metadata: authors: 0: author: Bussolo,Maurizio
1: author: Luongo,Patrizia
count: World
docna: 0: docna: The distributive impact of terms of trade shocks
docty: Policy Research Working Paper
subtopic: Labor Markets,Skills Development and Labor Force Training,International Trade and Trade Rules,Food Security,Inequality,Rural Labor Markets
teratopic: Agriculture,International Economics and Trade,Rural Development
abstracts: cdata!: The halving of oil prices, which
            happened in a short period between late 2014 and the first
            months of 2015, has generated major terms of trade losses
            for oil exporting countries. Even if the oil producing
            sector normally employs a small group of workers and oil
            export revenues tend to be concentrated in a few firms and
            in government accounts, these relative price changes have
            economy-wide effects and significant distributive impacts.
            This paper describes and quantifies the channels of
            transmission from the drop in oil prices, to changes in
            welfare distribution at the household level. Using a
            macro-micro simulation model, the paper assesses how this
            shock affects poverty, inequality, and shared prosperity for
            the case of the Russian Federation. The oil price reduction
            generates a reverse Dutch disease that impacts sectoral
            employment, factor returns, and consumption prices. It
            causes a contraction of employment and wages in more
            skill-intensive (non-tradable) sectors, and a reduction in
            consumption prices that is more pronounced for nonfood than
            for food goods. When these shifts are mapped to changes in
            incomes at the micro level, all households are affected.
            Poverty rates could increase by 1 to 4 percentage points,
            depending on the poverty line used. At the US$10 a day
            threshold, for example, 4.1 million additional people fall
            into poverty. Along the consumption distribution, richer
            people are affected more than those in the bottom 40
            percent. However, this minor progressive impact may be
            reversed due to increases in unemployment and cuts in social
            programs (transfers).
repnme: repnme: The distributive impact of terms of trade shocks
display_title: The distributive impact of terms of
            trade shocks
listing_relative_url: /research/2017/03/27312467/distributive-impact-terms-trade-shocks
topicv3: Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Poverty m1326687 695,Trade m1326924 698,Unskilled Workers m1504036 2733
publishtoextweb_dt: 2017-03-29T00:00:00Z
historic_topic: Agriculture,International Economics and Trade,Rural Development
no_of_pages: 35
author_keys: 0: author_key: 669228
1: author_key: 1368192
fullavailablein: node_id: 27312467
node_id: 27312467
model_extractions: WPS8016 Policy Research Working Paper 8016 The Distributive Impact of Terms of Trade Shocks _Maurizio Bussolo Patrizia Luongo_ Europe and Central Asia Region Office of the Chief Economist March 2017
Policy Research Working Paper 8016

# **Abstract**

The halving of oil prices, which happened in a short period between late 2014 and the first months of 2015, has generated major terms of trade losses for oil exporting countries. Even if the oil producing sector normally employs a small group of workers and oil export revenues tend to be concentrated in a few firms and in government accounts, these relative price changes have economy-wide effects and significant distributive impacts. This paper describes and quantifies the channels of transmission from the drop in oil prices, to changes in welfare distribution at the household level. Using a macro-micro simulation model, the paper assesses how this shock affects poverty, inequality, and shared prosperity for the case of the Russian Federation. The oil price reduction generates a reverse Dutch disease that impacts sectoral employment, factor returns, and consumption prices. It causes a contraction of employment and wages in more skill-intensive (non-tradable) sectors, and a reduction in consumption prices that is more pronounced for nonfood than for food goods. When these shifts are mapped to changes in incomes at the micro level, all households are affected. Poverty rates could increase by 1 to 4 percentage points, depending on the poverty line used. At the US$10 a day threshold, for example, 4.1 million additional people fall into poverty. Along the consumption distribution, richer people are affected more than those in the bottom 40 percent. However, this minor progressive impact may be reversed due to increases in unemployment and cuts in social programs (transfers).

This paper is a product of the Office of the Chief Economist, Europe and Central Asia 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 mbussolo@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 Distributive Impact of Terms of Trade Shocks

Maurizio Bussolo Patrizia Luongo<sup>1</sup>

**Keywords:** Terms of Trade, Poverty, Inequality, Shared Prosperity, Macro‐Micro Simulation. **JEL code:** C68; I32; D63

> 1 Maurizio Bussolo is with the World Bank: <u>mbussolo@worldbank.org; Patrizia Luongo is with the UNDP: patrizia.luongo@undp.org.</u>
# Introduction

The Russian Federation is a major producer and exporter of natural gas and crude oil. A number of authors (see Oomes and Kalcheva, 2007 and World Bank, 2005 among others) argued that – following the more than 300 percent increase in the oil price (from about 25 USD to 110 USD per barrel) between 2002 and 2008 – the economy had been showing symptoms of the Dutch disease. Increased revenues from oil exports pushed up the real exchange rate and hurt the competitiveness of the tradable sectors; manufacturing and other exportables shrunk, while services and non‐tradables expanded.<sup>2</sup> More recently the Russian economy has been facing the opposite situation; between July and December 2014 the oil price halved from around 110 USD to less than 60 USD per barrel (World Bank, 2015). The consensus is that the oil price will stabilize at the current lower levels (the average price in 2015 was 50 USD per barrel, and 42 USD in 2016) reflecting structural changes in the global oil markets rather than cyclical and short term adjustments.

The main objective of this paper is to assess the distributional impact of this terms of trade shock. Recent macroeconomic data suggest that a reverse Dutch Disease may be at work. In 2015, the exchange rate depreciated by more than 30% and terms of trade losses were large, equivalent to about 7% of GDP. Assuming that these income losses will be putting pressure on prices of non‐traded goods and services and will require related adjustments in the labor markets, this paper tries to identify who are the winners and losers, and to estimate the changes in income distribution and poverty.

