rafmacalaba/datause-encoder-data huggingface dataset:
20 statistically<sup>3 5</sup> , whereas with the second approach it is observed. The latter approach is, therefore, computationally more intensive and its results are susceptible to the frequency of rare events in the historical observation period, as well as to its length.

Finally, under the Monte Carlo (or stochastic simulation) approach, a bank tests the value of the portfolio under a large sample of randomly chosen combinations of price scenarios, whose probabilities are based on historical experience. This method is more flexible and is particularly useful in measuring the risk in instruments with nonlinear price characteristics, but it is less frequently used because of its time and cost demands.

Banks can use any of the three approaches to allocate capital between their various operations. So far, there is no industry consensus on the best method for calculating VAR. As with any statistical model, VAR depends on assumptions whose choice is dictated by the user's awareness and aversion to them.

The issue of correlations is extremely important for VAR models. Different 36 approaches capture different correlations . Measuring correlation is important because of the empirically well-established 'fat' tails in the distribution of market returns. Leptocurtosis means that measures of VAR based on a normal distribution of returns will likely understate actual VAR.

In addition, during extreme market movements, correlations change significantly which has implication for VAR measurement. During the October 1987 equity markets crash, the correlation between markets was close to 1 - all markets moved together. There is little benefit at such times from market diversification, but considerable benefit from having long and short positions in different markets. At other times - for example, after the 1987 Nikkei equity index crashed alone - the correlation between some markets was closer to zero, or even -1. The benefits from diversification then would much exceed the benefits from hedging.

> 35 For example, one version of the first approach assumes that the returns on risk factors are normally distributed, the correlations between factors are constant, and the delta of each portfolio constituent is constant. All of these can be criticized as unrealistic.

> 36 The variance/covariance approach, for example, is based on average correlations calculated for the whole data period, while the simulation approach reflects the actual correlations on particular days.
Policy Research Working Paper 6616

# **Abstract**

Using a newly compiled database of women’s property rights and legal capacity covering 100 countries over 50 years, this paper analyzes the triggers and barriers to reform. The database documents gender gaps in the ability to access and own assets, to sign legal documents in one’s own name, and to have equality or nondiscrimination as a guiding principle of the country’s constitution. Progress in reducing these constraints has been dramatic—half of the constraints documented in the 1960s had been removed by 2010. However, some sticky areas persist where laws have not changed or have even regressed. The paper analyzes potential drivers of reforms. A significant finding is that the relationship with a country’s level of development and the extent of its reforms is not straightforward. For the first half of the sample, there was no systematic connection; only in the last 25 years have increases in income been associated with higher probabilities for reform, but only in lower-income countries. With the remaining constraints as prevalent in middle- as low-income countries, increased growth is not necessarily going to spark additional reforms. Clearer patterns emerge from the momentum created by international conventions, such as the Committee to Eliminate All Forms of Discrimination against Women (CEDAW), women’s political representation at the national level, mobilization of women’s networks, and increasing labor force participation in sectors that provide a voice for women, which are positive forces for change. Conversely, conflict and weak rule of law can entrench a discriminatory status quo. And much is at stake; strengthening women’s legal rights is associated with important development outcomes that can benefit society as a whole.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mhallward@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Table 9: Summary Statistics - innovation index and its components before and after imputation_

||**N.**<br>**Obs**|**Mean**|**Std. dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**_Before imputation_**||||||
|Has the firm introduced new products/services?|15,845|0.328|0.469|0.000|1.000|
|Has the firm introduced new methods of production|1,496|0.309|0.462|0.000|1.000|
|Has the firm introduced new inputs|1,493|0.192|0.394|0.000|1.000|
|Has the firms Introduced new processes|15,701|0.261|0.439|0.000|1.000|
|Did the firms spend on formal R&D|1,489|0.203|0.403|0.000|1.000|
|**_After Imputation_**||||||
|Has the firm introduced new products/services?|10,808|0.005|0.073|-1.149|1.725|
|Has the firm introduced new methods of production|16,000|0.209|0.407|0.000|1.000|
|Has the firm introduced new inputs|16,000|0.123|0.328|0.000|1.000|
|Has the firms Introduced new processes|16,000|0.261|0.439|-0.758|1.276|
|Did the firms spend on formal R&D|16,000|0.172|0.377|0.000|1.000|
|**Latent measure of innovation**|**10,808**|**0.00**|**0.875**|**-1.254**|**4.078**|

26
develop a more nuanced understanding of the impact of informational provision in markets with multiple (public and private) providers, and how this may vary based on the pre-existing informational environment. Information provision in our setting improves consumer (parent) welfare by lowering markups and inducing lower quality schools to improve quality or shutdown. Public schools also respond positively by raising quality and overall village enrollment increases.

The remainder of the paper is structured as follows: Section I provides details on the data, the context and the report card intervention. Section II describes the conceptual and empirical framework. Section III presents the findings. Section IV discusses these results further and concludes.

