rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 21: Literacy rates and Education Level in rural and urban areas, for age group 15‐45, by regions, Togo, 2006.**

<!-- Start of picture text --> 120% 120%<br>100% 100%<br>80% 80%<br>senior high and above<br>60% 60% junior high<br>40% 40% primary<br>none<br>20% 20% literacy<br>0% 0%<br>maritime plateaux centrale kara savanes Total lomé maritime plateaux centrale kara savanes Total<br><!-- End of picture text --> _Source_ : Core Welfare Indicators Questionnaire (QUIBB/CWIQ) 2006 and author’s calculations.

# **5.3 Employment**

**As in other developing countries, most of the workers are employed in agriculture as self‐employed or unpaid family workers.** Table 5 presents the sector and status of employment in terms of wage and self employment in Togo, and two things stand out from the table. First, as we have seen, agriculture employs almost 60% of workers, followed by Commerce and trade, 22%. Second, the high share of self employment and family helpers (91%) points to a high incidence of low‐skilled and low‐productivity informal employment for those who cannot find other types of employment. Only in education and health and public administration it is more common to be wage employed, and the share is relatively high also for the mining, construction and transport sector. However, the share of these sectors in total employment is very low. As a result, only around 24% of urban workers receive a salary or are paid on a piece rate basis (27% in Lomé). The reminder are apprentices, non‐paid family workers or self‐employed.

**Table 5: Wage and Self Employment (LHS, and) Wage and Self Employment and Share of Laor Force (RHS), by Sector, Togo, 2006.**

||Wage or<br>piece‐rate<br>earner|Self‐<br>employed/<br>family<br>helper|Total||Wage or<br>piece‐<br>rate<br>earner<br>Shares ac|Self‐<br>employed<br>/ family<br>helper<br>ross sectors|Total<br>across<br>sectors|Total<br>Share on<br>total<br>workforce|
|---|---|---|---|---|---|---|---|---|
|Agriculture|0.9%|58.0%|58.9%|Agriculture|5.8%|94.3%|100.0%|10.6%|
|Mining|0.2%|0.4%|0.6%|Mining|63.4%|36.6%|100.0%|0.5%|
|Manufacturing|0.5%|3.2%|3.7%|Manufacturing|22.0%|77.9%|100.0%|7.1%|
|Construction|0.6%|1.2%|1.7%|Construction|37.1%|62.9%|100.0%|4.2%|
|Transport|1.1%|1.5%|2.6%|Transport|49.0%|51.1%|100.0%|6.9%|
|Commerce and Trade|1.3%|20.2%|21.5%|Commerce and Trade|8.3%|91.7%|100.0%|44.5%|
|Education/ Health|2.0%|0.6%|2.6%|Education/ Health|83.5%|16.5%|100.0%|5.5%|
|Administration|0.8%|0.1%|0.9%|Administration|97.1%|2.9%|100.0%|2.4%|
|Financial Services|1.7%|4.4%|6.1%|Financial Services|34.4%|65.6%|100.0%|14.2%|
|Other Services|0.3%|1.1%|1.4%|Other Services|22.7%|77.2%|100.0%|4.1%|
|Total|9.4%|90.6%|100.0%|Total|23.9%|76.2%|100.0%|100.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**Again as in other poor countries, workers tend to be underemployed rather than unemployed.** As Table 6 shows, overall unemployment rates are 7.4 and 1.4% in urban and rural Togo, respectively. At the same time 19% of rural and 25% of urban workers, are underemployed ‐ defined as those workers who declared to have tried to increase their incomes in the last 7 days.

24
large, suggesting that the model is statistically significant at the 1-percent level of significance. Similarly, the Chi-Squared statistics which tests the joint significance of all independent variables in the model rejects the null hypothesis of joint insignificance. Most explanatory variables are statistically significant at 1-percent level and the trend of per capita GDP (solid and dark line in Figure 4-A and B) is consistent with actual trend from empirical observations.

**Figure 4: Estimated GDP per capita for increasing R&D expenditures**

# **PANEL A: Hypothesis of 30% uniform increase in R&D expenses across Sub-Saharan Africa**

<!-- Start of picture text --> 900<br>Actual GDP per capita (in 2000 US$)<br>GDP per capita (in 2000 US$) with 30% growth rate of R&D and lower elasticity = 4%<br>800<br>GDP per capita (in 2000 US$) with 30% growth rate of R&D and elasticity = 8%<br>700<br>600<br>500<br>400<br>300<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text -->

