGDP countries develop and urbanize, whether due to industrialization broadly defined or resource windfalls, their urban sector also develops, which leads to some forms of formalization. But if we control for urban economic development ((8)), we find no correlations between the slum share and the variables of interest. Therefore, the slum share is not higher in NRXGDP countries. As a result, if any crowding takes places

> 33These results are not shown, but are available upon request.

> 34From (United Nations, 2020c) we know the GDP share of construction over time. We then obtain GDP using data from Bolt and van Zanden (2014), Gollin et al. (2016) and World Bank (2021).
26 in such countries due to the consumption of resources by the tall sector, the _non-tall nonslum_ sector must be disproportionately affected. However, these results should be taken with caution given the lack of global data on supply and crowding in this sector.

# **6. Mechanisms: Urban-Biased Policies and Consumption Cities**

Is the “rise of consumption cities” in some countries driven by their capital/largest city? In resource-rich countries, there is evidence that governments disproportionately invest resources in their capital/largest city (Bates, 1981; World Bank, 2020). Yet, industrialization/FIRE-ization may also lead to the disproportionate growth of a country’s largest city if it requires resources found there (e.g., skilled labor or an international airport).<sup>35</sup> In addition, resource exports cause the growth of small- and medium-sized mining towns or agro-towns (Jedwab, 2013), thus decreasing urban primacy. Therefore, _a priori_ it is unclear whether urban primacy is more prevalent among MFGSERV or NRXGDP countries.

**Cross-Sectional Regressions: Country-Level.** We use model (8) to study the correlations between the primacy rate – the share of the country’s urban population that lives in the largest city (source: World Bank (2021)) – in 2020 and the variables of interest: MFGSERV (2020), NRXGDP (mean 1960-2020) and DEINDU (change 1980-2020). We control for initial conditions, i.e. primacy, MFGSERV, and NRXGDP in 1960, thus capturing _longdifference_ correlations. We control for the urban share in 2020, add the controls for area, population, small islands and urban definitions, and use populations in 2020 as weights.

As seen in Col. (1) of Table 8, only slightly higher primacy rates are observed in NRXGDP and DEINDU countries relative to MFGSERV countries. The differences are not significant.<sup>36</sup> In Col. (2), we decompose NRXGDP into the export-to-GDP ratios of fuel & mining (FMXGDP) and agriculture (AGXGDP). The results do not show particularly high primacy rates in FMXGDP countries, but they show less primacy in AGXGDP countries. Yet, the high standard errors suggest that it is not the case for all agricultural exports.

**Panel Regressions: Country-Level.** We use the same 10-year panel regression as before

> 35In the longer run, as the cost of space increases and more stringent environmental regulations are adopted in larger cities, industrial activities move to small and medium-sized cities, thus reducing primacy.

> 36While some resource-rich countries such as Angola, Cˆote d’Ivoire, and Malaysia have high urban primacy rates (30%-40%), resource-poor countries such as Bangladesh, Japan and Thailand have similarly high rates. Argentina, the Arab Republic of Egypt and Peru – countries with intermediary levels of resource richness – also have high rates.
27 (eq. (9)) but now the dependent variable is the primacy rate in year _t_ (116 x 7 = 812 obs.). The variables of interest are again MFGSERV ( _t_ ), DEINDU (change 1980- _t_ ), and NRXGDP ( _t_ -1) (or FMXGDP and AGXGDP). In Cols. (3)-(6), we consider either three lags or four lags of the variables of interest (i.e., two or three extra lags in addition to the contemporaneous lag) and show the overall correlations across the various lags. We control for the urban share in _t_ , include controls for populations in _t_ , and cluster standard errors at the country level. Again, we find no significant correlation.

**City-Level Results.** We use the Functional Urban Area (FUA)-level data of the GHSL database to study whether the _largest cities_ , not just the _largest city_ , grow differently than other cities depending on the country’s “type”. We have population estimates for 7,422 FUAs c. 1975, 1990, 2000 and 2015 in 115 sample countries. We regress the log growth of their population between 1975 and 2015 – log (pop. + 1) 2015 - log (pop. + 1) 1975 – on the (country-level) variables of interest and their interactions with a dummy for whether the FUA is the capital/largest city (2015; we call this dummy “top 1”). We also consider the capital city and the two (top 2) or five (top 5) largest cities, or the capital city only (top 0) as well as add the controls of the long-difference regressions at the country level.

As seen in Web Appx. Table D.6, there are no significant correlations. However, point estimates suggest that the top cities in countries specialized in agricultural exports indeed grow slower relative top cities in industrialized countries. Yet, differences are not significant. We also do not find any significant correlation in a 10-year panel framework with one or two extra lags included (see Web Appx. Table D.7).

The results presented in this section suggest that even if some resources might be associated with urban primacy in some countries, it is not the case in general. While we do not discuss this above, we also find no correlation with deindustrialization. Thus, in resource-rich and deindustrializing countries, consumption cities are found along the urban hierarchy and, as shown in Fig. 4(a), the shares of urban tradables in their cities are lower than those in MFGSERV countries for _all_ population size categories of cities.

# **7. Concluding Discussion: Consumption Cities & Urban Human Capital**

Using census data for a large number of cities and countries, we established the following stylized fact: While some cities have high employment shares of urban tradables and can be characterized as _production cities_ , a relatively high number of cities have high shares of urban non-tradables and can be characterized as _consumption cities_ . This appears to be
28 the case not only for small cities, but also for many large cities, which are often seen as “engines of growth” in developing countries (World Bank, 1999, 2009).

Furthermore, we can use our city-level measures of _production/consumption city-ness_ (PCC; Section 1.) as well as population data for each city to obtain the populationweighted average level of PCC for each country c. 2000 (see Panel A of Web Appx. Table D.8 for the full list). While some developing economies have high aggregated PCC values, for example China, Costa Rica, Lesotho, Mauritius and Vietnam, other developing economies have low aggregated PCC values, especially Colombia, Indonesia, Nigeria, Tanzania and the Rep ́ublica Bolivariana de Venezuela. Some countries such as Brazil, India, Malaysia, Mexico and Turkey have both production cities and consumption cities, as indicated by a (city population-weighted) Gini index of PCC calculated for each country (Panel B for the full list).<sup>37</sup> Either their urban system is chronically “dualistic” or it is transitioning, whether away from production cities as in Latin America or towards them as in Asia (Section 1.).

**Urban Human Capital.** These facts pose the question of whether having an urban hierarchy dominated by consumption cities might have economic implications for a country, “beyond” the economic structure of its cities. If consumption cities are not as growth-enhancing as production cities, are they doomed to remain so or is it possible that they evolve into production cities in the future? An important factor that can shed light on these questions is the stock and quality of _urban human capital_ . Indeed, cities with a bigger stock of high-quality human capital may be better able to adapt to future economic conditions and grow regardless of their current economic structure.

We assess how urban human capital correlates with our structural variables of interest by obtaining from IPUMS the average number of years of education of adults (ages 25 to 65) living in the urban areas of 53 out of the 116 sample countries for the year closest to 2000. We use the same cross-sectional regression as in our study of sectoral or informal employment (eq. (10)) and include MFGSERV (2000), NRXGDP (average in 1960-2020) and DEINDU (change in 1980-2000), as well as the urban share (2000). The results, shown in col. (1) of Panel A in Table 9, suggest that NRXGDP and DEINDU countries might have

> 37For each country one by one, and since we know the PCC and population of each city c. 2000, we can estimate the degree of statistical dispersion of the PCC measure – i.e., the Gini index – within the country.
29 _more_ educated urban residents. However, the difference is not significant.<sup>38</sup> Angrist et al. (2019) provide a measure of education quality that is built on globally harmonized test scores, obtained for all areas of a country, for 101 countries (c. 2020).<sup>39</sup> If we use it as a proxy for urban education (human capital) quality, we find significantly lower values in NRXGDP countries ((2)). However, if we use the learning adjusted years of education,<sup>40</sup> we again find no significant differences at the country level ((3)).

Another imperfect proxy for education quality is a country’s return to education. Abstracting from human capital demand factors, a lower education quality should reduce the wage gains from higher educational attainment. We use the I2D2 data (1990-2017) and follow the methodology of Lagakos et al. (2018) and Jedwab et al. (2020a) to estimate the returns to education for urban areas, as well as the returns to experience for urban areas.<sup>41</sup> More precisely, for individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one (for 18-67 year-old “urban” workers only): The dependent variable is the log of monthly earnings ( _lnWit_ ). As in Lagakos et al. (2018), experience is categorized into seven bins ( _expite_ ): [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We include the number of years of education ( _eduit_ ) and country-year-sample fixed effects ( _θt_ ).<sup>42</sup> Finally, we omit samples without at least 10 observations in each bin.

We use _γ_ as a measure of the returns to education. To estimate the returns to work experience we follow Jedwab et al. (2020a). For each bin one by one (5, 10, etc.), we

> 38Likewise, if we use city-level data as we did in Section 1., we only find very weak correlations between the PCC residuals estimated using tradable employment and the residuals based on the number of years of education. These results are not shown but are available upon request. Thus, urban tradable employment and urban human capital seem _unrelated_ for a given city size and the level of urban economic development.

> 39From the notes of the Excel database containing the data, we learn that the “database harmonizes scores across major international student achievement testing programs measured in TIMMS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment.” Url for the database: https: //www.worldbank.org/en/publication/human-capital$ _\_ sharp$Data, last accessed 03-29-2022.

> 40From the notes of the Excel database containing the data (same Url as above), we learn that the “Learning-Adjusted Years of School are calculated by multiplying the [...] years of school by the ratio of most recent harmonized test scores to 625, where 625 corresponds to advanced attainment [...].”

> 41We calculate _potential work experience_ – which we call “experience” in the rest of the analysis – as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and calculate experience as age - years of education - 6; (ii) For individuals with less than 12 years of education, we assume that experience before age 18 is inconsequential and calculate experience as age - 18.

42Most countries having several samples, we use individual weights divided by the size of the sample.
30 estimate an annualized return. We then take the mean of the annualized returns across the seven bins to obtain the _mean annualized return_ throughout the experience distribution (remember that we use any available survey data during the 1990-2017 period).<sup>43</sup> The results in Col. (4) of Panel A, Table 9 show that urban returns to education might be lower in NRXGDP and DEINDU countries, however not significantly so. If we use the product of the years of schooling and the returns, to obtain an imperfect measure of the _value_ of education, we see a substantially lower value of schooling in DEINDU countries, but not in NRXGDP countries (col. (5)).

