metadata: authors: 0: author: Artuc,Erhan
1: author: Lederman,Daniel
2: author: Rojas Alvarado,Luis Diego
count: Chile,Paraguay,Mexico,Honduras,China,Brazil,Argentina
docna: 0: docna: The rise of China and labor market
            adjustments in Latin America
docty: Policy Research Working Paper
subtopic: Food & Beverage Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Mining & Extractive Industry (Non-Energy),Food Security,Rural Labor Markets,Labor Markets
teratopic: Macroeconomics and Economic Growth,Industry,Agriculture,Social Protections and Labor,Rural Development
abstracts: cdata!: This paper assesses the impact of the
            rise of China on the trade of Latin American and Caribbean
            economies. The study proposes an index to measure the impact
            on trade, which suggests sizable effects, especially in
            Argentina, Brazil, Chile, Honduras, Mexico, and Paraguay.
            The paper uses the index and a model of labor mobility, to
            calculate the impact of China's growth on labor markets
            in Argentina, Brazil, and Mexico. The resulting evidence
            suggests that the rise of China has had positive effects on
            agriculture and mining in Argentina and Brazil, which offset
            negative impacts on manufacturing industries, thus leaving
            total employment and real wages virtually unchanged in the
            long run. In contrast, the estimated impacts of China's
            rise on Mexico imply that the sizable shock to manufacturing
            was not offset by the positive shocks on mining and
            agriculture, reducing employment in the long run. The paper
            also discusses the effect of China on the degree of
            informality in these three economies and contrasts short-run
            and long-run effects on employment and wages across industries.
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repnme: repnme: The rise of China and labor market
            adjustments in Latin America
display_title: The rise of China and labor market
            adjustments in Latin America
listing_relative_url: /research/2015/01/23173043/rise-china-labor-market-adjustments-latin-america
topicv3: Agriculture and Food Security m1326205 435,Mining Services m1326410 1701,Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Mining Sector m1327113 554
publishtoextweb_dt: 2014-12-31T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Agriculture,Social Protections and Labor,Rural Development
no_of_pages: 58
author_keys: 0: author_key: 1140434
1: author_key: 125180
2: author_key: 1065547
model_extractions: WPS7155 Policy Research Working Paper 7155 The Rise of China and Labor Market Adjustments in Latin America _Erhan Artuç Daniel Lederman Diego Rojas_ Latin America and the Caribbean Region Office of the Chief Economist January 2015
Policy Research Working Paper 7155

# **Abstract**

This paper assesses the impact of the rise of China on the trade of Latin American and Caribbean economies. The study proposes an index to measure the impact on trade, which suggests sizable effects, especially in Argentina, Brazil, Chile, Honduras, Mexico, and Paraguay. The paper uses the index and a model of labor mobility, to calculate the impact of China’s growth on labor markets in Argentina, Brazil, and Mexico. The resulting evidence suggests that the rise of China has had positive effects on agriculture and mining in Argentina and Brazil, which offset negative impacts on manufacturing industries, thus leaving total employment and real wages virtually unchanged in the long run. In contrast, the estimated impacts of China’s rise on Mexico imply that the sizable shock to manufacturing was not offset by the positive shocks on mining and agriculture, reducing employment in the long run. The paper also discusses the effect of China on the degree of informality in these three economies and contrasts short-run and longrun effects on employment and wages across industries.

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://econ.worldbank.org. The authors may be contacted at dlederman@worldbank.org and eartuc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Rise of China and Labor Market Adjustments in Latin America**<sup>_∗_</sup>

Erhan Artu ̧c, Daniel Lederman and Diego Rojas

## JEL Codes: E24, F13, F16

Keywords: Informal and Formal Employment, China, Latin America, Labor Mobility

> _∗_ We would like to thank William Maloney, Pravin Krishna, Juan Carlos Hallak and Sergio Urzua for their insightful comments. We also like to thank comments from participants of the LACEA Labor Market Network’s 2<sup>_nd_</sup> meeting held in Colombia, the Authors’ Workshop for the World Bank’s LAC 2014 Flagship and participants of the CEMLA-Banco de Mexico Conference on“The Insertion of Asian Economies into International Trade: Challenges, Opportunities and Productivity Policies in Latin America & the Caribbean”. Artuç is an Economist with the Development Research Group, International Trade and Integration Unit (DECTI), Lederman is Deputy Chief Economist for Latin America and the Caribbean of the World Bank, and Rojas is a Consultant with the Office of the Chief Economist for Latin America and the Caribbean.
# **1 Introduction**

In recent decades China has experienced impressive growth. Over the last three decades GDP growth averaged 10% per year, and over 500 million people have moved out of poverty. China is now the world’s largest exporter, manufacturer and a substantial importer of raw materials<sup>1</sup> . The value of Chinese trade has practically doubled every four years in the last three decades<sup>2</sup> . Along with this impressive growth, there has been an increasing academic and policy debate about the effects of China on other countries’ performance, particularly in Latin America<sup>3</sup> . A cacophony of voices have been heard in the media discussing the economic and social impacts of the rise of China, with some arguing about the benefits while others are more skeptical<sup>4</sup> .

For Latin American countries, several questions regarding the growth of China remain unanswered. For instance, how much of the change in net exports and prices can be attributed to the growth of China? Did China’s growth affect relative wages, employment and informality in Latin America? How difficult was it for workers to move from a sector that received a negative shock to a sector that received a positive shock? This paper contributes to the debate providing some answers. More specifically, we conceptualize China’s growth as a negative trade shock for manufacturing exporters and a positive shock for mining and agricultural exporters. Then, we identify the size of these shocks for a large set of Latin American and Caribbean economies. Finally, using the identified shocks and a structural model of labor markets, we analyze the impact on wages and employment in three large countries: Argentina, Brazil and Mexico.

From the perspective of Latin American producers, the export and import growth of China was realized as a shift in aggregate demand faced by exporters. The size and direction of these

> 1See World Bank and Development Research Center of the State Council (2013).

> 2See Feenstra and Wei (2009).

> 3See for example Lora (2007) and Lederman _et al_ (2009).

> 4See for example the articles: “China and Latin America: Connected and Competing, _The European Financial Review_ , February 20<sup>_th_</sup> , 2013; “China and Latin America Magic,or realism?, _The Economist_ , January 1<sup>_st_</sup> , 2005 and “Deals Help China Expand Sway in Latin America”, _The New York Times_ , April 15<sup>_th_</sup> , 2009.