**PANEL B: Hypothesis of R&D expenses growing at OECD rate across SSA**

<!-- Start of picture text --> 2800<br>Actual GDP per capita (in 2000 US$)<br>GDP per capita (in 2000 US$) with 85% growth rate of R&D and lower elasticity = 4%<br>GDP per capita (in 2000 US$) with 85% growth rate of R&D and elasticity = 8%<br>2300<br>1800<br>1300<br>800<br>300<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> 20
# **V. The role of segregation in explaining the gender wage gap**

## Sector segregation

## _Public and private sector breakdown_

We first analyze the breakdown by public and private sectors. Over 40 percent of women and 30 percent of men in 2015 were employed in the public sector. Figure 4 depicts the movements in the wages of men and women and in the gender wage gap between 2004 and 2015 separately for the public and private sectors. Even though wages remain lower in the public sector, the gender wage gap in it is higher than in the private sector (Figure 4). Between 2004 and 2007 public sector wages grew faster than private sector ones. Because the increase in public sector wages was more pronounced for men, the gender wage gap in the public sector increased during this period. On the other hand, it unequivocally contracted in the private sector, resulting in the slight net decrease in the overall gap. The 2008 crisis precipitated the drop in wages in both sectors and hit men harder than women. As a result, the gender wage gap contracted in both sectors immediately following the crisis. Wages started to recover at the beginning of 2012 and continued growing until the end of 2013 when the wage growth stalled and turned negative for women in the private sector. The gap in the public sector remained flat whereas in the private sector it widened, resulting in a slight widening of the overall gap. This lies in contrast to the developments during the 2004-2007 expansionary period when the wages grew and overall gap slightly contracted.

Figure 4. Wages and gender wage gap in the public and private sectors, 2004-2015.

<!-- Start of picture text --> q1 '09 q1 '13 q1 '09 q1 '13<br>30 40 50 60 70 80 30 40 50 60 70 80<br>quarter quarter<br>public women public men private women private men<br>public gap Fitted values Fitted values Fitted values<br>Fitted values publicloggap Fitted values publicloggap<br>500<br>400<br>400<br>300<br>300<br>publicwage publicwage 200<br>200<br>100 100<br><!-- End of picture text --> The results of the BMZ decomposition reveal that the primary factors underlying the presence of the large gap in Georgia are considerable gaps within the private and public sectors. Sectoral segregation by public and private sectors appears to be inconsequential in explaining the presence of the gender wage gap in Georgia. Indeed, out of the 53.53 log points in the overall gap during 2004-2015, 53.55 log points (or 100.02 percent) are due to the wage inequality within sectors and, if anything, the inter-sectoral component is negative and the contribution 14
It should be noted that the costs only refer to economic opportunity costs related to loss of productivity, and do not capture costs related to emotional distress, risky health behavior and other factors that come with being jobless. These costs are an annual cost, rather than a lifetime cost, as estimated for early school leaving and adolescent pregnancy.

# **c. Methodology for computing the costs of adolescent pregnancy**

Adolescent pregnancy may affect future earnings through various channels. Maynard (1996) argues that adolescent pregnancy in the United States reduces young mothers’ future productivity and earnings through higher school dropouts (among other factors). More recent work on US women’s earnings by Anderson, Binder, and Krause (2003) identifies a ―motherhood tax‖ that results from less job experience of mothers due to time out of work to attend to childbearing and childrearing responsibilities. Neither of these factors is likely to be as important in developing countries as in the US partly due to early school leaving (unrelated to pregnancy) in many low-skilled countries, fewer safety nets that allow for women to not work for a period, and less structured labor markets (i.e. self-employment) that do not require consistency in job attendance, but they may have some impact. Following World Bank (2003) and Chaaban (2007), we compute the costs linked to adolescent pregnancies as measured through forgone lifetime earnings due to early pregnancies.

We estimate the opportunity cost of adolescent pregnancy using two methodologies, each subscribing to specific transmission mechanisms. First, based on the observation that teen mothers have lower levels of education than girls who delay pregnancy (McCauley and Salter 1995), we compute the opportunity cost by measuring the adolescent mother’s foregone annual income due to fewer years of schooling. We assume that if girls had postponed their first birth, they would earn a greater wage at every age throughout their working lives than those girls who did not postpone.

We assume a constant wage gap over their working lives between young mothers and girls who postponed their childbearing. Implicitly, we assume that the slope of the age-earnings profile is the same across groups but the intercept differs by the wage 11
households that are too poor to afford the minimal regulatory norms defining formal construction will have to settle in informal areas.

