rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Mobility and Underlying Poverty**

In face of the sizeable income and expenditure shocks, one might wonder how long-lasting economic positions are. Do the rich generally remain rich and the poor remain poor, or do individuals frequently switch positions? The degree to which individuals keep their position in the income distribution can be measured by the correlation between this period's income and the next period's. Table 3 shows these correlations for income and expenditures in Russia and Poland.

**Table 3. Correlations in Income and Expenditure**

||R|ussia|P|oland|
|---|---|---|---|---|
||Income|Expenditure|Income|Expenditure|
|**A. Traditional correlations**|||||
|Current month and 12 months later|0.335|0.479|0.535|0.627|
|Current month and 24 months later|0.254|0.431|0.549|0.626|
|Currentmonth and 36 months later|..|..|0.457|0.569|
|Current month and 48 months later|0.266|0.380|||
|B.**Underlying correlations**<br>Correlation between_underlying_<br>income/expenditure in current<br>month and 12 months later|0.884|0.924|0.934|0.937|
|Memo: "traditional" correlation<br>over the same time period.|0.338|_0.510_|0.575|0.678|

Notes: Equivalent income and equivalent expenditure are measured in logarithmic form. In panel A, the current month is taken from **the** first of the 4 waves of the data. The first wave of the Russian data was collected in November/December of 1994 and the first wave of **the** Polish data was collected between January and December of 1993. Correlations between persistent income/expenditure can only be calculated for waves 2 and 3 of the data. In both countries, wave 2 was collected 1 year after the first wave, and wave 3 was collected 2 years after the first wave. Standard error for these correlations are generally around 0.02 to 0.03.

Panel A of Table 3 shows the correlation between current log income or log expenditure, and its value in future periods. The correlation between a family's current economic situation and that 12 months from now is slightly less than 50% in Russia and somewhat more than 50% in Poland. This would suggest a lot of churning. However, these correlations fall only very little if we move out one, two or three extra years. Does this mean that the churning has largely ceased after the first year? The explanation for this pattern of correlations is that the correlation between any two years is less than unity for two reasons: (i) the transitory shocks that occur in each of the two years, and (ii) the underlying amount of churning - the persistent shocks between the two years. Whether 24
combining the IMF’s International Financial Statistics (IFS) data with information from the IMF’s Currency Composition of Official Foreign Exchange Reserves (COFER) database.

The IFS data on total holdings of international reserves (excluding gold) are available annually at the country level, without a breakdown across counterparty countries. The COFER database contains the currency breakdown of international reserves held in aggregate by advanced and emerging regions for 2001–14.<sup>32</sup> We use the COFER database to calculate the share of international reserves that North and South countries allocate in North and South currencies each year, assuming that this allocation in global currencies holds uniformly across countries within each region. That is, we assume that each country in the North (South) holds international reserves in North and South currencies in the same proportion as the region as a whole.

Using this methodology, we can construct country-to-region but not country-to-country data. For example, the data reveal the amount of international reserves that countries hold in euros, but not the amount of reserves held in assets of each euro area country. Similarly, COFER includes the share of international reserves in other currencies. We understand that this share corresponds to South currencies, but we cannot assign it to specific countries in this region.

The yearly value of international reserves that each country holds in the North and South results from multiplying the country-level IFS data by the corresponding share of international reserves in North and South currencies for each source country.<sup>33</sup> Implicit in these calculations is the assumption that holding reserves in a North (South) currency is equivalent to holding reserves in a North (South) country. For example, for a North country the value of international reserves held in

> 32 The IMF’s advanced and emerging regions are not identical to our definitions of North and South, but we explain below how we harmonize the two.

> 33 Because the IMF does not publish the currency breakdown for 2015–18 for confidentiality reasons, we carry forward the 2014 shares instead.

