rafmacalaba/datause-encoder-data huggingface dataset:
_-_ use the logarithm of the price of Brent _(BRENT)_ one of two commonly used crude oil reference prices for LNG<sup>2</sup> ' - to analyze the incidence of output prices on the riskiness of Ras Gas.<sup>22</sup> The contractual provisions of the off-take agreement permit us to separate demand volume from price risk because Kogas, by and large, has committed to buying a fixed amount of output _per annum._ Hence, demand risk essentially translates into breach of contract risk. Since Kepco is Ras Gas' effective off-taker and Kogas only an affiliated intermediary,23 we take -he mid-closing yield spread of the Kepco 7.75% global (Eurodollar and Yankee) bond maturing in April 2013 _(KORELES)_ over 10 year US Treasury yields to measure the economic and financial prospects of the LNG buyer as assessed by capital markets. From a statistical perspective, using Kepco credit spreads has the added benefit that they are an instrumental variable for Kogas spreads, which should be simultaneously determined with Ras Gas spreads because of the bilateral monopoly relationship between the two firms.

Ras Gas' fortunes also depend on Korea's macroeconomic environment through its impact on Kepco and Kogas. A severe recession might cast serious doubts on Kogas-Kepco's ability to honor their contractual commitments. We use the logarithm of the Korea Composite Stock Index _(KOSPI)_ as a proxy for the incidence of the Korean macroeconomic environmeni on electricity and gas demand. To control for Kepco's idiosyncratic (operational, regulatory and financial) risks, we include _KEPCO,_ the logarithm of its stock price. A further risk factor is the

> 20 See Zingales (2000) for a discussion for situations in which there might exist other residual claimants besides shareholders. Projects rarely issue publicly traded equity so that in their absence project riskiness is best assesseJ by the price of publicly traded debt, whenever available.

> 21 Gas prices turn out to be statistically non-significant when included in the regressions together with Brent prices, which is not really surprising given that about 0. I metric tons of LNG are priced as one barrel of crude oil. 22 Diagnostic testing reveals that logarithms offer superior fit over levels for several of the explanatory variables. 23 See Standard and Poor's (1999) for Ras Gas' financial dependence on the Korean electricity market and Kepco.

17
significantly over the period. The average per country share of subsidies in total expenditures substantially declined, particularly over the last five years of the period. The average share of subsidies steadily fell from 60% in 1985-90 to 58% in 1990-94, and to 44% in 1995–2000. Moreover, in practically all countries the proportion of subsidies in total government expenditures decreased or remained stable, the exceptions being Ecuador and Paraguay. The structure of public expenditures, not surprisingly, varies a lot across countries. Moreover it also exhibits a meaningful and large variability within countries over time. These large and seemingly meaningful variances are extremely important features which allow us to appropriately use panel data econometrics, including fixed effects methods (Nerlove, 2002).

Turning to the remaining variables used, the indicator of trade openness is intended to measure the degree of openness of trade _policy_ rather than trade volumes. Large countries (population and area wise) inherently tend to trade less than small countries, as they are naturally more self-sufficient, regardless of trade policy. If we want to use trade (imports plus exports) over GDP as a proxy for trade policy we must, therefore, normalize for country size and other structural characteristics (access to sea ports, oil wealth, etc.) that may affect the natural tendency of countries to trade. We normalize trade flows following a methodology used by Pritchett (1996), which consists in controlling for countries’ geographic size, population, per capita income and other non-policy variables that are likely to affect the volume of trade. The index can be interpreted as an indicator of a country’s trade policy openness with respect to the norm.<sup>21</sup> The data of agricultural land area per country over time is obtained from FAO sources, and the per capita non-rural income is estimated from national account data. Finally, since no data for agricultural labor exist, we use the total rural labor force as a proxy for L. Summary statistics for all the data used are provided in the appendix.

# **V. The Results**

We estimate the reduced-form for per capita agricultural GDP using various methods of estimations and aggregating the data in several alternative ways. Our interest is primarily in probing the robustness of the estimated coefficients. We present estimates using three specifications: (1) regressions using country data aggregated for relatively long periods of time

> 21  The actual cross-country regressions used to estimate the norm included more than 120 countries. Details about the methodology used are available fro m the author.

17
# **1.  Overview**

**Financial Inclusion is critical for the competitiveness of the Middle East and North Africa (MENA) region, for employment creation, and for raising incomes and reducing poverty** . Financial inclusion is through access to financial services such as credit, bank accounts, deposits, payments services, insurance, and pensions. Firms need access to financial services to be able to invest, innovate, take advantage of market opportunities, manage cash flow and costs, and reduce risks. Microenterprises constitute the vast majority of enterprises in MENA and are a significant employer (92.5 percent of enterprises in the case of Egypt, and 63.6 percent share of private employment<sup>1</sup> ). Individuals and households benefit from financial inclusion through a safe place to save, safer and cheaper access to remittances and other payments, loans or insurance payments to cover health or education expenses, and pensions for old age. High levels of financial exclusion hinder efforts to bring enterprises into the formal sector, and to increase investment and trade.

