In contrast, financial globalization and technological progress are shown to benefit mainly the richest 20 percent of the population. There exists a vast empirical and theoretical trade literature on the effects of globalization on inequality. Although the peak of this vibrant literature was reached in the mids with a series of major contributions for example, Feenstra and Hanson, ; Borjas, Freeman, and Katz, , there is a renewed interest in the topic for example, Broda and Romalis, ; Krugman, , including a great body of work on developing countries surveyed in Goldberg and Pavcnik , and a recent spike in theoretical research for example, Verhoogen, ; Egger and Kreickemeier, This paper contributes to the globalization-inequality literature by examining the effects of trade, financial globalization, and technology on income inequality in a comprehensive framework using a large panel of countries.
The rest of the paper is organized as follows. Section I examines the patterns in inequality and globalization across a broad range of developed and developing countries over the past two decades, and describes the unique inequality data set that is used in the empirical estimation. Section II discusses some of the channels through which trade and financial globalization may be expected to influence inequality within countries, whereas Section III analyzes the empirical evidence to identify the main factors explaining inequality.
Section IV discusses the implications of the empirical findings with particular emphasis on plausible mechanisms responsible for the rising income inequality.
Section V concludes. This section provides a brief review of the evidence on inequality and globalization over the past two decades and across income groups. Cross-country comparisons of inequality are generally plagued by problems of poor reliability, lack of coverage, and inconsistent methodology. This database uses a substantially more rigorous approach to filtering the individual income and consumption data for differences in quality than other commonly used databases, which rely on more mechanical approaches to combine data from multiple sources and render them somewhat less reliable for cross-country studies.
The end result is a unique data set that include 51 countries 20 developed and 31 developing over — that allows us to more comprehensively document inequality facts across a large number of countries.
Gini coefficients for all 20 developed economies in the sample were constructed using income survey data, while Gini coefficients for 16 out of 31 developing economies use consumption survey data. One unique feature of our data set is that no extrapolation was used. Given limitations of data availability, the analysis in this paper uses inequality data based on both income and expenditure surveys. Mixing these two concepts makes a comparison of levels of inequality across countries and regions potentially misleading.
In general, consumption-based Gini indices tend to show lower inequality and are more commonly used in developing countries in which higher rates of self-employment in business or agriculture where income fluctuates throughout the year make measurement of incomes difficult. Among other causes, lower measures of consumption-based inequality can result from consumption smoothing across time and greater measurement error for incomes for example, Ravallion and Chen, ; Deaton, When comparing income and consumption-based Gini indices, meticulous attention to concepts, definitions, and the details of survey methodology is required to improve comparability, and the World Bank's Povcal database used to construct our data set goes further than other databases in doing this see Chen and Ravallion, The database was created using primary data from nationally representative surveys with sufficiently comprehensive definitions of income or consumption.
Attempts were made to ensure survey comparability over time within countries, although cross-country and within-country comparisons are still not without problems because in many cases it was not possible to correct for differences in survey methods. Trends after are based on earnings data for full-time, year-round workers. Trends for pre Germany are based on data for West Germany. In summary, two broad facts emerge from the evidence.
First, over the past two decades, income growth has been positive for all quintiles in virtually all regions and all income groups during the recent period of globalization.
At the same time, however, income inequality has increased mainly in middle- and high-income countries, and less so in low-income countries. This recent experience seems to be a clear change in course from the general decline in inequality in the first half of the twentieth century, and the perception that East Asia's rapid growth during the s and s was achieved while maintaining inequality at relatively low levels. It must be emphasized, however, that comparison of inequality data across decades is fraught with difficulty, in view of numerous caveats about data accuracy and methodological comparability.
Tariff rates are calculated as the average of the effective rate ratio of tariff revenue to import value and of the average unweighted tariff rates. De jure financial openness, measuring a country's degree of capital account openness, is an index based on principal components extracted from disaggregated capital and current account restriction measures constructed by Chinn and Ito As with all principal component analysis the resulting index is computed using binary indicators and thus the unit of the measure can only be interpreted in relative terms.
Of note, the share of FDI in total liabilities has risen across all emerging markets—from 17 percent of their total liabilities in to 38 percent in —and far exceeds the share of portfolio equity liabilities, which rose from 2 to 11 percent of total liabilities over the same period. Not surprisingly, the share of international reserves in cross-border assets has also risen, reflecting the accumulation of reserves among many emerging market and developing countries in recent years.
ICT data are from Jorgenson and Vu This section discusses the channels through which the globalization of trade and finance could affect the distribution of incomes within a country, setting the stage for the empirical analysis that follows. The principal analytical link between trade liberalization and income inequality provided by economic theory is derived from the Stolper-Samuelson theorem resulting from the Heckscher-Ohlin model: it implies that in a two-country two-factor framework, increased trade openness through tariff reduction in a developing country where low-skilled labor is abundant would result in an increase in the wages of the low-skilled workers and a reduction in the compensation of the high-skilled workers, leading to a reduction in income inequality see Stolper and Samuelson, After tariffs on imports are reduced, the price of the importable high-skill intensive product declines and so does the compensation of the scarce high-skilled workers, while the price of the exportable low-skill intensive good for which the country has relatively abundant factors and the compensation of low-skill workers both increase.