37
# **3. Definition of Inflation Episodes and Inflation Starts**

Prior to proceeding with the empirical investigation, it is important to clarify the definition of inflation episodes and starts. To this end, we rely on Ball (1994) and Boschen and Weise (2003) and construct the trend inflation series by calculating the nine-quarter moving average of the quarterly consumer price inflation rate. Next, we turn to the determination of trough and peak dates of inflation, which are identified as dates at which trend inflation is lower (higher) than in the preceding and succeeding four quarters. An inflation episode is then defined as a period of time over which trend inflation rises by at least 1 percent from trough to peak and which is preceded by four or more quarters of stable or declining trend inflation.<sup>11</sup> In total, we capture 24 inflation episodes for which detailed information can be found in Appendix A (Figures 1 and 2).

Figure 1: Number of Inflation Starts per year <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>80 82 84 86 88 90 92 94 96 98 00<br><!-- End of picture text --> Following Boschen and Weise (2003), we define the start date for an inflation episode as the year following the year in which the trough took place. As can be seen from Figure 1, which depicts inflation starts, 17 out of the 24 inflation starts occurred

> 11 In the definition of an inflation episode, we require a 1 percent rise from trough to peak though Boschen and Weise (2003) employ a 2 percent threshold for the OECD countries. The rationale behind our choice of 1 percent rise is that emerging market economies included in this paper have experienced long lasting periods of high inflation and, as a result, a lower threshold—1 percent as opposed to 2 percent—seems to be more appropriate in capturing the periods of sustained increase in inflation. The fact that the average rise in inflation per quarter during inflation episodes in Boschen and Weise (2003) amounts to 0.36 percent, while the same figure in our case is 2.18 percent, supports this conjecture.  Nevertheless, the empirical results remain intact when we employ a 2 percent threshold in lieu of 1 percent.

9
positive association between HIV infection and education among women and none among men (Fortson 2008). However, it is puzzling why this gradient in mortality manifests in the 1980-84 period as well.

# _Socioeconomic mortality gaps in countries experiencing conflict and genocide_

We turn last to countries that have experienced an episode of heightened violence and conflict. As discussed above in the context of overall trends in these countries (Panels H and I of Figure 5) the experiences appear quite different across these countries—and the mortality “event” in question is potentially muted by longer-run trends. We focus here are four cases which illustrate a variety of profiles. First is the Democratic Republic of Congo (DRC) which, since the late 1990s (i.e. in the 1995-00 and 2000-04 periods) has been experiencing a civil war and episodic violence; second is Ethiopia where factional violence flared in the late 1980s, and a border war with neighboring Eritrea lasted from 1998 to 2000; third is Rwanda which suffered ethnic violence—which escalated into internationally recognized genocide—in 1994; last is Sierra Leone in which a civil war ran from 1991 to 2001.

Figure 11 shows the mortality trends in these countries by gender and location (left panels), and by gender and education (right panels).<sup>26</sup> In each case the conflict in question is apparent in the mortality rates—although not all socioeconomic groups experience the same mortality shocks. The clearest case is, perhaps unsurprisingly, Rwanda (notice the different scale for the vertical axis for Rwanda). In that country the mortality spike in the 1990-94 and 1995-00 periods is sharp. While mortality increased for both men and women, in both urban and rural areas, and among those with both more and less schooling—it was particularly pronounced for men, especially urban and more educated men (this case is discussed in greater detail in de Walque and Verwimp 2009).<sup>27</sup> In DRC mortality among rural men and among men with less than primary schooling has increased steadily since the mid-1990s—more than doubling from about 2 percent to over 4 percent in 2000-04. The mortality increases among other groups have been somewhat more

> 26 Figures for all countries and for subgroups (that is gender, location, education and their interactions) are available at http://go.worldbank.org/RIIWM53VS0.

> 27 The mortality spike during the Cambodian genocide of the late 1970s is similarly concentrated on urban and educated men as documented by de Walque (2005).

21
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previously discussed in the literature. Mincsr [1976] showed that, when the possibility of some workers queuing for formal sector jobs is considered, the direction of the effect of changes in the minimum wages on informal sector earnings cannot be determined a priori. In this essay, I explore the possibility of a non-standard result following a different avenue. In Mincer's approach, a non-standard result is associated with a large reduction in unemployment. Here, I will discuss the role of demand-factors, rather than jupply factors, in generating a non-standard result. This view of the matter, I suggest, is more likely to provide a more general explanation of some patterns observed among Latin American countries.