In Panel B and the first column of Panel C of Table 9, we investigate the same correlations for the urban returns to education or their interactions with the urban number of years of education when also including country-year-sample-location fixed effects in eq. (13). Locations correspond to macro regional areas (“Reg 01”), first level administrative areas (“Reg 02”), or primary sampling units, hence survey clusters (“PSU”).<sup>44</sup> Doing so allows us to compare individuals residing in the same regional or local urban labor market. While the number of available country estimates decreases, we tend to find lower returns to education in NRXGDP and DEINDU countries, and no significant differences for the interactions with the number of years (not shown). Finally, we tend to find lower returns to experience in NRXGDP and DEINDU countries, but not all the estimated differences are significant (see the last 4 columns, Panel C).

We then use data from IPUMS to obtain the urban number of of education for years adults (ages 25 to 65) in 54 countries. We end up with 151 country-years. Since we select countries with at least two years of data, the sample size decreases to 104 observations and therefore we cannot include lags. We run panel regressions with country and year FEs, regressing the education variable in _t_ on MFGSERV in _t_ , NRXGDP in _t_ -10, DEINDU between 1980 and _t_ (= 0 before 1980), the urban share in _t_ , and log population in _t_ and its square. We again do not find significantly different correlations for MFGFIRE, NRXGDP, and DEINDU. The results are similar for younger cohorts (ages 25 to 45).<sup>45</sup>

> 43 More precisely, for individuals belonging to bin _e_ , we obtain the _bin-specific annualized return_ as _rete_ = (( _βe_ +1)<sup>(1</sup><sup>_/e_)</sup> _−_ 1) _∗_ 100, with _βe_ being the estimated coefficient for bin _e_ in eq. (13). For this subgroup of individuals, it tells us by how many percentage points wages increased on average for each extra year of experience. We then take the average of these seven bin-specific annualized returns so that each bin is _equally_ represented. Therefore, the overall return _re_ is equal to (<sup>�7</sup> _e_ =1<sup>_rete_)</sup><sup>_÷_7.</sup>

> 44For example, “Reg 01” and “Reg 02” correspond to regions and states in the U.S., respectively. “PSUs” mostly likely correspond to neighborhoods or census blocks in the urban areas. 45All these results are available upon request.
31

**Wage Gaps.** Overall, it does not appear that consumption cities have significantly less human capital than production cities. In other words, for a given level of human capital, the workers of consumption cities are more likely to work in urban non-tradables (and informal employment). Now, inspired by Gollin et al. (2014), we use I2D2 to investigate the productivity gaps between urban tradables (UT) and urban non-tradables (UNT). For individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one, restricting the sample to 18-67 year-old urban workers belonging either to the UT sector or the UNT sector:

where the dependent variable is the log of monthly earnings ( _lnWit_ ) and _Tit_ is a dummy equal to one if the worker belongs to the UT sector. _δ_ measures the wage gap between the UT and UNT sectors. We first examine the gaps estimated without including controls for human capital ( _X_ ); then we re-estimate the gaps after adding human capital as a control and controlling for sorting. If we find large (conditional) wage gaps, that would suggest that the UT sector may provide “better” jobs than the UNT sector.

Focusing on the sample countries (N = data available for 54 countries in I2D2), we find an unconditional wage gap of about 22%.<sup>46</sup> If we control for gender, years of education, and the experience dummies, the gap reduces to 13%.<sup>47</sup> If we additionally control for (spatial) sorting by including first-level administrative area or primary sampling units FEs, we get 15-17% (N = 28-41). In other words, we find significant gaps even when we compare workers with similar human capital levels and workers residing in the same urban regional or local labor market. Of course, these results should be taken with caution since there may still be unobservable factors that we cannot control for.<sup>48</sup>

**Growth.** The higher wages for jobs in UT relative to UNT imply that workers in UT are more productive than workers in UNT. If most urban areas in a country are consumption cities, this might have negative implications for aggregate labor productivity in the

> 46Indeed, only 54 countries have large enough surveys with earnings data and detailed information on the respondent’s type of residence and subsector, allowing us to distinguish UT and UNT workers.

> 47Like Gollin et al. (2014), if we use national accounts data (source: United Nations (2020c); ISIC Rev. 3) and sectoral employment data (ILO (2022); ISIC Rev. 3.1) to estimate GDP per worker, we find for the sample countries much higher gaps around 120%-205%, depending on which sectors are included in UNT.

> 48If we use I2D2 data for the world instead, in order to increase sample size (thus adding developed economies), we find an unconditional gap of 24% (N = 91) and 16%-18% for the conditional gaps (N = 34-73).
32 country. But what is the implication of having consumption cities for growth?

Comparing the returns to experience for the UT and UNT sectors might be one way to answer it. Indeed, if the returns to experience capture human accumulation at work (Lagakos et al., 2018; Jedwab et al., 2020a), and if there is more human capital accumulation at work in the UT sector, we should observe higher returns in UT than in UNT. However, we do not find that this is the case.<sup>49</sup> Yet, in some specifications we found significantly lower urban returns to experience in the aggregate in NRXGDP and DEINDU countries, which may be due to more general factors in consumption cities.<sup>50</sup> Another way to answer the question about the growth implications of consumption cities, especially large ones, would be to globally estimate separately the agglomeration economies (AEs) for the two sectors, UT and UNT. We cannot estimate AEs by sector using the I2D2 because we cannot identify cities in the database (only or the number of the cluster are Since this regions survey given). paper identifies production/consumption city-ness (PCC) for many urban areas in a large number of countries, we could obtain from the literature and compare the estimates of AEs for countries whose cities are mostly consumption cities and the AEs of countries whose cities are mostly production cities. One can draw on many such urban studies, which consider one at a time and for a of cities or typically country sample regress log wages log productivity on the log of population, population density or employment. Chauvin et al. (2017) estimated the AEs for China – a country which our analysis shows has mostly production cities – at more than 16%, for India and the United States – two countries that we show have a mix of production, consumption and neutral cities – at 8% and 5%, respectively, and for Brazil – a country with mostly neutral and consumption cities – at 3%. The fact that these estimated AEs decrease as consumption cities become more dominant in a country’s urban hierarchy suggests that urban economic growth might also weaken.

Other studies corroborate the potential negative growth implications of countries’ having an urban hierarchy dominated by consumption cities. Venables (2017) show

> 49If we estimate eq. (13) separately for UT and UNT and focus on the sample countries for the period 1990-2017, we find similar returns at around 2% (N = 59-67). This is also the case if we consider the whole world including developed economies for which the returns might be better estimated.

> 50 Human capital could be accumulated at work _passively_ or _actively_ . While Jedwab et al. (2020a) think of human capital accumulation at work as being a primarily passive process, Ma et al. (2021) show that the process can be active in developed economies. Note that our sample focuses on developing economies.
33 theoretically that the supply-side benefits of co-location should be small for UNT firms. Likewise, Glaeser and Resseger (2010) find for the United States that AEs are strong in cities with higher levels of skill and nonexistent in less skilled metropolitan areas. Lastly, Burger et al. (2022, Panel H of Table 2) use firm-level data for 649 metro areas to estimate AEs for 98 economies. labor on urban developing They regress log productivity log density, finding an average AE of 10%-15%. They then interact density with dummies proxying for whether the firm belongs to the tradable sector, finding AEs that are twice higher than in the non-tradable sector. Congestion forces might also be stronger for the UNT sector than for the UT sector.<sup>51</sup> Finally, as shown by the trade literature, international trade affects endogenous innovation (Melitz and Redding, 2021). As such, having higher shares of firms and workers in UT rather than UNT may also promote growth.

Just like production cities can evolve into consumption cities through deindustrialization, consumption cities could still evolve into production cities in the future. We showed that they do not necessarily lack human capital, but other constraints that we do not observe constrain urban such constraints include may productivity growth; institutions, control of corruption, and investment climate, among others. There are many examples of successful transitions. The United Arab Emirates’ largest cities – Dubai and Abu Dhabi – have uncontestably become production cities by expanding FIRE. Likewise, the growth of Malaysia’s manufacturing reshaped the economic structure of the Kuala Lumpur metro area. However, since understanding what distinguishes dynamic consumption cities, which evolve into production cities, from stagnant ones is beyond the scope of this paper, we leave this question for future research.<sup>52</sup>

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51The UT sector (MFG & FIRE) is more formal than the UNT sector (verified using both IPUMS and I2D2 data; not shown). As such, it may have smaller congestion externalities on other sectors and the city as a whole. Akbar et al. (2018) find that the roads of Indian cities are particularly congested in the middle of the day when few workers are commuting. Indeed, the (more informal) wholesale and retail trade sector (incl. roadside vendors) often causes traffic congestion. Burger et al. (2022, Table 4) find that the AEs of UT firms are reduced to a lower degree by congestion than the AEs of UNT firms, possibly because UNT firms are more dependent on local consumer markets, while UT firms rely less on local markets and employ workers who are better able to locate close to their workplaces and/or afford faster transport services.