**The G20 leaders committed to ‘improving access to financial services for the poor’** at the Pittsburgh summit in September 2009, and set-up a Financial Inclusion Experts Group with World Bank Group support. The G20 Leadership Summit in Toronto in June 2010 endorsed the “Principles for Innovative Financial Inclusion” that the Group developed. The UN committee on building inclusive financial sectors urged central banks and governments in 2006 to add the goal of universal financial inclusion to the traditional goals of prudential regulation and financial system stability.  Universal access to finance has been adopted as a goal by India, Pakistan, South Africa, Mexico, Brazil, Colombia, Malaysia and a number of European countries.

## **While most MENA countries have taken steps to improve financial inclusion, there has not yet been a comprehensive high level commitment to financial inclusion across much of the**

**region.** Financial inclusion is a policy focus for several MENA countries, although it is not yet a priority objective alongside stability for MENA financial regulators and ministries of finance. Five MENA countries now have a financial inclusion strategy, and with three of these adopted since 2006 this seems to be a growing trend for region.

## **The enabling environment for financial inclusion in MENA has improved in the past two**

**years, but is still weak overall** . Deficiencies in financial infrastructure and regulatory frameworks make expanding access to finance costly and risky for banks. Lenders have to rely on collateral that is expensive to register and may not be readily enforceable, and on outdated credit assessment and risk management techniques. Credit information is improving in quality and availability throughout MENA, as credit registries are improved and credit bureaus established, but most microfinance institutions (MFIs) do not yet report to or have access to them. Interest rate caps on micro loans act as a disincentive for banks and investors, and limit

> 1 CAPMAS, 2006

4
sharp decline in the first half of the 2000s, and, although the differences narrowed again after 2010, the level recorded in 2014 was virtually the same as the one in 1992. Second, the relatively high _β_ -convergence coefficients across municipalities in border states seem to have reduced income dispersion along the U.S. border at a rate of 22 percent in 1992–2014.

# **5 Testing for poverty convergence**

Have poorer, converging municipalities been able to translate their relative income gains into poverty reduction? If per capita income follows a log-normal distribution, then any change in the poverty headcount ratio is determined, in a magnitude _η_ , by two components: one that is attributable to changes in income and one that is attributable to changes in the distribution of income. The relationship between each component and changes in poverty is illustrated in figure 4 over 1992–2014. As expected, those municipalities that experienced relatively higher rates of poverty reduction, according to the food poverty line, were those that experienced higher growth rates in mean per capita income (panel a), but also experienced progressive changes in the distribution of income (panel b), because such changes imply transferring resources from richer to poorer populations, thus stimulating poverty reduction.<sup>13</sup> Focusing on the first component, for now, let be the partial elasticity of poverty to growth in the mean per capita income of municipalities, representing the percent change in the poverty headcount ratio as a result of a 1 percent increase in income, holding the income distribution constant. _gi_ ( _Pit_ ) is the annualized change in poverty rates, calculated as in (2); _η_ is the elasticity parameter, with the expectation that _η <_ 0; _δ_ is a municipality-specific effect; and, _νit_ is a stochastic term.<sup>14</sup>

> 13The bulk of the following analysis focuses on extreme poverty as measured by the food poverty line. This is deliberate because the narrative is consistent, and conclusions hold when using higher thresholds such as the capabilities and assets poverty lines.

> 14Similarly, _gi_ ( _Pit_ ) = _δ_ + _ηgi_ ( _Git_ ) + _νit_ can represent the partial inequality elasticity of poverty or the percent change in the poverty headcount ratio as a result of a 1 percent increase in inequality, holding per capita income constant, with the expectation that _η >_ 0, and with _gi_ ( _Git_ ) as the annualized rate of change in inequality. The growth and inequality elasticity parameters can be denoted as _η_<sup>_y_</sup> and _η_<sup>_G_</sup> , respectively, and hence, under log normality, changes in poverty rates can be expressed as _gi_ ( _Pit_ ) _≈ η_<sup>_y_</sup> _gi_ ( _yit_ ) + _η_<sup>_G_</sup> _gi_ ( _Git_ ).

