rafmacalaba/datause-encoder-data huggingface dataset:
2 change in inequality. In Dar es Salaam, per capita consumption increased by 43%, headcount poverty dropped by a third, and inequality as measured by the Gini coefficient jumped from 0.30 to 0.34. All the changes in Dar are significant at the 95% confidence level.

**Table 1: Consumption Data from Household Budget Survey Data**

||**Mainland**<br>**Tanzania**|**Dar es**<br>**Salaam**|**Other**<br>**urban**<br>**areas**|**Rural**<br>**areas**|
|---|---|---|---|---|
|**% Below Basic Needs Pov. Line**|||||
|**1991/92**|38.6|28.1|28.7|40.8|
||(2.1)|(2.8)|(5.0)|(2.4)|
|**2000/01**|35.6|17.6|25.8|38.6|
||(1.6)|(2.7)|(2.2)|(2.0)|
|**Consumption per Adult Equivalent**|||||
|**Mean, 1991/92**|10223|11161|12445|9820|
||(290)|(410)|(842)|(326)|
|**Mean, 2000/01**|10880|15944|13536|10060|
||(250)|(779)|(487)|(273)|
|**Ratio (00/01) to (91/92)**|1.064|1.429|1.088|1.024|
|**Gini Coefficient**|||||
|**1991/92**|0.33|0.30|0.34|0.33|
||(0.01)|(0.01)|(0.02)|(0.02)|
|**2000/01**|0.34|0.34|0.35|0.32|
||(0.01)|(0.01)|(0.01)|(0.01)|

Notes: All figures shown are as calculated from Household Budget Survey data. Standard errors are given in parentheses. All figures were calculated from household-level data on a per adult equivalent basis, with weights calculated by multiplying household size by household sampling weights. 1991/92 figures were converted to 2000/01 Shillings by multiplying by 2.611811, which is the ratio of the poverty lines used to calculate the poverty levels, using nominal values in the official poverty report.

The poverty statistics calculated for this paper differ slightly from those in the official published report, _Household Budget Survey 2000/01_ (United Republic of Tanzania, National Bureau of Statistics, 2002). These are reproduced in Appendix Table 1. While the headcount rate figures are identical to those that were calculated for this paper, the published mean consumption figures differ slightly from those calculated for Table 1. This is partially because the published figures were calculated on a per capita (rather than per adult equivalent) basis.<sup>2</sup> 2 Also, for this analysis, the 1991/92 survey figures were converted to 2001/02 Shilling figures by multiplying by 2.611811, the ratio of the (Basic Needs) poverty lines from the two years. In the official figures, a price adjustment of 2.49 was used. The official figures and the analysis here also employ regional price adjustments. This combination of price adjustments may have been implemented differently for the official mean consumption figures than is done here. The report indicates explicitly that the mean consumption figures were calculated on a per capita basis, rather than the
TABLE 11 REAL ANNUAL COST OF CREDIT IN ISRAEL, 1979-1987 (PERCENT PER YEAR)

|Year<br>---------------|Overdraft<br>facili-<br>ties(1)<br>---------------------------------------|Free bank<br>credit,<br>short-<br>term(2)<br>-<br>------------|Short-term<br>directed<br>credit in<br>domestic<br>currency|<br>All short-<br>term<br>directed<br>credit(3)|All short-<br>term<br>credit(4)<br>|Long-term<br>investmen<br>credit<br>--------.|
|---|---|---|---|---|---|---|
|1978|<br>2%|NA|-24%|NA|-11%|__<br>-25%|
|1979|<br>-11%|NA|-45Z|NA|-10%|-26%|
|1980|<br>19%|NA|-39%|NA|1%|-9%|
|1981|<br>342|NA|-28%|NA|17%|-6%|
|1982|<br>5%|NA|-38%|NA|4%|-2%|
|1983|<br>-3%|NA|-44%<br>-|NA|8.5%|0.5%|
|1984|<br>67%|40%|-59%|6.37<br>-|26%|0.0%|
|1985|<br>100%|36%|-23%<br>|0.9%|20%|2.5%7|
|1986|<br>317|167.|_-37_|-9.0%|7%|1.6%|
|1987|<br>39%|27%|+3%|-0.6%|20%|0.92|

Source: Bank of Israel, Annual Report, 1980--1987 issues.