Two difficulties arise in the estimation of the distributive impact. The first is that a household survey for the period after the shock is not yet available. However, and this is even more important, even if survey data were available, they may not provide an adequate counterfactual given that, simultaneously to the oil price drop, the Russian economy has been influenced by other shocks, for example, economic sanctions. For these reasons, the analysis is performed using a macro‐micro simulation model which allows to generate a counterfactual scenario in which the oil price change is the only shock affecting the economy.

> 2 The economic effects of a natural resource boom were modeled by Corden and Neary (1984) who distinguished between a _resource movement effect_ – related to the reallocation of labor and capital from the tradable sectors to the booming natural resource sector – and a _spending effect_ – which brings a real exchange rate appreciation and further de‐industrialization. The real exchange rate appreciation is caused by the fact that higher incomes from natural resources raise demand for both tradable and non‐tradable goods, but while domestic prices of tradables are linked to international prices and thus remain constant, domestic prices of non‐tradables go up.

2
Overall, the results show that the oil price drop reduces the welfare of the bottom 40 percent by 6.5%. It also generates greater levels of poverty, whose magnitude depends on the poverty line considered, and a small reduction of inequality, explained by the relatively higher welfare loss for the top 60. These distributive impacts result from the transmission of the shock through two main channels. Through the labor market, the terms of trade shock causes a contraction of employment and wages in more skill‐ intensive (non‐tradable) sectors thus reducing welfare proportionally more for individuals in the upper part of the distribution. And through the goods market, the reduction of consumption prices – more pronounced for non‐food than for food goods – benefits more the richer households whose share of non‐ food items in total consumption is larger.

This distributional effect can be reversed if unemployment and reduction of government transfers are considered. Notwithstanding the real depreciation following the oil price drop, labor and other resources may not move frictionless to the tradable sectors, and thus unemployment may increase. Initial evidence shows that poorer workers may be more at risk of losing their jobs vis‐à‐vis higher paid ones. Similarly, the lower parts of the income distribution rely more heavily on government transfers that may be reduced because of budgetary pressures linked to lower oil royalties. For these reasons, the slightly progressive impact of the terms of trade shock may be reversed and become regressive.

These model‐based results are convincing because they represent, in reverse, what happened to sectoral employment and relative prices in the period when the oil price was growing (2003‐2008).

The rest of the paper is organized as follows: after a brief literature review, the following section introduces the model and data used in the paper; main results are illustrated in the next section, while conclusions and policy implications are presented in the last section.

# Literature Review

Terms of trade and, more in general, the abundance of natural resources, are subjects of a very large literature. This section briefly summarizes a few strands of this literature that are particularly relevant to this paper, namely: (i) the importance of natural resources to the overall development (growth) process in natural resources abundant countries; (ii) the Dutch disease issue and the impact of relative price changes on income distribution; and (iii) the modeling techniques employed to link terms of trade shocks and distributional change.

The impact of natural resource abundance on countries’ economic performances has been widely analyzed in the literature that has identified three channels through which it might affect economic 3
growth. The first one looks at the link between natural resource abundance and the quality and effectiveness of political institutions. The underlying idea is that abundance of a commodity generates rents that might favor rent‐seeking behaviors, corruption, conflicts and /or political instability which have adverse effects on long‐term growth (Duncan, 2006; Guriev _et al._ , 2008; Isham _et al._ , 2003; Lane and Turnell, 1996, 1999; Manzano and Monaldi, 2008). The second channel focuses on the fact that natural resource abundance might expose countries to price volatility (Sala‐i‐Martin and Subramanian, 2003). Commodity price volatility could lead to instability of public spending and of export income as well as under‐saving of natural resource revenues (Sinnott _et al._ , 2010). These in turn can lower economic growth by increasing the instability of aggregate output and demand in the short run. Finally, commodity dependence might affect a country’s economic performance as it increases countries’ risk of the so‐called Dutch Disease, i.e. an appreciation of the exchange rate in response to positive shocks, which brings to a contraction of the tradable sectors with potential negative effect on growth. The de‐industrialization caused by the Dutch Disease, in fact, might hamper a country’s growth perspective because it hurts mainly the manufacturing sector, which tends to be more competitive and innovative than the non‐tradable ones (Oomes and Kalcheva, 2000) or because it reduces the ability of the economy to absorb shocks via labor mobility (Hausmann and Rigobon, 2003).

Besides influencing economic growth perspectives, income distribution is also affected, via similar channels<sup>3</sup> by natural resource abundance (UNCTAD Annual Report on Trade and Development, 2012). Warr (2008), for example, uses a general equilibrium model and finds that the increase of staple food prices between 2003 and 2008 increased the incidence of poverty in Thailand, an exporter of agricultural goods. He shows that this happened because the main gainers of higher food prices were the landowners and not the poor; the expansion of the agricultural sector, in fact, increased the wage of unskilled workers but this was outbalanced by the negative effect higher staple food prices had on the poor. Essama‐Nssah _et al._ (2008) use a macro‐micro simulation approach to analyze the effect of the rising oil price on an oil importer country, South Africa, between 2003 and 2006. At the macro level, higher oil prices reduced the real GDP, caused a depreciation of the exchange rate and affected the labor market differently according to the sector considered, with fuel intensive sectors experiencing a bigger reduction in employment. The

> 3 Changes in relative prices, related to the resource effect and the spending effect (Corden and Neary, 1984) of the Dutch disease are the main channels through which distribution is affected and the focus of much of this paper. For an oil exporter country the two effects can be described as follows. Higher oil prices will lead to higher returns to labor and capital in the oil sector and this will cause a movement of the productive factors from the less remunerative manufacturing industries to the expanding oil activities. The higher wages and/or profits generated in the latter lead to a higher aggregate demand that will cause an increase in the prices of non‐tradables while the price of tradables, being determined above, remains unchanged. The combination of the two effects causes a de‐industrialization and an appreciation of the exchange rate.