> 52Nonetheless, Venables (2017) shows theoretically that a city is unlikely to produce UTs if it faces high urban and hinterland demand for UNTs. That is especially the case with increasing returns in tradable production that make cities specialized in UNT globally uncompetitive for the production of UTs.
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41 Table 2: Resources, Industrialization, Deindustrialization & Urbanization, 1960-2020

|**Specifcation:**|**Long-Diff.**|**Panel Anal**|**ysis (Country**|**FE & Year FE)**|
|---|---|---|---|---|
|Dep. Var. : Urban Share URB (%) in ...|2020|Year_t_|Year_t_|Year_t_|
|Timing for the Panel: Every ...||20 Years|10 years|5 years|
|**Panel A:**Baseline|(1)|(2)|(3)|(4)|
|NRXGDP (%) (1): 2020; (2)-(4):_t_-1|1.02***|0.28**|0.19**|0.14*|
||[0.239]|[0.106]|[0.089]|[0.072]|
|MFGSERV (%) (1): 2020; (2)-(4):_t_|1.09***|0.44**|0.43***|0.38***|
||[0.195]|[0.179]|[0.141]|[0.121]|
|DEINDU (%) (1): 1980-2020; (2)-(4): 1980-_t_|0.04|0.53|0.42|0.41*|
||[0.343]|[0.402]|[0.266]|[0.238]|
|Beta Coef. NRXGDP|0.49|0.16|0.10|0.07|
|Beta Coef. MFGSERV|0.57|0.26|0.26|0.24|
|Beta Coef. DEINDU|0.01|0.10|0.07|0.07|
|**Panel B:**Decomposing NRXGDP|(1)|(2)|(3)|(4)|
|FMXGDP (%) (1): 2020; (2)-(4):_t_-1|0.91***|0.29***|0.22**|0.17**|
||[0.235]|[0.101]|[0.090]|[0.076]|
|AGXGDP (%) (1): 2020; (2)-(4):_t_-1|1.22**|0.20|0.02|-0.03|
||[0.610]|[0.254]|[0.175]|[0.070]|
|Beta Coef. FMXGDP|0.40|0.15|0.11|0.08|
|Beta Coef. AGXGDP|0.22|0.05|0.00|0.00|
|Obs.; Controls; Country FE, Year FE|115; Y; N|347; Y; Y|693; Y; Y|1,387; Y; Y|

_Notes:_ Robust SE (clust. at the country level in cols. (2)-(4)) in parentheses. The six variables have the following summary statistics in col. (1): URB: mean = 52.1; SD = 19.0; min = 13.3; max = 100.0; NRXGDP: mean = 7.9; SD = 9.0; min = 0.4; max = 63.9; MFGSERV: mean = 70.5; SD = 9.9; min = 34.2; max = 93.6; DEINDU: mean = 4.6; SD = 5.0; min = 0.0; max = 21.3; FMXGDP: mean = 4.4; SD = 7.5; min = 0.0; max = 59.8; and AGXGDP: mean = 2.2; SD = 2.7; min = 0.0; max = 37.1. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
42 Table 3: Resources, (De-)Industrialization & Urban Employment, Cross-Section, c. 2000

|Dep.Var. =|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|**Empl. Sh. of:**|MFG|FIRE|SUM|NTR|NTR2|NTR3|GOVT|GOVT2|NRX|CONST|
|MFGSERV|0.18**|0.10**|0.28**|-0.21*|-0.29*|-0.23|-0.02|-0.01|0.05|-0.02|
||[0.09]|[0.05]|[0.11]|[0.12]|[0.14]|[0.15]|[0.03]|[0.06]|[0.10]|[0.04]|
|NRXGDP|-0.02|0.03|0.01|0.09|0.16|0.22|-0.13**|-0.09|0.11|-0.17**|
||[0.11]|[0.07]|[0.15]|[0.15]|[0.20]|[0.26]|[0.06]|[0.13]|[0.16]|[0.07]|
|DEINDU|-0.73*|0.09|-0.64*|0.38|0.88*|1.15*|-0.23|-0.20|0.20|-0.40*|
||[0.37]|[0.11]|[0.36]|[0.36]|[0.50]|[0.63]|[0.15]|[0.29]|[0.29]|[0.21]|
|**NRXGDP**|-0.20**|-0.07*|-0.27**|0.29*|0.45***|0.44**|-0.11**|-0.08|0.06|-0.15**|
|**- MFGSERV**|[0.10]|[0.04]|[0.11]|[0.15]|[0.15]|[0.20]|[0.05]|[0.10]|[0.14]|[0.06]|
|**DEINDU**|-0.91**|-0.01|-0.92**|0.58|1.17**|1.38**|-0.22|-0.18|0.15|-0.37*|
|**- MFGSERV**|[0.35]|[0.10]|[0.35]|[0.38]|[0.48]|[0.61]|[0.14]|[0.28]|[0.28]|[0.21]|
|Mean; Ctrls|20.7; Y|5.0; Y|25.7; Y|20.7; Y|25.3; Y|25.4; Y|6.5; Y|13.9; Y|13.0; Y|6.8; Y|

_Notes:_ Observations = 61 countries. This table shows the correlation between the employment share of each sector in urban areas c. 2000 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, also defined with respect to 2000. (3) SUM = MFG + FIRE. Robust SE.

Table 4: Resources, (De-)Industrialization & Urban Informality, Cross-Section, c. 2000

|Dep.Var. =|Empl. S|h. in Urba|n Empl.|Empl. S|h. in Urba|n MFG|Empl|. Sh. in Ur|ban NTR|
|---|---|---|---|---|---|---|---|---|---|
|Type of<br>Employment:|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|MFGSERV|0.54*|-0.62**|0.08|1.00***|-0.91***|-0.09|0.63*|-0.84***|0.21|
||[0.28]|[0.24]|[0.07]|[0.30]|[0.25]|[0.07]|[0.31]|[0.26]|[0.14]|
|NRXGDP|0.06|-0.22|0.16|0.56|-0.52|-0.05|-0.14|0.00|0.14|
||[0.37]|[0.28]|[0.12]|[0.37]|[0.32]|[0.09]|[0.43]|[0.40]|[0.13]|
|DEINDU|-0.33|0.19|0.15|-0.47|0.31|0.16|0.52|-0.88|0.36*|
||[0.69]|[0.57]|[0.17]|[1.08]|[0.90]|[0.21]|[0.88]|[0.80]|[0.21]|
|**NRXGDP**|-0.48|0.40|0.08|-0.44|0.39|0.05|-0.77**|0.84**|-0.07|
|**- MFGSERV**|[0.31]|[0.24]|[0.09]|[0.35]|[0.29]|[0.08]|[0.33]|[0.32]|[0.11]|
|**DEINDU**|-0.88|0.80|0.07|-1.47|1.22|0.25|-0.10|-0.05|0.15|
|**- MFGSERV**|[0.72]|[0.62]|[0.15]|[1.07]|[0.90]|[0.20]|[0.83]|[0.79]|[0.15]|
|Mean|58.0|34.8|7.2|61.2|33.9|5.0|35.8|58.4|5.8|
|Obs.; Ctrls|55; Y|55; Y|55; Y|54; Y|54; Y|54; Y|54; Y|54; Y|54; Y|

_Notes:_ This table shows the correlation between the employment share of each type of employment in urban areas or specific sectors in urban areas c. 2000 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, also defined with respect to 2000. NTR = nontradables (domestic wholesale and retail trade). Robust SE. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
43 Table 5: Natural Resources, Structural Change & Tall Building Construction, 1960-2020

|**Type of Buildings:**|All|Resid.|Non-Res.|Offce|Hotel|Retail|Gvt|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A:**<br>Core Controls|**L**|**ong-Differe**|**nce:** Dep. Va|r.: Log Urba|n Height D|ensity c. 202|0|
|MFGSERV2020|0.08***|0.09**|0.10***|0.11***|0.07**|0.07*|0.02|
||[0.024]|[0.040]|[0.029]|[0.029]|[0.034]|[0.043]|[0.029]|
|NRXGDP1960_−_2020|0.05|0.10*|0.06**|0.06*|0.02|0.05|0.04|
||[0.031]|[0.057]|[0.030]|[0.031]|[0.051]|[0.052]|[0.038]|
|DEINDU1980_−_2020|-0.01|-0.04|-0.00|-0.05|0.04|-0.06|-0.03|
||[0.028]|[0.050]|[0.035]|[0.036]|[0.039]|[0.054]|[0.047]|
|NRXGDP - MFGSERV|-0.03|0.01|-0.04|-0.05|-0.04|-0.02|0.01|
||[0.03]|[0.06]|[0.03]|[0.03]|[0.05]|[0.06]|[0.04]|
|DEINDU - MFGSERV|-0.08***|-0.13**|-0.10**|-0.16***|-0.03|-0.13**|-0.06|
||[0.03]|[0.06]|[0.04]|[0.04]|[0.05]|[0.06]|[0.05]|
|Obs; Controls|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|
|**Panel B:**<br>+ Urban Dvt Ctrl|s<br>**L**|**ong-Differe**|**nce:** Dep. Va|r.: Log Urba|n Height D|ensity c. 202|0|
|MFGSERV2020|-0.01|-0.02|0.01|0.02|-0.05|0.01|-0.02|
||[0.023]|[0.043]|[0.022]|[0.024]|[0.029]|[0.040]|[0.035]|
|NRXGDP1960_−_2020|0.02|0.03|0.05**|0.07**|-0.02|0.07|0.01|
||[0.027]|[0.050]|[0.023]|[0.026]|[0.046]|[0.056]|[0.051]|
|DEINDU1980_−_2020|-0.02|-0.04|-0.03|-0.06**|0.03|-0.09|-0.03|
||[0.026]|[0.042]|[0.024]|[0.025]|[0.031]|[0.056]|[0.048]|
|NRXGDP - MFGSERV|0.03|0.05|0.05**|0.04*|0.02|0.06|0.03|
||[0.02]|[0.05]|[0.02]|[0.02]|[0.04]|[0.07]|[0.06]|
|DEINDU - MFGSERV|-0.01|-0.01|-0.04|-0.09***|0.07*|-0.10|-0.00|
||[0.04]|[0.06]|[0.03]|[0.03]|[0.04]|[0.06]|[0.06]|
|**Panel C:**<br>+ Urban Dvt Ctrl|s<br>**10-**|**Yr Panel (w**|**/ 4 Lags):** De|p. Var.: Log|Urban Heig|ht Density i|n_t_|
|Sum for MFGSERV Lags|0.05|0.08|-0.01|0.01|-0.03|-0.11|0.05|
||[0.06]|[0.09]|[0.06]|[0.06]|[0.08]|[0.12]|[0.12]|
|Sum for NRXGDP Lags|0.10**|0.09|0.04|0.04|0.01|0.10|0.07|
||[0.04]|[0.06]|[0.04]|[0.05]|[0.05]|[0.07]|[0.05]|
|Sum for DEINDU Lags|-0.08|0.09|-0.06|-0.05|-0.03|0.40*|-0.79***|
||[0.07]|[0.14]|[0.07]|[0.07]|[0.15]|[0.23]|[0.30]|
|Obs; Cntry & Yr FE, Ctrls|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|

