rafmacalaba/datause-encoder-data huggingface dataset:
correlation coefficient between individual consumption and the asset index is only 0.374, and moving from consumption to the asset index as measure of socioeconomic status results in a considerable re-ranking. This can be seen from Table 3, which shows a crosstabulation of quintile membership under the alternative welfare indicators. Clearly, if there were no re-ranking, all the diagonal cells of the table would be 20, with the remaining cells being 0. This is far from the case.

**Table 3. Consistency of Ranking under Alternative Welfare Indicators**

|||||**Consumption**<br>|||
|---|---|---|---|---|---|---|
|||**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|
|**x**|**Q1**|5.39|4.55|4.91|4.31|2.17|
|**nde**|**Q2**|3.76|4.34|3.44|3.61|3.63|
|**et i**|**Q3**|5.08|4.16|3.90|3.76|3.02|
|**ss**|**Q4**|3.78|4.32|4.28|3.96|3.63|
|**A**|**Q5**|2.00|2.62|3.46|4.38|7.53|

However, in addition to re-ranking, the changes in the concentration index require that the re-ranking be correlated with the health service indicator of interest. This correlation seems to be present. In order to understand why this correlation arises, it is necessary to look more carefully at the re-ranking that is taking place. One way of doing this is to look at who it is that gains and loses rank as we move from a ranking based on consumption to one based on the asset index, and then to ask why these movements are likely to be related to service use.

This turns out to be a largely spatial effect. Moving from consumption to the asset index as the ranking variable substantially increases rank of households in urban areas (controlling for other factors). Moreover, controlling for the effect of living in urban areas, there is also a systematic re-ranking based on the province of residence, with households in two remote and poor province (Zambezia and Cabo Delgado) losing rank, while households in southern provinces (Inhambane, Maputo, Gaza, and Maputo City) and a central province (Sofala) gaining rank. Finally, households that live in remote areas (as measured by distance from a health center or health post) lose rank even after we control for these other factors.

Considering the type of assets and household characteristics included in the asset index, this re-ranking is not very surprising. Households in urban and more integrated areas of the country are more likely to live in more sophisticated dwellings, and to benefit from public or collective services such as running water. This may reflect lower cost and greater ease of acquiring (or renting) assets in these areas, and the complementarity of some assets and housing characteristics—electricity, refrigerator, running water, flushing toilet, etc.—with public infrastructure. It is also not surprising to find that this re-ranking is strongly correlated with the utilization of health services. This is due to the fact that, on average, households in areas where physical access to services tends to be greater (urban areas and the southern provinces) gain rank as we shift from consumption to the asset index as a welfare indicator. Conversely, households from rural and remote areas, who 13
so measures of adult morbidity or what constitutes functional good health that could be applied to men and women comparatively across societies. Hortality by age is therefore used as the preferred measure of health for men and women. It is typically summarized **as** life expectancy **at** birth, but infancy is the most vulnerable period, and infant mortality by sex is particularly sensitive to differential care in the family, as noted earlier.

Life expectancy appears to be greater for **women** compared to mon in populations where schooling is more equally distributed by gender, **as** shown by WHO estimates by region shown in Table 7. biological evidence suggests that women are genetically more viable than oen. Given similar consumption and self-investment opportunities, women have- lower death rates than men at all ages, except perhaps during the years women bear children if they have not had any education. Death due to bearing a child is twenty times higher in South and West Asia and North and subSaharan Africa than in developed countries, and women in these regions also bear two to three times as many children as do their counterparts in high income countries (column 7, Table 7). Nonetheless, infant and childhood mortality is quantitatively a larger factor io the low life expectancy of females in these low income regions. Differences in child mortality by sex are rarely known with great precision in poor populations, and tho official figures as averaged in columns 5 and 6 of Table 7 are not necessarily reliable. Greater confidence can probably be placed in the infant and childhood mortality estinates derived retrospectively from the recent round of World Fertility Surveys (Rutstein, 1983; Hobcraft It Al. 1985). Only two out of 29 surveys, those for Jordan and Syria, revealed greater female than male infant mortality, while three reported higher female than male child mortality between the ages of 2 and 5 years--Bangladesh,
_For labor or for divorce?_

# **4. Empirical Strategy**

Unilateral divorce laws gave women the right to file for divorce without having to prove fault, which was not possible prior in Morocco, The Arab Republic of Egypt, and Jordan. Their effects, however, are not necessarily the same for all women. In case of marriage dissolution, mothers of young children aged below an age cutoff set by legislators, are usually primarily assigned custody of their children, and guaranteed a legal right to child support from the non- custodial parent, even if the mother was to forfeit some of her financial rights to obtain the divorce. By contrast, mothers of children above the age custody cutoff run the risk of losing their child custody along with any financial support that may come with it. We exploit this setting to conduct a quasi-experimental differencein-differences strategy comparing the effect of unilateral divorce reform on mothers who are more or less affected by the reform.