Low-income residents may be able to outbid higher income groups by consuming less land per household, as in many U.S. cities, but this possibility declines when low-income residents cannot afford the higher construction costs of multistory building—that is, they cannot substitute capital for land. Poorly designed regulations often prevent low-income households from reducing their land consumption and therefore cause them to be systematically outbid by higher income groups. Their only alternative is to move into the slums in the informally developed areas. While the density in slums is produced by market forces, the total area developed informally and its location depend more on historical accidents than on consumer choices and market forces.

# **_The transformation of land into floor space_**

A city is defined by its buildings and by the economic activities or consumption in them. The end-product of urbanization is therefore not land but floor space. This truism is often ignored because of the difficulty of representing the third dimension on a map. Perhaps the threedimensional images of cities constantly updated on Google Earth will change that.

Land use regulations usually restrict the floor space that can be built on each parcel of commercial and residential land. Typically, regulatory limits on floor-area ratios prevent higher income groups and commercial users from building as tall a structure as they would like (reducing their ability to substitute capital for land), forcing them to consume more land than they would in the absence of regulations. This regulatory constraint, by increasing the land area used by commercial users and high-income groups, indirectly reduces the area of land available for lower income groups.

In the informal areas, the lack of regulation leads to intensive land use, but the lowest income households either cannot afford the cost of sturdy concrete construction required to build several floors or their property rights are so ambiguous that they are reluctant to make the investment. They compensate for the inability to build multistory buildings by minimizing plot size, open space, and road areas, even reducing further their consumption of land.

In Hanoi, where even poor households can afford to buy or rent multistory buildings, informal areas have buildings with up to five floors, multiplying the floor space affordable to low-income households. The examples of Hanoi and Bangkok suggest that a key factor in creating affordable housing is removing the regulatory constraint on floor-area ratios.

Removing constraints on land supply—be they imposed by deficient transport systems, poor land allocation practices, or arbitrary normative regulations—has two main objectives: improving urban productivity and improving the housing quality of the poor. Housing affordability is thus linked directly to land supply.

19
stays in the less risky, less demanding Mexican-side jobs that permit easier balancing of family responsibilities (Zabin and Hughes 1995). The literature does stress the importance of accumulated networking that appears to have caused some Mexican states to generate a disproportionate number of US bound migrants. To the degree possible, we controlled for such effects in the domestic regressions and, as detailed below, make additional adjustments in the simulations.

A second concern is whether the US variables are too “out of sample” to really treat the US as a “33<sup>rd</sup> Mexican State.” In fact, as table 4 shows, the ratio of the per capita income of Mexico’s richest state, the D.F. relative to its poorest is about 6.4 in nominal or 5.6 in real terms while the ratio of the average for the US relative to the D.F. is only about 1.9 or 1.5 PPP adjusted. That is, in terms of development the US is closer to Mexico City than Mexico City is to Chiapas. Nor are wage differentials radically different. The ratio of the Hispanic real wage in the US to the mean wage of the DF adjusted for PPP is roughly the same as the ratio of the real DF wage to that in Chiapas.

There may be some concern about the importance of the border representing fundamentally different “transport costs.” In fact, the evidence is strong that this is more a difference in magnitude than kind. Donato, Durand and Massey (1992) argue that “our data from Mexico reveal a fairly high probability of apprehension by INS combined with a near-certain probability of ultimately entering the United States.”(p 152) and that “ _every_ migrant who attempted a border crossing, whether apprehended or not, eventually gained entry” p 155 italics theirs. This suggests that border control serves more as a tariff than a quota.

The costs of movement are substantially different, but perhaps not so much as we might think at first look. The present (2002) cost of a second class bus from Quintana Roo to Coahuila, one of the longer trips in our sample, was roughly $US100 compared to very little between Mexico State and DF.<sup>10</sup> Anecdotal evidence about the cost of direct transport across the border in the 1980s was $150 (244 $US 2002; Conover 1987 cited in

> 10 White Star Bus line

14
# **1. Introduction**

A growing body of evidence indicates that financial services provide substantial benefits to users in terms of managing risks, absorbing financial shocks, accumulating savings, and investing in businesses, all of which can contribute to less volatile income and consumption patterns.<sup>2</sup> And as a result, promotion of broader financial inclusion has become an important objective for policy makers across the globe.<sup>3</sup> But transaction costs (e.g., time and expenses associated with travel) and actual costs (e.g., fees for account opening and for conducting financial transactions) associated with using financial services can be prohibitively high for many, and the available financial products from formal providers such as banks are often poorly suited to the needs of large segments of the population.<sup>4</sup> Those financial institutions that do cater to the poor are still mainly credit driven and do not offer a full set of financial products and services. This is especially true in developing countries where nearly half (46%) of all adults lack a formal banking account.<sup>5</sup> We therefore study an alternative model for providing financial services to poorer clients, agent banking. Agents, trusted local retailers selling everything from sundries to automotive parts, can also double as less formal, lower-cost alternatives to bank branches that enable customers to more conveniently make deposits, withdrawals, money transfers, and payments on loans (Lyman et al., 2006; Siedek, 2008; Mas and Kumar, 2008; Flamming et al., 2011). Because of their lower

> 2 See e.g. World Bank (2014) for an overview of that literature.

