rafmacalaba/datause-encoder-data huggingface dataset:
For that purpose, the paper is organized as follows. Section 2 presents the results of the stocktaking exercise on the countries with interest rate caps and the ones without them. The section also provides a general description of the main characteristics of the various interest rate regimes, including the source of rate-setting authority, the criteria for determining what is capped, and the ways the cap is applied in countries that currently use them. Section 3 presents further details on the use of interest rate caps by country. Section 4 presents some relevant financial sector characteristics in countries that cap interest rates on loans. Section 5 discusses alternatives to interest rate caps for avoiding predatory lending. Finally, section 6 concludes.

# **2. Global Stock-Taking Exercise**

In this section, we look at all available sources to determine which countries around the world are currently imposing interest rate caps on loans. We also look at various characteristics of the caps, such as the criteria, methodology of the calculus, regulatory changes, and other relevant aspects. In addition, we look at whether the cap applies to the banking sector or to nonbanking financial institutions. Our primary sources of information include other research on interest rate restrictions, blogs from the Consultative Group to Assist the Poorest (CGAP), websites of government agencies, and news sites such as Reuters and the _Financial Times_ . Finally, we provide some country evidence on the effects of interest rate caps.

Our study found the practice of regulatory controls on interest rates to be widespread. According to Helms and Reille (2004), for example, about 40 developing and transitional countries had interest rate ceilings in place. In European Union member countries, 14 states had some form of contractual interest rate ceiling in 2010, while 13 had no caps (iff/ZEW 2010). In addition, CGAP found 17 countries with interest rate ceilings in Sub-Saharan Africa (SSA) as of late 2010 (CGAP-MIX 2011).

## **_2.1. Overall Results of the Stock-Taking Exercise_**

The evidence from our review (figure 1) indicates that 61 out of the 152 countries (40 percent) that are inside the World Bank regions currently impose interest rate caps on loans. Most of these countries are in SSA and in Latin America and the Caribbean (LAC), accounting for 50 percent and 48 percent of the total countries in their respective regions, while only seven countries (or 44 percent) in East Asia and the Pacific (EAP) have interest rate caps. Evidence of interest rate caps was also found in 11 Western European countries and in other countries such as Australia, the Bahamas, Canada, and the United States.

5
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# **B Variable Descriptions**

- Excess Female Mortality: Female mortality - Male mortality

- Neonatal mortality: Death within one month of birth

- Under-5 mortality: Death before age five

- _Post_<sup>1</sup> _t_<sup>:indicatorvariablefor</sup><sup>_t ∈_1985</sup><sup>_−_1994</sup>

- _Post_<sup>2</sup> _t_<sup>:indicatorvariablefor</sup><sup>_t ∈_1995</sup><sup>_−_2005</sup>

- _Fi_ : child _i_ is female

- _Gj_ : first child of mother _j_ is female

- Allopathic treatment: an indicator variable that equals 1 if a sick child received medical help from an allopathic doctor during the past year, and 0 otherwise

- Expenditure on education: amount (in Rupees) spent on fees, books, uniform, hostel pocket money, transportation, and private coaching during last year

- Doctors’ fees: amount (in Rupees) spent on doctors’ fees last year

- Medicine and special food: amount (in Rupees) spent on medicine and special food last year

- Medical expenditure: amount (in Rupees) spent on doctors’ fees, medicine, and special food last year

- Ideal fraction of sons: (Ideal no. of boys + 0.5* Ideal no. of either sex) / (Ideal no. of boys + Ideal no. of girls + Ideal no. of either sex) (as reported by the mother)

- Ideal number of children: Mother’s self-reported ideal number of children of any sex

- Education categories: no education, incomplete secondary education, and secondary or higher education

- Categories for mother’s birth cohort: 1942-1960, 1961-1970, and 1971-1987

- Categories for Mother’s age at birth: 12-15 years, 16-18 years, 19-24 years, 25-30 years, and 31-49 years