2
shifts probably varied across countries and sectors. For example, Latin American manufacturing exporters probably faced lower prices and increased competition as China increased its market share in world markets. On the other hand, the increase of Chinese raw material imports probably contributed to the recent increase in commodity prices. As world manufacturing prices decreased, workers employed in manufacturing sectors faced downward pressures on their wages, forcing some of them to leave their sectors of employment. Unlike the manufacturing sector, as raw material prices increased, wages and employment in mining and agricultural sectors might have risen. These effects vary across countries and across sectors within a country. Moreover, the final effect on wages and employment depends on both the size of the shocks and the size of each sector as well as the costs workers face when moving between sectors of employment.

In the absence of reliable estimates of the impact of China on product-level prices, we develop vulnerability and opportunity indices for manufacturing, mining and agriculture. The objective of these indexes is to quantify the impact of China on each sector’s trade balance in a large sample of Latin American economies. Put differently, these indices give us the size of country-specific trade shocks that can be attributed to China’s increasing weight in world trade. In turn, we estimate (and partially calibrate) a neoclassical small open economy model with labor market adjustment costs using Argentine, Brazilian and Mexican data. Finally, we bring these two pieces together: We shock and calibrate the model using the vulnerability and opportunity indexes to calculate the effects of China’s export growth on wages, labor allocations, and informal employment.

The vulnerability and opportunity indexes show the potential effect of China on Latin American exports. For instance, the results suggest that China’s increased demand for mining products could have increased Brazil’s exports (of these goods) by 33% between 2001 and 2011, while Chile’s exports of mining products could have increased by 26%. Sizable increases in agricultural exports are expected for countries like Paraguay, Argentina, Uruguay and Brazil. Moreover, China’s competition in manufacturing could have significant effects on countries exporting textiles like Honduras, El Salvador and Haiti as well as countries exporting a more 3
diverse set of manufacturing products like Mexico.

After calculating the size of positive and negative shocks that can be attributed to China via the indices, we estimate and simulate a neoclassical small open economy model similar to Arias _et al_ (2013). The model is also related to Artu ̧c, Chaudhuri and McLaren’s (2010) labor mobility model but with a special emphasis on informal employment (which is especially important for Latin American economies). The model is empirically tractable, requiring only worker employment transitions across industries and employment status from labor-force surveys with panel data, which are available for Argentina, Brazil and Mexico. With these estimates of labor mobility costs, we simulate the effect of changes in labor demand across sectors to study labor-market adjustments caused by the growth of Chinese exports in global markets.

The estimation of labor mobility costs, as in Arias _et al_ (2013), highlights three common features in the labor markets of Argentina, Brazil and Mexico. First, it is less costly to become formal if the worker stays in the same industry. Second, the highest entry costs involve moving from informal to formal and changing the sector of employment. Third, the lowest entry costs are associated with movements from formal to informal within sectors. Moreover, in the three countries the highest mobility cost is found for workers moving out of informality into formality while changing sector towards agriculture or mining. This will limit the movement of workers to these sectors which were precisely the sectors that were positively affected by China. These high switching costs thus limited the reduction of informal employment of the positive shocks emanating from China.

From the simulations we conclude that, for Argentina and Brazil, the positive shocks in agriculture and mining offset the negative shock in manufacturing leaving the total level of employment and real wages almost at the same level. In these two countries, a larger positive shock in mining and agriculture is needed to offset a smaller shock in manufacturing because this sector employs a larger proportion of workers. In Mexico, the larger shock in manufacturing provokes a reduction of employment in the long run.

The export vulnerability index we introduce herein is closely related to the studies by 4
Lass and Weiss (2004), Hanson and Robertson (2009) and Freund and Ozden (2009). Lall and Weiss (2004) compare Latin American and Chinese exports, product by product at the 3-digit SITC level and identify categories where China is gaining market share at the expense of Latin America between 1990 and 2002. They find that in 1990, 30% of trade was in industries where Chinese exports were increasing and Latin American exports were decreasing. On the other hand, they find that China’s threat has been decreasing gradually: By 2002, only 11% of Latin American exports seemed to be negatively affected by China. They conclude that, over time Latin American export structures became relatively complementary to China’s exports.

Similarly, Freund and Ozden (2009) find that China’s export growth had only a small negative effect on overall Latin American exports. They show that China was affecting Mexico’s industrial exports negatively, but for the rest of Latin America, they did not find any significant impact. For Mexico, they found that a 10% increase in China’s industrial exports reduced Mexico’s industrial export growth by 7.9%. Moreover, they conclude that China’s continuing export growth might be affecting the wage distribution since it is concentrated in high wage industries.

Hanson and Robertson (2009) analyzed the effect of China’s export growth on Latin America using a gravity model, where exporters produce differentiated goods and compete with Chinese exporters under monopolistic competition. First, they estimate the changes in exporter fixed effects associated with export growth. Then, they simulate a counter-factual scenario for Latin America’s manufacturing export growth setting China’s export growth rate to zero. They find that China impeded growth of manufacturing exports in Argentina by 1.1%, Brazil by 1.4%, Chile by 2.3%, and Mexico by 3.1%.

Autor, Dorn and Hanson (2013) studied the implications of the rise of China on local labor markets (defined as “Commuting Zones”) within the United States through imports of Chinese goods. The authors argue that changes in Chinese imports by other high-income countries (used as an “instrumental variable”) caused higher unemployment, reduced labor-force participation and reduced wages in U.S. local labor markets that “house[d] import-competing manufacturing 5
industries” (Autor, Dorn, and Hanson, 2013). Similar research on local Mexican labor markets in currently underway at the Central Bank of Mexico, led by Daniel Chiquiar, with qualitatively similar results as those reported by Autor, Dorn and Hanson for the United States.<sup>5</sup> The rest of this paper is organized as follows. Section 2 describes the role of China as a competitor and source of demand in global markets. Section 3 explains the methodology, while section 4 contains the description of the data. The results are presented in section 5 and section 6 concludes.

# **2 China as a Competitor and Source of Demand in Global Markets**

Since the early 2000s China has experienced an astonishing increase in its exports. Between 2001 and 2011 China’s exports increased 400%, with manufacturing exports increasing 410%<sup>6</sup> . Moreover, Figure 1 shows that China increased its share in world exports from 7% to 12% in the same period, with manufacturing increasing from 7.6% to 14%. This large increase in exports suggests that China probably put downward pressure on world prices. For example, Amiti and Freund (2010) construct a price index for manufactured goods export from China to the US, finding a 12% decrease in the price (unit values) between 1997 and 2005. This change in prices and global shares probably affected other countries’ exports, mainly those competing in the same markets. For instances Mexico share in global manufacturing exports decreased from 2.6% to 2.1% between 2001 and 2011. Similarly, if we choose those products in which China’s share in world exports increase by 50% or more, and LAC represented at least 5% in world exports, we find that LAC’s share in global export of these products decreased by 16%, while the rest of the world (i.e excluding China) decreased its share by 14%.