> 3 Two-thirds of bank regulators surveyed in 143 jurisdictions report that they have a mandate to promote financial inclusion (World Bank, 2014). And more than 50 countries have set formal goals and targets for financial inclusion (Demirguc-Kunt, Klapper, Singer, and Oudheusden, 2015; _Maya Declaration Commitments_ , Alliance for Financial Inclusion, http://www.afi-global.org/maya-declaration-commitments).

> 4 See Collins et al. (2009) for detailed evidence from financial diaries on the vast array of financial arrangements (both formal and informal) used by the poor in developing countries to manage their economic lives, and the lack of fit between many formal products and their needs.

> 5 Demirguc-Kunt, Klapper, Singer, and Oudheusden (2015).

2
1

# I. INTRODUCTION

The Israeli stabilization program of 1985 is generally considered as one of the most successful programs of recent years, in that it rapidly disinflated the economy from inflation rates of about 400 percent per year to rates between 15-20 percent per year. While much attention has been devoted to the heterodox components of the program and their analytical foundations,1 relatively little work has been done quantitatively comparing the structure and performance of the macroeconomy before and after the program. Yet, such a comparison can be regarded as a key ingredient in an overall evaluation of the pros and cons of programs such as that of Israel.

Accordingly, the main objective of this paper is to examine how macroeconomic performance has changed after the implementation of the stabilization program and to determine whether these changes conform with the predictions of standard macroeconomic models. We investigate, using simple time series techniques applied to monthly data for 1980-83, the changes that have taken place in the time series properties of key macroeconomic variables after the stab4iization program of 1985. Special attention is given to changes in the tradeoff between inflation and unemployment which are apparent after the program.

The notion that a change in the policy regime should cause changes in the relationships among macroeconomic variables is quite familiar since Lucas' (1976) critique. We focus here on some of the empirical implications of this critique. Comparing policy regimes before and after 1985 suggests that the post-program regime is characterized by a tight fiscal policy, and by a 1 See, (.g., Blejer and Liviatan (1987), Bruno (1986), Bruno and Piterman (1988), Fischer (1987), Helpman and Leiderman (1988), Kiguel and L'viatan (1988), and Liviatan (1989).
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growth. In Indonesia, survey mean income fell dramatically between 1996 and 1999 at nearly 9 percent per year as a result of the East Asian financial crisis. Yet during this period, the pattern of growth in relative incomes was poverty-reducing. Inequality as measured by the Gini coefficient fell from 36.5 to 31.5. The relative growth incidence curve is downward sloping, indicating that incomes of the richer percentiles of the income distribution fell faster than incomes of poorer percentiles. In fact, below-average growth was recorded only for the richest 20 percent of the population. Despite this propoor pattern of relative income growth, the headcount measure of poverty increased from 8 percent to 13 percent of the population, driven by the large negative growth effect.

Consider next the sensitivity of the poverty measure to growth in different percentiles of the income distribution. In the case of the Foster-Greer-Thorbecke class, θ−1 ηt (p) = −θ ⋅ (y t (p/) z)⋅ (1− y t (p/) z) up to the headcount, and zero afterward. For the Watts index, ηt (p) = −1 up to the headcount, and zero afterward. Finally, for EDEI, we have ηt (p) = −θ ⋅ y t (p)θ . Note that these sensitivies depend not only on the poverty measure of interest, but also on the entire distribution of income as summarized by yt(p). Figure 2 graphs these sensitivities, using the actual distribution of income in China in 1990 as an example, to show how different poverty measures are sensitive to growth in different percentiles of the income distribution.

In the case of the headcount, this sensitivity is zero everywhere except just below the poverty line where it spikes down to minus infinity. This is because the headcount simply adds up the number of people below the poverty line – small increases in income of inframarginal poor people that do not bring them above the poverty line will not reduce the headcount. The same is true for increases in incomes of those above the poverty line, including the “near-poor” just above the poverty line. The case of the headcount already illustrates the broader point of Figure 2: whether a given pattern of growth is pro-poor or not depends crucially on the poverty measure of interest. In particular, if propoor growth in the sense of reducing the headcount measure of poverty is the objective, then a pro-poor growth strategy should focus exclusively on raising the incomes of those just at the poverty line, and should ignore everyone else.

8