rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6 - Microfinance indicators from CGAP financial access database**

||**Financial in**<br>**Promoting**<br>**rural areas**|**clusion:**<br>**access in**|**Deposits**<br>**Account**<br>**adults**|**:**<br>**s per tho**|**usand**|**Deposit**<br>**Value**<br>**of GDP**|**s:**<br>**as perc**<br>|**entage**|**Loans:**<br>**Accoun**<br>**adults**|**ts per th**|**ousand**|**Loans:**<br>**Value a**<br>**GDP**|**s percen**|**tage of**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Agency is<br>responsible<br>(Yes/No)|Dedicated<br>team<br>or<br>unit<br>(Yes/No)|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|
|**Malaysia**|√|√|2.78|1,146.06|n.a.|0.04|13.71|n.a.|87.26|113.07|19.03|0.63|12.09|0.22|
|**OIC countries**<br>**average**|68%|57%|30.86|224.11|68.14|3.98|5.33|0.61|24.94|31.00|34.68|2.59|3.62|0.79|
|**Developing**<br>**countries average**|47%|36%|100.53|216.71|48.93|1.99|4.27|0.78|27.54|25.01|30.14|1.71|3.35|0.97|
|**Low-income**<br>**countries average**|79%|62%|32.65|18.17|62.44|0.68|1.74|0.92|16.42|8.46|43.87|0.69|1.70|1.19|

Source: CGAP financial access report data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East Asia and the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered. For practical purposes, most small islands and economies at war were not included. The sample covers more than 94 percent of the world’s population and nearly 98 percent of global GDP.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions (for Yes/No questions), or means the average value for OIC countries. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries covered by the CGAP database.

51
oriented reforms: our independent variable is the liberalization index _(LIBERALIZATION)_ developed by De Melo et al. (1996), which is weighted average of three indices of liberalization in internal markets (liberalization of domestic prices and abolition of state monopolies), foreign trade liberalization (elimination of export controls and taxes; reduced import tariffs and elimination of import quotas; current account convertibility) and private sector development (privatization of small and large scale enterprises; banking reforms), respectively. The index assumes values from 0 (no liberalization) to 1 (complete liberalization).<sup>8</sup> Note that our independent variable represent the policies themselves and not the outcomes of these policies (i.e., growth or increase in material welfare). We do not presuppose any link between freedom and welfare. Rather we examine the link between freedom and the adoption of policies that can, under favorable circumstances, lead to increases in welfare.

Figure 1 shows the relation, over the period 1992-1997, between this index of liberalization and an index of civil and political liberties _(FREEDOM),_ obtained from various Freedom House reports, which assumes values from 2 (no freedom) to 14 (complete freedom). The figure indicates a clear, positive relation between freedom and economic liberalization. In fact, the linear correlation coefficient is 0.86 and the Spearman rank correlation is 0.84. Does this mean that political freedom has significantly affected the economic liberalization process in the twenty-five countries in our sample? To address this question, we estimate a model that evaluates the role of freedom vis-a-vis other economic and political variables, during the transition.

Our regression model builds on earlier work by De Melo et al. (1996, 1997), but we look more in depth. at the influence of political variables on economic 9 liberalization9.

On the basis of the arguments developed in section II, the first variable we include in our regression model is the index _FREEDOM._ In addition, we hypothesize that, not only did the democratization process have a positive impact on economic transition, but also freedom has been an important precondition for liberalization. We thus expect countries that enjoyed a higher level of freedom before the start of the transition

> 8 The variables and the data sources are described in the appendix.

> 9 We also consider a longer period (1992-97), thus increasing the number of observations, compared to the original De Melo et al. dataset.

7
Table 3 shows that using the unadjusted poverty line, couples with children and non-nuclear and other households with children are more likely to be in the bottom 40 percent of the expenditure distribution than other household types. However, this likelihood fell over time, especially for couples with children – in 2013-14, 54 percent of couples with children were in the bottom 40 percent, compared to 48 percent in 2018. On the other hand, unaccompanied children (although numbering very few – only 0.14 percent of all registered households by 2018) stand out as an increasingly vulnerable group, with the share of households below the 40<sup>th</sup> percentile going from 34 to 46 percent over the two waves.

Adjusting for economies of scale, single caregivers and single person household are most likely to be in the bottom 40 percent. The share of female single care givers in the bottom 40 percent rose from 38 to 48 percent between the two time periods. This is in contrast to couples with children where the share falls by around 5 percentage points. The share of non- nuclear households with children at the bottom of the distribution also decreased.