Notes: (1) Overdraft facilities are the most expensive source of free bank credit: this represents the marginal cost of credit after all the othler sources have been exhausted.

(2) Free bank credit includes the overdraft facilities as ofle"of is components, but the average cost of the entire mix is much lower.

(3) The weighted average of short-term directed credit in dbmestic and foreign currency (mainly US dollar-linked).

(4) The weighted average cost of all sourccs of short-term credit.

this source was then linked and its real cost became positive. Still the cost was negative for tax paying enterprises since linkage charges, even if not yet paid, were treated as tdx-deductible financing expenses. For many of those, linking of investment loans reduced and did not increase the cost of credit. This distortion, at the time unexpected, was eliminated in 1982 with the introduction of inflation

> **r** adjusted income tax regulations which basically recognized only real interest expenses as tax-deductible.

The information in Table 11 is fragmentary and collected from incomplete statistics. To double check, we consider the average cost of debt from all sources calculated from sample balance sheets.

33
**Does Access to Foreign Markets Shape Internal Migration? Evidence from Brazil**

Laura Hering and Rodrigo Paillacar

**JEL codes** : F16, F66, R12, R23.

**Keywords:** Migration, international trade, economic geography, Brazil

Laura Hering (corresponding author) is an assistant professor at the Erasmus School of Economics (Department of Economics) and a Tinbergen Research Fellow; her email address is laura.hering@gmail.com. Rodrigo Paillacar is an assistant professor at the University of Cergy-Pontoise (Laboratoire THEMA); his email address is rodrigo.paillacar@gmail.com. This research has been conducted as part of the project Labex MME-DII (ANR11LBX-0023-01). We thank the editor, two anonymous referees, Maarten Bosker, Matthieu Couttenier, Fabian Gouret, Philippe Martin, Sandra Poncet, Loriane Py, Cristina Terra, Vincent Rebeyrol and Gonzague Vannoorenberghe for their helpful suggestions.
||Direct|Partial|Total|
|---|---|---|---|
|Argentina||||
|As % ofHT exports in 2006|49.45|6.69|56.14|
|As% of all exports in 2006|1.21|0.16|1.oF|
|Brazil||||
|As % ofHT exports in 2006|41.24|52.42|93.66|
|As % of all exports in 2006|3.16|4.02|7.18|
|Chile||||
|As% ofHT exports in 2006|0.13|0.07|0.2|
|As % of all exports in 2006|0.05|0.03|0.08|
|Colombia||||
|As % ofHT exports in 2006|10.9|38.53|49.43|
|As % of all exports in 2006|0.26|0.92|1.18|
|Costa Rica||||
|As % ofHT exports in 2006|31.57|61.53|93.1|
|As % of all exports in 2006|9.96|19.42|29.38|
|Mexico||||
|As % ofHT exports in 2006|65.1|16.55|81.65|
|As % of all exports in 2006|32.3|8.21|40.51|
|LAC||||
|As % ofHT exports in 2006|68.7|26.66|95.36|
|As%ofallexportsin2006|8.28|ak|11.49|
TABLE 2

# Cross-Country Averages: 1960-89 (excluding oil countries)

||Fastgrowers|Slowgrowers|t-~-stat|
|---|---|---|---|
|Share<br>of<br>investment<br>in GDP|0.23|0.17|5.18|
|Secondary school enrollment<br>rate<br>(1960)|0.30|0.10|5.46|
|Primary<br>school enrollment<br>rate<br>(1960)|0.90|0.54|6.10|
|Government consumption/<br>GDP|0.16|0.12|3.26|
|Inflation<br>rate|12.34|31.13|-1.74|
|Black market exchange<br>rate premium|13.57|57.15|-3.79|
|Shareofexports<br>toGDP|0.32|0.23|2.31|

Note: Mean growth rate = 1.92 Fastgrowers: Greater than the mean growth rate. (n = 56) Slowgrowers: Less than the mean growth rate. (n = §3)