Although China’s exports can be seen as an increase in competition for some countries, its

> 5See Handley and Limao (2013) for the effect of China’s growth on US consumers through uncertainty channels.

> 6In current USD.

6
increasing imports offered export opportunities. China’s imports increased by 574 % between 2001 and 2011. As Figure 1 shows, China increased its world import share from a 4% to 9% in the same period. This increase was especially important in the mining and agricultural sectors<sup>7</sup> . China’s share in world imports of agricultural goods increased from 4.6% to 13.2%. China’s share in the world mining imports increased by 12 percentage points from 3% to 15%. Particularly, China’s imports from Brazil of mining goods in 2011 were 38 times its imports in 2001. At the same time, its imports of agricultural goods from Brazil in 2011 were 17 times its imports in 2001. Another example in Latin America is Argentina. China’s agricultural imports from Argentina in 2011 were 4 times its imports in 2001.

In sum, China has been playing two main roles in global trade: one as a large exporter of manufactured goods, probably pushing international prices downwards, and another as an important importer of mining and agricultural goods. These two roles impact Latin America exports and labor markets in opposite directions. The main aim of this paper is to analyze the net effect of these opposite forces on Argentina, Brazil and Mexico. Mexico is a good example of a country competing with China in the global manufacturing market, while Brazil and Argentina are good examples of countries that have increased they exports of mining and agricultural products.

# **2.1 China as a Competitor in Manufacturing**

The increase in China’s manufacturing exports probably had a greater impact on economies whose exports were more similar to China’s. To measure the degree of similarity between the exports of manufacturing goods between China and our three Latin-American countries we calculated the index proposed in Finger and Kreinin (1979). This index measures the proportion of exports from a pair of countries that are similar. More formally, the index is defined as:

> 7Mining refers to mining plus utilities.

7
where _i_ and _j_ are countries, _P_ is a group of products (for example manufacturing goods) and _xp,i_ is the share of product _p_ in total exports of goods _P_ . Figure 2 shows the similarity index for manufacturing goods between our three Latin-American countries (Argentina, Brazil and Mexico) and three major exporters: China, the United States (US) and the European Union (EU).

It is clear that Mexico is the country whose manufacturing exports are more similar to those of China. In fact, the bundle of manufacturing exports of Mexico is more similar to China’s than to the US, which it is not the case for Brazil and Argentina. The manufacturing export bundles of Brazil and Argentina are more similar to US and EU’s. Thus, it is expected that China’s increased in manufacturing exports will have a greater effect on Mexico’s exports than Argentina or Brazil.

In order to show that the similarity is driven by products in which China increased its exports, we now present a dynamic revealed comparative advantage index. This index gauges the change in the share one country’s exports represents in a trading partner’s total imports of a product. More precisely the index is defined as: where _Mc,p,t_ refers to imports of market _M_ from country _c_ , of product _p_ , in period _t_ . This index tells us if exports from a country are gaining global market share in a particular product. If the DRCA is greater than zero, then country _c_ is increasing its importance in country _M_ ’s imports of _p_ . Using this index we pay attention to two types of products: those under direct threat and those under strong-partial threat. We say that there is a direct threat if the DRCA of China is greater than zero and the DRCA of country _c_ is less than zero, i.e if China is gaining share in a particular market while country _c_ is losing participation in the same market. There is a partial threat if the DRCA is positive for both (China and country _c_ ). This partial threat is strong if the DRCA of China is greater than the DRCA of country _c_ , this is, although country _c_ is gaining participation in a particular market, China is gaining even more participation in 8
the same market.

Figure 3 shows the number of products under direct and strong-partial threat for Argentina, Brazil and Mexico in three different markets: US, EU and the world. Figure 3 shows the percentage of manufacturing exports under the same types of threat in the same markets. In both graphs, the DRCA was calculated for every year with respect to 2001.

From Figure 3 it is clear that the number of products in which China has been gaining import market share respect to LAC is increasing, particularly in the EU market. Moreover, in the world market more than 80% of Mexico’s manufacturing exports seem to be losing importance vis-`a-vis China. At the beginning of the 2000s about 80% of manufacturing exports from Mexico to US and the EU were also loosing importance against China. Thus, it seems that China’s manufacturing exports are not only similar to Mexico’s exports but also; they are similar in products in which China has been gaining market share.

For Mexico, Figure 3 shows a decreasing trend in the fraction of exports that are under threat, particularly in the US market. This trend can be due to a crowding out of Mexican products by Chinese products. For example, let us say that 100% of Mexico’s manufacturing products were crowded out by China’s exports, then the percentage of Mexico’s exports under threat is zero, but only because it is exporting zero goods. Thus, the decreasing fraction of Mexico’s exports under threat in the US market can be due to a reallocation of products, and China’s exports might be part of the explanation of this reallocation.

In sum, if China is behind the change in manufacturing exports of these three countries, it is expected to have a greater effect on Mexico due to their similarity in their manufacturing export basket.

# **2.2 China as a Source of Demand**

As noted above, China increased its share in world imports, particularly in agricultural and mining goods. Thus, China increased its importance as a trading partner for several countries, some of them in Latin America.

9
Figure 4 shows that in 2001 Brazil’s exports to China were about 4% of Brazil’s total exports, while in 2011 China represented around 20% in Brazil’s exports. In other words, China increased its importance as trading partner of Brazil by 16 percentage points in 10 years. This increase is mainly explained by the increase in mining exports. Exports of mining account for 11 percentage points of the 16 percentage points, while agriculture explains about 4 percentage points.

China also increased its importance as a trading partner of Argentina and Mexico. As Figure 4 shows, from 2001 to 2011, China increased its importance in Argentina’s exports by about 3 percentage points. An increase in agricultural exports explains 2 out of 3 percentage points. The increase of Mexico’s exports to China, in the same period, is about 1.6 percentage points, half of them explained by an increased in mining exports.

On one hand, China increased its exports of manufacturing goods, which increased the global supply these products, affecting Latin American exports. On the other hand, China has increased its imports of mining and agricultural goods, also affecting Latin American exports, but in the opposite direction. Which opposing force dominates the other: The increase in the global demand for Latin American agriculture and mining exports or the decrease in residual demand for manufacturing exports? The rest of the paper tackles this question.

# **3 Methodology**

## **3.1 “China Effect” on Net Exports**

Our goal is to develop an index that can capture the changes in net demand, faced by exporters, that are uncorrelated with local market conditions in Latin America. Unfortunately, it is quite difficult to identify the changes in net exports caused directly by China empirically, since there are many other relevant shocks that can shift import demand and export supply curves; domestic shocks can easily dominate the shocks caused by China. Therefore we deliberately abstain from using bilateral trade data between Latin American and other countries, 10
which are subject to shocks unrelated to China. However, we can conveniently assume that changes in China’s export market share in manufacturing and import market share in mining and agriculture (relative to the rest of the world) were exogenous to local conditions in Latin America.