_Notes:_ Robust SE in parentheses (clust. at the country level in Panel C). Summary statistics for LUHT in Panels A-B: All: Mean = 4.6; SE = 1.7; Min = -2.0; Max = 9.6. Resid: Mean = 3.3; SE = 2.7; Min = -3.7; Max = 9.3. Non-Res.: Mean = 3.9; SE = 1.8; Min = -2.4; Max = 8.6. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
44 Table 6: Natural Resources, (De-)Industrialization & Vanitous Tall Buildings, 1960-2020

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**Panel A:**<br>+ Urban Dvt Ctrls|**Long-Differ**|**ence:**Dep.|Var.: Log Non|-Residential|Height Densi|ty c. 2020|
|Buildings_≥_... Meters:|All|125|140|160|200|240|
|MFGSERV2020|0.01|-0.00|-0.03|0.06|0.03|0.01|
||[0.022]|[0.039]|[0.037]|[0.045]|[0.051]|[0.049]|
|NRXGDP1960_−_2020|0.05**|0.04|0.08**|0.09*|0.13**|0.14**|
||[0.023]|[0.035]|[0.036]|[0.043]|[0.063]|[0.062]|
|DEINDU1980_−_2020|-0.03|-0.06|-0.07*|-0.04|0.02|0.01|
||[0.024]|[0.044]|[0.038]|[0.047]|[0.065]|[0.061]|
|NRXGDP - MFGSERV|0.05**|0.05|0.11***|0.03|0.11|0.12*|
||[0.02]|[0.03]|[0.03]|[0.06]|[0.07]|[0.07]|
|DEINDU - MFGSERV|-0.04|-0.05|-0.04|-0.10|-0.01|-0.00|
||[0.03]|[0.06]|[0.05]|[0.06]|[0.08]|[0.08]|
|Obs; Controls|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|
|**Panel B:**<br>+ Urban Dvt Ctrls|**10-Yr Panel**|**(w/ 4 Lags):**|Dep. Var.: Lo|g Non-Resid|ential Height|Density_t_|
|Buildings_≥_... Meters:|All|125|140|160|200|240|
|Sum for MFGSERV Lags|-0.01|0.01|-0.03|0.01|-0.05|-0.15|
||[0.06]|[0.09]|[0.09]|[0.09]|[0.11]|[0.11]|
|Sum for NRXGDP Lags|0.04|-0.01|0.00|0.13*|0.13*|0.10|
||[0.04]|[0.07]|[0.07]|[0.08]|[0.08]|[0.08]|
|Sum for DEINDU Lags|-0.06|0.10|0.13|0.58**|0.46*|0.39|
||[0.07]|[0.13]|[0.12]|[0.26]|[0.27]|[0.25]|
|Obs; Cntry & Yr FE, Ctrls|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|
|**Panel C:**<br>**Long.Diff; Dep.Var.:**|<br>Vanity|Log No|n-Residential|Height Dens|ity c. 2020 Ba|sed on ...|
|+ Urban Dvt Ctrls<br>|(Tip-Occup.)|Steel|Concrete|Capital|Cap+Large|st<br>Other|
|MFGSERV2020|-0.06|0.13***|0.01|0.01|0.01|0.05|
||[0.080]|[0.040]|[0.038]|[0.024]|[0.041]|[0.038]|
|NRXGDP1960_−_2020|0.09|0.03|0.08**|0.05*|0.03|0.09**|
||[0.065]|[0.052]|[0.033]|[0.026]|[0.054]|[0.037]|
|DEINDU1980_−_2020|0.00|0.02|-0.04|-0.03|-0.03|0.09|
||[0.035]|[0.054]|[0.033]|[0.029]|[0.044]|[0.059]|
|NRXGDP - MFGSERV|0.15***|-0.10*|0.07**|0.03|0.03|0.04|
||[0.05]|[0.05]|[0.03]|[0.03]|[0.06]|[0.04]|
|DEINDU - MFGSERV|0.06|-0.11|-0.05|-0.04|-0.04|0.04|
||[0.07]|[0.07]|[0.06]|[0.03]|[0.06]|[0.07]|
|Obs; Controls|38; Y|115; Y|115; Y|115; Y|115; Y|115; Y|

_Notes:_ Robust SE in parentheses (clust. at the country level in Panel B).
45 Table 7: Natural Resources, (De-)Industrialization & Construction, Long-Diff., 1970-2020

|**Dependent Variable:**|**Log S**|**um Ceme**|**nt (Tons;**|**70-20)**|**Log Su**|**m Constr.**|**Slum**|**Share**|
|---|---|---|---|---|---|---|---|---|
|**Long-Difference**|**Produ**|**ction**|**Consu**|**mption**|**GDP (70**|**-20; PPP)**|**(%) c**|**. 2010**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MFGSERV 2020/2010|0.05***|0.01|0.05***|0.01|0.01|-0.01*|-0.55*|0.18|
||[0.018]|[0.014]|[0.019]|[0.014]|[0.02]|[0.01]|[0.32]|[0.31]|
|NRXGDP 1970-2020/2010|0.02|0.02|0.01|0.01|0.00|-0.01|-0.65*|0.14|
||[0.021]|[0.017]|[0.020]|[0.016]|[0.02]|[0.01]|[0.36]|[0.37]|
|DEINDU 1980-2020/2010|-0.01|0.01|-0.01|0.01|-0.01|-0.01|-0.16|0.06|
||[0.034]|[0.021]|[0.034]|[0.019]|[0.02]|[0.01]|[0.74]|[0.57]|
|NRXGDP - MFGSERV|-0.03|0.01|-0.03|0.01|-0.01|-0.02|-0.10|-0.04|
||[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|[0.03]|[0.36]|[0.33]|
|DEINDU - MFGSERV|-0.06*|-0.00|-0.06|0.01|0.01|0.00|0.39|-0.12|
||[0.04]|[0.02]|[0.04]|[0.02]|[0.01]|[0.02]|[0.70]|[0.61]|
|NRXGDP - DEINDU|0.03|0.00|0.03|0.00|0.01|0.01|-0.49|0.09|
||[0.04]|[0.02]|[0.04]|[0.02]|[0.03]|[0.03]|[0.62]|[0.46]|
|Obs.; Core Controls|80; Y|80; Y|80; Y|80; Y|115; Y|115; Y|91; Y|91; Y|
|Ctrls Urban Econ Dvt.|N|Y|N|Y|N|Y|N|Y|

_Notes:_ Robust SE in parentheses: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
46 Table 8: Natural Resources, (De-)Industrialization & Urban Primacy, Country-Level

|**Dependent Variable:**|Urban Pri|macy (%)|Urb|an Primacy|(%) in t (Pa|nel)|
|---|---|---|---|---|---|---|
||in 2020 (L|ong-Diff.)|3 Lag|s Incl.|4 Lag|s Incl.|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MFGSERV ((3)-(6): sum of lags)|-0.19|-0.2|-0.10|-0.05|-0.09|-0.05|
||[0.13]|[0.14]|[0.10]|[0.13]|[0.10]|[0.13]|
|NRXGDP ((3)-(6): sum of lags)|-0.11||0.02|0.05|||
||[0.15]||[0.07]|[0.09]|||
|DEINDU ((3)-(6): sum of lags)|-0.08|-0.11|-0.09|-0.05|-0.1|-0.05|
||[0.17]|[0.17]|[0.18]|[0.19]|[0.18]|[0.18]|
|FMXGDP ((3)-(6): sum of lags)||-0.09|||0.06|0.08|
|||[0.16]|||[0.08]|[0.11]|
|AGXGDP ((3)-(6): sum of lags)||-0.46|||-0.11|-0.11|
|||[0.36]|||[0.18]|[0.20]|
|NRXGDP - MFGSERV|0.08||0.12||0.10||
||[0.18]||[0.09]||[0.14]||
|DEINDU - MFGSERV|0.11|0.09|0.01|-0.01|0.00|0.00|
||[0.20]|[0.18]|[0.24]|[0.23]|[0.28]|[0.27]|
|FMXGDP - MFGSERV||0.11||0.15||0.13|
|||[0.20]||[0.10]||[0.16]|
|AGXGDP - MFGSERV||-0.27||-0.01||-0.06|
|||[0.36]||[0.19]||[0.21]|
|Country, Year FE|N|N|Y|Y|Y|Y|
|Observations|115|115|462|346|462|346|

_Notes:_ Robust SE (clust. at the country level in (3)-(6)) in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
47 Table 9: Resources, (De-)Industrialization, & Urban Human Capital, Cross-Section, c. 2000

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Panel A:**Dep. Var.:<br>(urban; c. 2000-2020)|Avg Number<br>Years Educ.|Harmonized<br>Test Score|Yrs Educ. x<br>Test Score|Returns<br>to Educ.|Yrs Educ. x<br>Returns|
|MFGSERV|0.04*|0.93|0.03|0.15***|2.06***|
||[0.02]|[0.62]|[0.02]|[0.04]|[0.49]|
|NRXGDP|0.07**|-0.30|0.04*|0.11**|2.01***|
||[0.03]|[0.93]|[0.03]|[0.05]|[0.55]|
|DEINDU|0.16**|1.82|0.14|-0.03|0.05|
||[0.07]|[3.14]|[0.12]|[0.13]|[0.93]|
|NRXGDP - MFGSERV|0.03|-1.23**|0.01|-0.05|-0.05|
||[0.03]|[0.54]|[0.03]|[0.03]|[0.55]|
|DEINDU - MFGSERV|0.11|0.89|0.11|-0.18|-2.00*|
||[0.08]|[3.00]|[0.12]|[0.13]|[1.01]|
|Observations|53|101|51|84|48|
|**Panel B:**Dep. Var.:|Ret. Educ.|Ret. Educ.|Ret. Educ.|Ret x Yrs Edu|Ret x Yrs Edu|
|(urban; c. 2000-2020)|Reg 01 FE|Reg 02 FE|PSU FE|Reg 01 FE|Reg 02 FE|
|MFGSERV|0.25***|0.18***|0.14***|2.00***|2.05***|
||[0.07]|[0.05]|[0.03]|[0.50]|[0.48]|
|NRXGDP|0.12|0.13**|0.17**|0.65|2.07***|
||[0.09]|[0.06]|[0.08]|[0.66]|[0.62]|
|DEINDU|0.16|0.09|-0.05|0.11|0.91|
||[0.14]|[0.15]|[0.37]|[1.40]|[0.86]|
|NRXGDP - MFGSERV|-0.14*|-0.04|0.03|-1.35|0.02|
||[0.07]|[0.02]|[0.07]|[0.99]|[0.61]|
|DEINDU - MFGSERV|-0.09|-0.08|-0.18|-1.89|-1.14|
||[0.13]|[0.14]|[0.37]|[1.15]|[0.89]|
|Observations|46|66|51|28|40|
|**Panel C:**Dep. Var.:|Ret x Yrs Edu|Returns to|Ret. Exp.|Ret. Exp.|Ret. Exp.|
|(urban; c. 2000-2020)|PSU FE|Experience|Reg 01 FE|Reg 02 FE|PSU FE|
|MFGSERV|1.35***|0.01|0.02|0.01|0.01|
||[0.30]|[0.01]|[0.02]|[0.01]|[0.01]|
|NRXGDP|1.67**|-0.02|0.02|-0.02|0.01|
||[0.75]|[0.01]|[0.01]|[0.01]|[0.02]|
|DEINDU|3.57|-0.02|-0.01|-0.03|-0.21***|
||[2.08]|[0.03]|[0.03]|[0.03]|[0.07]|
|NRXGDP - MFGSERV|0.32|-0.03***|0.00|-0.03***|-0.00|
||[0.69]|[0.01]|[0.03]|[0.01]|[0.01]|
|DEINDU - MFGSERV|2.22|-0.03|-0.02|-0.04|-0.22***|
||[2.22]|[0.03]|[0.03]|[0.03]|[0.07]|
|Observations|29|84|46|66|51|