4
distributive analysis, at micro‐level, shows that the oil price shock worsens both poverty and inequality. Moreover, they find that, even if the effect was not particularly strong on average, the impact of the shock varied along the distribution of income: wages and employment declined more across the poorest segment of the formal labor market and low skilled individuals were hit hard by the shock while the high‐ skilled, on average, benefitted from it.

When analyzing the impact of oil price changes there is an additional component to consider, as it causes a variation in energy prices and, through these, on the price of food and other commodities. There are several channels through which energy prices influence the price of other goods as energy is used directly or indirectly as an input factor in the production of several commodities. Recent empirical evidence shows that the prices of many commodities respond strongly to energy price variation and that the elasticity of non‐energy commodity prices with respect to energy price has strengthened over time (Baffes, 2007; Baffes and Haniotis, 2010; Borensztein and Reinhart, 1994; Gilbert, 1989).

The effect of an oil price change on food price in an important channel through which oil price variation influences the distribution of welfare and the level of poverty and inequality. Food is an important component of the consumption basket, especially for low‐income families, so a change in food price will affect the household’s welfare. Moreover, by influencing returns to agricultural activities, food prices influence the welfare of households active in this sector as landowners or wage earners. This implies that an increase in food prices, for example, might have a positive or a negative effect on poor households depending if they are net producers or net consumers (Deaton, 1999). However, as shown in the literature, in most cases the majority of poor households are net food buyers (Ravallion, 1990) even where agriculture is the main economic activity (Christiaensen and Demery, 2007), so that an increase in food prices worsens the welfare of the poor. The anti‐poor effect of rising food prices has been found at the country level (Hoelman and Olarreaga, 2007; Wodon and Zaman, 2008) as well as when poor countries are considered as a group (Ataman Aksoy and Hoekman, 2010). Oil and food price effects are jointly examined by De Souza Ferreira Filho (2008) and Estrades and Terra (2012). The first analyzed the effect of the increase in food and oil prices that occurred between 2004 and 2008 on poverty and inequality in Brazil, a food exporter and oil importer country. His results show that the negative impact on poverty of higher oil prices outweighs the positive impact of the expansion of the agricultural sector. Estrades and Terra (2012) consider both the economy‐wide and the distributive effect of terms of trade change between 2006 and 2008 in Uruguay, an exporter of agricultural goods and a fuel importer. Their results show that the Uruguayan economy has been positively affected by the increase in food prices but this has 5
been partly offset by the simultaneous increase in the oil price. As a result, poverty increased especially for extremely poor people. The reason, the author suggested, is that the increase of the price of the households’ consumption basket overcomes the income rise experienced by the poorest households.

By simulating the impact of the oil price reduction at both the macro and micro levels, this paper will allow us to map precisely the impact of a terms of trade shock – in this case the drop of the oil price in an oil exporter country – on income distribution and thus on poverty, inequality and shared prosperity.

# Methodology

Evaluating the distributive impact of the oil price shock requires the use of a simulation model for at least two reasons. First, being a recent phenomenon, there are still no micro data to compare the distributions of welfare across households before and after the reduction in oil price. Second, even if data were available, they might include other shocks<sup>4</sup> ; hence, to identify the distributive effect of the terms of trade loss, net of (possible) additional shocks, a counterfactual welfare distribution needs to be generated.<sup>5</sup> To obtain the counterfactual distribution the effect of the shock needs to be analyzed under two different perspectives: the macro‐economic perspective, which provides the impact of the terms of trade variation at the aggregate level; and the micro‐economic perspective, which is required to take into account the heterogeneous effect across households. The macro‐micro simulation model used here follows a top‐ down (from macro to micro‐level) approach, where the macro and micro part of the model are handled separately and then linked through changes in prices and quantity computed with the CGE (Lokshin and Ravallion, 2008; Chen and Ravallion, 2004). The CGE (macro) and the microsimulation modules are briefly described below.

## _The macro CGE model_

The CGE model used in this exercise is fairly standard and only a summary of its main features is described here, as its full detailed documentation can be found in van der Mensbrugghe (2014). Production is modeled using nested CES (Constant Elasticity of Substitution) functions that combine at various levels, with different substitution elasticities, intermediates and primary factors. Households’ consumption demand is derived from maximization of household utility producing a constant‐differences‐in‐elasticity

> 4 The labor market performances and the income distributions of Russian households, for example, are likely to be affected also by the economic sanctions.

> 5 This section relies mostly on previous works from Bourguignon and Bussolo (2013) and Bussolo, De Hoyos and Medvedev (2013) and Bussolo et al. (2013).

6
(CDE) demand function.<sup>6</sup> International trade is modeled assuming imperfect substitution among goods originating in different geographical areas. Imports demand results from a CES aggregation function of domestic and imported goods. Export supply is symmetrically modeled as a Constant Elasticity of Transformation (CET) function. Producers decide to allocate their output to domestic or foreign markets responding to relative prices. The assumptions of imperfect substitution and imperfect transformability grant a certain degree of autonomy of domestic prices with respect to foreign prices and prevent the model from generating corner solutions.

The labor market specification is an important driver of the distributional results, therefore its specification calls for some clarification and justification. Two types of labor are distinguished, skilled and unskilled. These categories are considered imperfectly substitutable inputs in the production process. Moreover, some degree of factor market segmentation is assumed: skilled workers are perfectly mobile across sectors, whereas the labor market for the unskilled is divided into agriculture and non‐agriculture segments.

The labor market segmentation by skill level has become a standard assumption in CGE modeling. The imperfect substitution in the production process for workers with different skills is likely to persist for the medium‐term time horizon, as unskilled workers cannot be ‘transformed’ into skilled ones, even with increased on‐the‐job training.

