rafmacalaba/datause-encoder-data huggingface dataset:
assistance spending brought the poverty rate down appreciably. Without the increases in social assistance, the poverty rate by the end of the period would have been 5% points higher than that actually observed (34% versus 29%). The cumulative effect of changes in the rate of inflation on poverty over this period, as well as the cumulative effect of the expansion in social security and social assistance—both of which reflect deliberate policy decisions taken by successive governments—were larger in magnitude than the effects of changes in the level and composition of economic growth, as depicted in Figure 5.

_5.4 On the role played by state-level initial conditions_ Lastly, we investigate whether the responsiveness of poverty to sector-specific growth depends on initial conditions, and whether our results on the importance of transfers and inflation, as well as the broader policy regime change around 1994 are robust to that dependence. We do this by replacing the state-specific<sup>β</sup> _iJ_<sup>coefficients in (4)—which were previously</sup> estimated as a linear function of a set of state dummy variables—with the coefficients of the following function on a set of initial characteristics of the state: We start by restricting our initial conditions to state-level GDP per capita and a measure of household income inequality (the Theil index) in 1970. As shown in Table 6, we find a negative coefficient for initial GDP per capita but the effect of initial inequality is insignificant for both H and PG regression.<sup>29</sup> For H, other results are largely maintained: the impact of inflation remains positive (poverty increasing) and significant; federal social security and assistance remains negative and significant, but state-level public social spending becomes insignificant. For PG, the only result that does not survive is a negative and significant agricultural elasticity post 1994, but the direction of the change remains as before.

Restricting the model with initial conditions to initial output and inequality levels is not terribly informative, however. In the next step, we replace these variables by what we hypothesize to be their likely determinants, notably the initial levels of human capital, initial infrastructure prevalence or access to public services, and variables related to the distribution of political power and voice at a local level. In Table 7, we replace 1970 per capita GDP and

> 29 The income measures used to compute the Theil indices are from the 1970 Census, and thus measured with even greater error than in standard household surveys. The lack of significance might reflect this greater measurement error.

22
Our main interest lies in the evolution of these spatial income disparities in the postapartheid period, when coercive institutions imposed on marginalized racial groups were progressively dismantled and labor union activity was empowered. Examining relative changes in real per capita income between 1996 and 2011, we find that spatial income convergence was considerably stronger among previously marginalized communities with higher initial exposure to resource rents—as measured by a larger share of initial employment in the mining industry. This finding holds irrespective of whether we consider: (1) the universe of local communities; (2) a restricted sample of local communities located just-inside and just-outside the former homelands; or (3) a sample composed only of communities from the former homelands.

The evidence we document is in line with bargaining theory emphasizing the interaction between the strength of coercive institutions and the distribution of resource rents through collective wage negotiations. In the model, domestic unionized producers and foreign producers supply a homogeneous commodity to a global integrated product market. Imperfect competition in the product market generates rents that workers may capture in the form of higher wages. The wage premia—defined as wages in excess of what workers would earn elsewhere in the labor market—is determined through Nash bargaining, and then the firms set the employment (and output) levels unilaterally as part of the product market game with domestic and foreign competitors. The advent of democracy, accompanied by several legislative interventions aimed at leveling the historically uneven bargaining field, leads to significant improvements in the negotiating position of the union, which is therefore able to capture a larger share of resource rents.

In this framework, unionized wages would also be expected to rise in the face of increased resource rents following international price shocks. However, in the absence of changes in institutions governing collective bargaining, these marginal rents would accrue mainly to workers and mine owners from non-marginalized racial groups, who had stronger bargaining power to begin with and resided outside the former homelands. The evidence we provide reveals that initial exposure to resource rents—as measured by the initial employment share in the mining industry—is a robust positive driver of local income growth for black communities in the former homelands, not of local per capita income growth in general. This points to a key role of the interaction between coercive institutions and the distribution of rents, rather than just variation in the volume of resource windfalls.