**Table 3.** Share of households below 40<sup>th</sup> percentile, by demographic characteristic

||Unad<br>expen<br>θ|justed<br>diture<br>= 1|Expen<br>adjus<br>economi<br>θ=|diture<br>ted for<br>es of scale<br> 0.5|
|---|---|---|---|---|
||2013-14|2017-18|2013-14|2017-18|
|Male HHH|41.0%|39.8%|35.7%|32.6%|
|Female HHH|37.8%|40.3%|38.0%|49.0%|
|Couples with children|54.1%|47.9%|39.9%|34.8%|
|Couples without children|14.7%|15.9%|25.6%|31.8%|
|Single caregivers|40.0%|42.3%|37.9%|47.9%|
|Single person households|10.3%|19.9%|29.1%|51.2%|
|Unaccompanied children|34.5%|46.4%|57.5%|73.2%|
|Non-nuclear and other households with children|46.7%|45.9%|36.3%|35.3%|
|Non-nuclear and other households without children|21.0%|20.3%|26.4%|31.6%|
|No children in the hh|13.4%|19.4%|28.1%|42.1%|
|Children in the hh|50.6%|47.2%|39.5%|37.7%|
|Dependency ratio <1|18.1%|21.7%|27.0%|32.3%|
|Dependencyratio >=1|54.3%|50.9%|42.0%|42.7%|

_Note_ : Own calculations based on ProGress and JD-HV database. This table shows the percentage of households below the 40<sup>th</sup> percentile of the expenditure distribution, for each wave and each demographic group.

Table 3 also compares households with and without children over time. Using the unadjusted expenditure measure, households with children in both waves are significantly more prone to be at 20
- Ryan, Nicholas (2018). “Energy Productivity and Energy Demand: Experimental Evidence from Indian Manufacturing Plants”.

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- Schlenker, Wolfram and Charles A. Taylor (2021). “Market expectations of a warming climate”. _Journal of Financial Economics_ 142, 627–640.

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Somanathan, E. et al. (2021). “The Impact of Temperature on Productivity and Labor Supply: Evidence from Indian Manufacturing”. _Journal of Political Economy_ 129, 1797–1827. Song, Malin, Licheng Peng, et al. (2022). “Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation”. _Technological Forecasting and Social Change_ 174, 121276.

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50
Nations Statistics Division (UNSD). This dataset provides detailed information on global merchandise trade, encompassing the import and export of goods and services between countries and regions. In this study, we limit the analysis to trade in merchandise only, since data on trade in services are very limited. Moreover, following the standard convention in empirical trade studies, we use the value of imports reported by Jordan’s trade partners to capture the value of Jordan’s exports to its partners.

Trade trends provide insights into Jordan's economic health and growth prospects. Analyzing trade data helps understand how Jordan is integrating into the global economy and which sectors are driving export growth. Moreover, understanding trade patterns helps Jordan identify opportunities to diversify its economy. Over-reliance on a few key exports or trading partners can make the economy vulnerable to external shocks. By analyzing trade trends, Jordan can strategize to broaden its export base and reduce dependency on specific markets.

Jordan has pursued a policy of trade liberalization in the past few decades. Between 1997 and 2024, Jordan signed eight trade agreements (see Table 2). The most important is the PanArab Free Trade Area, signed in 1997 with the Middle East and North Africa (MENA) countries. In 2021, it accounts for a third of Jordan’s total exports.

**Table 2. Jordan’s Trade Agreements**

|**Agreement Name**|**Signature**|**Entry**<br>**into**<br>**force**|**Partners**|
|---|---|---|---|
|Pan-Arab Free Trade Area<br>(PAFTA)|01/19/1997|01/01/1998|MENA|
|EU - Jordan|11/24/1997|05/01/2002|EU, Jordan|
|United States - Jordan|10/25/2000|12/17/2001|Jordan, United States|
|EFTA Agreement|06/21/2001|09/01/2001|Iceland,<br>Liechtenstein,<br>Norway, Switzerland, Jordan|
|Agadir Agreement|02/25/2004|03/27/2007|Arab<br>Republic<br>of<br>Egypt,<br>Jordan, Morocco, Tunisia|
|Jordan - Singapore|05/16/2004|08/22/2005|Jordan, Singapore|
|Canada - Jordan|06/28/2009|10/01/2012|Canada, Jordan|
|United Kingdom - Jordan|11/05/2019|05/01/2021|Jordan,United Kingdom|

**Source:** World Trade Organization (WTO). <u>WTO | Regional trade agreements</u>

percent in 2000 to 4.0 in 2020 (Figure 10). Even though trade openness (measured as the sum of exports and imports as a percentage of GDP) has historically been above 100 percent (110.3 percent in 2000), it has been decreasing, hitting a low of 65.3 percent in 2020 (due to the pandemic) and reaching 81.4 percent in 2021 (Figure 11) toward pre-pandemic values.

