rafmacalaba/datause-encoder-data huggingface dataset:
performers in the United States can skew the mean; if we care about a wide pool of the population acquiring basic skills, however, the mean is a distortionary metric. This effect is similar when comparing South Africa to Tanzania, where South Africa has fewer students meeting the minimum threshold, but more students reaching intermediate and advance thresholds, so has a higher mean performance. A notable comparison of distributions is Zimbabwe and Swaziland. Zimbabwe has a lower percentage of students meeting the minimum threshold relative to Swaziland, but the percentage of students meeting the intermediate benchmark is much higher, clustered close to the percentage meeting the minimum, whereas in Swaziland the percentage of students meeting the intermediate benchmark is clustered closer to those meeting the advanced threshold. Thus, Zimbabwe, has a higher mean score.

When analyzing the entire dataset, we see that less than 50 percent of students reach the minimum global threshold of proficiency in developing countries relative to 86 percent in developed countries. For intermediate benchmarks, 25 percent of students reach the threshold relative to 66 percent in developed countries; and for advanced benchmarks only 2 percent of students reach the global threshold relative to 10 percent in developed countries.

Overall, distributional information reveals critical information on the absolute level of education quality attained by a larger pool of society, the egalitarian nature of education quality, and can shed light on the debate over whether economic performance is driven by a few innovative members of society at the top, or an education society at large.

# _- -_ _<u>4.2. Long term Performance Trends (1965 2015) using the Panel Data Set</u>_

Our database provides the largest, most current globally comparable panel database on education quality. Table 4 provides summary statistics for each year and level of schooling in our _Harmonized Learning Outcomes_ (HLO) database. We observe that at the primary level, mean scores have fluctuated over time, but overall increased. Moreover, variation has also fluctuated but has stabilized more recently. At the secondary level, performance overall has decreased. This is likely driven by enrollment selection effects, where more poor performers stay in the system.

In Table 5, we rank countries by the variation in their secondary schooling quality from 1980 and 2015. We observe gains for most countries with data availability over this period, with annual growth rates in achievement ranging from 0.10 to 0.62 percent. We see the largest gains for Hong Kong, followed by Iran and Finland. Notably, a few countries have experienced a decline in performance, including France, Hungary, Thailand and Chile. Figure 8 present results 25
Preferences, Purchasing Power Parity, and Inequality: Analytical Framework, Propositions, and Empirical Evidence Amita Majumder<sup>a</sup> Ranjan Ray<sup>b</sup> Sattwik Santra<sup>c</sup>

**Keywords:** Non-homothetic Preferences, Tradeable Items, Penn Effect, Counterfactual PPP. **JEL Classification** : D11, D63, E31, F01, I31.

# Acknowledgements:

The work on this paper started when Ranjan Ray visited the World Bank in June 2015. The authors thank seminar participants at The Centre for Studies in Social Sciences, Calcutta, and at the World Bank, Washington D.C., for helpful comments and, especially, Kaushik Basu, Francisco Ferreira, Nada Hamadeh, Dean Jolliffe, Sugata Marjit and Abhirup Sarkar for useful discussions. The disclaimer applies and the authors retain full responsibility for the contents of this paper.

> a Professor, Economic Research Unit, Indian Statistical Institute, Kolkata, India. amita@isical.ac.in

> b Professor, Department of Economics, Monash University, Melbourne, Australia. ranjan.ray@monash.edu; corresponding author

> c Assistant Professor of Economics, Centre for Studies in Social Sciences, Calcutta. sattwiks@gmail.com
Table 6: Brazil: 2002–2005–2008 complete rankings and inequality

||2002|2005|2008|||
|---|---|---|---|---|---|
|_N_|366,388|390,046|372,581|||
|_μ_(_y_) eq.|934.66 (333.55)|937.057 (324.13)|1113.48 (355.26)|||
|mld (all)|0.4738 (0.0014)|0.4327 (0.00131)|0.3922 (0.0010)|||
|mld (between)|0 0.0672 (0.0004)|0.0618 (0.0004)|0.0512 (0.0003)|||
|||||’02-’05|’05-’08|
|avg. growth.||||0.26%|6.36%|
|_GY s_||||0.7547 (0.0910)|7.4937 (0.1169)|
|_OGY s_||||0.4384 (0.0559)<br>|0.7891 (0.0566)<br>|
|_GY_||||-1.0213 (0.6120)|9.5304 (1.0758)|

_Source: Authors’ calculations on PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica). mld_ = mean logarithmic deviation or generalized entropy index with parameter 0, _GY s_ = EOp consistent aggregate measures of growth (eq. 9), _OGY s_ = EOp consistent aggregate measures of growth progressivity (eq. 10), _GY μ_ = Aggregate measure of between type inequality of growth (eq. 11), 95% bootstrapped standard errors are reported in parenthesis.