21
pandemic. We focus here on , which captures the difference in achievement change per week of closures. ππ As discussed in the data section, we use four measures of the length of school closures: (1) UNESCO full closures; (2) UNESCO full and partial closures; (3) PISA student reported closures; and (4) PISA principal reported closures. All are expressed in terms of weeks of school closures. We use and to calculate the learning loss for countries with shorter or longer school closures, comparing results at the percentiles of each school closure measure ττ ππ reported.

This model can be further expanded to test for differences in the impact of the pandemic on boys with girls as a baseline, immigrant students with natives as a baseline, or students with different socioeconomic background. To achieve this, we add interaction terms between all variables in the regression model above and individual student characteristics. In this case, the estimated coefficient for the interaction between 2022 and a dummy for boys, for example, shows the differential impact of the pandemic on boys, after controlling for separate time trends DD by gender in each country. Moreover, the interaction term between a dummy for boys and 2022 w shows how boys were differently affected by weeks of school closures. ii DD ∗wwtFinally, we investigate heterogeneity in the impact of school closures by achievement Finally, we investigate heterogeneity in the impact of school closures by achievement level. Some country studies reported more significant losses among low-achieving students, while others showed similar losses across achievement spectrum. To check how globally learning losses vary among students of different proficiency, we re-estimate the main models using quantile regressions, fitting regressions to explain achievement trends for students at different percentiles of PISA scores. To deal with many dummy variables and interactions, we use recent implementations of fast quantile regression algorithms (Chernozhukov et al. 2022).

PISA data are collected through a complex stratified survey with schools sampled as primary sampling units and students sampled at the second stage. We use the sets of replicate 10
_Where Do the Unemployed Go?_ When young people leave unemployment, most go into informal salaried work or leave the labor force. Young men exit unemployment into informal salaried work in all three countries (about one-quarter), as do most young adults (Table 8). The exception is that Mexican males age 19-24 mostly enter formal sector employment (27.2%), followed by informal sector employment (23.9%). About one-third of women of all ages, on the other hand, leave the labor force entirely after a spell of unemployment. Among women who find a job, one- half to two-thirds become informal sector employees (Table 8).

The propensity to transition from unemployment to informal salaried employment is not markedly different among young people from poor and non-poor families. Taking young adults as an example, 25 percent of poor Argentines who had been unemployed six months previously now hold informal salaried employment, as compared to 22 percent of young adults from non-poor families. This compares to 15 percent for Mexican young adults from poor and non-poor families and 21 and 14 percent for young Brazilian adults, respectively.

Three to 22 percent of youth who are unemployed go back to school. The propensity is the highest among Argentine youth and lowest among Brazilian youth. The difference in propensities may be due to the length of the period between observations for each sample. While the Argentines may have left school, spent time in job search, spent time working, and decided that they needed more school over a six month period, we observe the Brazilians over a shorter period, where they may be in the school-to-break transition, having not yet decided whether their future holds employment or more school.<sup>9</sup> Although the informal sector is the main labor market segment to receive unemployed youth, they do not want to be there. Brazilian data show that only one-quarter of informal salaried youth and young adults prefer to be in that sector, compared to the formal salaried sector (Perry et al, 2007). These are lower rates than prime aged adults (32 percent for men and 43 percent for men) and than older adults (55 and 70 percent for men and women age 55-70, respectively). Among youth and young adults who do prefer the informal salaried sector, the motivation for such a preference is that they like their current job (75 percent), or they need time for other activities (5-10 percent).

g 15
Nigeria and Uganda, just to name a few. However, to the best of our knowledge no study has examined the extent and root cause of the gender productivity gap in Niger. Niger is a prime candidate for study since it is very poor and has one of the lowest agricultural yields in the world. In addition to filling an important gap on gender differentials in productivity in Niger, the paper has three interesting features derived from recent papers on this topic, namely Kilic et al (2013). The first is that we use a national household survey, not data limited to a specific region. The findings then reflect the diversity of agricultural practices and cultures across the country. Second, the gender productivity gap is considered at the plot level and not at the household level. Peterman et al (2011) have shown that household level gender indicators tend to underestimate gender differences in productivity. For this reason, Peterman et al recommend that particular attention be paid to survey design and that gender differences be examined at the plot level and not only at the household level. Third, the methodology uses the Oaxaca-Blinder decomposition at the aggregate and detailed level, providing the possibility to identify the factors that explain the productivity gap. Identifying these factors can help to inform policy interventions aimed at narrowing the gender productivity gap in Niger.

We found that in Niger on average plots managed by women produce 19 percent less per hectare than plots managed by men. We also find that the gender gap tends to be widest among Niger’s most productive farmers, ranging from 4 percent among the least productive farmers (at the 10th percentile) to 34 percent among highly productive farmers (at the 90th percentile). The primary factors that contribute to the gender productivity gap in Niger are: (i) farm labor on which women face significant challenges in accessing, using, and supervising male farm labor; (ii) the quantity and quality of fertilizer use with men using more inorganic fertilizer per hectare than women and (iii) land ownership and characteristics with men owning more land and enjoying higher returns to ownership than women.