Moreover, the competition faced by manufacturing exporters can be quite different based on the products they export. Some countries’ exports overlap with China’s export composition more than others. For example, Mexico’s manufacturing exports are similar to China’s exports while Brazil’s manufacturing exports are quite different. The same logic applies to imports. Therefore, export and import composition of a country can affect its exposure to the potential threats and opportunities brought by the rise of China in global markets.

Appendix I presents a detailed derivation of the vulnerability index. In short, to calculate the export vulnerability of a sector, for each four-digit industry subcategory we calculate the net increase in China’s share of world trade adjusted by the size of the ROW in world’s exports. Then, we calculated a weighted average of these increases, where the weights are the initial share of each product in country _i_ ’s total manufacturing exports. The following equation gives us the export vulnerability index, which is expressed as a percent change in country _i_ ’s total sectoral exports: where _Xg,t_<sup>_C_is China’s exports of product</sup><sup>_g_at time</sup><sup>_t_,</sup><sup>_X_</sup> _g,t_<sup>_W_is the total exports in the world,</sup><sup>_X_</sup> _g,t_<sup>_i_</sup> is country _i_ ’s exports, and _G_ is the set of all products in a particular sector, according to the ISIC classification. The minus sign at the beginning of (1) tells us that exports in the ROW decrease if China’s share in world’s exports increases.

As mentioned, the index is a function of the export composition of the origin country. For example, if Mexico’s exports and China’s exports are unrelated, then the index will be zero. It 11
is important to note that we assume perfect substitutability of Chinese exports and country _i_ ’s exports at the 4 digit level of aggregation.

We develop a similar index to calculate the increase in the import demand faced by Latin American economies due to China’s growth. This opportunity index is equal to where _Mg,t_<sup>_C_istheproduct</sup><sup>_g_importsofChinaattime</sup><sup>_t_and</sup><sup>_M W_</sup> _g,t_<sup>istotalimportsintheworld.</sup> This is the same index as (1) assuming that China’s exports are equal to the -additive- inverse of imports and that world’s imports equal world’s exports.

Note that we use the increase in China’s share in world imports instead of using the increase in country _i_ ’s exports to China. We avoid using bilateral data because they are affected by both import demand and export supply shocks. The objective is to isolate the exogenous import demand shock from export supply shocks.

In order to have an estimate of the increase or decrease in net exports of a sector in country _i_ , we calculate the difference between the opportunity and vulnerability indices. In reality, the actual change in country _i_ ’s net exports can be quite different from the amount implied by the indices. Because the indices only capture the shocks caused by the growth of China, assuming there are no other exogenous changes, particularly in local market conditions. Note that we are not interested in calculating the observed change in exports _per se_ , but the change in exports that can be attributed to China, net of all other exogenous shocks.

It is useful to discuss the underlying assumptions under which the indices can capture changes in country _i_ ’s net exports: (1) All products from all origins within category _g_ are perfect substitutes produced in perfectly competitive markets, and they are unrelated to products in other categories. The level of aggregation of the trade data might affect the validity of this assumption. For example, 12
if the level of disaggregation is high, then different categories of products can become closely related to each other. In order to avoid this problem, we do not go beyond the 4 digit level of the ISIC classification<sup>8</sup> .

(2) The exogenous product _g_ shocks originating from China are approximately equal for the Latin American producers and for the rest of the world. This assumption may be violated if competition is localized due to high transportation costs. For example, the increase in Chinese exports might affect Vietnamese exporters more severely than Mexican exporters as a result of Vietnam’s proximity to China.

This said, we calculated the vulnerability and opportunity indexes for manufacturing, mining and agricultural products in Argentina, Brazil and Mexico. The difference between these indices tell us how much net exports were expected to increase (or decrease) because of China’s rapid growth for a typical country with given initial export structure, assuming everything else stays the same. Then we use the model presented in the next section to calculate corresponding price changes in each economy. This is the required price change to match the change in net exports within our neo-classical model calibrated with Latin American data. For example, we calculate how much the manufacturing prices should decline to account for the change implied by the net export index (i.e the difference between the opportunity and vulnerability indices in manufacturing).

# **3.2 A Model of Labor Adjustment in a Small Open Economy**

We use the informal and formal labor mobility framework introduced by Arias _et al_ (2013). Consider an economy with perfectly competitive _N_ +1 sectors and _N_ products. The last sector is the residual (or non-market) sector, which is not related to any product. Each sector, apart from the non-market sector, has an informal and a formal sub-sector. For example, manufacturing goods can be produced either in the formal manufacturing sector or in the informal manufacturing sector. In addition to perfectly competitive producers, there is a fixed

> 8We have experimented with different aggregation levels, such as ISIC 6, and found that the proposed index is robust.

13
number of risk-neutral workers whose decisions constitute the labor supply equations.

# **3.2.1 Labor Supply**

Workers are distributed across 2 _N_ + 1 sub-sectors: A worker in sector _i_ receives wage _wt_<sup>_i_</sup> at time _t_ . In addition to the wage, workers receive a sector specific common utility _η_<sup>_i_</sup> while working in sector _i_ . The fixed utility _η_<sup>_i_</sup> accounts for compensating differentials, such as working conditions, hours, benefits, on-the-job hazards, etc. Therefore, the sector with the highest wage is not necessarily the most attractive one. We assume that both the wage _wt_<sup>_i_andthefixed</sup> utility _η_<sup>_i_</sup> are common for all workers in sector _i_ , hence workers are ex-ante identical (within sectors).

Workers have an option to change their industry of employment in every time period. However, workers face moving cost frictions, and have to pay a utility cost if they decide to move. The moving cost has two components: a fixed moving cost common to all workers, and a random idiosyncratic component that is worker-specific in every time period. When a worker moves from sector _i_ to _j_ , she pays the fixed moving cost _C_<sup>_ij_</sup> and a random idiosyncratic worker-specific cost _ε_<sup>_i_</sup> _t_<sup>_−ε_</sup> _t_<sup>_j_, where the random variable</sup><sup>_εi_</sup> _t_<sup>follows an “extreme value” distribution</sup> with scale parameter _ν_ , which is proportional to the standard error. The moving cost captures all labor market frictions that restrict worker mobility across sectors, and it should not be interpreted (only) as the literal cost of moving. Stayers do not pay the moving cost, i.e. _C_<sup>_ij_</sup> = 0 if _i_ = _j_ . Due to the idiosyncratic component of the moving cost, workers are ex-post from each other.