The assumption that the market for unskilled labor is further segmented into agricultural and non‐ agricultural activities is more controversial. However, econometric analysis indicates that a gap in remunerations between these two segments remains even after controlling for education, gender, experience and other variables including cost of living differentials (between rural areas, where agricultural activities are predominantly located, and urban ones). Some barrier to mobility – land ownership providing economic security to farmers, specificity to human capital acquired in agriculture, or others – must exist and hinder equalization of wages across the two segments. In the model, this segmentation is implemented with some flexibility. Using a Harris‐Todaro specification, a certain number of unskilled workers migrate from one segment to the other in response to changes of the wage differentials across the segments.

This rich set‐up allows to capture changes in wages for workers of different education level and employed in different segments. And since skilled‐unskilled and rural‐urban (or, more precisely, agriculture‐non

> 6 This is the standard demand function in GTAP model, see www.gtap.agecon.purdue.edu/models/current.asp.

7
agriculture) wage gaps represent important drivers of inequality, this set‐up allows to explain changes in distributions.

For the goods markets, equilibrium for domestically produced goods sold domestically is assumed through market clearing prices. And the small country assumption is assumed for export and import prices and thus they are exogenous, i.e. export levels do not influence international prices and import demand does not influence (CIF) import prices. For the factor markets, wages equate demand and supply of the various segments (agriculture and non‐agriculture) of labor markets with the assumption of full employment, i.e. a vertical labor supply. Capital supply is assumed to be fixed and mobile across sectors and a market clearing rental rate is calculated by the model. Finally, a sector‐specific factor, representing natural resources, is employed exclusively in production in the oil and gas sector; in the current version its supply is sensitive to the international price of oil, therefore its reduction triggers a reduction of the supply of the natural resource and some contraction of the output of the oil sector.

The version of the CGE model used here has a 2011 base year and relies on the Social Accounting Matrix for the Russian Federation and on bilateral trade flows from the Global Trade Analysis Project (GTAP) 9 database to calibrate initial parameters.

The scenario of the oil shock is implemented by reducing the exogenous international price of oil by 50 percent and a new equilibrium is calculated.<sup>7</sup> The results of the model, in terms of changes between the initial equilibrium and the oil shock one of: (i) prices (for food and non‐food items), (ii) wages (for the four labor market segments), (iii) unskilled labor migration from agriculture to non‐agricultural activities, and (iv) per capita consumption are passed to the household survey data and, in the microsimulation, a new hypothetical global income distribution is generated for the oil shock equilibrium.

# _The microsimulation model_

The ultimate focus of analysis is household welfare, and household real per capita income is here assumed as its indicator:

> 7 The model is solved in a comparative statics approach. This means that the model assumes no changes in the demographic structure of the population nor in its skill endowment. Likewise, the supply of capital and land is assumed fixed. The only variable factor of production is natural resources that, as mentioned in the main text, responds to changes in the international price of oil. The economy is also assumed to operate in full employment.

8
On Ww) Yh, Yn = D1 9ni wit Yr Py Png Onis Pr = PrOn¢ + Png (1 — On) Ong dw, D, = {Wie oo Wwe} = {f (Yi. Prt) f Une Pye) Oni Wi Pp Png D, = {W, oe We} = {f (Pie Pic) wf (Yue Pye)
Note that all the variables linking the CGE and the microsimulations are at the aggregate levels apart from the household specific employment bundle ��,� . Given that the model employed here is of a static nature, the total amounts of skilled and unskilled workers do not change, and neither do the endowments of these two labor types at the household level. However, because of the shock, employment by sector changes. The CGE model produces the economy‐wide new allocation of workers across sectors, and the microsimulation is used to determine which specific worker moves from one sector to the other. More specifically, at the micro level, workers are reallocated among the agriculture and non‐agriculture sectors by means of a probit model, where the probability of switching sectors is estimated as a function of several personal and household characteristics. Workers are allowed to switch between labor market segments until the CGE‐estimated differences in labor allocations between the benchmark and oil price scenarios are achieved. For workers who switch, a labor income is imputed on the basis of observable characteristics and the return of them prevailing in the receiving labor market segment. For example, if a worker joins a new sector, that worker will be imputed a wage based on his or her observable characteristics such as age, gender, and education.

The impact of the oil shock can be assessed by comparing the standard inequality and poverty indicators estimated for both �� and �<sup>�</sup> � .

# Results

## _Macro‐simulation: A reverse Dutch Disease_

Oil revenues are concentrated in a few companies and in the government accounts and a small percentage of the total work force; less than 2 percent in the case of the Russian Federation, is employed in the oil sector. However, the fall of the international price of oil deeply affects the structure of the Russian economy and has a pervasive impact on all households through several channels.<sup>8</sup> The first channel is represented by the terms of trade loss. Because imports have become more expensive, the cost of living rises and reductions of real income and consumption are much larger than contraction of production (GDP) and employment. The income loss and reduction of demand triggers a series of additional effects in factors and goods markets in line with a reverse Dutch disease. Economic activity

> 8 It actually has an impact far beyond its borders as it is affecting neighboring countries through a reduction in the value of the remittances they are receiving. See for example World Bank (2016) where the case of the Kyrgyz Republic, a remittance dependent country with most of its migrant workers employed in Russia, is examined.

10
shifts out of non‐tradables. This can create unemployment, and even if new jobs are created in tradable sectors, relative wages will likely change. Asset prices, including real estate prices, will likely drop sharply. Due to the overall reduction and shift of final demand, relative prices of consumption goods will also change: prices of non‐tradables (for example of services) will contract more severely than prices of tradables. Finally, a fiscal channel may also be at work as transfers from the government, which is facing shrinking oil revenues, may be under pressure.