_Notes:_ Cross-sectional regressions c. 2000 (2020 for the test scores). See text for details. Robust SE.
48

## **<u>WEB APPENDIX NOT FOR PUBLICATION</u>**

# **A Regional Patterns of Production & Consumption Cities**

**Asia.** Web Appx. Fig. D.1 shows the location of production cities and consumption cities in Asia. Unlike China, India has a mix of specialized production cities (e.g., Bangalore and Kanpur) and consumption cities (e.g., Kolkata). Apart from some large production cities in Malaysia and Vietnam, other cities in Asia (e.g, in Indonesia, the Islamic Republic of Iran, Iraq, and the Philippines) are either consumption or neutral cities.

**Africa.** Data are sparser in Africa (Web Appx. Fig. D.2). Nevertheless, one can see the dearth of production cities. With the exception of South Africa, most African cities are consumption cities, with more extreme consumption cities (in red) in Nigeria, Sierra Leone, Sudan or Mozambique. This is expected given the reliance of many African countries on resource exports, which fuels consumption in urban areas.

**Europe.** In Europe (Web Appx. Fig. D.3), countries have production and neutral cities. Productive urbanization increases as one moves away from the “edge” of Europe, i.e. Southern Spain or Eastern Turkey, in line with the division of labor and production sharing within the EU bloc and the importance of intra- and inter-regional trade.

**North America.** North America (Web Appx. Fig. D.4) has many production and neutral cities located in the Northeast or the East Coast of the United States and the North and Center of Mexico (so either where there are maquiladoras or close to Mexico City). Production cities can be seen in Central America, where production sharing brought in manufacturing activities from the United States (or Mexico) following reforms in the 1990s. Californian cities are neither consumption nor production centers, while Southern U.S. cities (e.g., Miami and Houston), coastal cities in Mexico (e.g., Acapulco and Cancun), and cities in the Dominican Republic or Haiti are (non-extreme) consumption cities (in yellow or orange).

**South America.** In South America (Web Appx. Fig. D.5), only the Southeastern areas of Brazil have production cities (S _a_  ̃o Paulo is, however, a neutral city). The rest of Brazil has consumption cities (Rio is a clear consumption city). In Argentina, Bolivia, Chile and Paraguay, cities are either neutral (e.g., Buenos Aires and Santiago de Chile) or consumption cities. Ecuador and Peru have slightly more consumption cities and Colombia and the Rep ́ublica Bolivariana de Venezuela have many large and small
49 consumption cities (Bogot ́a is a “medium” consumption city but Barranquilla is an “extreme” consumption city).

# **B Robustness for the Mapping Analysis**

**Non-Linearities.** We obtain similar residuals to those in the main text if we presented also include the square, cube, and perfect fourth of the urban share in 2000, and their interactions with the population dummies (coefficient of correlation = 0.99), in case there are non-linearities in the relationship between MFG+FIRE employment, urban economic development, and city size.

**Per Capita GDP.** We obtain a coefficient of correlation of 0.97 if we use log per capita GDP in 2000 (PPP and constant international dollars) instead of urbanization (including the square, cube, and perfect fourth of log per capita GDP, and their interactions with the population dummies). This is not surprising since urbanization rates and log per capita GDP are highly correlated cross-sectionally (correlation of 0.91 in 2000; N = 178).

**Omitting Urbanization.** The correlation is 0.99 if we do not control for the urban share and do not interact the population dummies with it. We then do not allow the relationship between city employment and size to change with urban economic development.

**Using the Raw Employment Shares.** We obtain a coefficient of correlation of 0.89 if we simply consider the raw, i.e. non-residualized, city-specific employment shares. While the residualization was a priori important to ensure we compare apples with apples, the very high correlation indicates the residualization is not entirely necessary.

**Weights.** We obtain a coefficient of correlation of 1.00 if we do not modify the weights so as to over-weigh developed countries (which are under-represented in IPUMS). In that case, the weights are only based on the FUAs’ population levels c. 2000. We obtain a coefficient of correlation of 1.00 if we do not use weights at all.

**Alternative City Categorizations.** If we do not combine the top two population categories into one category, we obtain a coefficient of correlation of 1.00. If we use 5 population categories instead of 10 categories, we still get a correlation of 0.99.

**Urban Definition.** We can control for the urban definition used by the country c. 2010. We include dummies identifying whether the definition is based on a population threshold, another condition, an administrative function, or a combination of these, and the log of the threshold (U.N., 2011). We then interact these variables with the population dummies. The correlation of the residuals remains very strong (about 0.9). Another related question
50 is whether we could instead of focusing on urban observations in FUAs consider all observations in administrative units with a population density above a certain threshold. However, this would include rural workers. In addition, population densities in urban areas are much higher in developing countries than in developed countries (Jedwab et al., 2021c). It is likely similar in rural areas. A high threshold would exclude rich country cities. A low threshold would then include rural areas/workers in poorer countries.

**Urban Non-Tradables.** The correlation with the residuals when the dependent variable is the employment share of the non-tradable domestic “wholesale and retail trade” sector (DWRT) is -0.51 (we include “other services” as it appears that IPUMS mistakenly reclassified some DWRT activities for a few countries). Adding “household services”, it becomes -0.53. It is lower than -1.00 as other sectors see their share increase when MFG+FIRE decreases. Also including “public administration”, it becomes -0.60.

**Informality.** The correlation with the residuals when the dependent variable is the selfemployment share (estimated including unpaid workers) is about -0.45. It is lower than -1.00 because self-employment is an imperfect proxy for informality. Indeed, high-skilled workers of the MFG+FIRE sector could be self-employed despite belonging to the formal sector. Nonetheless, the fact that the correlation is almost equal to -0.5 is reassuring.

# **C Theory Appendix for Propositions 1-4**

In this appendix we explain how we obtain propositions 1-4.

## **C1. Resource Revenues and Consumption Cities**

Our Proposition 1 reiterates GJV16’s result that resource revenues _R_ offer a path to urbanization U and the emergence of “consumption cities”. Indeed, employment in urban non-tradables _Ln_ is increasing in _R_ whereas employment in manufacturing and tradable services _Lm_ is decreasing in _R_ . In other words, a positive shock to _R_ leads to the emergence of “consumption cities”. The overall effect on urbanization is also positive.

**Proposition 1 (Urbanization through commodity rents and “consumption cities”)**

From (C.23), (C.17), (C.19) and (C.21) below, we have the following:

**Proof:** From (6) we have the following implicit function for _Ln_ :
51 From the implicit function theorem: The partial derivatives of _F_ with respect to _R_ and _Ln_ are respectively: From (C.16) we obtain that: Both the numerator and the denominator are positive. The denominator is positive not only when the country is resource rich and _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_>_0, but also when</sup><sup>_R −p∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0</sup> because in this case we can show that the following inequality holds using the fact that since both _α <_ 1 _, βn <_ 1 then _αβn <_ 1. Replacing _αβ_ with 1 results in a smaller expression because _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0.Then from (6) and because both</sup><sup>_Ln<_1 and</sup><sup>_βn<_1 we have:</sup> After substituting (6) in (7a), we obtain: Differentiating with respect to _R_ , from (C.18) we obtain: Eq. (C.19) shows the (urban) Dutch Disease effect of an increase in resource revenue. The effect is larger for countries with small non-tradable sectors (these are mostly lowincome countries) and for countries with relatively productive tradable urban activities. Substituting (6) in (7b) we obtain:
<!-- Start of picture text --> —- —(—)<br>( —C » )<br>—~ —| (— )<br><!-- End of picture text -->
53 _x_ = _p_<sup>_∗_</sup> _m_<sup>_Am_forurbantradablesand</sup><sup>_y_=</sup><sup>_p∗_</sup> _f_<sup>_Af_forurbannon-tradables.Thisallowsus</sup> to explore not only the effect of productivity changes on urbanization and employment, but also the effect of price shocks affecting the agricultural and manufacturing sectors. For example, agricultural exports could increase because of productivity, _Af_ , increases and/or because of increases in world demand, and therefore the world price ( _p_<sup>_∗_</sup> _f_<sup>)for</sup> the country’s agricultural product. It is important to clarify that, in our mind, the world agricultural price, _p_<sup>_∗_</sup> _f_<sup>,intheagriculturalsubsistenceconstraint,differsfromthe</sup> world price for the agricultural commodities exported by the country. The price in the subsistence constraint represents the price for the basket of goods consumed locally which can be assumed to be fixed or to change less in response to global demand changes than the prices of the country’s main agricultural exports.