In the dynamic equilibrium, workers choose the optimal sector after taking wages, fixed utility, fixed moving costs, random moving costs into account by calculating their expected future values. Workers are rational, risk neutral, live infinitely and discount the future with parameter _β_ . The optimal sector for a worker may not be the one with the highest wage and fixed utility because of the moving costs and workers’ expectations about the future. Under the rational expectations assumption, workers have an expectation about future wages but their 14
expected wages do not necessarily equal the revealed wage in the data. That is, workers can make errors in their guesses about the future, but the errors cannot be systematic, i.e workers cannot persistently underestimate or overestimate future wages. Our econometric strategy does not require parameterization of workers’ expectations, however.

The optimization problem is expressed by the following equation where _Vt_<sup>_i_isthepresentdiscountedvalueoftheworkers’utilityinsector</sup><sup>_i_.Thechoice-specific</sup> Bellman equation is where Ω<sup>_i_</sup> _t_ +1<sup>isthe“optionvalueofmoving”,whichisequaltotheexpectedbenefitofmoving</sup> conditional on the net benefit of moving being positive. The “option value of moving” can be solved analytically when _ε_<sup>_i_</sup> _t_<sup>is an i.i.d random variable that follows a “extreme value”distribution.</sup> The labor flows can be expressed as where _yt_<sup>_ij_isthenumberofworkersmoving to sector</sup><sup>_j_from sector</sup><sup>_i_and</sup><sup>_Li_</sup> _t_<sup>is the totalnumber</sup> of workers in sector _i_ .

15
# **3.2.2 Production, Consumption and Net Exports**

We assume that formal and informal sub-sector products are perfect substitutes. However, consistent with existing literature, workers are less productive when they are employed in the informal sector as opposed to formal sectors. This assumption is also consistent with the lower informal wages observed in the data. The effective total human capital in an industry _i_ can be expressed as where _j_ and _k_ are the formal and informal sub-sectors of industry _i_ . _a_<sup>_j_</sup> is the productivity parameter in the formal sector _j_ , and _a_<sup>_k_</sup> is the productivity of the workers in the informal sector _k_ . Assuming Cobb-Douglas production, output _qt_<sup>_i_inmain-industry</sup><sup>_i_attime</sup><sup>_t_isequal</sup> to where the labor share is _γi_ and the stock of capital is _K_<sup>_i_</sup> .

From the production function (7) and equation (6), we can derive the marginal product of labor for a worker of type _k_ in sector _i_ . Equating the wage to the value of the marginal product of labor, we obtain the wage equation: where _s_ is the formal or informal sub-sector of sector _i_ , _Pt_ is the aggregate price index, _p_<sup>_i_</sup> _t_<sup>is</sup> the unit price of sector _i_ output and _wt_ is the real wage in terms of _Pt_ .

We close the model with the following consumer price index equation 16
with consumption shares _θi_ .

Given the Cobb-Douglas preferences, the domestic consumption of sector _i_ product, _c_<sup>_i_</sup> _t_<sup>,canbe</sup> expressed as where the income _Mt_ equals _Mt_ =<sup>�</sup> _i_<sup>_p_</sup> _t_<sup>_iq_</sup> _t_<sup>_i_.</sup> We pin down prices by setting _qt_<sup>_i_=</sup><sup>_ci_</sup> _t_<sup>inthenon-tradablesectors9.Inthetradedsectors,the</sup> net exports are equal to It is useful to write the net exports relative to time _t_ 0 variables for calibration purposes: where _q_ ̃ _t_<sup>_i_</sup> 0<sup>and ̃</sup><sup>_c_</sup> _t_<sup>_i_</sup> 0<sup>are production and consumption of product</sup><sup>_i_, and they can be directly plugged</sup> in from the data<sup>10</sup> .

> 9In this paper, the non-tradable sectors are: construction, retail and other services.

> 10Particularly from Input-Output tables

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# **3.3 Estimation, Parameterization and Calibration**

Our econometric strategy relies on estimating expected values from the data. Previously in the literature, Hotz and Miller (1993) showed how expected values can be imputed from conditional choice probabilities. They introduce an inversion equation that maps probabilities to values and estimate conditional choice probabilities with a non-parametric method. In a departure from the literature, we use a Poisson Pseudo Maximum Likelihood (PPML) regression to estimate expected values instead of the Hotz and Miller (1993) inversion equation. PPML is used in the international trade literature to estimate the gravity model as in Santos Silva and Tenreyro (2006) and Anderson (2011). The use of PPML is intuitive and simplifies the estimation procedure significantly. With an infinitely large sample, Hotz-Miller inversion equation and PPML give identical results. However, PPML performs better if the sample has many zeros due to small sample (as it is usually the case with developing country data).

The following variables are taken directly from the data: Wages, _wt_<sup>_i_,thedistributionof</sup> workers, _L_<sup>_i_</sup> _t_<sup>,andlaborflows,</sup><sup>_y_</sup> _t_<sup>_ij_.</sup> The discount factor _β_ is fixed at 0.95 and the variance parameter _ν_ is set to 0.6 following the literature.<sup>11</sup> The moving cost parameter, _C_<sup>_ij_</sup> , the values _EVt_<sup>_i_andtheoptionvaluesΩ</sup><sup>_i_</sup> _t_<sup>areestimated.Theremainingfixedutilityparameters,</sup><sup>_ηi_,and</sup> production function parameters are calibrated from the data.

## _Stage 1: The Flow Equation_

The labor flow equation (5) can be estimated with a PPML regression similar to the gravity equation, where the entry costs create a bilateral resistance term similar to distance. The labor flow equation can be expressed as a PPML regression such that

> 11 It is possible to estimate _ν_ with longer time series. For example, Artu ̧c _et al_ (2010) use a time series of 26 years. Although, our estimator is more efficient, we do not have sufficiently long time series to pin down _ν_ . See the technical note by Artu ̧c (2013) for details.

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Γ<sup>_i_</sup> _t_<sup>is the coefficient of the origin-industry fixed effect; Λ</sup> _t_<sup>_j_is the coefficient of destination-industry</sup> fixed effect<sup>12</sup> ; and _δ_<sup>_ij_</sup> is the normalized fixed entry cost to sector _j_ from sector _i_ . **1** _i_ = _j_ is the indicator function that equals one if _i_ is different from _j_ and zero otherwise (i.e. when _yt_<sup>_ij_</sup> corresponds to movers rather than stayers). _e_<sup>_ij_</sup> _t_<sup>istheregressionresidual.</sup> The structure of the moving cost matrix _δ_<sup>_ij_</sup> is flexible. Workers pay an entry cost that is a function of the destination sector and workers’ original formality status. Thus, we assume the following moving cost structure:

- ∆<sup>_j_</sup> 1<sup>_,_fromaninformalsectortoformal</sup><sup>_j_withoutchangingthemainindustry,</sup>      ∆<sup>_j_</sup> 2<sup>_,_fromaformalsectortoinformal</sup><sup>_j_withoutchangingthemainindustry,</sup>  

