rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4. Change in gender gaps in time use over time (men minus women). Hours per day Panel A. Market work Panel B. Unpaid domestic work <!-- Start of picture text -->
-7.0 -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0<br>6.0<br>0<br>5.0 -<br>4.0 -<br>3.0 -<br>-<br>2.0<br>-<br>1.0<br>-<br>0.0<br>0.0 1.0 2.0 3.0 4.0 5.0 6.0 -<br>Gender gap - WDR Gender gap - WDR<br>Gender gap - Our study Gender gap - Our study<br><!-- End of picture text -->

Panel C. Personal care <!-- Start of picture text -->
-1.0 0.0 1.0 2.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>Gender gap - Our study<br><!-- End of picture text -->

**Gender gap - WDR**

Source: Authors’ elaboration based on survey data.

Notes: Gender gaps corresponding to our study were calculated for the sample of men and women aged 18-65 in order to match the sample definition used in the 2012 WDR. The time difference between the surveys used in the 2012 WDR and our study in years is of 4 years for Ghana, 5 years for Guatemala, Iraq, Mexico and Timor-Leste, 6 for Uruguay, 8 years for Argentina, 10 years for South Africa and USA, and 12 years for West Bank and Gaza.

41
**Figure 6: Prices and Volumes of Non-Traditional Exports 1993-2009**

<!-- Start of picture text --> (indices 1994=100)<br><!-- End of picture text --> <!-- Start of picture text --> 700<br>Price<br>600<br>Volume<br>500<br>400<br>300<br>200<br>100<br>0<br>Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09<br><!-- End of picture text -->

# **4.  Trends in export diversification**

Although the performance of Peruvian exports has been notable during the last decade, one large concern is the diversification of the range of export products, as the economy has suffered historically from strong swings in its terms of trade due to excessive product concentration. This section presents an analysis of the evolution of the degree of concentration of Peruvian exports since the start of the economic reform phase in the early 1990s until 2009. The analysis relies on the computation of annual time series of diversification indicators between 1993 and 2009<sup>12</sup> , as well as a more disaggregated comparison of the degree of export diversification at the product level in two years: 1993 and 2009. The empirical evidence obtained from both approaches indicates that over these two decades, Peru has seen a trend towards a greater concentration of its exports, especially in the post-2000 period. The silver lining is that this greater overall concentration of aggregate exports has gone hand in hand with a greater diversification of the non-traditional export category, which is reflected in the emergence of a large number of new export products.

## **Concentration indices**

This section looks at the evolution of diversification using two different methods of measurement. The first is the proportion of the value of exports of the major export products, a widely used simple and intuitive indicator. The second one is the Herfindahl-Hirschman (HH) index, a well-known indicator used extensively in the literature.<sup>13</sup> The indicators are calculated using nominal trade information and the 12 The analysis starts from 1993, the year as from which the Superintendency of Tax Administration has systematized and integrated information with data on the level of subheadings.

13 The Herfindahl Index is defined as ; where _n_ is the number of observations and _x_ the fraction of exports in relation to total non-traditional exports of the j-th SIC; in this case, the range of the index fluctuates from (maximum diversification) to 1 (maximum concentration). Nonetheless, if the idea is to compare 12
In the gridded version of WatBal, the relative importance of water storage on the hydrological regime of a cell is expressed as: where: Smax is expressed as millimeter of water stored per meter depth of soil and is dependent primarily on the type of soils in the cell. AWCmult is dependent on the type of vegetation and hence is primarily a function of land use within the cell. The storage variable, z, is given as the relative storage state and is a value between 0 and 1. Consequently, when CSmax is multiplied by z, it gives the volume of water stored in the cell at any given time.

As recommended by the UN Food and Agriculture Organization (FAO), potential evapotranspiration is computed using the Penman-Monteith method (FAO 1992). A nonlinear relationship (based on Kaczmarek 1993) is used to compute actual evapotranspiration from potential evapotranspiration.

As z decreases the ratio of actual to potential evapotranspiration declines (Figure 3).