# **Proposition 2 (Productivity growth in agriculture and “consumption cities”)**

So long as _R < p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>, from (C.26), (C.27), (C.29) and (C.30) below, it follows that:</sup>

**Proof:** From (C.15) and the implicit function theorem and noticing that _y_ = _p_<sup>_∗_</sup> _f_<sup>_Af_,we</sup> have:

From (C.15) the partial derivatives of _F_ with respect to _y_ is respectively: From (C.16b), (C.24) and (C.25), we have: As long as _R < p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>,i.e.thecountryisnotparticularlyresourcerich,theincrease</sup>
54 in agricultural productivity shifts resources into non-tradables (<sup>_<u>∂L</u>_</sup> _∂y_<sup>_<u>n</u>_</sup> _>_ 0). In this case, the numerator is positive and, as shown above, the denominator is positive too. Differentiating (C.18) with respect to _y_ , we get: The first term in the brackets in (C.27) is negative and so is the second one in resource poor countries as _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0.Therefore,</sup><sup>_<u>∂L</u>_</sup> _∂y_<sup>_<u>n</u><_0</sup> Differentiating (C.22) with respect to _y_ , we obtain: Rearranging the terms in (C.28) we get: From (C.29) we see that the effect of agricultural productivity growth on urbanization _−_ is negative in resource-poor countries where _R p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0 and manufacturing productivity</sup> <u>1</u> is relatively high ( _x_ _<u>α</u>_ <u>1</u> _<u>x</u>_ + _<u>αy α</u>_ <u>1</u><sup>_>_</sup> 1+ _<u>αα</u>_<sup>).Inthiscase,thefirstandsecondtermsinthesquare</sup> brackets are negative and there is a shift in employment away from urban areas (<sup>_<u>∂U</u>_</sup> _∂y_<sup>_<_0).</sup> However, when manufacturing productivity is low, the first term is small and the second term is positive. In this case, the agricultural productivity shock spurs urbanization (<sup>_<u>∂U</u>_</sup> _∂y_<sup>_>_0).Differentiating (C.20) with respect to agricultural productivity y, we obtain:</sup> In resource-poor countries (i.e. _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0)withsufficientlyhighmanufacturing</sup> <u>1 1</u> productivity so that _α y α − x α <_ 0, a productivity boost in agriculture shifts resources <u>1 1</u> into rural areas. In this case,<sup>_∂L_</sup> _∂y_<sup>_<u>f</u>>_0.Please note that whenever condition</sup><sup>_α y_</sup> _α − x α <_ 0
55 <u>1</u> is satisfied so is _x_ _<u>α</u>_ <u>1</u> _<u>x</u>_ + _<u>αy α</u>_ <u>1</u><sup>_>_</sup> 1+ _<u>αα</u>_<sup>,whichensuresthattheproductivityboostinagriculture</sup> has an opposite effect on urbanization, i.e. _<u>∂U∂y</u>_<sup>_<_0.However,whenmanufacturing</sup> productivity is low (i.e. _α y α_ <u>1</u> _− x α_ <u>1</u> _>_ 0 and _x_ _<u>α</u>_ <u>1</u> _<u>x</u>_ + _<u>α</u>_ <u>1</u> _y_ _<u>α</u>_ <u>1</u><sup>_<_</sup> 1+ _<u>αα</u>_<sup>.thefirsttermsin(C.29)</sup> and (C.30) are small so the second terms dominate. In (C.29) the second term is positive implying a shift of labor into urban areas, i.e.<sup>_<u>∂U</u>_</sup> _∂y_<sup>_>_0, while in (C.30) the second term is</sup> negative, implying a shift of labor away from agriculture, i.e.<sup>_∂L_</sup> _∂y_<sup>_<u>f</u><_0.</sup>

**End of Proof** : In sum, if the level of agricultural productivity is high enough, agricultural development leads to de-industrialization but enables urbanization and the shift from rural and urban tradables to urban non-tradables in “consumption cities”.

# **C3. Industrial and/or Service Revolution and Production Cities**

We discuss how a manufacturing/FIRE revolution leads to production cities.

## **Proposition 3 (Urbanization through industrialization and “production cities”)**

From (C.33), (C.34), (C.35), and (C.36) below, we have: so long as _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0 and agricultural productivity is sufficiently high:</sup> <u>1 1</u> ( _α_ ( _p_<sup>_∗_</sup> _m_<sup>_Am_)</sup> _α <_ ( _p_<sup>_∗_</sup> _f_<sup>_Af_)</sup> _α_ ).

**Proof:** From (C.15) and the implicit function theorem and noticing that _x_ = _p_<sup>_∗_</sup> _m_<sup>_Am_:</sup>

The partial derivatives of _F_ with respect to _x_ is: Using (C.16b), (C.31), and (C.32), we obtain the following result: The numerator in (C.33) is negative because _R_ is low in resource-poor countries; as shown before, the denominator is positive. Thus, (C.33) is positive and<sup>_<u>∂L</u>_</sup> _∂x_<sup>_<u>n</u>>_0, implying</sup> that a positive productivity shock in manufacturing shifts resources into non-tradables. Differentiating (C.18) with respect to x, we obtain:
56 The first term in the square brackets is positive. The second term is positive when the country is resource poor, i.e. _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0, and agricultural productivity is high enough</sup> <u>1 1</u> so that _α x α − y α <_ 0. The latter reflects the importance of the Green Revolution for industrial development. Industrialization in countries with low agricultural productivity is slower than in countries with higher agricultural productivity. Thus, the effect of a positive productivity shock in manufacturing is an expansion of employment in _<u>∂Lm</u>_ manufacturing and tradable services, i.e. _∂x >_ 0. This suggests that productivity growth in manufacturing and/or tradable services in resource poor countries fosters an expansion in the total employment of these sectors.

Differentiating (C.22) with respect to x and using (C.34), gives us the following expression: Both terms in (C.35) are positive when countries are resource-poor ( _R − p_<sup>_∗_</sup> _f_<sup><u>c</u></sup> _~~f~~_<sup>_<_0)</sup> and agricultural productivity is high enough. In this case, productivity growth in manufacturing and/or tradable services fuels urbanization. Finally, from (C.20) we get:

**End of Proof:** In (C.36), if agricultural productivity is sufficiently high, then both terms in the square brackets are negative. Thus, a productivity boom in manufacturing / tradable services leads to a shift of resources away from agriculture and into urban tradables.

A shock that reduces the country’s relative level of manufacturing productivity should reduce manufacturing employment according to (C.34). For instance, it could
57 be that manufacturing productivity decreases ( _Am_ ) or that manufacturing productivity stays the same but other countries’ manufacturing productivity increases, thus lowering manufacturing prices ( _p_<sup>_∗_</sup> _m_<sup>).Inbothcases,(</sup><sup>_x_=</sup><sup>_p∗_</sup> _m_<sup>_Am_)woulddecrease.Ofcourse,this</sup> applies to both manufacturing and FIRE.

Various factors could account for a decrease in _x_ . First, many countries, in particular in LAC, have adopted in the past ISI policies that artificially increased manufacturing productivity and employment at the expense of other sectors, and also raised the urbanization rate. When these policies were removed, productivity _Am_ declined, but urbanization rates decreased little. Second, increased trade competition in the world, especially with the growth of China (e.g., in manufacturing) and India (e.g., in business services), reduced the world price levels of urban tradables. In countries where urban tradable productivity was initially unchanged, _x_ likely decreased, resulting in the same effects as the removal of ISI policies. Third, the production functions of eq. (2) implicitly assume complementarities between technology and labor. However, new labor-saving technologies have appeared over time in urban tradable sectors, especially in more developed countries. While our model does not explicitly account for this mechanism, _x_ In it could be interpreted in our model via a lower , with again the same consequences. _x_ the end, regardless of the “origin” of the reduction in , production cities see their sectoral composition change as employment in urban tradables declines. If we assume that urban residents do not migrate to rural areas, for example because skills acquired in the urban sectors have no value in the agriculture sector or because agricultural productivity is high, a negative shock to manufacturing will not shift resources from urban to rural areas (as in Proposition 3). Instead, it will shift resources from urban tradables to non-tradables, resulting in the transformation of a production city into a consumption city. Thus, we formulate Proposition 4.

# **D Data Creation: Aggregate Data**

**Sample.** We focus on 116 countries that were still “developing” countries (i.e., had not reached high income status) in 1960. We obtain data every 5 years between 1960 and 2010. The full sample thus consists of 116 countries times 13 years = 1,508 observations. **GDP Share of Manufacturing and Services.** When available we obtain the GDP _share of manufacturing_ and the GDP share of manufacturing and services from the Beta version
58 of the World Development Indicators (WDI) database of the World Bank (2021).<sup>53</sup> More recent versions of the WDI do not report these GDP shares for earlier decades, only the older versions of the WDI do. The Beta version has the merit of showing all available estimates for all versions of the WDI. For each we yearly simultaneously country-year, then take the mean of the available estimates. To maximize the number of available estimates for the years 1960, 1965, ..., 2015, 2020, and in order to minimize fluctuations due to year-specific measurement issues, we rely on five-year moving averages.

After doing so, of the 1,508 observations in our data, for 189, 159 and 195 observations we still do not have an available estimate for the GDP share of MFG, services (SERV), and MFG+SERV, respectively. For the 2010-2020 period, we complete the data using estimates from Central Intelligence Agency (2021) and reports from international organizations or governmental agencies. Even after doing this, for 188, 153 and 189 observations we still do not have an available estimate, respectively.

For the long-difference regressions, we need data c. 1960-1970. For these years, we use United Nations (1960-1980). However, for 87, 75, and 88 observations, we do not have an available estimate for the GDP share of MFG, SERV, and MFG+SERV, respectively.

The _System of National Accounts_ (SNA) - _Analysis of Main Aggregates_ (AMA) database of United Nations (2020c) reports the GDP share of aggregated sectors, including MFG and services, for all countries from 1970 to 2020. When needed, we use this database to complete the missing country-years of our main data set, after verifying that the newly added estimates are consistent with the estimates that we already had for other years.<sup>54</sup> **GDP Share of FIRE.** The _National Accounts Official Country Data database_ of United Nations (2020a) reports when available the GDP share of various sectors – using both the ISIC Revision 3 and Revision 4 – from the 1960s to date. The data are patchy, however, and we employ them only to obtain the GDP share of FIRE c. 2020 (observations from 2015-2020).

> 53For manufacturing, we use the series “Manufacturing, value added (% of GDP)”. For services, we use as our baseline the series “Services, etc., value added (% of GDP)”. When estimates of the service share are not available, we rely on another WDI series: “Services, value added (% of GDP)”.