- Caste categories: Scheduled Caste (SC), Scheduled Tribe (ST), and Others

- Religion categories: Hindu, Muslim, and Others

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|Urpelainen<br>& Yang<br>2017|22|2012||37|24|10|24|11|41|
|---|---|---|---|---|---|---|---|---|---|
|Urelainen|||T&D losses|21|29|5|8|11|22|
|p<br>2017|23|2011|Generation<br>capacity|35|26|9|18|10|41|
|Vagliasindi<br>2012|24|2009||5|3|3|3|2|6|
|Zhang et al.<br>2005|25|2001||13|2|2|3|2|3|
|Zhang et al.<br>2008.|26|2003||14|6|4|5|2|3|

_Notes: the number in brackets indicates the total number of countries in each World Bank region._ Take‐aways from regression models

1. **The main finding is that private sector participation was often associated with improvements in sector performance.** This was most marked for labor productivity and operational efficiency. (Andrés et al., 2006; Ba and Gasmi, 2011; Gassner et al., 2009; Vagliasindi, 2012). It is striking that very few studies analyzed bill collection rates and quality of service despite the well documented and understood magnitude of these dimensions of underperformance.

2. **The effects of regulation and competition were more ambiguous.** Fewer studies tested for their effects and, with the exception of the impact of regulation on access (Ba and Gasmi, 2011, Balza et al., 2013; Vagliasindi, 2012), the majority of studies found no significant beneficial impact of these policies on performance.

3. **The interaction between policies has largely been ignored or incompletely treated.** Many of the studies used simple (0/1) dummy variables for the four key reform steps, and included no interaction dummies, or not all the interaction dummies corresponding to the set of policy dummies. As explained earlier, this specification error can lead to bias in the estimation of the effects of the various reform steps.

4. **The degree of reform is important.** Those studies that have introduced a degree of reform variable, rather than an on/off measure, have obtained significant results (Erdogdu, 2011b; Nagayama, 2010; Zhang et al., 2005). Taken across the set of studies examined, all four policies can be expressed in a way that allows for increasing impacts.

5. **There is considerable agreement among the various studies that performance indicators internal to the firms are impacted by private sector participation, but not by regulation.** The lack of impact of regulation is not surprising given that regulation tends to focus on performance criteria that impact users rather than on those internal to the firm.

6. **The impact of competition is not often significant.** Fewer studies test for this variable and most failed to identify a significant impact on any of the performance indicators. The majority of these studies treated competition as an on/off variable rather than one which can vary 47
but also in terms of logistics: They require securing the land for new schools (if needed), managing the construction projects, hiring and allocating trained teachers, etc. For the Chilean program, for instance, it took ten years to go from covering 5 percent of students to 80 percent of students. Therefore, if the program coverage goal is ambitious, it requires long-term commitment to ensure the sustained flow of financial resources and effective management. Otherwise, the roll-out speed will be negatively affected and will produce an immediate slowdown on essential indicators such as coverage.

**Figure 3. Coverage of Extended Day Programs in LAC, 2001-2014 (% net enrollment)**

<!-- Start of picture text --> 100<br>90<br>80<br>Chile<br>70<br>El Salvador<br>60<br>Mendoza<br>50<br>Mexico<br>40<br>Rio Negro<br>30 Sao Paulo<br>20 Uruguay<br>10<br>0<br>2001 2006 2011<br><!-- End of picture text --> Source: Authors’ construction, with data from Alfaro & Holland (2012).

Second, there is great variety in the number of hours that systems choose to add. Figure 4 shows the seven cases reviewed, ranging from a low of 10 hours per week (Chile) to a high of 25 hours (São Paulo). Coincidentally, those programs in the mid-range (orange bars in Figure 4) that add around 18 or 20 hours per week are those we would consider Full-time School programs.