In the CGE model, the terms of trade shock is implemented as a 50 percent reduction of international oil prices<sup>9</sup> and, as shown in Table 1, this causes a reduction of consumption per capita of about 7 percent. This is a one‐off large welfare reduction that materializes even if production and employment do not contract. In fact, the model simulates the impact of the oil shock as moving to a new long term equilibrium with full employment of labor and physical capital. Gross domestic production marginally contracts because of a reduction in the supply of natural resource in the oil sector, but employment does not go down. It may be possible that factor markets do not adjust to this new equilibrium in the short run and unemployment can rise with additional negative impacts for GDP. However, this exercise aims at modeling the impact of the shock as if it were permanent and after the economy has fully adjusted to the new relative prices.<sup>10</sup>

> 9 The 50% reduction was the one estimated at the end of 2014. During 2015 the price of oil dropped even more, reaching 36 USD per barrel in December 2015 (World Bank Commodity price data).

> 10 Further below the assumption of full employment is (partially) relaxed and the distributional consequences of an increase in unemployment are assessed.

11
_Table 1: The aggregate impact of the oil shock_

||Percent change<br>with respect to<br>no shock|
|---|---|
|Consumption per capita|‐6.9|
|GDP per capita|‐1.1|
|_Labor demand_||
|Unskilled (Agriculture)|6.9|
|Unskilled (No‐Agriculture)|‐0.6|
|_Wages_||
|Unskilled (Agriculture)|‐3.9|
|Unskilled (No‐ Agriculture)|‐10.6|
|Skilled|‐11.5|
|Skill Premium|‐1.5|
|Urban Premium (for unskilled)|‐7.0|
|_Consumer Price Index_||
|Food|‐7.8|
|Othergoods|‐12.7|

Source: GTAP and ROSSTAT data, and CGE simulation results Following the reduction of demand, prices of non‐tradables decrease making tradables relatively more expensive. This real exchange rate depreciation induces shifts in economic activity and employment that are quite different across sectors. Table _2_ shows that, in terms of output contraction, besides the oil sector, the hardest hit sectors are non‐tradables, namely construction, transport and communication, other business services and public services. While import competing industries, and export oriented sectors, benefitting from the real depreciation, experience output expansions. The important caveat of the model operating as if the economy adjusts to its new long term equilibrium without frictions needs to be reiterated here. The gains of the expanding sectors are genuine opportunities, but they may not necessarily be realized quickly in the real economy. The analytical framework used here to model the oil shock is signaling what could happen if the markets were fully flexible and resources were allowed to respond to the new price signals; whereas, in reality, it may not be possible to reallocate resources as markets are just imperfectly flexible. For example, closure of firms in urban shrinking sectors such as in construction may be costly, or banks and other financial intermediaries may not easily start lending to new firms in expanding sectors. Nevertheless, if the relative prices change triggered by the oil shock is, as it is believed, not a temporary fluctuation but a long‐term shift, not adjusting to these sectorial reallocations – by, for example, imposing price controls or other restrictions – will be futile and counterproductive.

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In terms of employment, heavy job losses are recorded for the construction and transport‐communication service sectors, about 0.9 and 1.2 percent of total employment, respectively (see Table _2_ , third column). These, translated in actual levels of employment, are close to 500,000 and 700,000 jobs, a considerable loss. However, new opportunities arise in import competing and export oriented sectors. A potential large gain is highlighted in the manufacturing sector which may create close to 800,000 jobs.

Table 2: Oil shock triggers sectoral adjustments in favor of tradables and against non‐tradables

||**Production**<br>(% change with<br>respect to no shock)|**Employment**<br>(% change with<br>respect to no shock)|**Employment**<br>(change with respect<br>to no shock, as % of<br>tot employment)|**Factor Intensity**<br>(number of skilled<br>per 100 unskilled)|
|---|---|---|---|---|
|Oil|‐13.4|‐18.9|‐0.4|211|
|Construction|‐5.3|‐9.7|‐0.9|182|
|Transport and Trade Services|‐1.3|‐6.0|‐1.2|139|
|Business Serv. & Publ. Admin.|‐0.4|‐0.8|‐0.4|182|
|Food products|2.3|3.6|0.1|145|
|Other agriculture|3.4|6.3|0.3|89|
|Other mining|5.1|20.4|1.2|145|
|Other manufacturing|8.2|8.6|0.3|89|
|Export agriculture|9.5|15.6|0.2|211|
|Export manufacturing|12.7|23.2|0.7|145|

Source: GTAP and ROSSTAT data, and CGE simulation results Sectors use different inputs in their production, and specifically employ skilled and unskilled<sup>11</sup> workers in different proportions; these reallocations produce imbalances in labor markets. In particular, as on average skilled workers are employed more intensively in shrinking sectors – such as Business Services and Public Services, Oil, and even in Construction (where for every 100 unskilled workers, 182 skilled workers are employed, see rightmost column in Table _2_ ) – job losses affect more severely skilled workers than unskilled ones. Similar imbalances influence the urban premium, defined here as the ratio of non‐ agriculture activities (essentially urban) wages over agriculture (mainly rural<sup>12</sup> ) wages. These imbalances in the labor markets put downward pressure on the skill and urban premia which, as shown in Table 1, decrease by a few percentage points.

Another relevant aggregate result estimated by the CGE model is the change in relative prices of goods and services. The oil price reduction and the real exchange rate depreciation make energy cheaper,

> 11 The partition of the population into skilled and skilled workers is based on the highest level of education attained; specifically, unskilled are all those who have completed, at most, secondary education while all the others are considered skilled (even if they did not completed tertiary education). Based on this definition, unskilled workers account for 40 percent of the population and skilled for 60 percent. Among the unskilled, 20 percent has only primary education and 17 percent has junior education; 20 percent have been enrolled in junior vocational education and the highest share (38%) has secondary education.

> 12 Agricultural activities are mainly (76 percent) located in rural areas.

13
importables more expensive, and non‐tradables less expensive. Energy is an important input in the production of many final goods, and imports can be, in part, substituted by domestic varieties. The model results take into account these general equilibrium effects and, even if production of food tends to be energy intensive, the reduction of prices of other goods is expected to be larger (see last two rows of Table 1).