The essay will be-organized as follows. Section I will review the literature on the impact of minimum wages on inequality. Section 2 will discuss the role of labor realloc ..on and demand factors in determininq the relationship between minimum wages and informal sector earnings. In section 3 the model will be developed. Conclusions will be discussed in section 4.

2
Policy Research Working Paper 6248

# **Abstract**

Between 2000 and 2010, the Gini coefficient declined in 13 of 17 Latin American countries. The decline was statistically significant and robust to changes in the time interval, inequality measures, and data sources. Indepth country studies for Argentina, Brazil, and Mexico suggest two main phenomena underlie this trend: a fall in the premium to skilled labor and more progressive government transfers. The fall in the premium to skills resulted from a combination of supply, demand, and institutional factors. Their relative importance depends on the country.

This paper is a product of the Poverty, Equity and Gender Unit, 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 author may be contacted at lflopezcalva@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
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<!-- Start of picture text --> sie<br>20%<br>319% 14.2% 15.0% iLTs<br>Fj 10.2% 10.0%.<br>10% ion<br>S% asx, ey<br>|2 EEERn=<br>Poorest 2 3 4 5 6 7 8 9 Richest<br><!-- End of picture text -->
take many forms, but two relevant ones are the increased installation of air conditioning (the extensive margin response) and improved housing conditions that make cooling less costly and more efficient on the intensive margin (for instance, better insulation, passive cooling, central air vs. window units, etc.). As a result, the increase in violent crime arrests is likely to be smaller than a naive projection of our marginal impacts would suggest. It is even possible, if adaptation is sufficiently effective, that the number of arrests due to hotter temperatures is smaller by mid-century than today.

Different areas will, however, adapt at different rates. Wealthier areas may be more able to adapt because of better access to credit to make the necessary investments. These differences in adaptive capacity increase the dimensions along which the impacts of climate change may be regressive. It is well known that poorer areas are more exposed to marginal climates and are likely to be more exposed to future climate extremes (Hallegatte et al., 2018; Hsiang et al., 2017a). But even if more vulnerable areas are not more exposed to future climate extremes, the impacts of climate change may be regressive if these areas are less able to adapt as effectively as a wealthier or less vulnerable area.

To examine this question, we account for the adaptive response to climate change in concert with projections of how future increased temperatures will impact arrests for violent crimes. We allow adaptive responses to evolve individually for every administrative unit in our data, in line with their baseline income and housing quality, as in Carleton et al. (2020). This approach allows us to examine the regressivity of future exposure to temperature and account for the adaptive response of an individual block group. Our projections for future temperatures come from data created by Rasmussen et al. (2016) and used in Hsiang et al. (2017a). These data collect the output of between 28 and 44 global circulation models (GCM) for each of the RCP scenarios and measure the number of days the maximum temperature is in each of the 1<sup>_◦_</sup> C bins for every county in the continental United States from 1981 to 2100. We use data on temperatures in Texas from 2000 to 2050 to examine how future climate change will change arrests.

23
We begin by reviewing the ongoing debate on land markets in the transition economies of East Asia. Then we address a key issue in that debate: Is rising rural landlessness retarding the country’s progress against poverty? Section 3 uses a simple model of occupational choice to see how we might expect both landlessness and poverty to be affected by introducing a land market. We then turn to various empirical methods. After describing our data in section 4, we study the evolving relationship between landlessness and living standards in section 5. Section 6 notes some aspects of how participation in labor and credit markets has changed. The role played by rising landlessness in reducing poverty is examined in section 7. Section 8 concludes.

# **2. The debate on land market reform in East Asia**

Similarly to China’s “household-responsibility system” introduced in the late 1970s, Vietnam returned decision-making powers over farm outputs to households in the early 1980s. In both countries, the farm-household became the residual claimant on all output in excess of its contracted quota, with the collectives and local cadres setting those quotas and allocating land across households for fixed periods; households were not free to transfer, exchange or sell their allocated land. It is widely agreed that this major agrarian reform increased agricultural output in both countries (see the survey by Rozelle and Swinnen, 2004).