We provide further evidence on the specific mechanisms emphasized by the model by examining complementary individual-level data from the 10% sample of the 1996 population census. Consistent with the existence of large resource rents in the mining industry, we document a large wage premium associated with employment in this sector. In line with the expected impacts of coercive institutions, we document a large income gap against 3
# **References**<sup>*****</sup>

Agamile, P., Ralitza, D., & Golan, J. (2021). Crop Choice, Drought and Gender: New Insights from Smallholders’ Response to Weather Shocks in Rural Uganda. _Journal of Agricultural Economics_ . 72(3), 829-856. **[1]** <mark>Alam, G. M., Alam, K., & Mushtaq, S. (2016). Influence of institutional access and social capital on adaptation decision: Empirical evidence from hazard-prone rural households in Bangladesh.</mark> _<mark>Ecological Economics</mark>_ <mark>,</mark> _<mark>130</mark>_ <mark>, 243-251.</mark> **<mark>[</mark> 1]** <mark>Auffhammer, M. (2018). Quantifying economic damages from climate change.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(4), 33-52.</mark> **<mark>[</mark> 3]** <mark>Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyzes of climate change.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 181-198.</mark> **<mark>[</mark> 3]** <mark>Azzarri, C., Haile, B., & Letta, M. (2022). Plant different, eat different? Insights from participatory agricultural research.</mark> _<mark>PloS one</mark>_ <mark>,</mark> _<mark>17</mark>_ <mark>(3), e0265947.</mark> **<mark>[</mark> 4]** <mark>Bardsley, D. K., & Hugo, G. J. (2010). Migration and climate change: examining thresholds of change to guide effective adaptation decision-making.</mark> _<mark>Population and Environment</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(2-3), 238-262.</mark> **<mark>[</mark> 1]** <mark>Barrett, C. B., & Carter, M. R. (2013). The economics of poverty traps and persistent poverty: Empirical and policy implications.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>49</mark>_ <mark>(7), 976-990.</mark> **<mark>[</mark> 4]** <mark>Bazzi, S. (2017). Wealth heterogeneity and the income elasticity of migration.</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(2), 219-55.</mark> **<mark>[</mark> 1]** <mark>Beauchemin, C. (2020).</mark> _<mark>Towards Migration Modules for the 50x2030 Survey: Literature Review and Recommendations</mark>_ <mark>. Mimeo.</mark> **<mark>[</mark> 2]** <mark>Becerra-Valbuena, L. G., & Millock, K. (2021). Gendered migration responses to drought in</mark>

> * To help readers, we report, next to each reference, a number in bold and square brackets depending on the specific topic to which each reference belongs. The legend is as follows: [1] Migration and climate change; [2] Migration data sources, gaps, and methods; [3] Climate change impacts and weather data; [4] Other topics.

38
independence are tested at p = 0.05, then 0.05 is not the probability of erroneously finding some dependent set of variables when in fact all pairs are independent.

However, simulation methods can be used to reliably test for the error probabilities associated with the outcome search. Specifically, given the model in Figure 3A from a sample of 158 countries, we first estimate the model and then use the estimated model to generate a number of samples of varying sizes. We then run the search procedure on each sample and calculate the frequency with which the relation we are interested in, Ethnic Fractionalization � Property Rights, is incorrect in the output. Alternatively, we can generate a hypothetical model M' where say in addition to Ethnic Fractionalization � Property Rights, Common Law � Property Rights and Latitude � Property Rights. We estimate M' and use M' to generate a number of samples of size n, run the search procedure on each sample and calculate the frequency with which we only find Ethnic Fractionalization � Property Rights (and not Common � Property Rights or Latitude � Property Rights) in the output. For all the simulations we generate 100 samples at five different sample sizes, 50, 100, 150, 200 and 250.

Table 7 presents the results from the Monte Carlo simulations performed on 2000 randomly generated datasets. In Panel A, the simulations were carried out on a model similar to Figure 3A imposing the 2-Tier temporal order where Tier 1 consists of Latitude, Common Law, Catholic Religion and Ethnic Fractionalization and Tier 2 consists of Property Rights. The significance levels at which the tests were carried out were 5% and 1%. The number in each cell presents the frequency with which we don’t find Ethnic Fractionalization � Property Rights. At both the 5% and 1% level, the results show that at sample sizes above 100, Ethnic Fractionalization is found to be a strong 31
child mortality rates. In particular, they show that universal immunization could have reduced rates of mortality under age two by as much as one-third in these countries. A strong effect of vaccination is also shown by Ssewanyana and Younger (2007) who study the determinants and trends of infant mortality in Uganda using three rounds of DHS surveys. They estimate logit models and include district-level proxies for health infrastructure. District-level vaccination rates, in particular for childhood diseases, turn out to be one major determinant of (infant) mortality; mothers’ educational achievement another.