- (14) _δ_<sup>_ij_</sup> = ∆<sup>_j_</sup> 3<sup>_,_fromanyinformalsectortoformal</sup><sup>_j_whilechangingthemainindustry,</sup>     ∆<sup>_j_</sup> 4<sup>_,_fromanyformalsectortoformal</sup><sup>_j_whilechangingthemainindustry,</sup>     ∆<sup>_j_informal</sup><sup>_j_whilechangingthemainindustry(fromanysector),</sup> 5<sup>_,_to</sup>

-

where _i_ is the origin sector and _j_ is a formal or informal destination sector, such as informal manufacturing or formal services. The main industries are: 1. Agriculture, 2. Mining and Utilities 3. Manufacturing, 4. Construction, 5. Trade, 6. Hotels and Restaurants and, 7. Other Services. Each main industry consists of an informal and a formal sector. Therefore there are 2 _×_ 7 + 1 = 15 sectors including the “residual” sector. For example, if a worker moves from informal manufacturing to formal manufacturing (that is, without changing the main industry), she faces the moving cost ∆<sup>_j_</sup> 1<sup>.Ifaworkermovesintoinformalmanufacturingfrom</sup> formal services, she faces the moving cost ∆<sup>_j_</sup> 5<sup>.If a worker moves from formal manufacturing to</sup> formal service she faces the moving cost ∆<sup>_j_</sup> 4<sup>.</sup> The _j_ -specific term or the destination fixed effect, Λ<sup>_j_</sup> _t_<sup>,isequalto</sup>

> 12Note that, we need to drop either destination or fixed effect for one choice (i.e. normalize the choice specific values). Otherwise, the regression matrix becomes singular because only the value differences between choices are from the choice data.

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the _i_ -specific term or the origin fixed effect, Γ<sup>_i_</sup> _t_<sup>,isequalto</sup> and the moving cost term, _δ_<sup>_ij_</sup> , is equal to Finally, using destination and origin fixed effects, we construct the option value term, Ω<sup>_i_</sup> _t_<sup>,as</sup> See Appendix II for the details of the estimation strategy. The remaining parameters _η_<sup>_i_</sup> and _ν_ are calibrated in the second stage. After calibrating, _η_<sup>_i_</sup> , and the production functions, the model can be fully parameterized.

# _Stage 2: Parameterization and Calibration_

The estimation procedure gives us the dynamic labor supply equations. In order to derive the labor demand and net export equations, we need to parameterize the production and consumption functions. Without loss of generality, we normalize the productivity parameter to unity for formal workers, i.e _a_<sup>_j_</sup> = 1 for formal sector _j_ . Also, by assumption, the informal productive multiplier is equal to _a_<sup>_k_</sup> = _wt_<sup>_k/w_</sup> _t_<sup>_j_where</sup><sup>_k_and</sup><sup>_j_are,respectively,theinformaland</sup> formal sub-sectors of the same sector. The _a_<sup>_k_</sup> ’s are easily pinned down from observed average 20
wages.

We pin down the Cobb-Douglas labor shares from the ratio of an industry’s wage bill over the industry’s value added (which comes from national input-output tables<sup>13</sup> ). The wage equation multiplier _A_<sup>_i_</sup> is calibrated such that the aggregate labor allocation reflects the observed average wage in the data from each country. Finally, the fixed utility parameters, _η_<sup>_i_</sup> , are calibrated by matching the simulated labor allocations with the labor allocations observed in the data.

# **3.4 Simulations**

We simulate the effect of China on each country with a one-time change in the world prices of manufacturing, mining and agriculture at the same time. We assume that China’s growth in exports and imports causes the price faced by Latin American exporters to change, since producers face perfectly elastic demand in a perfect competition setting. Consistent with this, we assume that Argentina, Brazil and Mexico are small open economies, i.e. they take prices as given. However, the difference between the vulnerability and opportunity indices gives us the change in net exports but not in prices. Therefore, we need to map the change in net exports (which is endogenous in our model) to a exogenous price change.

The price effect of China is endogenous estimated with the calibration of the model. The calibration is performed with the estimated moving-costs parameters and the other parameters computed from the observed data as discussed above. In the first iteration, we shock the model with a guess of the price effect of (only) one sector and we use it to calculate the reduction in net exports over time (see equation 12). Then we compare the change in net exports with the difference between the opportunity and vulnerability indices we described previously (see equations 1 and 2). If the change in net exports equals the difference between the indices we stop. If the change in exports is not equal to the difference between indices we apply another price reduction. The algorithm tries different price shocks until it finds an equilibrium where

> 13We used the IO table of 2003 for Mexico, 2005 for Brazil and 1997 for Argentina. The sources are the INEGI, IBGE and INDEC respectively.

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the long run reduction in simulated net exports is equal to the reduction implied by the indices, for each sector separately.

Then we simulate the model applying the change in prices of all the three sectors at once. The simulations are conducted by calculating the equilibrium values of _Vt_<sup>_i_,</sup><sup>_Li_</sup> _t_<sup>,</sup><sup>_pi_</sup> _t_<sup>for every</sup><sup>_i_and</sup> _t_ , using the equations described in the previous subsection. The simulation method is based on a shooting method similar to Lipton _et al_ (1982).

When we shock the system by changing the price of one product (i.e through a demand shock), labor supply and wage differentials change for all sectors in the general equilibrium setting. Therefore, a demand shock for one sector creates supply shocks in other sectors. Our goal is to calibrate the price change in a particular sector to match a specific demand shock implied by the index, _cetirus paribus_ . Therefore, we have to calibrate price changes separately for each demand shock (as opposed to calibrating them together at once), without contaminating the system with shocks originating from other sectors<sup>14</sup> .

We compute the perfect-foresight path of the adjustment following the one-time price shock. The simulated adjustment process continues until the economy effectively reaches the new steady state. This requires that each worker –taking the time path of wages in all sectors as given– optimally decides at each date whether or not to switch sectors, while taking into account workers’ own idiosyncratic or personal shocks. This computation yields a time path for the allocation of workers, as well as the time path of wages, since the wage in each sector at each date is determined by market clearing conditions from equation (8)<sup>15</sup> .

> 14To check robustness of the results, we calibrated price changes simultaneously for all demand shocks and found that the general equilibrium effects are not large enough to affect the results significantly.

> 15The simulations were also run using the opportunity indexes of agriculture and mining as well as the vulnerability index of manufacturing instead of the net effect. Moreover, we also simulate each price change separately. The main results are qualitatively similar to those of the net effects. Results are available upon request.