# _The distributive effects of the oil price shock_

The oil price shock will affect all households, but the final impact will likely be larger for some groups than others. In terms of per capita consumption, the poorest bottom 40 percent loses 6.2 percent of its welfare and the top 60 percent suffers a loss of 7.0 percent (see Table 3). This minor progressive impact, i.e. the fact that richer people experience welfare losses slightly larger than those for poorer people, amounts to a marginal change of the Gini coefficient, a standard indicator of inequality, which decreases from 41.06 to 40.86. Worryingly, poverty rates rise for all the usual poverty lines. For example, when measured at the low 2.5 $ a day line, the oil shock increases poverty by dragging about 100,000 additional people below that line. At higher poverty lines, such as 5 and 10 $ a day, 900,000 and 4.1 million additional people fall behind and poverty rates increase to almost 4 and 22 percent, respectively.

_Table 3: Welfare, poverty and distributional impact of the oil shock in the Russian Federation_

||Without Oil<br>Shock|With Oil<br>Shock|Change|
|---|---|---|---|
|Per capita Consumption|USD in consta|nt 2011 PPP|%|
|All Population|8,971|8,354|‐6.88|
|Bottom 40|3,619|3,395|‐6.20|
|Top 60|12,539|11,660|‐7.01|
|Poverty headcount|Headcoun|t ratios (%)|% Points|
|2.5 $ a day line|0.25|0.33|0.07|
|5 $ a day line|3.14|3.77|0.64|
|10 $ a day line|19.06|21.98|2.92|
|Gini coefficient|41.06|40.86|‐0.20|

Source: HBS data and micro‐simulation results. Note: per capita consumption is expressed in 2011 US PPP $; poverty headcounts and Gini in percentages The impact of the oil shock along the full welfare distribution of the Russian population can be seen in the growth incidence curve (GIC) of Figure 1. For all percentiles, per capita consumption is measured with and without the oil price shock and the difference between these two situations is plotted in the graph. The welfare distribution obtained from the household survey of 2011 is assumed to represent the situation without the oil shock. The microsimulation model is used to generate the counterfactual distribution with 14
the oil shock. The negative slope of the curve denotes the progressive impact of the shock. However, the poorest percentiles still lose significantly so that the equalizing effect of this result should not be overstated.

_Figure 1: Growth incidence curve due to the oil shock_ <!-- Start of picture text --> ‐4<br>‐5<br>Overall Change<br>‐6<br>‐7<br>‐8<br>‐9<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>consumption)<br>Change in welfare (% change in per capita<br><!-- End of picture text --> Source: HBS data and micro‐simulation results. Note: the points represent the difference in per capita consumption measured with and without the oil shock; the line is obtained as a polynomial fit of the points.

What explains this heterogeneous impact of the shock? Table 1 offers some clues. The skill and urban premia are decreasing and one may expect skilled workers and (among the unskilled) urban ones to be towards the richer tail of the distribution. This table also highlights that among the workers displaced by the shock, about 100,000 unskilled ones will have to move from better paid urban occupations to rural lower paid jobs. However, the aggregate results of this table do not inform from which part of the distribution these displaced unskilled workers are more likely to come. Finally, the lower reduction of food prices vis‐à‐vis prices of other consumption items suggests a potential regressive impact given that poorer households spend a larger share on food than richer households.<sup>13</sup> Other channels of transmission of the shock can also contribute to its uneven incidence. For example, rents in oil producing and closely related sectors (such as refineries) will be directly hit. Given the large capital stocks of the oil sector, capital returns may also suffer larger losses vis‐à‐vis labor returns. This shift

> 13 Note that if rich consumers purchase a larger share of imported goods, they may be impacted more severely than poorer ones by the increase in import prices. Some authors [Broda and Romalis 2008] have studied this issue for the case of the US. They found that lower import prices of goods originating in China have actually helped the poorer consumers in the US. However, the current household data for Russia do not allow to investigate this channel.

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in factorial distribution may translate into a reduction of personal income disparities (and thus a negatively sloped GIC), as capital incomes may be more concentrated among the rich groups of the country.<sup>14</sup> Asset prices, including real estate prices, will likely drop sharply. Moreover, those who have borrowed in foreign currency suddenly experience a rise in real debt. Finally, transfers from the government are not distributionally neutral as some groups, for example retirees, receive proportionally more than others. A change in transfer amounts, and coverage, caused by budgetary difficulties can thus change the distribution.

Using the microsimulation model, it is possible to untangle these multiple effects and study their individual separate impacts. Consider first the adjustments in the labor market. Unskilled workers displaced from jobs in non‐tradable services are among those who lose the most: their welfare is almost halved; before the crisis the average consumption for a displaced worker was around 9,700 USD but with the oil shock it declined to around 6,000 USD.<sup>15</sup> Although the impact is clearly large for individual displaced workers, the consequences for the overall distribution depend on the number of displaced workers and whether they were initially concentrated in a specific part of the distribution. About 100,000 unskilled workers, around 0.1 percent of total employed workers, move back to agriculture sectors because of the shock. These are not randomly selected, as some will have a higher probability of moving than others. Nevertheless, as shown in Figure 2, they are coming from all parts of the distribution. They tend to be aged above 40 and, in most cases, males with at most junior secondary or secondary education, but they are not necessarily poorer.<sup>16</sup> For these reasons, the impact on the overall distribution is muted as shown by the GIC 1, the dashed line in Figure 3.<sup>17</sup>

> 14 Note, however, that this effect cannot be currently assessed given the data limitations of the available household survey where income from capital is not accurately measured.

> 15 Because of the segmentation, two separate wages are clearing the rural (agriculture) and urban (non‐agriculture) segments. The urban premium, i.e. the ratio of the wage of unskilled workers in non‐agriculture sectors over that of unskilled workers in agriculture is initially equal to 1.3. A labor migration function allows for some imperfect mobility across segments and this, together with sectoral demand for labor, determines the shift in the urban premium shown in Table 1.