## _4.1.Empirical setting and assignment to treatment_

Periods before and after the introduction of the right to unilateral divorce in Morocco, The Arab Republic of Egypt, and Jordan are analyzed. For this, a comparison is made between the change in labor outcomes between mothers of children younger than the age-cutoff for custody and mothers of children older than the same age-cutoff, and whom should be less favored by the reform. Are assigned to treatment women for whom the age of the youngest child is below the legal age cutoff for child custody in each country. In Morocco, this age-cutoff is set to 15<sup>10</sup> for both boys and girls, in The Arab Republic of Egypt, it was first set to 10 for boys and 12 for girls in 2000 and then raised to 15 for both in 2005<sup>11</sup> and in Jordan it was raised to 15<sup>12</sup> for both sexes after 2010. Mothers, whose youngest child is older than the age-cutoff are assigned to control groups.

In all specifications, panel fixed effects methods to estimate the magnitude of the relation between unilateral divorce laws and outcomes of women’s labor are considered : i (p i i ii ii jj ii ii<sup>) + β+ ττ</sup> ii<sup>. ii+ εε</sup><sup>**(1)**</sup> YY = αα + θθ + δδ ppii . iit ttiit

> 10 Article 166 the Moroccan family code (Moudawana) of February 5, 2004. See unofficial family code translation here <u>http://www.hrea.org/wp-content/uploads/2015/02/Moudawana.pdf</u> 11Amendment to the law No. 25 of 1929 in 2005. (Art. 1 of Law No. 4). See Bernard-Maugiron, 2022.

> 12 Article 170, 173 of the Personal Status Law.  For more details, see UNESCWA report here - <u>https://www.unescwa.org/sites/default/files/event/materials/jordan adjusted.pdf</u>

14
Table 9: Political Aid Cycle in Mission Areas

||At le|ast one Wo|rld Bank pr|oject com|mitted in ce|ll-year|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Election _×_ Mission|_−_0.00||0.01|0.01||0.03<sup>_∗∗_</sup>|
||(0.01)||(0.01)|(0.01)||(0.01)|
|Turnover _×_ Mission||_−_0.03<sup>_∗∗_</sup>|_−_0.04<sup>_∗∗_</sup>||_−_0.03<sup>_∗∗_</sup>|_−_0.05<sup>_∗∗∗_</sup>|
|||(0.01)|(0.02)||(0.01)|(0.02)|
|Country-year fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Cell fxed efect|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|No|No|No|Yes|Yes|Yes|
|Mean dep. var. of non-mission cells|0.03|0.03|0.03|0.03|0.03|0.03|
|Mean dep. var. of mission cells|0.07|0.07|0.07|0.07|0.07|0.07|
|Number of cells|6,819|6,819|6,819|6,811|6,811|6,811|
|Observations|136,380|136,380|136,380|136,220|136,220|136,220|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; elections from Varieties of Democracy; and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Results are from a balanced annual panel over 20 years. Standard errors are clustered at the country level. Controls included in columns 4–6 are the following variables interacted with the Election and/or Turnover dummy: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude).<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 down and these areas experience a temporary drop in aid. The (less robust) increase in election years without turnover could constitute a reward from the incumbent in exchange for political support, but the instability of the estimate makes it to draw conclusions.

The findings in table 9 cannot be explained by a general slowdown of project commitments due to government officials being busy with the electoral campaign, because the regressions include country-year fixed effects. One might argue that the effect is driven by turnover between heads of state who belong to different religions, which would imply religious favoritism as opposed to political favoritism. However, this interpretation is feasible only if all (or most) turnovers are from a Christian to a non-Christian head of state. A qualitative check of this explanation using multiple sources (e.g., Encyclopedia Britannica and Wikipedia) suggests that most turnovers happen between individuals of the same religion, making this explanation implausible. Yet another possible explanation has to do with heads of state coming disproportionately from mission cells (or having had a missionary education). However, to be consistent with the findings, this interpretation would also require (most) turnovers to happen between individuals with different 36
# **_Absenteeism does not reflect an implicit contract with teachers_**

Grant, then, that teacher absence has costs in terms of student learning. But what if it is part of a tacit understanding between teachers and the public, in which teachers offset low pay by being allowed to miss a substantial number of school days? To paraphrase the colloquial US expression, perhaps the government pretends to pay teachers, and the teachers pretend to work. Teachers’ unions sometimes offer a version of this story when they argue that salaries are so low that absence in unavoidable, either because teachers need to supplement their salaries through outside jobs or because low pay makes their logistics (transportation or housing, for example) especially challenging.