For an advanced economy where high-skill factors are relatively abundant, the reverse would hold, with an increase in openness leading to higher inequality. The implications of the Stolper-Samuelson theorem, and in particular the ameliorating effects of trade liberalization on income inequality in developing countries, have been extensively studied but generally not been verified in economy-wide studies.
A particular challenge has been to explain the increase in skill premium between skilled and unskilled workers observed in most developing countries. This has led to various modifications to the Heckscher-Ohlin model, including the introduction of multiple countries where poor rich countries may also import low-skill high-skill intensive goods from other poor rich countries; the introduction of a continuum of goods, implying that what is low skill-intensive in the advanced economy will be relatively highly skill-intensive in a less developed country see Feenstra and Hanson, ; and the introduction of intermediate imported goods used for the skill-intensive product.
However, these extensions have themselves presented additional challenges for empirical testing. Recent theoretical and empirical studies try to rethink the effects of trade on inequality in the context of heterogeneous firms and provide quite different insights from the Heckscher-Ohlin model. Recent contributions include Egger and Kreickemeier , Verhoogen , and Yeaple , just to name a few.
Difficulties in explaining observed increases in inequality by between-sector shifts gave rise to a parallel and competing literature showing evidence of other nontrade factors such as skill-biased technical change. Another explanation of how the spread of technology may affect inequality is that technology may increase capital intensity in production, thereby increasing the returns to capital and the relative income of capital owners see Krusell and others, , for an analysis of the impact of capital-skill complementarity in the United States.
Any empirical estimation of the overall effects of globalization therefore needs to explicitly account for changes in technology in countries, in addition to standard trade-related variables. An additional important qualification to the implications deriving from the Stolper-Samuelson theorem relate to its assumption that labor and capital are mobile within a country but not internationally.
If capital is assumed to be mobile across borders, then the implications of the theorem are weakened substantially. This channel would appear to be most evident for FDI, which is often targeted at high-skilled sectors in the host economy see Cragg and Epelbaum, Moreover, what appears to be relatively highly skill-intensive inward FDI for a less developed country may appear relatively low skill-intensive outward FDI for the advanced economy.
An increase in FDI from advanced to developing countries could thus increase the relative demand for skilled labor in both countries, increasing inequality in both the advanced economy and the developing country. The empirical evidence on these channels has provided mixed support for this view, with the impact of FDI seen as contributing to inequality, at least in the short run, or inconclusive. In addition to FDI, there are other important channels through which capital flows across borders, including cross-border bank lending, portfolio debt, and equity flows.
Within this broader context, some have argued that greater capital account liberalization may increase access to financial resources for the poor, while others have suggested that by increasing the likelihood of financial crises greater financial openness may disproportionately hurt the poor. Some recent research has found that the strength of institutions plays a crucial role: in the context of strong institutions, financial globalization may allow better consumption smoothing and lower volatility for the poor, but where institutions are weak, financial access is biased in favor of those well-off and the increase in finance from tapping global and not just domestic savings may further exacerbate inequality.
Overall, and taking a longer perspective, the impact of non-FDI flows would depend on the extent to which it is accompanied by domestic financial development that broadens the access to finance rather than serves to deepen it. If financial flows make resources available to a broader cross-section of the work-force, they would serve to reduce inequality by allowing investment in skills and human capital.
However, if they make more financial resources available to those who already have capital and collateral, this would likely exacerbate inequality. It should be noted that the link between income inequality and the two channels discussed above, that is, through trade liberalization and skill-biased technological change, is argued in the literature to operate through labor income.
To the extent that financial globalization allows agents to borrow and invest more easily in the production of goods and services or in human capital, this would be expected to boost the future incomes of these agents more than those who are unable to access such financing.
In summary, analytical considerations suggest that any empirical analysis of the distributional consequences of globalization must take into account both trade and the various channels through which financial globalization operates, and also account for the separate impact of technological change. In this section we use cross-country estimation to investigate how much of the rise in inequality seen in developing and high-income countries in recent decades can be attributed to increased globalization, and how much to other factors, such as the spread of technology and domestic constraints on equality of opportunity.
While country studies can certainly take advantage of more disaggregated and more detailed data to study the effects of globalization on inequality, they cannot capture the broad relationship as each study focuses instead on some parameters of particular interest. In contrast to most existing studies that focus on within-country variation in inequality in a particular country, 12 this study is unique because it uses a large panel of advanced and developing countries.
The analysis relates the Gini coefficient to various measures of globalization and a number of control variables including technological progress. It is important to caution, however, that offshore outsourcing can be viewed as a measure of trade openness as well see, for example, Feenstra and Hanson, , and the effect of offshoring on inequality could be interpreted as the effect of trade. The analysis also includes a number of control variables that can be important in determining how inequality changes in countries over time and that have seen significant changes in recent years.