> 16 Moving workers are identified using a probit estimation, as explained in the methodological section, see the Appendix for more details.

> 17 The line ‘GIC 1’ in the figure almost overlaps with the horizontal straight line that intercepts the vertical axis at ‐6.9. This horizontal line represents the average percentage welfare loss, i.e. the loss that everyone would incur were the shock uniformly distributed. The case represented by GIC 1 takes into account the losses of displaced unskilled workers, but the impact is minor, visible only because of the small blips on the GIC 1 line.

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_Figure 2: Unskilled workers displaced by the shock come from all parts of the distribution_ <!-- Start of picture text --> 20,000<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>2,048 3,262 4,152 5,014 5,986 7,115 8,568 10,686 14,552 28,369<br>Deciles (identified with their per capita consumption in 2011 rubles)<br>Number of displaced workers<br><!-- End of picture text --> Source: HBS 2011 and authors calculations using microsimulations In addition to displacement of workers, the oil shock indirectly but more significantly affects labor markets by changing relative returns to human capital and to sector specific skills. While the whole population will be negatively affected by the oil shock, in terms of incidence those depending on the returns to skills will lose about 1.5 percent more than the others, and, among the unskilled, those in urban activities will lose 7 percent more than those in expanding rural sectors. Figure 4 shows that the human capital is not uniformly distributed. Skilled employed workers (mainly in non‐agriculture sectors) represent about 31 percent of the population and the majority of them, about 18 percent, are found in the upper 60 percent of the distribution. And even within the bottom 40 and the top 60 the share of skilled workers is not uniform, as shown by the upward sloping line in the figure.<sup>18</sup> Conversely, the unskilled workers are overrepresented at the bottom of the distribution, especially those employed in the agriculture sectors. In light of the shares shown in Figure 4, the reason for a progressive impact of the reduction of the skill and the urban premia – illustrated by the downward sloping GIC 2 line in Figure 3 – becomes apparent.

> 18 On average for every 100 individuals, 9 are skilled among the bottom 40, while 21 are skilled among the top 60. However, these proportions are 5 and 26 for the bottom 10 and top 10 respectively.

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_Figure 3: Decomposition of the incidence of the oil shock_ <!-- Start of picture text --> ‐4<br>‐5 GIC 2: GIC 1 + Δ in<br>Returns to Skills<br>‐6<br>GIC tot: GIC 1 + GIC 2<br>+ Δ Goods Prices<br>‐7<br>GIC 1: Labor Movement across sectors<br>‐8<br>‐9<br>0 20 40 60 80<br>Population percentiles<br>consumption)<br>Change in welfare (% change in per capita<br><!-- End of picture text --> Source: HBS 2011 and authors calculations using microsimulations The progressive effect of the change in wage premia is _reduced_ by another round of unequal adjustments: the change in relative prices of consumption goods. Not all households consume the same proportions of food items. As shown in Figure 4, the share of total expenditure devoted to food is much higher for poorer households: the average Russian household spends 33 percent of its total expenditures on food, while a household in the bottom 40 percent spends 43 percent, and one in the top 60 only 16 percent. Therefore, richer households benefit disproportionally more from the stronger reduction of prices of non‐food items, including non‐tradables. This is reflected by a counterclockwise tilting of the GIC (from GIC 2 to GIC tot in Figure 3) which makes the overall incidence less progressive.

# _Robustness and validation_

The incidence results discussed up to this point rely on two assumptions: the first is that the shock affects the allocation of labor across sectors but without generating unemployment; the second is that the reduction in oil royalties does not influence the level of government expenditure and its household transfers. The implications of the two assumptions are related, as it can be inferred by looking at the employment share along the welfare distribution depicted in Figure 4. The intensity of the use of labor, the main source of income for most households, is not uniformly distributed. Households at the bottom 40 percent record an average employment rate of 45 percent against an average of 53 percent for 18
households at the top 60 percent. An increase in unemployment and a reduction of social government programs may hit poorer households harder, given their more intense dependency on transfers and higher risk of becoming unemployed (or lower employability).

_Figure 4: Unequal Distribution of Employment and Assets, such as human capital, and of consumption of food across the Russian Population, 2011_ <!-- Start of picture text --> 0.6 0.6<br>Total Occupation<br>0.5 0.5<br>Share of food in total<br>0.4 consumption (right axis) 0.4<br>Unskilled in<br>0.3 Non Agri 0.3<br>Skilled in No Agri<br>0.2 0.2<br>0.1 0.1<br>Skilled in Agri<br>Unskilled in Agri<br>0 0<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>Share of food<br>Percent share of total population<br><!-- End of picture text --> Source: HBS data (2011) To get some insights on the implications of these assumption we carried out two additional simulations. In the first one we assume that all those who lose their jobs in the contracting sectors – up to 3.1 percent of total employment – become unemployed. In the second one we analyze the implication of a cut in government transfers.

Abstracting from multiplier effects,<sup>19</sup> what would be the distributional impact of the increase in unemployment? The first step of the simulation was to identify those who are more likely to become unemployed.<sup>20</sup> Not surprisingly, the results of the econometric specification show that less qualified,

> 19 Clearly, if employment goes down (or equivalently if unemployment increases), GDP will also go down and that would trigger a reduction of incomes, then consumption will decrease with another round of reductions. This negative multiplier effect is not accounted here.

> 20 Specifically, workers more likely to be unemployed are selected through a multinomial probit estimation that computes the probability of either being employed in tradable sectors, employed in nontradable sectors, or being unemployed according to individual and household characteristics. These characteristics include gender, education level, age, marital status, household size, living in urban or rural areas, and headship.

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younger and female workers are more likely to lose their job. However, exposure to unemployment is not concentrated in particular income groups, as these individuals are found across the whole welfare distribution (see Figure 5).