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# **4 Data**

The estimation of the labor mobility costs is conducted using Labor Force Surveys (LFS) from Brazil, Mexico and Argentina. A common feature of these surveys is their rotative panel structure that permits the construction of employment transition matrices. We restrict the sample to individuals between 15 and 65 years of age. The industries are aggregated into seven sectors: i) Agriculture, ii)Mining and Utilities, iii) Manufacturing, iv) Construction, v) Commerce, vi) Hotels and Restaurants, and vii) Other Services. Each sector is further divided into formal and informal. A worker is considered informal if she is not registered in the social security system. Additionally, there is a residual sector that captures individuals who are either unemployed or out of the labor force.

The “Encuesta Nacional de Ocupacion y Empleo” (ENOE) is used to compute the transition matrices for Mexico. The ENOE is a household survey that collects detailed information on labor force status, wages, occupational and demographic characteristics. It is collected quarterly since 2005, and it is representative at both the national and state levels. The sample size is around 120,260 households in each quarter. Each household is interviewed in five consecutive quarters. In each quarter, one fifth of the sample (e.g., households in their fifth interview) is replaced. We construct the transition matrix by looking at the first and fifth interview. We derive six year-on-year transition matrices, the first matrix reflecting transitions between 2005 and 2006 and the most recent between 2010 and 2011.

The “Pesquisa Mensal de Emprego” is the Brazilian LFS. The survey collects detailed information of Brazil’s labor market and it is representative of only six metropolitan (urban) regions. Each wave of the survey has approximately 120,000 individuals. The panel structure consist on each household being interviewed 12 times over an 18 months period. In each wave of the survey, we identify individuals in their first interview and we follow them a year later at the time of their fifth interview. We computed four year-on-year transitions matrices that cover the period between 2007 and 2011.

The “Encuesta Permante de Hogares” (EPH) is used to compute the transition matrices 23
for Argentina. It is collected quarterly since 2003, and it is representative of the urban areas. The sample size is around 17,000 households in each quarter. Each household is interviewed in two consecutive quarters and then again in the same two quarters of the following year. We construct the transition matrices by comparing the sector of employment of the individual in the first and third interviews. We derive seven year-on-year transition matrices, the first matrix reflecting transitions between 2004 and 2005 and the most recent between 2010 and 2011.

The simulation exercises require information on key parameters, including labor shares and consumption shares by sector for each country. Complementary data sources were required to compute these parameters. The labor shares were defined as the ratio of the wage bill over total value added for each sector. These parameters were calculated from input-output (IO) tables from Mexico, Brazil and Argentina. We used the IO table of 2003 for Mexico, 2005 for Brazil and 1997 for Argentina. Average formal and informal wages were calculated with data from the Labor Force Surveys (LFS). We use CPI hasket shares along with input-output tables on consumption expenditures to calibrate the utility function parameters. The parameters for the simulations are shown in Table 1.

# **5 Results**

This section presents three sets of results. The first set of results is related to the direct effect of China’s trade on LAC’s exports. This direct effect of China is calculated for three sectors (manufacturing, agriculture and mining) and 29 Latin American countries, using our vulnerability and opportunity indexes. The second set of results are the PPML estimation of the workers’ perceived entry costs. These costs are associated with movements across industries as well as with movements in and out of formality. Finally, the estimated entry costs and the net-exports effects are utilized to simulate the total effect of China’s trade on Argentina, Brazil and Mexico’s labor markets.

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# **5.1 The China Effect on Exports**

As explained in the methodology, the vulnerability index before China’s change in manufacturing, agricultural and mining exports, was calculated as a weighted average of the change in China’s share in global exports of the each sector (adjusted by the size of the rest of the world (ROW), i.e the world excluding China). Similarly, the opportunity index of China’s increase in imports -of manufacturing, mining and agriculture- was calculated as a weighted average of the change in China’s share in world’s imports (adjusted by the size of the ROW). The net index is defined as the difference between the opportunity and vulnerability indices. The net index shows that Brazil, Argentina and Mexico are between the countries with the largest effect in mining, agriculture and manufacturing, respectively; making them suitable to analyze shocks in these sectors.

Figure 5 shows the opportunity and net China effect on LAC’s mining exports. Brazil appears with the highest net effect among all the countries in the sample, mainly due to the increase of iron ore exports. According to our index, China’s increase in mining imports -from 2001 to 2011- would increase Brazil’s exports of mining by 33%. Following Brazil, the second country with the highest effect is Chile, one of the most important exporters of copper. The net effect on Chile’s exports of mining is about 26%. At the bottom are countries like Costa Rica and El Salvador, which do not have an important extractive industry.

Figure 6 shows the opportunity and net effect of China on LAC’s agricultural exports. At the top are countries like Paraguay, Argentina, Guyana, Brazil and Uruguay. The largest effect is found for Paraguay, with a (positive) effect of about 16%. The second largest effect is found for Argentina, with 13%, followed by Guyana and Brazil, both with an effect close to 11%.

Figure 7 shows the vulnerability and net effect on LAC’s manufacturing exports. The most affected countries were Haiti and Honduras. For these countries, textiles where among the most important manufacturing exports in 2001. The calculated effect was 19% and 16% for Haiti and Honduras respectively. Mexico was hit by 11%. Different from Haiti and Honduras, the effect on Mexico was not concentrated in textile exports; it was distributed across more products, 25
including office and accounting machinery as well as television and radio transmitters. With a positive effect are countries like Bolivia, Paraguay, Suriname and Cuba, which exported few manufactured products that overlapped with China.

Table 2 shows the implied change in prices for Argentina, Brazil and Mexico, which were estimated with the iterative simulation procedure. The decrease of 11% in Mexico’s manufacturing exports is associated with a price shock of 12%. Similarly, the implied change in mining price for Brazil is about 27%, while Argentina’s agricultural price change is about 10%.

# **5.2 Labor Mobility Costs**

Table 3 shows the estimates of (normalized) labor mobility costs. Assuming _ν_ = 0 _._ 6, these costs should be interpreted as entry costs in terms of multiples of annual average real wages times 0.6 in each country. As in Arias _et al_ (2013), there are three common results. First, it is less costly to become formal if the worker stays in the same industry. Second, the highest entry costs involve moving from informal to formal and changing the sector of employment. Third, the lowest entry costs are associated with movements from formal to informal within the same sector.

In Argentina and Mexico the lowest entry costs are found in movements from formal to informal employment within the restaurant-and-hotels sector. In Brazil the lowest entry cost is associated with movements from formal to informal within agriculture. Moreover, in the three countries the highest cost is found in movements from informal to formal employment while changing sector into agriculture or mining. This limits the movement of workers into these sectors, which are precisely the ones that were positively shocked by China.