## TABLE 3

## CROSS-COUNTRY CORRELATIONS

||GYP|INV|RES**|x|GOV|PI|BMP|REVC|CIVL|
|---|---|---|---|---|---|---|---|---|---|
|GYP|1.00|0.59*|0.73*|0.32*|0.09|-0.16|-0.38*|-0.36*|-O0.41*|
|INV||1.00|0.00|0.50*|0.28*|-0.04|-0.43*|-0.40*|-2,.33|
|RES**|||1.00|0.09|-9.13|-0.17|-0.13|-0.16|-D.ls|
|X||||1.00|0.15|~0.15|-0.22*|~0.34*|-l 238|
|GOV|||||1.00|-0.16|-0.19|-0.29*|-3.19|
|PI||||||1.00|0.18|0.46*|2.92|
|BMP|||||||1.00|0.47*|1.38*|
|REVC||||||||1.00|9.508|
|CIVL|||||||||1.009|

**RES The residual of the OLS regression of average per capita growth (GYP) on the I-Variables: initial income (RGDP60), population growth (GPO), secondard school enrollment rate (SEC), and the investment share (INV).

* significantly different from zero at the 0.05 significance level.

37
and _loans_ . While the deposit market has become more concentrated mostly due to the UCB privatization, there has been no significant change in concentration in the lending market over the past six years (Figure 2).<sup>7 8</sup> Market concentration in Uganda is higher than in the Kenyan and Tanzanian banking sector as measured by the Herfindahl index in deposits and loans (Cihak and Podpiera, 2005).<sup>9</sup> Further, the Ugandan banking market has experienced a significant increase in foreign ownership over the past years (Figure 3). While the increase in foreign banks’ market share in deposits has been mostly due to the privatization of UCB to Stanbic, the increase in foreign banks’ market share in the lending market has been independent of this event.

While the formal financial system in Uganda contains not only commercial banks (Tier 1), but also bank-like institutions (Tier 2) and since 2004 microfinance deposit-taking institutions (Tier 3), banks are still the dominating part of the financial system, at least in terms of intermediated funds. Further, Tier 2 institutions are specialized financial institutions whose spreads and margins might not be comparable to banks. We will therefore focus on Tier 1 banks in our empirical analysis.

# **3. Methodology and Data**

We utilize a panel of commercial banks’ interest spreads and margins that allows us to formally investigate which bank- specific, industry and macroeconomic characteristics are the main drivers for the persistently high spreads and margins observed in Uganda. Following Ho

> 7 UCB had to a very large market share in the deposit market but its share in the lending segment was comparatively small so the UCB privatization did not lead to an increase in lending concentration.

> 8 The Herfindahl index is the sum of squared market shares and varies between zero and 10,000 with higher values indicating more concentrated banking systems.

> 9 According to Cihak and Podpiera (2005), the Herfindahl indices in loans were 1045 for Kenya, 1169 for Tanzania and 1597 for Uganda in June 2002.

8
# Figure 7 Monthly Charges for Local Services, Flat Rate System, 1970 **-** 92

<!-- Start of picture text --> 1985 Ch$ per llne<br><!-- End of picture text --> <!-- Start of picture text --> 3,500<br>3,000 -- -<br>2,000 _ - - -- - -<br>\ ~ ~ ~~~~~~ 8 I asc r i Reskdentil<br>10500 - - - - - - - _ - ---- - - - - - - -<br>S o o --- - - - --- - - - - - - - - - - - -<br>0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992<br><!-- End of picture text --> Note: December rates of each year, except for August 1992.