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beneficiaries, thus providing an estimnate of the impact of titling on this type of investment rather than just correlations with little indication of causality. Availability of production data allows us to make inferences on the extent to which any tendency towards increased investment, which may have resulted from the land titling effort, helped to counteract previous under-investment. With regard to land values, we are able to control for household fixed effects, in addition to tenure characteristics, for observable plot characteristics such as topography, slope, and distance to the homestead, thereby eliminating unobserved factors that may, in a simple cross-sectional equation, easily result in biased coefficients which would overestimate the impact of titling.

# **3.1 Titling and land-related investment**

The notion that the greater tenure security accorded by possession of registered land title will be associated with higher levels of investment is a key element in the literature (e.g. Feder et al. 1988). The relationship between possession of title and higher levels of land-attached investments has repeatedly been confirmed in cross-sectional equations (see Binswanger, Deininger, and Feder 1995). Even though numerous studies have demonstrated that land tenure has an investment-enhancing effect (Besley 1985, Rozelle et al. 1998; Gavian and Fafchamps 1996), the inability to control for unobservable householdsspecific characteristics may, however, have resulted in biased coefficients. For example, it is likely that producers with better access to credit or infrastructure were more likely to acquire title. This would lead to an overestimate and thus limit the ability to use the results of such analysis to extrapolate on the impact of changes in title status brought about by titling programs. Availability of data on investment before and after title was received at the plot level allows us to deal with this concern by using the difference in investment levels rather than the simple amount of investment.

In other words, we use the monetary value of all the land-attached investments that were available in 1990 and 1999. These include irrigation facilities; processing and storage structures such as sheds, silos, warehouses and coffee processing plants; and livestock structures such as stables, silos, and fencing. Subtracting the value of such investments in 1990 from what was available in 1999 allows to construct the net investment on plot _i Ali._ Regressing this variable on initial title status, the change in title status during the period, and a vector of time invariant characteristics, helps to obtain an estimate of the impact of an exogenous change in tenure status on investment. Formally, we estimate an equation of the form _Ali_ = a _+6 fXj,_ + A _Z,_ + £i where Xi, is a vector of time varying characteristics (e.g. whether the plot has a title or is registered), and **Z** is a vector of time invariant characteristics such as soil quality and other physio-geographic plot features. Note in particular that this specification allows us to test empirically between two competing 12
Nevertheless, the program’s design is imperfect in at least three ways. These imperfections, either singly or jointly, are not unique to this program. First, the program does not use teacher value-added measures which adjust gains in student exam scores for existing differences in relevant student and school characteristics (Koedel, Mihaly, and Rockoff _forthcoming_ ).<sup>14</sup> Second, while the ASC survey and PEC exam data are used for both monitoring and accountability, the program makes these data more high stakes, increasing the risk that gaming and data manipulation undermine data reliability (even if this risk is limited to the relatively small sample of program schools). Third, gains in student exam scores may be due to teachers coaching students in examtaking strategies rather than teachers engaging in actions that produce actual student learning.<sup>15</sup>

# **III. Data, sample, and empirical strategy**

# _A. Data and sample_

Four annual rounds of the ASC survey and PEC exam data were linked together using the government’s unique school identification codes (the Education Management Information System [EMIS] code). The October 2009 ASC survey and the January–February 2010 PEC exam rounds serve as our baseline data. (The program came into effect in July 2010.) The October 2012 ASC survey and January–February 2013 PEC exam rounds serve as our data for the third program year.

Evaluation sample schools were linked largely without issue across the various databases. We could not link a few schools because they were merged into other schools as part of Punjab’s school merger program.<sup>16</sup> As a result, 583 schools (or 97 percent of the original evaluation sample) remain in the evaluation sample in the third program year. The shares of schools “attrited out” from the original evaluation sample do not differ between treated and untreated samples (2.7 percent versus 3.3 percent, respectively).

> 14 The use of value-added measures for teacher performance management is however contentious in education research and practice communities (see, for example, Goldhaber 2010).

> 15 Neal (2011) argues that exam preparation can be defensible if performance pay shifts teachers from ineffective or lack of teaching—which presumably applies in our case (see Chaudhury et al 2006 for evidence from low- and middleincome countries on teacher absentee and off-task rates)—to exam preparation than from effective teaching to exam preparation.

> 16 Acquired schools lose their status as separate schools and are assigned the same EMIS codes as their respective acquiring schools.