> 54 We do not use SNA-AMA as our baseline database. Indeed, when comparing WDI + the yearbooks and SNA-AMA, it appears that many SNA-AMA estimates were extrapolated.
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63 Table D.1: Employment Share of Urban Tradables by City Size, Cross-Section, c. 2000

|Dep. Var. = MFGFIRE_a,_00|Coef.|SE||Coef.|SE|
|---|---|---|---|---|---|
|Capital City_a_(CAP)|-5.46**|(2.44)|URB_c_|0.03|(0.05)|
|Pop. Size CAT_a_= 2|-0.66|(1.42)|URB_c_* Pop. Size CAT_a_= 2|0.07**|(0.03)|
|Pop. Size CAT_a_= 3|1.03|(2.35)|URB_c_* Pop. Size CAT_a_= 3|0.08*|(0.04)|
|Pop. Size CAT_a_= 4|3.69|(3.99)|URB_c_* Pop. Size CAT_a_= 4|0.04|(0.06)|
|Pop. Size CAT_a_= 5|5.79|(4.41)|URB_c_* Pop. Size CAT_a_= 5|0.01|(0.07)|
|Pop. Size CAT_a_= 6|13.09***|(4.10)|URB_c_* Pop. Size CAT_a_= 6|-0.06|(0.06)|
|Pop. Size CAT_a_= 7|8.59*|(4.50)|URB_c_* Pop. Size CAT_a_= 7|0.03|(0.07)|
|Pop. Size CAT_a_= 8|11.70*|(5.98)|URB_c_* Pop. Size CAT_a_= 8|0.05|(0.10)|
|Pop. Size CAT_a_= 9|21.76***|(5.35)|URB_c_* Pop. Size CAT_a_= 9|-0.09|(0.08)|
|Constant|16.24***|(4.06)||||

_Notes:_ Obs. = 6,865 urban agglomerations. R2 = 0.20. The dependent variable is the employment share of MFG+FIRE in each urban agglomeration _a_ belonging to country _c_ circa 2000. The other variables are also defined in 2000. Robust SE clustered at the country level in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

Table D.2: Additional Correlations for the Long-Difference Regressions

|**Dependent Variable:**|**Urban**|**ization Rate (%**|**) in 2020**|
|---|---|---|---|
||(1)|(2)|(3)|
|NRXGDP (%) (Mean 1960-2020)|1.02***|0.74***|0.74***|
||[0.239]|[0.236]|[0.275]|
|MFGSERV (%) (2020)|1.09***|||
||[0.195]|||
|DEINDU (%) (1980-2020)|-0.04|-0.70|-0.88|
||[0.343]|[0.480]|[0.628]|
|MFG (%) (2020)||1.57***|1.06***|
|||[0.279]|[0.395]|
|SERV (%) (2020)||0.41*||
|||[0.244]||
|FIRE (%) (2020)|||0.69**|
||||[0.327]|
|SERV (non-FIRE) (%) (2020)|||0.57*|
||||[0.303]|
|Beta Coef. MFGSERV|0.57|||
|Beta Coef. MFG||0.62|0.22|
|Beta Coef. SERV||0.16||
|Beta Coef. FIRE|||0.14|
|Beta Coef. SERV (non-FIRE)|||0.16|
|Controls|Y|Y|Y|

_Notes:_ Obs. = 115. We control for initial conditions c. 1960 – i.e., the urban share and the value of the variables c. 1960 (except for FIRE and SERV (non-FIRE) since there is no existing data for the FIRE sector in the 1960s – and add the controls for area, population, small islands, and urban definitions. Robust SE in parentheses.
64 Table D.3: Timing of the Correlations btw Urbanization & the Measures, 10-Year Panel

|**Dependent Variable:**||**Urbaniz**|**ation Rate (%)**|**in Year**_t_||
|---|---|---|---|---|---|
|Correlations with:|Baseline|1 Lead|l Lag|2 Lags|2 Lags|
|NRXGDP (%)_t_||0.05||||
|||[0.062]||||
|NRXGDP (%)_t_-10|0.19**|0.18***|0.12*|0.03||
||[0.089]|[0.065]|[0.065]|[0.080]||
|NRXGDP (%)_t_-20|||0.23***|0.23***||
||||[0.072]|[0.068]||
|NRXGDP (%)_t_-30||||0.14***||
|||||[0.046]||
|MFGSERV (%)_t_+10||0.15||||
|||[0.096]||||
|MFGSERV (%)_t_|0.43***|0.26***|0.33**|0.37**|0.37**|
||[0.141]|[0.090]|[0.142]|[0.159]|[0.168]|
|MFGSERV (%)_t_-10|||0.26**|0.16**|0.17**|
||||[0.103]|[0.077]|[0.078]|
|MFGSERV (%)_t_-20||||0.31**|0.32**|
|||||[0.127]|[0.125]|
|DEINDU (%) 1980-_t_+10||0.27||||
|||[0.238]||||
|DEINDU (%) 1980-_t_|0.42|0.15|0.37|0.11|0.10|
||[0.266]|[0.152]|[0.252]|[0.271]|[0.272]|
|DEINDU (%) 1980-_t_-10|||0.01|-0.09|-0.08|
||||[0.295]|[0.170]|[0.162]|
|DEINDU (%) 1980-_t_-20||||0.35|0.35|
|||||[0.443]|[0.440]|
|FMXGDP (%)_t_-10|||||0.05|
||||||[0.090]|
|FMXGDP (%)_t_-20|||||0.23***|
||||||[0.069]|
|FMXGDP (%)_t_-30|||||0.14***|
||||||[0.047]|
|AGXGDP (%)_t_-10|||||-0.04<br>|
||||||[0.160]|
|AGXGDP (%)_t_-20|||||0.23|
||||||[0.195]|
|AGXGDP (%)_t_-30|||||0.15|
||||||[0.161]|
|Sum for NXGDP|||0.35***|0.40***||
||||[0.12]|[0.15]||
|Sum for MFGSERV|||0.60***|0.85***|0.85***|
||||[0.20]|[0.29]|[0.29]|
|Sum for DEINDU|||0.37|0.37|0.37|
||||[0.32]|[0.46]|[0.46]|
|Sum for FMXGDP|||||0.42***|
||||||[0.15]|
|Sum for AGXGDP|||||0.33|
||||||[0.34]|
|CntryFE,Yr FE,Ctrls;Obs|Y;694|Y;578|Y;578|Y;462|Y;462|

_Notes_ : Robust SEs are clustered at the country level. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
65 Table D.4: Resources, Industrialization & Sectoral Employment / Informality, Panel

|Dep.Var. =|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Empl. Sh. of ... (_t_)|MFG|FIRE|SUM|NTR|NTR2|NTR3|GOVT|GOVT2|WAGE|SELF|
|MFGSERV_t_|0.34**|0.06|0.40|-0.29|-0.54**|-0.51***|-0.10|-0.15**|0.14|-0.27|
||[0.154]|[0.111]|[0.243]|[0.239]|[0.233]|[0.099]|[0.083]|[0.063]|[0.337]|[0.271]|
|NRXGDP_t−_10|0.30*|-0.04|0.26|0.21|0.49***|0.38***|0.18**|0.04|-0.31|0.30|
||[0.166]|[0.071]|[0.217]|[0.153]|[0.152]|[0.104]|[0.076]|[0.167]|[0.257]|[0.228]|
|DEINDU1980_−t_|-0.42|-0.03|-0.45|-0.23|-0.47***|-0.12|0.10|-0.17|0.30|-0.51***|
||[0.368]|[0.204]|[0.563]|[0.161]|[0.153]|[0.191]|[0.126]|[0.127]|[0.199]|[0.17]|
|**NRXGDP**|-0.04|-0.10|-0.14|0.51|1.03***|0.90***|0.28*|0.19|-0.45|0.57|
|**- MFGSERV**|[0.19]|[0.13]|[0.29]|[0.35]|[0.30]|[0.15]|[0.14]|[0.18]|[0.49]|[0.42]|
|**DEINDU**<br>-|0.76***|-0.10|-0.85**|0.06|0.07|0.40*|0.20|-0.03|0.16|-0.24|
|**- MFGSERV**|[0.27]|[0.14]|[0.41]|[0.20]|[0.15]|[0.21]|[0.15]|[0.13]|[0.28]|[0.23]|
|Observations|124|124|124|124|124|124|120|93|99|99|
|Country, Year FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Controls|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ This table shows the correlation between the employment share of each sector/type of employment in urban areas in _t_ and measures of natural resource exports ( _t_ -10), industrialization/FIRE-ization ( _t_ ), and deindustrialization (1980- _t_ ). Robust SE clustered at the country level. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
66 Table D.5: Resources, Industrialization & Employment, Cross-Section, I2D2 Database