Figure 8 National Long Distance **Tariffs,** 1970 **-** 88 <!-- Start of picture text --> 1988 Chilean pesos, net of taxes<br>P Per 3 mins. call, via operator Per minute, via operator<br>2,000 500<br>* - - 400<br>1,500 _ Ion<br>. ;<br>D ; 300<br>1,000 -_ D= 3,250 kms - - - *.<br>v '~~~~, %. D=3,2SOkms<br>500 -- - - - - - - - - -- - - -20 - -<br>D=400kms *.%. --D=80bmns - - - - - ----- , - - 100<br>D= 80 Ions<br>D= 20 kms<br>D= 20 kms _- - - -<br>0 1 ~~~~~~~~~~~~~~~~~~~~~0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br><!-- End of picture text -->
be construed as a dependence on assets _per se_ but rather on being from a wealthier (or poorer) household. Wealthier households do not just have more assets, but generally they have more educated parents, more educational related resources, a higher value towards education, more information, healthier members, access to better schools, etc. Assets alone probably have little impact on the development of cognitive skills once these other factors are taken into account. Hence, the dependence on wealth which needs to be measured is really a dependence on all the observable and unobservable household and school characteristics relevant to the development of cognitive skills that are associated with being from a wealthier household. Of the various indexes in the PISA dataset, the wealth index seems to provide the best measure for this purpose. Since it is constructed from the same set of possessions in all countries (PISA: 217-49), the measure is comparable across countries. Other measures which proxy for household wealth usually include education level of the parents, but this may not be comparable across countries.

This analysis uses cognitive production function theory to develop a means to estimate the dependence of cognitive skills on wealth and gender. We adopt a basic model of cognitive skills from Todd and Wolpin (2003), but, in order to be applicable to PISA, we equate cognitive skills to _competency_ in one of the subject areas and model its production.

Competency in a particular subject area, without loss of generality, for the _i_<sup>th</sup> student in the _j_<sup>th</sup> school, θ _i,j,_ is modeled as a function of a vector of household inputs received over the entire life of the student, **_h_** _i,j_ , a vector of school inputs received over the life of the 13
<mark>Rogoff, B., Moore, L., Najafi, B., Dexter, A., Correa-Chávez, M., & Solís, J. (2007). Children's development of cultural repertoires through participation in everyday routines and practices. In J. E. Grusec & P. D. Hastings (Eds.),</mark> _<mark>Handbook of socialization: Theory and research</mark>_ <mark>(pp. 490-515). New York, NY, US: Guilford Press.</mark> Roorda, D. L., Koomen, H. M., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students’ school engagement and achievement: A meta-analytic approach. _Review of Educational Research_ , _81_ (4), 493-529.

Rosenberg, M. (1986). _Conceiving the self._ Reprint Edition. Melbourne, FL: Krieger.

<mark>Rosenshine, B. (1968). To explain: A review of research.</mark> _<mark>Educational Leadership</mark>_ <mark>, 275-280.</mark>

- Rosenshine, B. (2012). Principles of instruction: research-based strategies that all teachers should know. _American Educator_ , _36_ (1), 12.

- Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom. _The Urban Review_ , _3_ (1), 16-20.

- Rosenthal, T. L., & Zimmerman,  B. J. (1978). _Social learning and cognition_ . New York: Academic Press.

- Roth, M. W. (1996). Teacher questioning in an open-inquiry learning environment: Interactions of context, content, and student responses. _Journal of Research in Science Teaching, 33,_ 710-735. http://dx.doi.org/10.1002/(SICI)1098- <u>2736(199609)33:73.0.CO;2-R</u>

- Rubie-Davies, C. M., Weinstein, R. S., Huang, F. L., Gregory, A., Cowan, P. A., & Cowan, C. P. (2014). Successive teacher expectation effects across the early school years. _Journal of Applied Developmental Psychology_ , _35_ (3), 181-191.

- Sadker, M., & Sadker, D. (1994). _Failing at fairness: How our schools cheat girls._ New York: Touchstone.

- Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. _International journal of Educational Research_ , _13_ (1), 89-99.

- Samson, G. K., Strykowski, B., Weinstein, T., & Walberg, H. J. (1987). The effects of teacher questioning levels on student achievement. _The Journal of Educational Research_ 80(5), 290–295.

- <mark>Schon, D. A. (1987).</mark> _<mark>Educating the reflective practitioner. Toward a new design for teaching and learning in the professions.</mark>_ <mark>The Jossey-Bass Higher Education Series. San Francisco, CA: Jossey-Bass Publishers.</mark>

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Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children's self-efficacy and selfregulation of reading and writing through modeling. _Reading & Writing Quarterly_ , _23_ (1), 7-25.