13
enrollment and the available micro-level evidence on dowry payments suggest that such incentives are neither strong nor generalized. Dasgupta, Maitra, and Mukherjee (2008) argue that marital arrangements may be responsible for these patterns. In South Asia, married sons typically live with their parents in a subordinate capacity. Thus, when parents seek wives for their sons, they may value characteristics that facilitate the continuation of parental control over their sons after marriage. A lack of education on the part of the bride may constitute such a characteristic. Hence, parents may prefer uneducated brides unless educated brides have significantly larger dowries. This situation reduces parental incentives to educate daughters. Therefore, the success of the human capital investment policies identified by previous studies crucially depends on the living arrangements and cultural norms of each society.

In the developed world, women have caught up with men in terms of human capital accumulation, and the gender gap is shrinking (Heathcote et al. 2010). This substantial economic empowerment of women has been accompanied by a decreasing trend in female domestic violence. Aizer (2010) proposes an economic theory of household bargaining that incorporates domestic violence, suggesting that an increase in a woman’s relative wage increases her bargaining power and reduces levels of violence by improving her outside options. Using new sources of administrative data for the United States, the author finds that the decline in the wage gap witnessed over the past 13 years can explain 9 percent of the reduction in violence and the costs associated with it. Although Aizer’s model seems appropriate to describe the situation in developed countries, it does not consider the role of culture, which is likely to have a crucial effect on domestic violence in developing countries. Female economic empowerment may threaten the image of men as breadwinners, and men may respond by increasing violence against their wives (Angelucci 2008).

Information and media exposure have proved useful in affecting individuals’ attitudes toward a variety of issues. Several papers by Fernandez et al. show that the increase in female labor force participation after the 1950s led successive cohorts of women to enter the market at higher rates. The dramatic increase in female participation shaped men’s views toward working women (Fernández, Fogli, and Olivetti 2004) as well as the views of society in general toward the conciliation of work and family life (Fernández 2007; Fogli and Veldkamp 2010).

6
**Table 2. Demographic Composition of the Sample (2008-2016)**

|**Characteristics of Head**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|---|---|---|---|---|---|---|---|---|---|
|**Cohort group**||||||||||
|**1974 and earlier**|22.2|23.7|25.5|26.7|28.2|29.2|30.4|31.2|31.7|
||(0.4)|(0.4)|(0.4)|(0.4)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**1964-1973**|34.1|34.4|34.1|34.0|33.4|33.1|33.4|33.2|33.6|
||(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**1954-1963**|31.2|29.7|28.7|27.9|27.4|26.7|26.1|25.4|24.7|
||(0.5)|(0.5)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
|**1948-1953**|12.5|12.2|11.7|11.5|11.0|11.0|10.1|10.2|10.0|
||(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|
|**Gender**||||||||||
|**Female**|24.8|26.1|27.1|28.3|29.7|30.5|32.0|32.7|32.9|
||(0.4)|(0.4)|(0.4)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**Male**|75.2|73.9|72.9|71.7|70.3|69.5|68.0|67.3|67.1|
||(0.4)|(0.4)|(0.4)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**Education level**||||||||||
|**No education**|6.6|6.9|6.9|6.9|7.1|6.9|6.7|6.6|6.6|
||(0.2)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.2)|(0.2)|(0.2)|
|**Primary education**|39.8|40.3|39.6|39.2|38.7|37.9|37.5|37.6|36.9|
||(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**Middle school**|16.6|16.7|16.3|16.0|15.9|15.6|15.4|15.1|14.9|
||(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
|**Secondary**<br>**education**|20.8|20.7|20.8|21.4|21.5|21.3|21.4|22.2|22.7|
||(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
|**Higher education**|16.2|15.4|16.4|16.4|16.8|18.3|19.0|18.6|18.8|
||(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|
|**Household size**||||||||||
|**1-2 people**|9.4|9.9|10.4|10.7|11.3|11.8|12.8|13.2|13.8|
||(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|
|**3-4 people**|42.4|43.3|43.5|43.6|43.6|44.5|44.3|44.1|44.0|
||(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**5-6 people**|32.5|31.9|31.6|31.6|30.5|29.7|29.6|29.7|28.9|
||(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|(0.5)|
|**more than 6 people**|15.8|15.0|14.5|14.2|14.6|14.0|13.2|13.0|13.4|
|**Note:**Estimates based on s<br>adjusted accordingly for lat<br>in parenthesis.|(0.4)<br>ample of<br>er waves.|(0.4)<br>head of h<br>The estim|(0.4)<br>ouseholds<br>ates repre|(0.3)<br>aged 25<br>sent the s|(0.4)<br>to 60 year<br>hare of the|(0.3)<br>s in the fi<br>househol|(0.3)<br>rst survey<br>d heads. S|(0.3)<br>(GEIH-2<br>tandard d|(0.3)<br>008) and<br>eviations|

21