# **I. Data, Context and Intervention**

Private schooling has increased dramatically in low-income countries. In India, private school enrollment doubled between 1993 and 2005, with a market share of above 50 percent in urban areas (Andrabi et al, 2010). Similar patterns are observed in African countries; for example, in The Gambia, private school primary enrollment increased from 14 to 28 in just over 10 years (World Bank Data, 2014). In Pakistan, the setting of this study, the number of private schools increased sharply from 3,800 in 1983 to 47,000 in 2005; such schools currently account for a third of all primary school enrollment. These private schools are coeducational and instruct children in English, Mathematics, and Urdu using a curriculum and textbooks similar to that in public schools. In contrast to public schools however, private schools face little government oversight or regulation and operate in (de facto) lightly regulated markets with no administrative guidance on pricing. Sixty percent of the rural population in the province we study resided in a village with at least one private school in 2001, with on average 7.3 schools in these villages. Thus, parents face substantial choice for their 7
18

# **Fig. 1.3. Types of households living in privately owned** (i) **and non-privatized (ii) housing.**

# **Vladimir,** 1996

<!-- Start of picture text --> 38.A4% 69.3%<br>M Pensioners<br>14.0%<br>& _O. % ( 14.0% E Aduilts without<br>* \ __ ~~~~~~~~~~~children<br>_40 w.1% fl - CFarmnlies 0 with<br>* 21.5% ~ 16.7% children<br>39.3% 67.0%<br>P* Pensioners<br>10.4%/<br>MAdults without<br>children<br>1-. 43.3 1/9 22.6% ....... 3.. ClFain llles w ith<br>17.4% _ _ 43.3% 22.6% ............... children<br><!-- End of picture text --> Gorodets, 1996 Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

The impact of the household's demographic category on privatization proved so significant that it practically invalidates the other factors. For instance, apartments in the low-quality Khrushchev-era buildings were privatized quite eagerly because a large proportions of their tenants are older persons (this is true of all old buildings). The concentration of pensioner households in the middle-income groups resulted in quite high rates of privatization of the apartments occupied by such households.

# SECTION 2. **The Development of the Housing Market** in **the Context of Public Priorities**

This section considers the social and economic aspects of the developrment of the housing market. The findings of sociological surveys of the past few years are used to trace the changes in households' attitudes to the methods of looking for housing over the period of reform. Also examined is the influence of the housing market on household residential mobility. And, finally, the availability of housing depending on
# **1. Introduction**

The current crisis has put the financial sector again at the center of policy makers’ attention across the developed and developing world. While in recent years, the financial sector debate across the African continent has been dominated by policies to increase access to financial services, minimizing the impact of the crisis currently tops the agenda. Financial systems across Africa have seen a deepening and broadening over the past years, partly benefiting from the Great Moderation and global liquidity glut, but also from improvements in macroeconomic policies and progress in institutional reforms (Beck, Fuchs and Uy, 2009). This paper discusses the recent development of the financial sector in the major East African economy of Kenya, in the context of recent reforms.

By African standards and in comparison with other East African economies, Kenya’s banking sector has for many years been credited for its size and diversification. Private credit to GDP – a standard indicator of financial development, was 23.7% in 2008, compared to a median of 12.3% for Sub-Saharan Africa (Table 1). While this number is not higher than it was in 2005, the quality of lending has significantly improved, as can be seen in the increasing ratio of loans net of provision relative to GDP (Table 2).<sup>2</sup> Unlike most other countries in the region, Kenya has a variety of financial institutions and markets – banks, insurance companies, stock and bond markets - that provide an array of financial products. Notwithstanding this relative advantage, Kenya’s financial system has failed to provide adequate access to banking services to the bulk of the population. While the larger proportion of savings comes from small depositors, lending is skewed in favor of large private and public enterprises in urban areas. Financial services are expensive, as evidenced by high interest rate spreads (see Figure 1 below) and account fees.<sup>3</sup>

> 2 The figure for Sub-Saharan Africa is taken from Allen, Carletti, Cull, Qian, and Senbet (2009).

> 3 We note, however, that Kenya’s net interest margins are on par with the rest of Sub-Saharan Africa (Table 1). While there are some countries in Latin America that have higher interest margins and spreads than Kenya, a recent comparison shows that Kenya ranked 8<sup>th</sup> of 37 countries in interest spreads in 2008 and 12<sup>th</sup> of 66 countries in net interest margins from 2001-2009. Also, Uganda’s spreads and margins, which are higher than those in Kenya rank in the top five on both dimensions. In all, the evidence points to Kenya’s margins and spreads as being relatively high. See Jorgensen et al. (2010) for the international comparisons of interest spreads and net interest margins.

2
interventions in this area might have no net effect. Unfortunately, there is no way to determine whether this is the case.

In the Egyptian context, microcredit consists of extending modest amounts of credit to individuals. Microloans may not exceed LE 5,000 (about US$900), and most are much smaller: 48% of the microloans made in 2005 were for no more than LE 1,000, and only 7% were for more than LE 3,000.