In terms of curricular design, the cases are all similar: morning activities tend to remain mostly unchanged, and the additional hours are invested in a mix of curricular and extracurricular activities. Additional activities are usually distinctive in terms of either content or format. For example, activities can remain focused on the core curriculum while experimenting with novel 8
**Table B3: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2) between Tanzanian Markets and Randfontein (South Africa)**

||**--------------**|**---------- Regr**|**ession results**|**, eq. (1) --------**|**----------------**|**--- Price spr**|**ead, eq. (2)---**|
|---|---|---|---|---|---|---|---|
||μ|pt<br>W|_R-square_|ADF|PP|ADF|PP|
|_Arusha_|1.59***<br>(2.87)|0.86***<br>(15.72)|0.57|-2.85*|-2.77*|-3.10**|-2.79*|
|_Bukoba_|1.19***<br>(3.16)|0.91***<br>(24.00)|0.69|-2.43|-3.03**|-2.70*|-3.09**|
|_Dar es Salaam_|1.25***<br>(2.82)|0.90***<br>(20.06)|0.60|-2.63*|-2.48|-2.88**|-2.65*|
|_Dodoma_|0.87<br>(1.42)|0.93***<br>(15.36)|0.55|-2.83*|-2.62*|-2.95**|-2.65*|
|_Iringa_|1.21**<br>(2.37)|0.88***<br>(17.17)|0.55|-2.79*|-2.97**|-3.08**|-3.01**|
|_Lindi_|1.49**<br>(2.50)|0.87***<br>(14.91)|0.53|-3.32**|-3.72***|-3.68***|-3.74***|
|_Mbeya_|-0.19<br>(0.47)|1.02***<br>(24.50)|0.68|-2.53*|-2.66*|-2.48*|-2.64*|
|_Morogoro_|1.74***<br>(3.19)|0.85***<br>(15.62)|0.53|-2.98**|-2.86*|-3.16**|-2.92**|
|_Moshi_|1.61**<br>(2.56)|0.86***<br>(13.92)|0.53|-2.73*|-2.34|-3.00**|-2.43|
|_Mtwara_|20.7***<br>(3.31)|0.82***<br>(13.34)|0.51|-3.10**|-4.18***|-3.52***|-4.10***|
|_Musoma_|1.52***<br>(2.96)|0.88***<br>(17.23)|0.64|-2.62*|-3.11**|-3.03**|-3.14**|
|_Mwanza_|0.29<br>(0.58)|1.00***<br>(20.33)|0.72|-2.79*|-3.11**|-2.79*|-3.11**|
|_Shinyanga_|0.76<br>(1.31)|0.95***<br>(16.61)|0.66|-2.49|-3.59***|-2.63*|-3.58***|
|_Singida_|0.54<br>(0.86)|0.96***<br>(15.51)|0.62|-2.82*|-3.12**|-2.90**|-3.12**|
|_Songea_|1.68***<br>(3.08)|0.82***<br>(14.87)|0.50|-3.11**|-3.19**|-3.43**|-3.23**|
|_Sumbawanga_|0.95**<br>(1.99)|0.89***<br>(18.48)|0.63|-3.14**|-3.08**|-3.50**|-3.16**|
|_Tabora_|0.69<br>(1.35)|0.95***<br>(18.76)|0.70|-3.16**|-3.60***|-3.27**|-3.62***|
|_Tanga_|1.37**<br>(2.17)|0.88***<br>(14.02)|0.52|-2.92**|-3.03**|-3.14**|-3.04**|

**Notes** : ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The numbers in parentheses are absolute (robust) t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent .

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deviations below that of a reference population, increases dramatically from about the age of 6 months to 24 months, and stabilizes (but does not recover) thereafter (Shrimpton et al. 2001). Are there comparable patterns in other dimensions of child wellbeing? Do specific health insults, inadequate resources, or low levels of stimulation have especially large negative effects on outcomes if they occur at a particular age in the life of a child? What deficits can be made up later in life? These are hard questions to answer with a single cross-section of data, both because it is not possible to disentangle age and cohort effects, and because a variety of child outcomes tend to be correlated over time. There are therefore important benefits to the collection of panel data that span the life of children from birth onwards. Indeed, because in-utero conditions are likely to have an effect on subsequent child development, panels would ideally begin during a mother’s pregnancy.