The evidence does not generally support this view of teacher absence. First, in many developing countries, teacher pay does not appear to especially low, at least as measured by the teachers’ wages in alternative professions. Teachers’ salaries often reach two to five times their country’s per capita GDP, so wages that seem low in absolute terms are quite reasonable in relative terms (Bruns, Mingat, and Rakotomalala, 2003) _._ And while part of this premium may reflect the scarcity of educated workers in the economy, which may bid their wages up, this argument applies more to the minority of teachers with a university degree than to the majority who may have less education. Although comparisons are difficult, recent work on Indonesia – which at the time had a relatively poorly paid teaching corps – suggests that teachers with only a high school education earn more than similarly educated workers in other professions (Filmer, 2002).

Another indication of the level of teacher salaries relative to those of outside opportunities is the level of potential supply for the positions: there are often large numbers of candidates with the requisite qualifications queuing up for positions in the teaching corps. Indeed, private schools often take advantage of this excess supply by hiring teachers at a fraction of the wage paid by the government schools. In India, for example, teachers in rural private primary schools typically make one‐ quarter to one‐fifth as much as their public‐school counterparts (Kremer and Muralidharan Forthcoming).

A second reason to doubt the tacit‐understanding of absence story is that key stakeholders – including, most importantly, average citizens – show no recognition of such an implicit bargain with teachers. In recent international polling, citizens in many countries cite teacher absenteeism as a problem.<sup>12</sup> Anecdotally, households in some high‐absence countries are clearly outraged by the flagrant disregard that some 12 The source for this statement is unpublished poll data provided by Stephen Knack.
data on cross-border trade in services from various sources. These data report bilateral trade flows for more than 200 countries in a broad set of activities (according to BOP classifications). The data are not flawless, however. First, not all modalities of trade in services are covered—see Francois and Pindyuk (2013) for a description of all the modalities of trade in services. Second, coverage at the country-product level has improved dramatically over time, which implies that the data quality in the early years is not as strong as in later years.

These caveats aside, we use the data in Francois and Pindyuk (2013) to examine how sensitive the relationship between the two indicators of diversification is to the inclusion of services. To do so, we calculate the measure of distance defined above (i) for the COMTRADE data (merchandise trade) at the 5 digit level of disaggregation; and (ii) for the COMTRADE data merged with the Francois and Pindyuk (2013) data for trade in services. The latter will have the 944 5-digit product classifications defined in the SITC Rev. 1 classification plus the 63 services defined in Francois and Pindyuk (2013). Similar to when we analyzed the effect of the different levels of aggregation on our measures of diversification, we rescale the variables that measure the number of latent products and the average number of products per year so that both variables are comparable between the two datasets. The calculations are made using the period 1995-2012, where the coverage of the trade in services dataset improves substantially.

Figure 6 presents results for the relationship between the number of latent export lines and the average number of export lines per year for both the data including and excluding services. There does not seem to be a disproportionate attenuation problem between the two measures of diversification when using data with and without services; D is less than 5 percent higher when we include services versus when we exclude services (Figure 6). This suggests that leaving out services, as measured by Francois and Pindyuk (2013), does not seem to change the main conclusion of the paper.

# **5. Latent Diversification, Economic Size, and Economic Development**

The previous section proposed a new measure of trade diversification and studied how it compares to traditional diversification measures, such as the number of products exported in a given year. The results suggest that there are indeed large differences between our proposed measure, which we call latent trade diversification, and standard measures. This section expands the comparison between the two diversification measures by analyzing the extent to which country characteristics, such as the size of the 17
# **Data Appendix:**