|Dep.Var. =<br>**Empl. Sh. (c. 2005):**|(1)<br>MFG|(2)<br>FIRE|(3)<br>MFGFIRE|(4)<br>NTRI|(5)<br>NTRI2|(6)<br>WAGE|(7)<br>SELF|(8)<br>UNPAID|
|---|---|---|---|---|---|---|---|---|
|**Panel A:**<br>Vars. in 2000|||||||||
|MFGSERV2000|0.12***|-0.04|0.08*|0.09|0.14|0.44**|-0.48***|-0.15|
||[0.035]|[0.039]|[0.043]|[0.127]|[0.144]|[0.180]|[0.136]|[0.118]|
|NRXGDP1960_−_2000|-0.06|0.03|-0.03|0.18|0.03|0.41|-0.30|-0.15|
||[0.056]|[0.045]|[0.076]|[0.127]|[0.209]|[0.294]|[0.221]|[0.121]|
|DEINDU1980_−_2000|-0.26|0.14|-0.12|0.12|1.42**|-0.77|1.08*|0.54|
||[0.167]|[0.105]|[0.204]|[0.364]|[0.597]|[0.715]|[0.557]|[0.326]|
|**NRXGDP**|-0.18***|0.07|-0.11|0.08|-0.11|-0.03|0.18|-0.00|
|**- MFGSERV**|[0.04]|[0.05]|[0.07]|[0.17]|[0.20]|[0.27]|[0.22]|[0.15]|
|**DEINDU**|-0.38**|0.18|-0.20|0.03|1.28**|-1.21|1.56***|0.69*|
|**- MFGSERV**|[0.16]|[0.12]|[0.21]|[0.40]|[0.60]|[0.74]|[0.57]|[0.36]|
|**Panel B:**<br>Vars. in 2010|||||||||
|MFGSERV2010|0.10|-0.03|0.07|0.09|0.26|0.69***|-0.62***|-0.23|
||[0.061]|[0.043]|[0.061]|[0.145]|[0.199]|[0.217]|[0.157]|[0.142]|
|NRXGDP1960_−_2010|-0.13**|0.04|-0.10|0.14|0.16|0.42*|-0.28|-0.13|
||[0.065]|[0.045]|[0.070]|[0.143]|[0.223]|[0.244]|[0.195]|[0.134]|
|DEINDU1980_−_2010|-0.09|0.14|0.05|0.10|1.11**|-0.02|0.47|0.23|
||[0.134]|[0.085]|[0.159]|[0.309]|[0.503]|[0.652]|[0.564]|[0.242]|
|**NRXGDP**|-0.23***|0.07|-0.16***|0.05|-0.09|-0.27|0.34*|0.09|
|**- MFGSERV**|[0.05]|[0.07]|[0.06]|[0.20]|[0.20]|[0.22]|[0.18]|[0.18]|
|**DEINDU**|-0.19|0.17|-0.02|0.02|0.86|-0.71|1.09*|0.45|
|**- MFGSERV**|[0.15]|[0.10]|[0.17]|[0.36]|[0.55]|[0.69]|[0.57]|[0.30]|
|Observations|93|90|90|94|91|94|93|93|
|Controls|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ This table shows the correlation between the employment share of each sector/type of employment in urban areas c. 2005 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, defined with respect to 2000 or 2010. Robust SE. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
67 Table D.6: **Resources, Industrialization & Growth of FUAs, 1975-2015, Cross-Section**

|**Dependent Variable**||L|og FUA P|op. 2015|- Log FU|A Pop. 19|75||
|---|---|---|---|---|---|---|---|---|
|Capital + Largest City:|Top 1|Top 2|Top 5|Top 0|Top 1|Top 2|Top 5|Top 0|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MFGSERV*TOP|-0.02|-0.01|0.00|-0.01|-0.02|-0.01|0.00|-0.01|
||[0.025]|[0.024]|[0.025]|[0.025]|[0.026]|[0.025]|[0.026]|[0.026]|
|NRXGDP*TOP|-0.01|-0.01|-0.01|-0.00|||||
||[0.029]|[0.023]|[0.018]|[0.039]|||||
|DEINDU*TOP|0.04|0.03|0.01|0.05|0.04|0.03|0.01|0.05|
||[0.047]|[0.046]|[0.044]|[0.051]|[0.047]|[0.046]|[0.044]|[0.051]|
|FMXGDP*TOP|||||-0.01|-0.01|-0.01|-0.00|
||||||[0.031]|[0.024]|[0.019]|[0.042]|
|AGXGDP*TOP|||||-0.06|-0.04|-0.05|-0.05|
||||||[0.057]|[0.052]|[0.046]|[0.061]|
|TOP*(NRXGDP - MFGSERV)|0.00|0.00|-0.01|0.00|||||
||[0.05]|[0.04]|[0.04]|[0.06]|||||
|TOP*(DEINDU - MFGSERV)|0.06|0.04|0.01|0.06|0.06|0.04|0.01|0.06|
||[0.07]|[0.06]|[0.06]|[0.07]|[0.07]|[0.07]|[0.06]|[0.07]|
|TOP*(FMXGDP - MFGSERV)|||||0.01|0.00|-0.01|0.01|
||||||[0.05]|[0.04]|[0.04]|[0.06]|
|TOP*(AGXGDP - MFGSERV)|||||-0.04|-0.03|-0.05|-0.04|
||||||[0.05]|[0.05]|[0.04]|[0.05]|
|Observations|7,422|7,422|7,422|7,422|7,422|7,422|7,422|7,422|

_Notes:_ Robust SE in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

# Table D.7: **Resources, Industrialization & Growth of FUAs, 1975-2015, Panel**

|Dependent Variable:|||Lo|g FUA P|op. in Yea|r_t_|||
|---|---|---|---|---|---|---|---|---|
|Extra Lags Included||1 Extr|a Lag|||2 Extr|a Lags||
|Capital + Largest City:|Top 1|Top 2|Top 5|Top 0|Top 1|Top 2|Top 5|Top 0|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Sum of Lags for MFGSERV|0.01|0.00|0.00|0.00|0.02**|0.01*|0.01*|0.01|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Sum of Lags for NRXGDP|0.01|0.01|0.01|0.01|0.02|0.02|0.02|0.03|
||[0.02]|[0.01]|[0.01]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|
|Sum of Lags for DEINDU|-0.02|-0.01|-0.01|-0.02|-0.01|-0.01|-0.00|-0.02|
||[0.02]|[0.02]|[0.02]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Observations|29,681|29,681|29,681|29,681|22,259|22,259|22,259|22,259|
|City FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ Robust SE clust. at the city level in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
68 Table D.8: Country-Level Production City-Ness (PCC): Average and Gini Index c. 2000

|||**Panel**|**A: Aver**|**age Production City-**|**Ness fo**|**r Each**|**Country**||
|---|---|---|---|---|---|---|---|---|
|Rank|Country|PCC|Rank|Country|PCC|Rank|Country|PCC|
|1|Romania|24.6|26|South Africa|1.4|51|Peru|-4.7|
|2|Lesotho|20.3|27|Togo|1.0|52|Ecuador|-5.0|
|3|Slovenia|18.6|28|Jamaica|0.7|53|Myanmar|-5.3|
|4|Mauritius|14.7|29|Papua New Guinea|0.2|54|Haiti|-5.6|
|5|Ireland|12.4|30|<br>Morocco|0.2|55|Ethiopia|-5.6|
|6|Belarus|12.0|31|Thailand|-0.1|56|Egypt, Arab Rep.|-5.7|
|7|Honduras|10.8|32|India|-0.6|57|Malawi|-6.2|
|8|China|8.8|33|Philippines|-0.9|58|Venezuela, RB|-6.4|
|9|Malaysia|8.2|34|Chile|-1.0|59|Rwanda|-7.5|
|10|Costa Rica|8.2|35|Dominican Rep.|-1.2|60|Zambia|-7.7|
|11|Israel|6.7|36|<br>Benin|-1.4|61|Cameroon|-8.3|
|12|Poland|6.2|37|Italy|-1.4|62|Colombia|-9.7|
|13|France|6.0|38|United States|-1.9|63|Cambodia|-10.2|
|14|Portugal|5.9|39|Guinea|-2.0|64|Sudan|-10.3|
|15|Spain|5.5|40|Armenia|-2.3|65|Uganda|-10.3|
|16|Guatemala|4.5|41|Botswana|-2.5|66|Indonesia|-10.5|
|17|El Salvador|4.3|42|Paraguay|-3.1|67|Mozambique|-10.8|
|18|Lao PDR|3.8|43|Brazil|-3.1|68|Mali|-11.0|
|19|Fiji|3.6|44|Bolivia|-3.2|69|Liberia|-11.3|
|20|Nepal|2.9|45|Argentina|-3.3|70|Senegal|-12.1|
|21|Turkey|2.7|46|Panama|-3.6|71|Sierra Leone|-14.5|
|22|Vietnam|2.7|47|Iran, Islamic Rep.|-3.6|72|<br>Tanzania|-14.8|
|23<br>|Canada<br>|2.3<br>|48<br>|<br>Ghana<br>|-3.8<br>|73<br>|Nigeria<br>|-15.0<br>|
|24|Mexico|2.2|49|Jordan|-3.8|74|Iraq|-17.2|
|25|Nicaragua|1.9|50|Kyrgyz Republic|-4.2||||
||**Pa**|**nel B:**|**Gini In**|**dex of Production Ci**|**ty-Ness**|**for Eac**|**h Country**||
|Rank|Country|Gini|Rank|Country|Gini|Rank|Country|Gini|
|1|Nigeria|0.30|26|Honduras|0.12|51|France|0.06|
|2|Colombia|0.25|27|Italy|0.12|52|Canada|0.06|
|3|Indonesia|0.20|28|Mozambique|0.11|53|Dominican Rep.|0.06|
|4|India|0.20|29|Nicaragua|0.11|54|<br>Armenia|0.06|
|5|Egypt, Arab Rep.|0.18|30|Peru|0.11|55|Poland|0.06|
|6|<br>Iran, Islamic Rep.|0.17|31|Tanzania|0.11|56|Belarus|0.06|
|7|<br>Guatemala|0.16|32|Myanmar|0.11|57|Nepal|0.05|
|8|Venezuela, RB|0.15|33|Sierra Leone|0.10|58|Senegal|0.05|
|9|<br>Sudan|0.15|34|Panama|0.10|59|Portugal|0.04|
|10|Turkey|0.15|35|Philippines|0.10|60|Ghana|0.04|
|11|Brazil|0.14|36|Vietnam|0.10|61|Haiti|0.04|
|12|Ethiopia|0.14|37|Ecuador|0.10|62|Uganda|0.03|
|13|Mexico<br>|0.14|38|United States|0.09|63|Mali<br>|0.03|
|14|Malaysia|0.13|39|Iraq|0.09|64|Israel|0.03|
|15|Kyrgyz Republic|0.13|40|Thailand|0.09|65|Ireland|0.03|
|16|<br>Papua New Guinea|0.13|41|Jamaica|0.09|66|Togo|0.03|
|17|<br>Morocco|0.13|42|Chile|0.09|67|Guinea|0.02|
|18|Lao PDR|0.13|43|Bolivia|0.08|68|Slovenia|0.02|
|19|Spain|0.12|44|Benin|0.08|69|Rwanda|0.02|
|20|China|0.12|45|South Africa|0.07|70|Costa Rica|0.01|
|21|Botswana|0.12|46|El Salvador|0.07|71|Liberia|0.01|
|22|Argentina|0.12|47|Romania|0.07|72|Lesotho|0.00|
|23|Cameroon|0.12|48|Cambodia|0.07|73|Mauritius|0.00|
|24<br>|Jordan<br>|0.12<br>|49<br>|Paraguay<br>|0.07<br>|74|Fiji|0.00|
|25|Zambia|0.12|50|Malawi|0.07||||