59
**Table 4.5a** **<u>(continued): Effectiveness of Bias Correction</u>**

||**Full ESS**||**Ethiopia**<br>**HFPS(n**|**=3,247)**||**Full IHPS**||**Malawi**<br>**HFPS(n**|**=1,729)**||
|---|---|---|---|---|---|---|---|---|---|---|
||**(n=6,774)**|_Adjust_|_ed Weights_<br>_†_|_Unadju _|_sted Weights_<br>_†_|**(n=3,181**|_Adjust_|_ed Weights_<br>_†_|_Unadju _|_sted Weights_<br>_†_|
||_Mean_|_Mean_|_Difference_<br>_(2) -(1)_|_Mean_|_Difference_<br>_(4) -(1)_|_Mean_|_Mean_|_Difference_<br>_(7) -(6)_|_Mean_|_Difference_<br>_(9) -(6)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|_(9)_|_(10)_|
|**_Consumption_**|||||||||||
|Mean expenditure(LCUper capita)|14,943|14,400|(543)|17,437|2493.996***||||||
|**_Quintiles_**<sup>**_a_**</sup>|||||||||||
|Quintile 1 -poorest|0.200|0.214|0.013|0.144|-0.056**|0.201|0.165|-0.048*|0.093|-0.109***|
|Quintile 2 -poorer|0.200|0.199|-0.001|0.166|-0.034**|0.199|0.177|-0.010|0.147|-0.051***|
|Quintile 3 - middle|0.200|0.212|0.012|0.204|0.003|0.200|0.171|0.006|0.195|-0.005|
|Quintile 4 - richer|0.200|0.199|-0.001|0.234|0.034**|0.200|0.174|0.033|0.273|0.073***|
|Quintile 5 - richest|0.200|0.176|-0.024|0.252|0.052**|0.200|0.162|0.018|0.292|0.092***|
|**_Housing_**|||||||||||
|Improved roof|0.689|0.728|0.039|0.795|0.106***|0.589|0.540|0.006|0.691|0.102***|
|Improved floor|0.177|0.183|0.006|0.274|0.097***|0.333|0.290|0.012|0.435|0.102***|
|Improved toilet|0.313|0.339|0.026|0.415|0.102***|0.043|0.025|0.002|0.057|0.014|
|Improved water source|0.747|0.774|0.027|0.831|0.084***|0.844|0.808|-0.005|0.848|0.004|
|Access to electricity|0.559|0.633|0.074*|0.711|0.152***|0.150|0.112|0.013|0.208|0.058*|
|**_Asset ownership_**|||||||||||
|Solar|0.153|0.174|0.021|0.148|-0.005|0.207|0.178|0.044*|0.291|0.084***|
|Generator|||||||||||
|TV|0.178|0.195|0.017|0.296|0.118***|0.152|0.121|0.012|0.224|0.072***|
|Refrigerator|0.072|0.075|0.003|0.122|0.05***|0.080|0.055|0.004|0.110|0.031|
|Bicycle|0.012|0.013|0.001|0.021|0.010|0.416|0.374|0.035|0.504|0.087***|
|Motorbike|0.009|0.009|0.000|0.013|0.004|0.044|0.032|0.012|0.075|0.031***|
|Car or other vehicle|0.009|0.009|-0.001|0.015|0.005*|0.025|0.013|0.004|0.040|0.015|
|Mobile Phone|0.553|0.736|0.183***|0.800|0.247***|0.582|0.544|0.178***|0.839|0.257***|
|**_Other activities_**|||||||||||
|Owns land|0.824|0.827|0.003|0.753|-0.071***|0.890|0.861|0.000|0.865|-0.025|
|Cropfarming|0.658|0.683|0.024|0.561|-0.097***|0.812|0.772|-0.018|0.787|-0.025|
|Owns anylivestock|0.684|0.705|0.021|0.594|-0.089***|0.476|0.431|0.008|0.510|0.035|
|Operates nonfarm enterprise|0.229|0.244|0.015|0.287|0.058**|0.409|0.372|0.028|0.484|0.075**|
|Anymember has financial account|0.457|0.545|0.088***|0.634|0.177***|0.339|0.300|0.007|0.422|0.083***|
|Income from remittance(int'l & domestic)|0.145|0.145|0.000|0.161|0.016|0.528|0.489|0.003|0.521|-0.007|

aFor Malawi, the quintiles are based on a wealth index, not consumption expenditures.