This considerable change in the overall inequality for the time span considered is well known in the literature. Ferreira et al. (2008) suggest a number of determinants of this change: the decline in inequality between educational subgroups, a reduction in the urban-rural gap, a reduction of inequalities between racial groups, a dramatic increase in the minimum wage, and improvements in social protection programs. Clearly, these variables have a direct impact on inequality of outcome and on the distribution of opportunities. Moreover, our analysis shows that these growth processes have been beneficial in terms of improving opportunities and that Brazil has experienced an impressive increase in the degree of EOp, particularly during the 2002–05 period. Our conclusions complement the findings of Molinas et al. (2011), who look at the development of IOp in Brazil with a specific focus on the opportunities of children.

# **III. CONCLUSIONS**

In this paper, we have argued that a better understanding of the relationship between inequality and growth can be obtained by shifting the analysis from final achievements to opportunities.

To this end, we have introduced the individual OGIC and the type OGIC. The former can be used to infer the role of growth in the evolution of IOp over time. The latter can be used to evaluate 34
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29
to measure outcomes at the completion of a program, and yet the program might yield additional and unintended outcomes in the longer run. Second, while the assignment of individuals or groups of individuals to a treatment can be defined, program beneficiaries may not actually take up an intervention—or may not do so until after they have learned more about the program. Third, with time, control groups or groups other than the intended beneficiaries might find a way of obtaining the treatment, or they may be affected simply by learning about the existence of the program, possibly because of expectations that the program will be expanded to their area. If non-compliance is correlated with the outcome of interest, then the difference in the average outcomes between the treatment and the control groups is a biased estimate of the average effect of the intervention. We discuss these three examples below.

# **_Short-run and long-run outcomes_**

Programs that invest in cumulative processes such as a child’s physiological growth and accumulation of knowledge require the passage of time, implying that longer program exposure would yield greater gains, though probably with diminishing marginal returns. In addition, such cumulative processes could lead to outcomes beyond those originally intended—and possibly beyond those of immediate interest to policymakers. Early childhood development (ECD) programs are an excellent example of short-run outcomes that could lead to long-run outcomes outside those envisioned by the original design. These programs aim to mitigate the multiple risks facing very young children and promote their physical and mental development by improving nutritional intake and/or cognitive stimulation. The literature review by GranthamMcGregor et al. (2007) identifies studies that use longitudinal data from Brazil, Guatemala, Jamaica, the Philippines and South Africa that establish causality between pre-school cognitive development and subsequent schooling outcomes. The studies suggest that one standard 13
**Table 8 Job creation and job destruction: Croatia against**<sup>**selectedcountries**</sup> **(as percent of total employment)**

||Croatia|Bulgaria<br>|Lithuania|Poland|France<br>|Germany|
|---|---|---|---|---|---|---|
||2001|2000|1998-99|1998-99|1984-91|1983-90|
|Jobcreation|3.5|6.8|9.7|5.3|6.6|6.5|
|Jobdestruction|4.9|10.8|10.7|10.1|6.3|5.6|
|Employmentgrowtha)|-1.4|-4.1|-0.9|-4.8|0.3|0.9|
|Jobturnover|8.4|17.6|20.4|15.4|12.9|12.1|
|Jobreallocation|7.0|13.5|19.4|10.5|12.6|11.2|

Note: Continuing establishment only (i.e. business start-ups<sup>andclosures are not included).</sup> Definitions: a) The employment growth rate refers to the sample<sup>(whichis limited tolegalpersons)</sup> and is not necessarily consistent with the overall change<sup>in employment.</sup>