_Figure 5: Unemployed workers by decile of the welfare distribution_ <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Household income decile<br>population<br>Newly unemployed as a share of total<br><!-- End of picture text --> This result suggests that the ultimate distributive impact of the unemployment increase would depend heavily on the social protection programs available to those who lost their job and, of course, to the whole population. However, it is unlikely that these programs and, more generally, public transfers and expenditure will not suffer from the shrinking oil royalties following the drop in oil price. The oil price shock, indeed, puts government expenditure under pressure and its reduction is likely to have affected differently individuals located in different parts of the welfare distribution.

Taking advantage of the availability of data on public transfers in a different survey, the RLMS (Russian Longitudinal Monitoring Survey), our second simulation estimates the effect of a 10 percent cut in public transfers. The impact on poverty and shared prosperity depends on the incidence of the transfers along the welfare distribution: if poor households depend relatively more than the richer ones on public transfers, the final effect of the oil shock could be reversed once the cut of transfers is taken into account.

Most of the beneficiaries of public transfers<sup>21</sup> and pensions are indeed located in the first decile of the consumption distribution (see Figure 6) while the share is much smaller for households located in the top 30 percent of the distribution.

> 21 Transfers are defined as the sum of children, rent, utilities, fuel and unemployment benefits.

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_Figure 6: Share of transfers by decile of the consumption distribution_ <!-- Start of picture text --> 25.00<br>20.00<br>15.00<br>10.00<br>5.00<br>0.00<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>consumption<br>Transfer as share of<br><!-- End of picture text --> Source: RLMS (2011) Due to the different incidence of public transfers for households located in bottom 40 and top 60 of the income distribution, the “progressive” effect of the oil price shock shown above is reversed as poor families lose more (‐7.13) than the top 60 of the income distribution (‐6.82). (See Figure 7.) 21
<!-- Start of picture text --> 4<br>S<br>2-5<br>c<br>r=}<br>e<br>Ss GIC 3: GIC1+GIC2+A /<br>g 6 Goods Prices .<br>=e-Z ./<br><!-- End of picture text --> <!-- Start of picture text --> ~— . “<br>5 A<br>£ -“GIC final: GIC3+ A Transfers<br>9 7<br>= .<br>seyou<br>4<br>S<br>-9<br>0 20 40 60 80<br>Population percentiles<br><!-- End of picture text -->
<!-- Start of picture text --> Oil price and and exchange rate, 2002-2008<br>120 34<br>100 Official exchange exchange rate 32<br>30<br>r) 80 yg<br><!-- End of picture text --> <!-- Start of picture text --> Oil price and and exchange rate, 2002-2008<br>120 34<br>100 Official exchange exchange rate 32<br>30<br>r) 80 yg<br>& 28 2@<br>wy 60 Qa<br>¥ 26 =<br>=<br>40 «<br>24<br>20 Crude oil price, Brent 22<br>0 20<br>2002 2003 2004 2005 2006 2007 2008<br>Variation in employment and and value added by sector, 2003-2008<br>20<br>_° oy Sy 8 6 = &<br>x -10<br>-20<br>-30<br>-40<br>ey,, oyTMTM e we oe eS ro &<br>> 2 4 NY v EN<br>xe ¢++ 3s‘3‘3 esSS s>> ssSS<br>So ° seRSRS oCss ~~>><br>mw Aemployment mAShareof VA VA<br><!-- End of picture text --> <!-- Start of picture text --> wy 60 Qa<br>¥ 26 =<br>=<br>40 «<br>24<br>20 Crude oil price, Brent 22<br>0 20<br>2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> <!-- Start of picture text --> Variation in employment and and value added by sector, 2003-2008<br>20<br>_° oy Sy 8 6 = &<br>x -10<br>-20<br><!-- End of picture text --> <!-- Start of picture text -->
-30<br>-40<br>ey,, oyTMTM e we oe eS ro &<br>> 2 4 NY v EN<br>xe ¢++ 3s‘3‘3 esSS s>> ssSS<br>So ° seRSRS oCss ~~>><br>mw Aemployment mAShareof VA VA<br><!-- End of picture text -->
<!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>Ce 3.5<br>F 3.0<br>> 2.5<br>@ 2.0<br><!-- End of picture text --> <!-- Start of picture text --> = 1.5<br>1.0<br>0.5<br>0.0<br>Non-Agri/Agri<br>2003 m 2008<br><!-- End of picture text -->
<!-- Start of picture text -->
1.5<br>&<br>£<br>2<br>1.4<br>a<br><!-- End of picture text -->

<!-- Start of picture text --> =<br>rr)<br>1.3<br>Skill-Premia<br>2003 @ 2008<br><!-- End of picture text -->
the overall effect on households’ welfare is computed by the money metric variation of welfare due to the prices and wage changes.

The distributive analysis shows that the two channels (the consumption prices and the labor market changes) work in the opposite direction. The food prices decrease less than non‐food prices, increasing more the welfare of better‐off households. On the other side, a lower skill premia and a reallocation of the labor force across sectors generates a bigger loss, in terms of wage premia, for the skilled workers usually located in the upper part of the welfare distribution. Together with the GDP contraction, the loss in terms of trade will cause a considerable decline in the average consumption per capita but, as suggested by the results on inequality and shared prosperity, individuals in the upper part of the distribution are those who will be more strongly hit by these negative changes.

However, this distributional effect can be reversed if one takes into account employment losses and reduction of government transfers. The real exchange rate depreciation generates the incentives for labor and other resources to move from non‐tradables to tradables sectors, but inter‐sectoral resource movements may not be as smooth. There may be frictions (due to imperfections in financial markets, for example) and other adjustment costs (a worker in a non‐tradable sector may have sector specific skills and thus not able to be immediately productive in a tradable sector) which may create unemployment. Or to decrease the pressure on its budget, the government may reduce its transfers. These two additional effects would impact the lower parts of the income distribution – which rely more heavily on labor incomes or transfers – more severely than the richer deciles.

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