Harttgen and Misselhorn (2006) use a multilevel approach to explain child mortality and malnutrition in South Asia and Sub-Saharan Africa on the basis of DHS data for South Asia and four Sub-Saharan Africa countries (Mali 2001, Nigeria 1999, Uganda 1995 and Zimbabwe 1994). Their special covariates include a health facility index (whether mother received tetanus vaccination before birth, whether mother received prenatal care, whether child was born at home without assistance of a doctor or a nurse, average number of vaccination per child within a household). Their results indicate that determinants of child mortality differ significantly from those of under-nutrition: access to health infrastructure appears to be more important for child mortality, while individual features (e.g. wealth and educational and nutritional characteristics of mothers) play a larger role for anthropometric shortfalls.<sup>4</sup> In their study on Zambia, Derose and Kulkarni (2005) identify similar mechanisms. They find that full immunization considerably improves infant survival chances, although material resources appear to become more crucial beyond infancy.

Differences in health infrastructure between rural and urban areas have also been under scrutiny of empirical research. Using Ghana’s Living Standard Survey of 1988, Lavy et al. (1996) find big urban-rural disparities concerning health status, which they show to mainly stem from differences in the quality and accessibility of health services. Examining survival and health outcomes of children born during the ten years preceding the survey, they use nonparametric Kaplan-Meier methods to estimate hazard functions while time-dependent hazard rate Weibull models measure the rate at which the hazard rate changes. Investigating the same question for three Sahel countries (Burkina Faso, Mali and Senegal), Lalou and LeGrand (1997) reach a somewhat different conclusion. Similar to Lavy et al. (1996), they find child mortality rates to be substantially higher in rural than in urban areas using a special survey on infant mortality. According to their analysis, this is not only due to a lack of health infrastructure, but also a result of under-usage of health services in rural areas.

> 4 However, the use of the health facility index masks the precise transmission channel of different components of health infrastructure and services that may have very different effects on child mortality, such as vaccination (programs) and the availability of a basic health services, respectively.

11
lower real lines—indeed, a (non-official) line that is roughly comparable with prevailing poverty lines today in the world’s poorest countries.<sup>24</sup> A new “supplementary” poverty line was introduced by the US Census Bureau in 2011 that explicitly acknowledges the relevance of relative poverty in the US (Short, 2012). The next section will return to this new measure.

It is probably no surprise to readers that we see higher real lines in richer countries as evident in Figure 2. In identifying who is considered “poor” within its borders, a rich country tends to use a more generous allowance—just as one finds in survey data on individual perceptions of poverty.<sup>25</sup> The food bundles are almost always anchored to stipulated nutritional requirements, although these vary, with higher mean requirements in places and times with better nourished populations and often with higher activity levels. The food menus identified in practice for attaining given requirements also vary greatly, and are typically more generous (such as with larger allowances for protein and more diversified diets) in less poor places. Past research has also found that a large share of the mean-income gradient in national poverty lines is due to more generous allowances for non-food needs in richer countries (Ravallion et al., 2009). However, these observations can be interpreted in two very different ways: either a line with higher purchasing power is needed to attain the same level of welfare in a rich country as a poor one, or richer countries use a higher welfare threshold in defining poverty.

It is also notable that there is a positive intercept in Figure 2. This pattern seems intuitively plausible, as it is unlikely that the poverty lines used by countries could fall to zero in the limit as mean consumption falls to its lowest level. Using the non-OECD sample, the predicted poverty line based on a linear projection is $0.96 (s.e. = $0.25) for the country with the lowest mean, which is $0.76, for the Democratic Republic of Congo (DRC). The DRC has an unusually low mean (Figure 2(b)). If one uses the country with the next lowest mean, Madagascar, with a mean of $1.45, the predicted poverty line is $1.28.

So these data are more suggestive of weakly-relative lines, with an elasticity less than unity, but rising with the mean; using the linear projection for non-OECD countries, at the lowest mean consumption the elasticity is 0.36 (s.e.=0.12) while it approaches unity in high-income

> 24 While the US did not have an official poverty line 100 years ago, the most credible and widely-cited estimate at the time by Hunter (1904) was only a small fraction of the current official line; indeed, the Hunter line appears to be close to the “$1 a day” international line (Ravallion, 2016a, Chapter 1). Kilpatrick (1973) found evidence that the mean subjective poverty line in the US (based on survey data) rose over time with average income with an elasticity of around 0.6. Also see the discussion in Blank (2008).

> 25 For a survey of the literature see Ravallion (2016a, Part 2).

12
volatility characteristics of commodity prices have attracted less attention. In this paper, we therefore address the following question: has commodity price volatility increased? We do this by exploiting a newly compiled unbalanced panel dataset on 45 individual commodity prices spanning the 1784-2009 period at a monthly frequency.