9
# **Table 6: Wage Changes Among Manufacturing Workers Observed 2 Periods Who Switch Industry, 1983-2002**

Dependent Variable: Log Wage Change Between Periods

||All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Panel A: Sample of Workers<br>|who Stay in Man<br>|ufacturing both<br>|Periods<br>||
|Switched Industry|-0.003|0.0001|-0.002|-0.015*|
|Classification (1=yes)|(0.003)|(0.003)|(0.005)|(0.008)|
|Observations|147,865|83,026|41,827|23,012|
|Panel B: Sample of Workers<br>|who Switch Indu<br>|stry Classificati<br>|on between Peri<br>|ods<br>|
|Left Manufacturing|-0.031***|-0.036***|-0.025***|-0.028***|
|(1=yes)|(0.004)|(0.006)|(0.006)|(0.010)|
|Observations|170,545|93,689|49,015|27,841|
|Panel C: Sample of Workers<br>|who Leave Manu<br>|facturing betw|een Periods||
|Switched Occupation|-0.059***|-0.044***|-0.042***|-0.106***|
|(1=yes)|(0.010)|(0.011)|(0.013)|(0.021)|
|Observations|22,680|10,663|7,188|4,829|

_Source_ : Sample is composed of CPS MORG workers observed in two consecutive samples and employed in manufacturing in the first period.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. Standard errors are clustered by occupation. All models include year, state and education level fixed effects. Other demographic controls are age, sex, non-white, and union status in the first period. Industries and occupations are defined by 3-digit census classifications. Classification of routine is based on first period occupation. The classification of occupations into routine categories is determined by the proportion of tasks which are routine in each occupation, with low being occupations with more than 2/3, intermediate being between 1/3 and 2/3, and high being occupations with less than 1/3 of tasks designated routine. See appendix for details on how workers are matched across samples.
- **Halpern, Lszl, Mikls Koren, and Adam Szeidl** , “Imported Inputs and Productivity,” CeFiG Working Papers 8, Center for Firms in the Global Economy 2011.

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- **and Joel Rodrigue** , “Does the use of imported intermediates increase productivity? Plant-

- level evidence,” _Journal of Development Economics_ , 2008, _87_ (1), 106 – 118.

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- , “Product Differentiation, Multiproduct Firms, and Estimating the Impact of Trade Liberalization on Productivity,” _Econometrica_ , 2011, _79_ (5), 1407–1451.

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30
**Table 3.12: Education Spending, 1997**

|**Average Spending Pe**<br>**Preschool, Primary, Secondar**<br>**(R$)**<br>|**r Stud**<br>**y, and**<br>|**ent at**<br>**Tertia**<br>|**ry levels**<br>|
|---|---|---|---|
|**Pre.**|**Pri.**|**Sec.**|**Ter.**|
|**Alagoas**<br>396|350|1058|9423|
|**Bahia**<br>438|322|395|6939|
|**Ceará**<br>323|361|543|6649|
|**Maranhão**<br>324|319|546|8040|
|**Paraíba**<br>454|354|731|6995|
|**Pernambuco**<br>450|376|557|3917|
|**Rio Grande do Norte**<br>369|381|646|14288|
|_Source:_Secretaryof Education,Rio Gra|nde do N|orte.||

Total educational spending in Paraíba as elsewhere in Brazil is biased toward higher education. In 1997, spending per student was only R$454 at the preschool level, R$354 at the primary level (less than a quarter of what was spent in the Federal District), R$731 at the secondary level (around the median for state spending), and R$6,995 at the tertiary level (Table 3.12). This indicates a distortion in spending, biased toward the higher levels of education and regressive in nature, because far more students from the top income quintiles attend secondary and tertiary education than students from lower-income quintiles.

While data on the incidence of spending are not available, the high number of children in poverty, relatively high enrollment rates in the state and the fact that 70 percent of spending goes toward primary education would indicate that expenditures on primary education are progressive. Contrary to this progressive spending pattern is the high percapita spending on tertiary education where few poor attend and hence tertiary spending is more regressive in nature.

Efforts to improve equity in the education system in Brazil are likely to pay dividends in social as well as in human capital. _Fundef_ has been a turning point for the Northeast and its states and poor municipalities have gained substantial educational resources. At the municipal level, data on expenditures are not available, but it is possible to use proxies to spending—per-capita numbers of schools. It is also possible to compare this information with living standards at the municipal level as measured by the Human Development Index (HDI) with the aim of determining if education spending is pro-poor.<sup>11</sup> Table 3.13 presents a sample of municipalities with a range of HDI rankings.

> 11 The HDI is a composite index containing 9 indicators of socio-economic well being related to education, health, water supply, electricity, telecommunications, vehicles, dependency ratios, and agricultural production.