These include technological development , measured by the share of ICT capital in the total capital stock, access to education , measured by the average years of education in the population ages 15 and older, and the share of this population with at least a secondary education, sectoral shares of employment , measured by the shares of employment in agriculture and in industry, and domestic financial development , measured by the ratio of private credit to GDP.
As shown in Figure 6 , ICT capital has risen rapidly over the past 20 years across all income country groups. For a given level of technology, greater access to education would be expected to reduce income inequality by allowing a greater share of the population to be engaged in high-skill activities. Both educational variables considered in the analysis have tended to increase across all regions, but with considerable cross-country variation. In developing countries, a move away from the agricultural sector to industry is expected to improve the distribution of income by increasing the income of low-earning groups.
This reflects two offsetting effects of globalization: while increased trade tends to reduce income inequality, FDI tends to exacerbate it. Heshmati, Almas, Greta: the voice of climate activism who says 'don't listen to me' Sean Fleming 23 Sep Furthermore, looking at average income levels across quintiles, real per capita incomes have risen across virtually all income and regional groups for even the poorest quintiles Figure 3 shows per capita income by quintile in selected regions. Rogoff, S. It is interesting to note, once again, that the coefficient estimates of labor productivity in agriculture and in industry on inequality have opposite signs.
Similarly, an increase in the relative productivity of agriculture is expected to reduce income disparities by increasing the income of those employed in this sector. Briefly, with a fixed wage differential, a movement of labor between the two sectors will tend to raise inequality over some region of the employment share and reduce it beyond it, as the relative sizes of the two groups change see Robinson, Ideally, our estimation methodology should be motivated by a particular theoretical framework, even if the estimation is not structural.
However, there is no formal theory that incorporates the effects of trade and financial globalization, and technology in a model of income inequality. Therefore, our estimation will not be linked directly to any one existing theory, but will incorporate key ingredients of the prominent theories in the literature.
Country fixed effects allow us to focus on within-country changes instead of cross-country level differences. In addition, time dummies are included to capture the impact of common global shocks such as business cycles or growth spurts. The resulting baseline model is estimated using fixed effects panel regressions with standard errors clustered at the country level.
Before moving on to reporting results, some cautionary remarks with regards to our estimation specification are warranted. Although our panel regression estimation has the advantage of controlling for country fixed effects particularly important in our analysis given that in some countries inequality is measured using income data while in other countries using consumption data , there are also some concerns: First, using changes rather than level data may result in magnified measurement error Bound and Krueger, In our estimation, using deviations rather than levels is necessary given that merging consumption and income data sets is significantly more problematic than the potential magnification error.
Second, we acknowledge that while our estimation approach may successfully suppress business cycle effects, this may not be entirely desirable in the present study given that part of the variation shown in the illustrative part of the paper may be coming from business cycle. Third, an alternative strategy is to try to identify regressions based on between-country variation. Notes: The regressions were estimated using panel regressions with country fixed effects and time dummies. Standard errors are clustered at the country level.
All explanatory variables are in natural logarithm, except the tariff measure, the capital account openness index, and the population share with at least a secondary education. Trade openness is replaced by the individual import and export shares of GDP, while financial openness is decomposed into the outward FDI stock, the inward FDI stock, the inward portfolio equity stock and the inward debt stock.
Neither export- nor import-to-GDP are significant at conventional levels; these two variables are in fact highly correlated at 87 percent. On the financial openness side, the variable that shows significance at the 5 percent level is the ratio of inward FDI stock to GDP. We further modify the estimation model, after a joint test shows that imports and other components of financial openness with the exception of inward FDI are insignificant.
The coefficient on exports implies that a one standard deviation increase in the export-to-GDP ratio from its sample mean would reduce inequality approximately by 3. Similarly, a one standard deviation decrease in tariffs would reduce inequality by 2. To better understand the inequality-reducing impact of exports, the export-to-GDP ratio is split by sector of origin agriculture, manufacturing, and services column 4 of Table 1.
We find that it is the agricultural component of exports that is especially important to reduce inequality. The effects of agriculture, manufacturing, and services exports are statistically not significantly different from one another, but agricultural exports have the largest coefficient and are statistically significant. The coefficient on exports thus seems to reflect the fact that in many developing countries a lot of the poor are still employed in the agricultural sector, so that an improvement in the export prospects of this sector tends to reduce inequality.
This significant book presents an original examination of the theoretical and empirical interactions between globalization, technology and poverty. Jeffrey James. Abstract: This significant book presents an original examination of the theoretical and empirical interactions between globalization, technology and poverty.
The inequality-raising impact of inward FDI, although puzzling at first, appeared to make a lot of sense upon examination of data on the sectoral composition of FDI. These suggest indeed that FDI mostly takes place in relatively higher skill- and technology-intensive sectors, and thereby increases the demand for, and wages of, more skilled workers.
Most of the control variables are also found to be statistically significant and—except for the education variables and the share of agricultural employment—these estimates are broadly robust across different models. First, technological progress and domestic financial deepening both significantly increase inequality.