The paper is organized as the following. Section 2 presents the country context and the data used for this analysis. The third section deals with the global decomposition and the detailed decomposition of the gender differential. Since the differences in productivity between men and women are not necessarily the same along the distribution, we take into account this potential heterogeneity in section 4 to examine the differences at different levels of productivity. In section 5, we formulate alternative hypotheses to check the robustness of the previous findings and section 6 concludes.

# **2. The Country Context and the Data**

3
**Table 1. Effects of Market Access on Land Inequality: OLS and Bias-Adjusted OLS  Estimates**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. var.|**ln**|**(Landgini 20**|**12)**|**ln**|**(Landless 20**|**12)**|
||||Bias-|||Bias-|
|Estimator|OLS|OLS|Adjusted|OLS|OLS|Adjusted|
|**ln(MA1996)**|**0.108*****<br>**(0.02)**|**0.111*****<br>**(0.02)**|**0.115*****<br>**(0.04)**|**0.360*****<br>**(0.07)**|**0.357*****<br>**(0.07)**|**0.352*****<br>**(0.11)**|
|ln(Slope)||-0.037|||-0.078||
|||(0.03)|||(0.09)||
|Elevation||-0.138|||0.148||
|||(0.14)|||(0.43)||
|Crop Suitability||-0.002|||-0.005||
|||(0.00)|||(0.00)||
|ln(Rain)||0.039|||0.153||
|||(0.05)|||(0.16)||
|ln(Temperature)||-0.188|||0.621||
|||(0.35)|||(0.96)||
|ln(Rain CV)||0.089|||0.256||
|||(0.13)|||(0.39)||
|ln(Temp. seasonality)||-0.056|||-0.064||
|||(0.08)|||(0.21)||
|Pop. density, 1961||0.023***|||0.037||
|||(0.01)|||(0.02)||
|Constant|-1.942***|-0.980||-6.262***|-11.059*||
||(0.25)|(2.08)||(1.14)|(5.84)||
|State Dummies|No|Yes|Yes|No|Yes|Yes|
|Observations|200|200|200|212|212|212|
|R-squared|0.166|0.380||0.167|0.428||

Note: Columns (1), (2), (4) and (5) are estimated by OLS. Columns (3) and (6) are estimated by Oster's biasadjusted OLS (Oster (2019)). Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
commercial banking and corporate securities underwriting. The reasons for separating banking and insurance, and the arguments for retaining the barriers to such linkage include the following:

1. Permitting banks to conduct insurance operations, or insurance companies to conduct a banking business, could produce an unhealthy concentration of economic power either nationally or at the local level. Combining financial powers under a single ownership could reduce competition in loan markets, particularly in localized markets for home mortgages, farm mortgages or business loans.

2. Commercial banks would gain an unfair competitive advantage over insurance company agents, since they could draw upon irside information regarding a depositor's financial status in drawing up prospect lists for insurance sales. Also, businesses and consumers seeking bank loans could be subject to subtle pressures to buy their insurance from the bank, in order to gain approval of their loan application.

3. Since bank deposits are federally insured, and the public feels that their funds are safe, they mav also favor purchase of insurance from a commercial bank in the belief that the federal backing also applies. Insurance companies would thus be left at a disadvantage because of this likely confusion in the public mind.

4. Banks should not be allowed to engage in non-banking activities such as insurance or securities underwriting, since they could expose the bank to financial risks that could jeopardize the safety and soundness of the banking system on which the public relies.

On the other side of the argument, those who favored bank entry into the insurance field offered the following views:

1. Competition would be enhanced, and the public better served, if banks were allowed to offer insurance products to the public. Some supporters suggested that the cost of insurance would be lowered by such bank activities because of cost-saving efficiencies.

2. Commercial banks have been losing market share in the financial markets, not only in domestic markets, but also in relation to foreign banking enterprises such as the Japanese. Permission to enter the insurance and securities businesses would enlarge the financial size and scope of commercial banks in the United States, and allow them to compete more effectively on a global basis.

3. Entry into insurance and security sales would enhance bank profits and thereby improve the financial soundness and capital base of commercial banking.

4. These enlarged powers for commercial banks would improve their diversification and thereby enhance the financial stability of the commercial banking system.

_5._ Allowing financial institutions to conduct financial business of all kinds, without the present compartmentalization, would serve the public's need for "one-stop financial services' and provide a "level playing field" among financial institutions on a fair and equal basis.

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