However, agrarian policies in the two countries diverged from the late 1980s. In China, the collectives retained their powers in setting quotas and allocating (and re-allocating) land.<sup>2</sup> There have been concerns about the efficiency costs of China’s non-market land allocation (Li et al., 1998; Carter and Estrin, 2001; Brümmer et al. 2006). By contrast, Vietnam allowed a free market in land-use rights, with the state retaining formal ownership. This was done in two stages. In the first, land was assigned to households. While this appears to have been done in a

> 2 The history of China’s (rural and urban) land policies is reviewed in Ho and Lin (2003).

3
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**Figure 3: National Poverty Rates in Tajikistan (left-note not comparable to international poverty estimates); Number of quarters in poverty (right)**

<!-- Start of picture text --> 45% Number of Quarters a Household is<br>40% Poor During the Year<br>35% 100%<br>90%<br>30%<br>80%<br>25%<br>70%<br>20%<br>60%<br>15% 50%<br>10% 40%<br>30%<br>5%<br>20%<br>10%<br>0%<br>2013 2014 2015 2016 2017<br>Poverty Ext. Poverty No Poor Qtr One Poor Qtr Two Poor Qtr<br>Qtr. Poverty Qtr. Ext. Poverty Three Poor Qtr Four Poor Qtr<br><!-- End of picture text --> _Source: the Tajikistan HBS, Author’s calculations_

**Kyrgyzstan** struggles with poverty rates nearly as high as those in Tajikistan, and in some cases higher. Although poverty fell rapidly for the first half of the past decade, progress stagnated thereafter. Measured at $3.2-a-day, the poverty rate fell from about 79 percent of the population in 2000 to 20 percent in 2008. However, the declining trend nearly halted at that point, and over the following years has remained nearly flat. Changes in the poverty rate are strongly associated with economic growth in the Kyrgyzstan, and recent slow economic growth has translated into little poverty reduction. The country is also relatively undiversified, and in the past, boom-bust cycles in economic activity have been closely linked to periods of progress and stagnation in poverty reduction.

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We observe large heterogeneity in the share of self-employed individuals at home that remain self-employed at destination, ranging from 20% in the US over the period 2001-17 to 73% in Egypt in 1998-2006. Pooling together all the countries, 40% of individuals who are self-employed at home maintain this status after migrating. Thus, the majority of the self-employed do not stay self-employed after migrating. Nevertheless, their self-employment rate at destination is substantially higher than that of migrants who were wage workers at origin (10% become self-employed at destination), or who were not working at origin (only 6% become self-employed at destination).

# _Are the self-employed more or less likely to migrate?_

We begin with simple unconditional comparisons that tell us whether the average selfworker is more or less to than the worker or employed likely migrate average wage individual not working at origin. Figure 2 shows that, for the majority of the countryperiods considered, the self-employed are less likely to migrate than wage workers, and even more so than individuals not employed. Indeed, on average, 8% of selfemployed respondents migrates internally or internationally, as opposed to about 12% of wage workers, and 13% of the individuals with no job. We draw similar conclusions when we look separately at internal migration rates (figure 3) and international migration rates (figure 4), although tracking people who move abroad is a particularly difficult task, making international migration a rare outcome in the survey data available.<sup>11</sup> The considerable heterogeneity in country migration rates emerging from each of the previous figures is partly coming from place- and period-specific differences, but it is also due to the fact that our migration definition varies across surveys. For example, while in the Chinese panel data (CFPS) we can only measure migration as changes in home province or county, in the Egyptian panel data (ELMPS) we additionally account for moves between ”sheyakha”, a relatively small administrative division. We return to this more in detail at the end of this section, where we show that we find similar results when we separate observations by distance of migration.

We next ask whether this relationship still holds once we condition on basic observable characteristics. First, we consider age and gender. Figure 5 shows that our conclusion generally holds when we separate between female and male respondents, and group observations into five age bins.<sup>12</sup>

> 11Average internal migration rates are 6.8% for the self-employed, 9.9% for wage workers, and 11.6% for the unemployed. Average international migration rates are instead 0.7%, 1%, and 0.9%, respectively. 12We do not show results for Egypt, as we observe no instance of migration for female respondents in the 18 to 24 age category. We similarly omit data from Tanzania because the sample does not include migrants for several gender-age groups.

9