As argued above, the sector most directly affected by China within Brazil is the mining sector, while the agricultural and manufacturing sectors are the main affected sectors in Argentina and Mexico, respectively. Given the estimated entry costs, what are the main movements expected to/from other sectors? An increase in labor demand in the Brazilian mining sector is expected to increase people moving to mining. A higher labor demand in the agricultural 26
sector in Argentina is expected to increase informal employment rather than formal; while an increase of mining labor demand in Mexico is expected to provoke a movement of labor from other formal sectors to formal employment in mining. Moreover, a decrease in labor demand in the manufacturing sector, in general, is expected to provoke a movement of labor to commerce or to the residual sector.

# **5.3 Simulations**

As in Arias _et al_ (2013), the analysis assumes that: 1) the domestic price of each good at date _t_ = _−_ 1 is unity, 2) at _t_ = 0 the domestic prices change according to Table 2, and, 3) the changes in prices are assumed to be permanent. We computed the perfect-foresight path of the adjustment, following the shock in prices. The simulated adjustment process continues until the economy effectively reaches the new steady state. This computation yields a time path for the allocation of workers and wages in each sector.

Figures 8 to 10 show the simulations for Argentina. As expected for Argentina, the employment in the agricultural sector increases in both the formal and informal sector. The agricultural formal sector increases by 14% and the informal agricultural sector increases by around 6%. The labor in the mining sector also increases, but less than in agriculture. The formal employment in mining increases by 4%, while the informal by 1%. Moreover, the shocks provoke a decrease in the manufacturing sector. Formal manufacturing decreases by 1%, while informal manufacturing decreases by 0.5%.

The percentage effects are greater for agriculture and mining than manufacturing but manufacturing is a larger sector. Formal manufacturing employs about 9 times the number of workers that formal mining employs and about 45 times the number of workers in formal manufacturing. Informal manufacturing employs about 41 times more workers than informal mining and 8 times more than informal agriculture. Since manufacturing employs more workers than the other two sectors, its negative effect on employment offset the positive effect of agriculture and mining, at the aggregated level. Thus, the aggregate effect on employment is near zero, 27
as shown in Figure 9. The decrease in the residual sector is about 0.3%. Thus, in general, the negative shock in manufacturing is offset by the positive shock in agriculture and mining.

Figure 10 shows the adjustment path of real wages in Argentina. Right after the shock, wages increase in agriculture and mining and fall in manufacturing, as the demand for labor increases in the two first sectors and decreases in manufacturing. In the long run, as labor is reallocated between sectors, wages tend to move towards their initial level. In the new steady state there is an increase in agriculture real wage (of about 2.6%), while the real wages in the other sectors remain at the original level.

In sum, in Argentina, the positive shock on agriculture and mining is offset by the negative shock in manufacturing, keeping wages and the aggregate level of labor almost at the same original level before the shocks. Although the aggregate level of labor remains constant in the long run, we should notice that there is a reallocation of it from the manufacturing sector to the agriculture and mining sectors.

Similar results are found for Brazil. Figure 11 shows increases of about 35% in the employment level of the formal mining sector and 14% in informal mining. Moreover, there is an increase of about 2% in the number of workers in formal agriculture. In contraposition to these increases in the positively shocked sectors, there is a decrease in the number of workers in the negatively shocked sector, namely manufacturing. The number of workers in formal manufacturing decreases by about 3%, while they decrease by 2% in the informal manufacturing sector. As in Argentina, the manufacturing sector employs substantially more workers than mining and agriculture, which offset the positive effect in employment. As Figure 12 shows, the residual sector decreases by about 0.1%. Thus, as in Argentina, the negative shock on manufacturing is offset by the positive shock in mining and agriculture, leaving the aggregate level of employment similar to the level before the shock. Moreover, in _t_ = 0, when the shocks hit the economy, there is an increase in mining real wages of about 28%, although, as people move out of manufacturing to this sector, increasing the labor supply, real wages start to drop increasing only 2.3% in the new steady state.

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In short, there is a reallocation of labor from manufacturing to mining and agriculture, leaving real wages and the aggregate level of employment almost without change in the long run. In the short run there is an increase in real wages in the mining sector.

Figures 14 to 16 shows the simulations for Mexico. Similar to Brazil and Argentina, there is an increase in formal and informal employment in agriculture and mining, as well as a decrease in formal and informal manufacturing employment. In the informal sector, the employment in manufacturing decreases by 2.6%, while mining increase by 6% and agriculture by 3%. In the formal sector, manufacturing decrease its number of workers by about by 6.3%, while mining increase their number of workers by 15% and agriculture by 2.5%. At the aggregate level, total employment decreases. As Figure 15 shows, the residual sector increases by 2% in the long run. In the short run, there is an important increase in mining real wages (of 16.6%) and a substantially decrease in manufacturing real wages of about 5%. But in the long run, there is only a slightly decrease in manufacturing real wages and a slightly increase in mining real wages.

In sum, for Argentina and Brazil, the positive shocks in agriculture and mining offset the negative shock in manufacturing leaving the total level of employment and real wages almost at the same initial level. Note that, in these two countries, a larger positive shock in mining and agriculture is needed to offset a smaller shock in manufacturing, because this sector employs a larger proportion of workers. In Mexico, the larger shock in manufacturing provokes a reduction of employment in the long run. In Argentina and Brazil the re-allocation of labor goes from the manufacturing sector to the agricultural and mining sectors, while in Mexico the re-allocation of labor goes from the manufacturing sector to the agriculture and mining sectors.

# **6 Conclusions**

This paper analyzed the effects of China on Latin America’s trade and labor markets. Regarding the change in exports, the opportunity and vulnerability indexes suggest sizable effects on Latin America. Specifically we found important negative effects on manufacturing 29
exports for countries such as Haiti, Honduras and Mexico. On the other hand, we found positive effects on agricultural exports for countries like Paraguay, Argentina, Guyana, Brazil and Uruguay and also positive effects on mining exports for Brazil, Chile, Honduras and Peru.

The China-related trade shocks affected LAC labor markets characterized by high labor mobility costs, which drive a notable wedge between the short- and long-run impacts on relative wages and employment in Argentina, Brazil and Mexico. The highest mobility costs were found for workers switching from informality into formal jobs in agriculture or mining. This high mobility cost limited both the number of workers absorbed by these sectors and the reduction in informality induced by the favorable China effect.

The results of the simulations indicate that for Argentina and Brazil the positive shocks on agriculture and mining offset the negative shock on manufacturing, thus leaving the total level of employment and real wages almost at the same initial level in the long run. In these countries, a larger positive shock in mining and agriculture was needed to offset a smaller shock in manufacturing because the latter employed a larger proportion of workers. In the short run there was an increase in real wages in the mining and agricultural sectors.

In Mexico, however, the shock to manufacturing provoked a reduction in employment in the long run. In the short run, there was an increase in mining real wages and a decrease in manufacturing real wages.

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