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

35
for Sub-Saharan Africa. Another study, Alvaredo et al. (2013), finds that the share of income held by the top 1% in the USA rose dramatically.

# 2.2 The engine of the education wave: Education and demographic transitions by region

Looking forward, what does an increase of skilled labor supply driven by developing countries mean for global inequality? The first step in the analysis of the impact of the forthcoming education wave is to assess the geographically diverse increases in the supply of skilled workers. These increases depend on two key factors: the intergenerational education gap – i.e. the difference in education attainments of the old cohorts that are exiting the labor market vis-à-vis the young entering cohorts – and the intergenerational demographic gap – i.e. the difference in population size between the young and the old cohorts. Large intergenerational education and demographic gaps, and thus a large increase in the supply of skilled workers, are observed for countries that have boosted their investment in education and that have still high population growth, i.e. a large share of young people. Conversely, aging countries where most of their population has already entered advanced secondary and tertiary education will not experience increases in the supply of skilled workers.

# Figure 2: Education transition by region

<!-- Start of picture text --> a) ECA, LAC, SSA, and High-income b) EAP, MENA, and South Asia<br>Education gap Education gap<br>between age groups 20−24 and 60−64 between age groups 20−24 and 60−64<br>Europe & Central Asia<br>Latin America & Caribbean East Asia & Pacific<br>Sub−Saharan Africa Middle East & North Africa<br>High Income South Asia<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Source: Barro Lee Source: Barro Lee<br>6 6<br>5<br>4<br>4<br>3<br>2<br>difference in years difference in years<br>2<br>0 1<br><!-- End of picture text --> Source: Authors elaboration based on Barro and Lee (2013) Note: Old cohort refers to the population between 60 and 64 years old. Young cohort refers to the population between 20 and 24 years old.

6
While the data we rely on come from the most reliable cross-country datasets on income distribution currently available, there nevertheless are some spells where the changes in average income and/or changes in decile shares over the spell are extreme. To prevent these from unduly influencing our results, we clean the data by truncating the sample at the first and 99<sup>th</sup> percentiles of the distribution of average growth and of each of the 10 decile shares. This leaves us with a final sample consisting of 117 countries, with a median length between the first and last available surveys of 16 years. For the sample of spells at least five years long, we have a total of 285 spells, with a median spell length of 6 years.

# **4.  Results**

# **4.1  Basic Description**

We begin with some very simple descriptive analysis of the relative importance of growth and changes in inequality for growth in social welfare. The eight panels of Figure 3 plot growth in social welfare (on the vertical axis) against growth in average incomes (on the horizontal axis) in the sample of minimum 5-year long spells, for eight measures of social welfare: average incomes in the bottom 10, 20, 40 and 90 percent of the income distribution, the Sen index, and the Atkinson index with values of θ equal to 1, 2 and 3. Since growth in social welfare is the sum of growth in average incomes and the growth rate in the relevant inequality measure, the vertical distance between each point and the 45 degree line reflects the contribution of inequality changes to growth in social welfare. The striking feature of these graphs is that, for most social welfare functions, the data points cluster very closely around the 45-degree line. This indicates that changes in social welfare due to inequality changes (in either direction) are small, particularly in comparison with the large variation in growth rates in average incomes apparent on the horizontal axis of each graph. Only in the case of very bottom-sensitive social welfare functions that place a very high weight on the poorest deciles (such as average incomes in the bottom 10 percent, or the Atkinson A(3) measure), do we begin to see more dispersion around the 45degree line.

Figure 4 provides a complementary perspective on the relative size of changes in growth and changes in inequality. We first plot the distribution of growth rates across all episodes in our sample of spells at least five years long. This distribution has a mean of 1.5 percent per year, and very substantial variation: the standard deviation of growth in average incomes is 4.2 percent, and the 10<sup>th</sup> and 90<sup>th</sup> percentiles of the distribution of growth in average incomes are -2.8 percent and 6.2 percent 11