The expectation is that such loans provide a way for households to generate income, and in the case of poor households, even rise out of poverty. In 2005, 44% of those who took out microloans worked in agriculture, and a further 30% were traders. Fully 55% of such loans went to women, and 42% of borrowers said they were illiterate. On the other hand, most of the loans are short-term; in 2005, 70% of microloans had a duration of less than a year, and 22% were repayable over 1-2 years. When asked (in the 2004/05 HIECS survey) whether microloans increased their income, 80% of households said yes, while the remaining 20% said no.

Our analysis of the impact of microcredit is disaggregated to the level of five regions – metropolitan areas, urban and rural Lower Egypt, and urban and rural Upper Egypt. This is done because of the belief that the effects of microcredit may vary substantially, depending on the underlying economic structure. We measure the impacts using propensity score matching; separate propensity score equations are estimated for each region, using household-level data – unlike our earlier estimates, which are based on village-level aggregates – and the models adapted until they were balanced. We employ the propensity scores to match “treated” (i.e. borrower) individuals in the region of common support with comparators, using kernel matching, nearest-neighbor matching, and a regression model. The results of the kernel matching are shown in Table 14, and in almost all cases were substantially the same as those found using the other two techniques; the two exceptions are noted in Table 14.

One clear pattern does emerge from Table 14: microlending appears to lead to substantially higher levels of non-farm income per capita, especially in urban areas. It is also associated with widespread selfemployment and, more modestly, with lower literacy rates.

Most of the other effects are not consistent from one region to the next. The sharpest distinction is between the metropolitan areas and urban Lower Egypt on the one hand, and the rest of the country on the other. In the former, microcredit is associated with higher levels of expenditure, food expenditure, and _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 22 of 36_
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21
88 port of Colombo ( _pColombo,k_ ), in USD/MT (which we then convert to USD/kg).<sup>115</sup> Since data on port prices ( _pColombo,k_ ) is only available for five crops, we use equation (35) to compute _B_<sup>ˆ</sup> _k_ for each of these five crops, then impute the _B_<sup>ˆ</sup> _k_ of the other three crops (cinnamon, cloves, and groundnuts) with the average _B_<sup>ˆ</sup> _k_ among the five crops for which data is available.

## **D.C Production Function Parameters**

As explained in Section VI.A.3, we estimate Cobb-Douglas coefficients _γk_<sup>_f_by matching fertilizer</sup> cost shares (see equation 36). For that, we use data on national crop prices _p_<sup>_LKA_</sup> _k_ , national annual crop production _Q_<sup>_LKA_</sup> _k_ , and crop-level national annual fertilizer expenditure _Mk_<sup>_f,LKA_</sup> .

As shown in equation (37), we then apportion residual input shares to land and labor using data from IO tables on value added and labor compensation. To do that, we map our eight crops to the IO table’s agricultural sectors, which include “minor export crops”, “highland crops”, “tea growing” (separately for high, medium, and low elevations), “paddy”, and “potatoes” as sectors. We classify cinnamon and cloves as “minor export crops”, groundnuts, maize, and onions as “highland crops”, and rice as “paddy”. For tea, we aggregate high, medium, and low elevations into a single sector.

# **E Inversion Details**

In Section VI.B we describe our general approach to backing out unobserved exogenous variables (productivities, preference shifters, fertilizer endowments, and number of estate workers) by combining observed variables with the equilibrium structure of the model. In this section, we explain the details of this “inversion” procedure.

## **E.A Inversion Procedure**

It is possible to rewrite the market clearing equations (22, 25, 26, 27) of the model without QRs to obtain the following system of equations:<sup>116</sup> 115This data is from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023. 116More specifically, to obtain the system of equations, substitute equation (20) and _p_<sup>_f_</sup> _i_<sup>=</sup><sup>_pf_</sup> _RoW_<sup>into fertilizer market</sup> clearing condition (22), substitute equations (7), (17), and (18) into crop market clearing condition (25), and substitute equations (16) and (19) into labor market clearing condition (26).
<!-- Start of picture text --> US FDI to 87 COUNTRIES AND 5 SECTORS<br>8 8 .08<br>e)<br>© ° 8<br>7 6 e) Q .06<br>q S<br>1S} ©,<br>é°2<br>~ e) Oo © &<br>se} 4 oO fe) o ra 04<br>2 6° gf 4<br>z |? 8 ° S<br>ae) eS,<br>eS 2 ° fe) =<br>7 axe) ca, & a<br>f°) 5o)6lCUCOOté‘ o)*‘C*Sw<br>0 6 BoSo Q<br>© O 0<br>0 25 50 75 100 0 25 50 75 100<br>Equity Allowed (%) Equity Allowed (%)<br>Source: IAB and US Bureau of Economic Analysis (www.bea.gov)<br>Note 1: 87 countries, 5 sectors; 133+ values (out of 435)<br><!-- End of picture text -->