31
decreases, and the domestic financial system deteriorates. This effect may or may not generate losses from trade to the poor country, but the deterioration of the financial system may be important for a wide variety of reasons that are beyond the scope of this model. Indeed, the importance of financial development to fostering long-run growth and reducing output volatility has received a great deal of attention in the literature.

In the model we presented, the comparative advantage that generates the key effect comes purely from differences in wealth between countries. However, in practice institutional quality — contract enforcement, property rights, investor protection, etc. — has been shown to be quite important to financial development (La Porta et al., 1997). Institutional differences will tend to reinforce the financial comparative advantage in favor of rich countries, and exacerbate the effects we highlight here.

We provide empirical evidence that trade openness affects countries’ financial systems differentially. In richer countries trade promotes financial system growth, in poorer ones the effect is the opposite. While the results in this paper are suggestive, there are a number of important caveats. The empirical proxy of financial development we use is the ratio of total lending to GDP, which is a measure of the size of the financial system and not its quality. Thus, while the results are consistent with the model we presented in this paper, they do not allow us to conclude that the quality of the financial system is affected as well as its size.

The strength of financial comparative advantage was proxied crudely by the interaction of aggregate trade openness and per capita income. Perhaps the revealed financial comparative advantage can be measured more precisely by looking at industry-level import and export data and the implied “financial content of trade.” Implementing more sophisticated empirical tests of the influence of trade on the financial system remains on the research agenda.

# 5 Appendix 1

Proof of Lemma 1: γ<sup>t</sup> is a random variable with the following probability distribution: and this implies that 18
insurance, industry and commerce suffer from undue exposure to fire and other hazards, which when they occur can debilitate a company.

# **III. STATE OF SOCIAL SECURITY**

**Design Faults.** The development of pensions funds and life insurance companies is also often adversely affected by the existence of social security systems paying or promising to pay generous pension benefits. This is more a problem for middle and high income countries, since in low income countries social security systems are less well developed and where they exist they usually have a very small coverage.

Apart from discouraging the development of long-term contractual savings, the social security systems of most developing countries also suffer from their own internal financial and structural problems. These cause considerable distortions in incentives and the allocation of resources and cast doubt on their long-term viability.

**Demographic Aging.** Much has been written recently about the pressing problems of social security systems in Latin American and Eastern Europeal countries'. Considerable attention has also been focused on the problems caused by the progressive aging of the populations of developing countries, although demographic aging in most developing countries is by no means a current problem but rather a poteintial future problem of the second half of the next century. In fact, the current problems facing the social security systems of developing countries stem from major faults in design and implementation and have little to do with the heavy burden imposed by an aged population and labor force.

I For a comprehensive worldwide review of social security issues, see World Bank (1994). For more specific discussions of the experience of individual countries or regions, see Arrau and SchmidtHebbel (1994), Arrau et al (1993), Davis (1993), Diamond and Valdes-Prieto (1994), Demirguc-Kunt and Schwarz (1995), Fox (1994), Kane (1995a and 1995b), Mesa-Lago (1989), McGreevey (1990), Queisser (1991 and 1995), Schmidt-Hebbel (1994), Vittas (1993c), Vittas and Iglesias (1992), and Vittas and Michelitsch (1995).

4
Figure 2: Regression Discontinuity First Stage (a) Sample Frame (N=5,554) <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>14<br>13<br>12<br>11<br>10<br>9<br>Log relief<br>8<br>7<br>6<br>5<br><!-- End of picture text --> (b) Surveyed Households (N=2,897) <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>13<br>12<br>11<br>10<br>9<br>Log relief<br>8<br>7<br>6<br><!-- End of picture text --> Notes: Panel (a) plots the log relief amount for households benefiting from 100% debt relief (left) and conditional 25% debt relief (right) for all 2,897 surveyed households. The estimated discontiˆ nuity at _x_ = 0 is _γ_ = 1 _._ 330, SE( _γ_ ˆ) = _−._ 064. Panel (b) plots the log relief amount for households benefiting from 100% debt relief (left) and conditional 25% debt relief (right) for all 2,897 surˆ veyed households. The estimated discontinuity at _x_ = 0 is _γ_ = 1 _._ 330, SE( _γ_ ˆ) = _−._ 064. Dashed lines represent local linear regressions with a bandwidth of .01 ha to each side of the program cutoff. 44
_Thirdly, there are suggestions to introduce more rural satellite auction markets which will invariably reduce the congestion at the three main auction markets in Lilongwe, Mzuzu and Limbe._ Currently, there are 4 satellite markets that have been in operation for the past 2 years. There is need to increase the number, but this requires collateral investments by the private sector players, through some form of private-public partnerships.