# Sources:

Croatia: Central Bureau of Statistics, Author's<sup>calculations.</sup> Bulgaria: Rutkowski (2002b) Lithuania: Rutkowski (2002a) Poland: Rutkowski (2001) OECD countries: OECD (1996) The low rate of job creation is associated with a low rate of<sup>jobdestruction.</sup> Croatian firms yearly close 4.9 percent of all jobs,<sup>comparedwiththe job destruction</sup> rate of 10-11 percent in the restructuring transition economies<sup>and around6 percentin</sup> mature market economies. At the first sight the low job destruction<sup>ratemayseem a</sup> positive phenomenon, preventing larger employment declines.<sup>Thisisnotthecase</sup> however. The reason is that the low rate of job<sup>destructiontranslates into alowrate</sup> of job creation. This is because the creation of new jobs necessitates<sup>destructionof</sup> old jobs (World Bank, 2002).9 Or alternatively, hiring is bound<sup>tobelimited if firing</sup> is difficult and costly, as employers avoid hiring to minimize<sup>thefuturecostsof</sup> employment adjustment during the downturn.

Economic costs of low job flows (turnover) can be substantial.<sup>Limitedjob</sup> turnover implies that there are limited efficiency gains associated<sup>with reallocationof</sup> jobs away from less productive toward more productive<sup>uses. In other words,low job</sup> turnover entails less enterprise restructuring and less<sup>associated productivity</sup>

> 9 This is because resources (including labor) locked in old jobs need<sup>to be releasedto become</sup> available for newjobs. This argument assumes that either the pool of<sup>unemployedis small(whichis</sup> not the case in Croatia), or that unemployment is structural,<sup>i.e.that theskillrequired (and/orlocation</sup> of) by new jobs differ from the skills possessed (or location of)<sup>by theunemployed.</sup>

_-19-_
Figure 6. Country Cases: Weakening Severity (Rows Ranked by High, Medium, Lower and Low Peak Severity) <!-- Start of picture text --> Belgium Spain United Kingdom Italy<br>150<br>100<br>50<br>1 1 1 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Germany Iran, Islamic Rep. Turkey United States<br>50<br>40<br>30<br>1<br>20 1 1<br>1<br>10<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>China Czech Republic Algeria Greece<br>10.0<br>2<br>7.5<br>3<br>5<br>5.0<br>7<br>2.5<br>18<br>0.0<br>Feb 1Mar 1 Apr 1May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Australia Korea, Rep. Morocco Malaysia<br>2.5<br>9<br>2.0 12 11<br>1.5 20<br>1.0<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

13
Phase out of groundnut trade barriers under NAFTA and URAA is limited in scope, but it continues to have a dramatic impact on U.S. imports. From virtually no imports to the U.S. prior to 1994, edible groundnut imports have increased dramatically (Table 10). Argentina and Mexico averaged a fill rate of 87 and 77 percent respectively, but over-of-the quota imports were quite important, averaging 25,000 tons over the last six years. Edible groundnut imports comprised 6 percent of U.S. total domestic consumption of groundnuts in 2001.

**Table 10. US Imports of edible groundnuts**

|year|Import<br>Arge|Quota<br>ntina|Import<br>Mex|Quota<br>ico|**Total**<br>**Import**|**Total **<br>**Quota**|**Over-the-Quota**<br>**Imports**|
|---|---|---|---|---|---|---|---|
|1996|38270|29853|4710|3583|57000|38478|18522|
|1997|40622|33365|6148|3690|64000|43088|20912|
|1998|34465|36875|4834|3801|70000|47702|22298|
|1999|39494|40388|4916|3915|82000|52318|29682|
|2000|72230|43901|4864|4032|97000|56938|40062|
|2001|37557|43901|3611|4153|81000|57059|23941|
|2002|29927|43901|4406|4278|46795|57184|Not filled|
|2003|4692|43901|292|4278|5698|57184|Not filled|

Source: USDA The initial impacts of the new Farm bill are also visible here with the collapse of imports in 2003 (Table 10). The elimination of production quotas decreased the price paid by US processors and thus, increased domestic use of groundnuts (Figure 8). It also removed the incentive to import edible groundnuts.

**Figure 8. The decline in US domestic groundnut prices after the Farm Bill 2001**

<!-- Start of picture text --> 1100<br>1000 Rotterdam price<br>900<br>800<br>700<br>600 US domestic price<br>500<br>400<br>New Farm Bill<br>300<br>93 94 95 96 97 98 99 00 01 02 03<br><!-- End of picture text -->

# **Groundnut Policies in India, China, and Argentina**

For India, China and Argentina, a general trend since the mid-1990s is a gradual reduction of potentially market-distorting direct government intervention in production and marketing of groundnut products. However, in response to declines in groundnut product prices, 17
the fixed effects regression. Moreover, the cohort coefficients, although significant, show no particular pattern. A similar pattern does emerge, however, for the age dummies, although the pooled OLS exhibits a more pronounced shape. Separate regressions by gender, on the other hand, yield no major pattern differences even though men tend to have higher overall levels of wellbeing. The corresponding average marginal effects for the non-functional forms in Table 4 and the gender split analysis are plotted in Figure 4.