Descriptive work is a critical building block for more ambitious attempts to combine economic theory and empirical analysis in an attempt to understand the causal pathways whereby characteristics of households or children affect a given dimension of development in early childhood. In the literature on the United States, there is considerable controversy about _how_ low incomes lead to poor outcomes in early childhood. Low income is often associated with a lack of resources that can affect child development—for example, toys or reading material that stimulate cognitive development, or high quality day care. Children in low income households also tend to have worse health and nutritional status, and generally have parents with lower levels of education. In addition, lower incomes are associated with higher levels of maternal depression and home environments which are less nurturing, both of which are believed to have direct causal effects on outcomes. If there is a gradient between socioeconomic status and the formation of skills in early childhood in Latin America and the Caribbean, as seems likely, careful descriptive work informed by sound economic theory about how (and why) households make choices about childhood investments will be required to disentangle causal effects. Rich data sets with long histories and detailed information on household resources, maternal and child characteristics, parenting environments and access to social programs are particularly important in this regard.

In some cases, experimental evidence may also help recover “structural” parameters with a causal interpretation. The evaluation of PROGRESA is a good case in point. For the first years of the program, households were randomly assigned into a treatment group that received cash transfers and a control group. The program has been shown to have had positive effects on enrollment and attendance in school (Schultz 2004; Behrman et al. 2005). If the increase in child enrollment and attendance translates into higher school attainment as adults, it should be possible to (eventually) collect data on the outcomes of children born to the (randomly-selected) 22
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# 6 Conclusions

This paper aims to estimate to what extent violence explains the extremely low female labor force participation (FLFP) rates in South Asia.

This is relevant for both academics and policy makers as FLFP is well-known to contribute to development. In addition to providing extra labor input and therefore increasing gross domestic product, women having earning power has been shown to increase investment in children, including education, which promotes future GDP growth (Duo [2012]).

The direction of causality between violence and FLFP and its implications for identication represents a crucial challenge. One the one hand violence could increase FLFP through the added worker eect whereby women increasingly work outside the home to help support households during a crisis. On the other hand violence may increase the cost of working for women, either directly due to the risk of violence or indirectly by reducing economic opportunities. Therefore, economic theory does not oer a clear prediction about the relationship.

At rst glance, the evidence supports the latter rather than the former eect, because in a cross-section of countries or regions a negative correlation between violence and FLFP emerges; that is, FLFP is lower in areas where violence is higher. Thus, looking at aggregate data shows little evidence for the added worker eect.

We evaluate the validity of this argument using disaggregated data that track FLFP rates over repeated crosssections. On the "extensive margin" of labor supply - that is, on women's decision whether to work or not work - we nd that one extra-attack reduces FLFP by 0.008 percentage point. This results in a widening of the gender labor participation gap as the eect of violence on men's labor participation is up to seven times smaller in our study. On the intensive margin - that is, on women's decision regarding how many total hours they work - the presence of violent attacks encourages married women to exert more working hours, but when the environment gets more risky, as the number of dead and people wounded increases, women reduce their working hours. We also nd that violence has a greater impact on discouraging women from working in areas where female work was more prevalent before the advent of violence. Moreover, the eect of violence is non-linear as a greater number of attacks generates a greater impact on FLFPR.

Although we nd a robust negative eect of terrorist attacks on FLFP using dierent estimation approaches, we recognize that the sizes of the eects found are not high enough to attribute the extremely low FLFP rates in the region solely to violence. Although we provide evidence that violence is indeed one of the determinants of low FLFP, further research and richer data are needed to establish how the presence of violence inuences FLFP compared to drivers such as values, cultural norms and human capital dierences, among other factors.

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2

# **Abstract**

Trade policy reforms in recent decades have sharply reduced the distortions that were harming agriculture in developing countries, yet global trade in farm products continues to be far more distorted than trade in nonfarm goods. Those distortions reduce some forms of poverty and inequality but worsen others, so the net effects are unclear without empirical modeling. This paper summarizes a series of new economy-wide global and national empirical studies that focus on the net effects of the remaining distortions to world merchandise trade on poverty and inequality globally and in various developing countries. The global LINKAGE model results suggest that removing those remaining distortions would reduce international inequality, largely by boosting net farm incomes and raising real wages for unskilled workers in developing countries, and would reduce the number of poor people worldwide by 3 percent. The analysis based on the Global Trade Analysis Project model for a sample of 15 countries, and nine stand-alone national case studies, all point to larger reductions in poverty, especially if only the non-poor are subjected to increased income taxation to compensate for the loss of trade tax revenue.
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