|**Variable**|**Code**|**Data source**|**Note**|
|---|---|---|---|
|Initial GDP per capita|LGDP|Summers and Heston, 1991,<br>updated using GDPG|Natural logarithm of GDP/capita for first year of<br>period; constant 1985 dollars|
|Institutional quality 1|Prop, Reg|Fraser Institute:<br>http://www.freetheworld.com/2005<br>/2005Dataset.xls|Disaggregate indicators, available on the web.|
|Institutional quality 2|ICRG|PRS Group’s IRIS III data set (see<br>Knack and Keefer, 1995)|<br>Revised version of variable. Computed as the av-<br>erage of the three compo-nents still reported|
|Institutional quality 3|CIM|Contract Intesive Money,<br>IFC data, 2005|M2=M1+quasimoney=34+35<br>Currency comes from line 14a of International<br>Financial Statistics, “currency outside deposit<br>money banks.”_M_2 is defined by IFS as the sum<br>of money and quasi-money, or the sum of lines<br>14a (currency outside banks), 24 (demand<br>deposits), 15 (time deposits|
|Sub-Saharan Af-rica|SSA|World Bank, 2003|Codes nations in the south-ern Sahara as sub-<br>Saharan|
|East Asia|EASIA|Dummy for China, Indonesia,<br>South Korea, Malaysia,<br>Philippines, and Thailand,<br>following Burnside and Dollar||
|Effective Devel-opment Assis-<br>tance/ real GDP|AID|Chang et al., 1998; DAC, 2002;<br>IMF, 2003; World Bank, 2003;<br>Summer and Heston, 1991|Available values for 1975–95 from Chang et al.<br>Miss-ing values extrapolated based on<br>regression of EDA on Net ODA. Con-verted to<br>1985 dollars with World Import Unit Value<br>index from IMF, series 75. GDP computed like<br>LGDP above|
|Net Overseas De-velopment<br>Assistance/real GDP|ODA|DAC, 2002; IMF, 2003; World<br>Bank, 2003; Summer and Heston,|Like AID exception using ODA from DAC<br>Real GDP expressed in PPP|
|Population|LPOP|World Bank, 2003|Natural logarithm|
|Mean years of secondary<br>schooling among those over 25|SYR||Barro and Lee, 2000|

39
# **1 Introduction**

The strong relationship between financial development and economic growth is well documented in the literature (see, for example, King and Levine, 1993; Beck _et al_ ., 2000; Demirgüç-Kunt and Maksimovic, 1998; Beck _et al_ ., 2004; Levine, 2005; Klapper _et al_ ., 2006; Demirgüç-Kunt _et al_ ., 2008). In more recent years the debate expanded to include the notion of financial “exclusion” as a barrier to economic development and the need to build inclusive financial systems (Beck _et al_ ., 2008).

Recent empirical evidence using household data indicates that access to basic financial services such as savings, payments and credit can make a substantial positive difference in improving poor people’s lives (Caskey _et al_ ., 2006; Dupas and Robinson 2009). For firms, especially small and medium enterprises (SMEs), access to finance is often the main obstacle to growth (Schiffer and Weder, 2001; Cressy, 2002; IADB, 2004; and Beck _et al_ ., 2005, 2006, and 2008).

Accordingly, financial sector reforms that promote financial inclusion are increasingly at the core of the international development agenda for policy makers and development institutions at the global level. The United Nations (UN) has declared 2005 the Year of Microfinance, and the recent Pittsburg and Korea G-20 communiqués increasingly underscore the importance that this topic has gained in the international arena. The Nobel Institute awarded the Nobel Peace Prize to the founders of microfinance, Muhammad Yunus and the Grameen Bank, in 2006. New international bodies, such as the Alliance for Financial Inclusion (AFI) have emerged whose primary objective is to advance financial inclusion for the world’s poor. The International Monetary Fund (IMF) and the International Finance Corporation (IFC) also increasingly pay attention to this debate. The IMF has launched a new database on financial inclusion,<sup>1</sup> and the IFC together with Consultative Group to Assist the Poor (CGAP) and AFI have been leading the G-20 discussion around financial inclusion for households and SMEs. The Bill & Melinda Gates Foundation has just pledged $500 million per year over the next five years to expand access to saving services.

Against this backdrop, the World Bank Group (WBG) has initiated the _Financial Access_ indicators and reports to respond to a higher demand for data and measurement of financial inclusion. This initiative aims to fill the gap in the data landscape by collecting supply-side data on financial access as well as data on financial inclusion policies and regulations from financial regulators around the world. The pilot round of surveys was implemented and published as _Banking the Poor_ in 2008, which was based on data from both regulator and bank surveys, covering 54 countries around the world with a strong focus on Sub-Saharan African countries. Subsequently, CGAP and the WBG launched _Financial Access 2009_ , the first in an annual series of reports to inform policy debate and monitor statistics of financial access worldwide. _Financial_

> 1 http://fas.imf.org.

2