**Figure 4. Average marginal effects of a methodical and gender comparison of well-being over the lifespan:**

**RLMS data (1995–2013)**

<!-- Start of picture text --> (1) POLS (2) FE<br>full sample full sample<br>4.5 4.5<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(3) FE (4) FE<br>men women<br>4.5 4.5<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>Age Age<br>Linear Prediction<br>Linear Prediction<br><!-- End of picture text --> 16
C6te d'Ivoire is one of the few countries in Sub-Saharan Africa to have conducted household surveys over a ten year period between 1985 and 1995 that allow one to trace the evolution of poverty in urban and rural areas and among different socioeconomic groups. This ten year period spans an economic recession of major proportion and ends with the beginning of an economic recovery following the devaluation of the CFA franc in January 1994. Poverty increased substantially over the course of the recession, and did not diminish in the year following the devaluation in part because much of increase in export crop prices, especially cocoa, was taxed away.

Moreover, the impact of the recession and devaluation was not uniform. Between 1988 and 1993, urban poverty increased more rapidly than rural poverty, even though mean expenditure in the urban areas remained substantially above mean expenditure in rural areas throughout the period. And food crop farmers appear to have suffered more than export crop farmers. These trends persist after the devaluation.

However, an exploration of the poverty trends over time across regions and socioeconomic groups raises a number of questions about the pattern of change and about the reliability of the data or the methodology used to derive the poverty estimates. Secondary data cast some doubt on certain of the survey findings, suggesting that they may be more an artifact of the survey data than a reflection of real economic trends. These problems are not unique to C6te d'Ivoire; results from surveys in Uganda and to a lesser extent Ghana have also raised issues of comparability.' The ability to draw reliable conclusions from time series data is crucial for our understanding of the impact of policy reforms on poverty. Far more attention needs to be paid to the issues of comparability in designing surveys and analyzing data and also to the development of better regional and temporal price indices if reliable time series data on poverty is to be generated for sub-Saharan Africa.

# **_Economic Crisis and the Increase in Poverty_**

C6te d'lvoire's economic history is marked by a long economic boom period that ended in the early 1980s with the onset of a brutal economic recession. In the two decades following independence, C6te d'Ivoire achieved an impressive economic growth record. Real GDP per capita grew at 5.7 percent per year between 1960 and 1979, buoyed by strong growth in cocoa and coffee exports, favorable terms of trade, and growth in the manufacturing sector.

A decline in the coffee harvest in Brazil led to a dramatic increase in coffee and cocoa prices in 1976. The terms of trade increased by 58 percent between 1975 and 1977. The cocoa and coffee boom generated large surpluses for the Agricultural Price Stabilization Fund (CSSPA) of more than 10 percent of GDP. The government undertook a major investment program which was financed in part by the export crop revenues and a large increase in external borrowing. Investment rose to 26 percent of GDP on average between 1975 and 1979 (table 1).

See, for example, Appleton (1994) and Jones and Ye (1996).

1
# TABLE 3

First stage results (exports /sales on _z_<sup>0</sup> )

|||ex<br>|port intensit<br>|y<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|exchange rate<br>|–0.149***|–0.148***|–0.149***|–0.149***|–0.150***|
|(_z_<sup>0</sup><br>_jt_<sup>)</sup>|(0.023)|(0.023)|(0.022)|(0.022)|(0.022)|
|exchange rate * initial sales<br>|–0.0029*|–0.0030*|–0.0029*|–0.0029*|–0.0029*|
|(_z_<sup>0</sup><br>_jt _<sup>_∗sj_0)</sup>|(0.0017)|(0.0017)|(0.0016)|(0.0016)|(0.0017)|
|_R_<sup>2</sup>|0.4129|0.4147|0.4144|0.4146|0.4165|
|_F_-statistic|888.09|882.96|888.42|884.31|884.60|
|Prob _> F_|0.0000|0.0000|0.0000|0.0000|0.0000|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: First-stage results of IV-FE regressions of (log) employment (for various types of skilled and unskilled workers) on export intensity (exports/sales). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

17