# **VIII. COTTON**

86. Cotton is one of the Government of Malawi’s declared strategic crops. Government aims to promote cotton production as a way of broadening its agricultural export base. Prospects in cotton production have for the past decade been hampered by poor incentives on the market in terms of low prices and limited profits, mainly due to fewer ginners on the market<sup>14</sup> . The cotton sector has about 120,000 smallholder farmers, three ginning companies and three main input providers. Up until 2003/04, cotton yields averaged about 600 kg/ha, but since then, through a number of emerging cotton development initiatives and the slight increase in the ginners, average yield has improved to about 900 kg/ha and production has considerably increased to about 50,000 MT in 2007/08 season (see Table 18).

87. The increased production response after 2003/04 was as a result of the establishment of the Cotton Development Association (CDA) involving the major ginners. The CDA provided treated seed and pesticides to cotton farmers under contract farming arrangements. A further important change was the improved ginning out turn (GoT) up from 33% to 38%, which improves the overall crop value as lint is significantly more valuable than seed. Due to these positive developments, Cotton is now the fourth biggest crop by value. There is also realistic potential to double the volume and value in the coming years, through the initiatives to improve cotton seed, adoption of Bt-cotton (after the initial trials) and ensure fairness and transparency in setting prices. Partly, the bright prospects are also a result of the improvements in the cotton price on the international market (see Table 20) and the favorable incentives being provided by the ginners that ensure that increase in international prices is passed through to the cotton producers.

**Table 19:  Malawi cotton production (metric tons)**

||2002-03|2003-04|2004-05|2005-06|2006-07<br>Final|2007-08<br>Estimate|
|---|---|---|---|---|---|---|
|Seed cotton|14,700|33,000|45,000|46,000|43,000|50,000|
|Lint (avg. 38%)|5,600|12,540|17,100|19,760|16,340|19,500|
|Cottonseed (avg.<br>57%)|9,100|18,810|25,650|29,640|24,510|28,000|

Source: Kadale Consultants, 2007

> 14 From 2007/08 season, Government started to intervene in cotton markets by setting minimum prices (at a level higher than parity).

41
also documents the methods used for aggregating poverty globally and the alignment of estimates to a particular reference year.

The remainder of the paper is structured as follows. The next section briefly reviews the literature on global poverty measures, with a particular emphasis on the use of PPP conversion factors and the impact they have had on international poverty measures in the past. Section 3 describes in detail the data and methods used in the latest revision of the global poverty numbers. The fourth section describes how the $1.25 line has been updated to $1.90 based on the 2011 PPPs, and the final section presents the 2012 count and reports on the revised global poverty numbers from 1990 to 2012.

# **2.  A brief history of global poverty measurement using PPPs**

The precise methods used for measuring global poverty have changed over time, but one guiding principle has endured, namely that the global extreme poverty line should reflect how the world’s poorest countries estimate a minimum threshold of living which meets basic needs in their societies. One instrument that has consistently been used for assessing minimum basic needs has been national poverty lines. Absolute national poverty lines, when well constructed, are anchored in core biological (e.g. caloric) needs, but also reflect country context and values, and often are robustly debated by the press and citizens. This section describes each of the major revisions to the international poverty line, which are also synthesized in Table 1.

One of the first estimates of internationally comparable poverty measures was conducted by Ahluwalia, Carter and Chenery (1979) who use India’s national poverty line (46<sup>th</sup> percentile of the per capita income distribution) to estimate the world’s poverty incidence, using the 1975 PPPs from the ICP. Their estimate was based on consumption and income data from 25 countries. While the global coverage of this estimate was limited,<sup>8</sup> this was the first attempt to measure global poverty with a common absolute poverty line. It also began the practice of measuring international poverty based on national lines, converted at PPPs.

Since the 1990s, the international poverty lines used by the World Bank have been defined on the basis of information from a sample of the national poverty lines used in some of the world's poorest countries. Ravallion, Datt and van de Walle (RDW, 1991) examine 33

> 8 Including the imputed data for 11 countries, their sample covers 80 percent of the population of countries of the developing world, excluding China.

5