# **2. Literature Review**

Ever since Prebisch (1950) and Singer (1950), a vast literature has grown around the issue of identifying a secular trend in commodity prices. This research has been mainly concerned that commodity prices tend to decline relative to the price of manufactures with obvious implications for the primary producers.<sup>5</sup> In contrast, the literature on the volatility of commodity prices and its public policy implications is relatively small. This research is mainly focused on understanding how commodity price volatility affects income volatility, especially in poor countries (Koren and Tenreyro (2007)), among others). Other authors have been concerned with how commodity price volatility generates instability in international markets (Blanford (1983), Heifner and Kinoshita (1994) among others).

While mainly concerned with identifying price cycles – booms and slumps – Cashin and McDermott (2002) also test to see if variability in prices is higher or lower across cycles. Using The Economist’s index of industrial commodity prices over the period 1862 – 1999, they find evidence of a ratcheting up in the variability of commodity price movements around 1899 and then again in the early 1970s.<sup>6</sup> The finding of higher variability in commodity prices after the end of the Bretton Woods era is in line with hypotheses of a link between nominal (and real) exchange rates and the volatility of dollar denominated commodity prices Chu and Morrison (1984), Reinhart and Wickham (1994), Cuddington and Liang (1999). Comparing three different datasets<sup>7</sup>

> 5 Interest about a possible negative long-term trend in commodity prices has occupied development economic literature since the late 1940s. For instance in Prebisch (1950) and Singer (1950), Grilli and Yang (1988), Cuddington and Urzua (1989), Cuddington (1992), Powell (1991), Reinhart and Wickham (1994). 6  For annual data, Cashin and McDermott define large booms as a sequence of generally increasing prices that have had a price movement of at least 25 percent over the phase, and large slumps as a sequence of generally decreasing prices that have had a price movement of at least 25 percent over the phase.

> 7 The datasets are the following: (1) the annual data set of Grilli and Yang from 1900 to 1992, (2) Boughton’s dataset with annual observations from 1854 to 1990, (3) the IMF’s _International Financial Statistics_ (IFS) which covers the post World War II period with monthly observations.

3
# 1 Introduction

Over the last two centuries, there has been much debate about the role of trade policy in food crises. Governments of food-exporting but famine-aected areas have often been called upon by their citizens, and have often implemented, food export bans in the hope of protecting vulnerable people. Classical economists were inuential in arguing against such policies in favor of free trade. For example, Aykroyd (1974) describes how the Governor of Bombay in the early nineteenth century quoted Adam Smith's The Wealth of Nations in defending his policy stance against any form of trade intervention during the famines that aicted the region. Various Famine Commissions set up by the British Raj argued against the trade interventions that were being called upon to help protect vulnerable populations. Similarly, Woodham-Smith (1962) describes the inuence that Smith and other classical economists had on British policy responses to the severe famines in Ireland in the mid-nineteenth century. In modern time, free trade has been advocated as a means of stabilizing domestic food consumption in the presence of output shocks (World Bank, 1986). Others have been less supportive. Sen (1981) and Ravallion (1987) pointed to the possibility that real income declines in the famine aected areas can generate food export while people starved.<sup>1</sup> Regulated trade through taxes or even export bans may then be a defensible policy response in helping vulnerable groups relative to feasible alternatives (Ravallion, 1997).

This debate re-surfaced during the latest food price crises. Between 2007 and 2011 an estimated 33 countries resorted to restrictions on exports of grains and other food commodities (Sharma, 2011). The international policy community mobilized itself against these practices. Then World Bank president Robert Zoellick, at the 2008 High-Level Conference on World Food Security, advocated for an international call to remove export bans and restrictions. These controls encourage hoarding, drive up prices, and hurt the poorest people around the world who are struggling to feed themselves.<sup>2</sup> U.S. Secretary of State Hillary Clinton echoes the same concerns in a 2011 speech on global food security: We also saw how unwise policy also had an impact. Some policies that countries enacted with the hope of mitigating the crisis, such as export bans on rice, only made matters worse. (...) And that sounder approach includes (...) abstaining from export bans no matter how attractive they may appear to be, using export quotas and taxes sparingly if at all (...).<sup>3</sup> This paper revisits these claims and argues that trade policies are not necessarily the fundamental source of the macroeconomic amplication decried above. Rather, we show that an

> 1Though in Ravallion (1987), the analysis of the time series data for famines in British India indicated that the aggregate income eects were not strong enough to undermine the consumption-stabilizing eects of unrestricted trade in that specic period.

> 2http://go.worldbank.org/BUEP7C3NC0

> 3http://www.state.gov/secretary/rm/2011/05/162795.htm

2