rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table 2: Summary statistics**

|**Variable name**|**Variable description**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|**_Dependent variable_**|||||||
|lib_m|Count of import liberalizing measures on medical products|133|1.91|2.68|0|22|
|res_m|Count of import restrictive measures on medical products|133|0.24|1.12|0|10|
|lib_x|Count of export liberalizing measures on medical products|133|0.16|0.37|0|1|
|res_x|Count of export restrictive measures on medical products|133|1.49|2.98|0|30|
|**_Control variables_**|||||||
|pop|Population (mln)|128|57|179|0.04|1390|
|ge|Government effectiveness|127|0.06|0.88|-1.81|2.23|
|mp|Market penetration (USD mln)|130|168|481|0|5200|
|Msh|Share of medical imports in total imports|129|0.06|0.03|0.01|0.17|
|RCA|Standard RCA for medical products|129|-0.47|0.44|-0.99|0.58|
|Tar|Simple average applied tariff rate on medical products|119|5.93|4.57|0|27.59|
|COVID_cases|Cumulative count of COVID-19 cases|130|107070|400949.8|3|3805524|
|COVID_deaths|Cumulative count of deaths due to COVID-19|130|4294|15539.5|0|140437|
|**_Procurement variables_**|||||||
|tot_steps|Total steps required to complete procurement process|122|17.72|1.76|12|21|
|tot_time|Total time to complete procurement process (# of days)|122|716.61|245.10|270|2062|
|eproc|Share of e-procurement in total procurement|124|0.69|0.32|0.25|1|
|gpa|Membership of WTO's GPA|133|0.24|0.43|0|1|
|num_dpa|Numberofdeep procurement agreements|133|4.36|8.71|0|26|

20
Estimated Equation for PIGC PIGC - 9686. - 2041. Ql + 2855. Q2 + 248.6 Q3 + 1.645 BRHOGKE - 181600. RPCPP (1500.) (455.7) (458.8) (456.6) (.2708) (40230.) R<sup>2</sup> _ .739, K<sup>2</sup> * .714, DW = .735, **ay** - 1239., Data = 1973:1 - 1987:4 The poultry component of the meat supply block includes the following estimated equations.

Estimated Equation for PRDBR PRDBR - -800.0 + 148.6 Q1 + 148.1 Q2 - 124.8 Q3 + .003463 BRCH (115.3) (28.68) (28.28) (29.08) (.00007286) + 570.7 RPCPBR (1511.) R2 = .984, K<sup>2</sup> - .983, DW - 1.03, ay = 77.43, Data = 1973:1 - 1987:4 Estimated Equation for CPL CPL **=** 3981. - 103.0 Q1 + 357.0 Q2 - 1040. 03 + .7616 CPL (919.2) (216.5) (219.5) (226.2) (.06795)

- 41080. RPCPBR (11460.)

R<sup>2</sup> = .846, K<sup>2</sup> = .832, DW = 2.44, cy = 591.1, Data = 1973:1 - 1987:4 Estimated Equation for BRCH BRCH = -166900. + 50220. Q1 + 93450.Q2 + 40980. Q3 + 36.91 CPL (98360.) (27050.) (26110.) (27430.) (13.25) + ^4.79 CPL(T-1) + 22.25 CPL(T-2) + 20.55 CPL(T-3) (13.64) (13.69) (13.72) + 21.75 CPL - 745100. RPCPBR (11.92) (1173000.) R<sup>2</sup> = .921, K<sup>2</sup> _ .906, rW = .246, Cy = 538300., Data = 1973:1 - 1987:4

# **Quarterly Meat Demand Block**

The meat demand block includes two components of estimated equations. The domestic meat demand component of the meat demand block forms a simultaneous multivariate system where each demand equation is represented in 22
MULTILINGUAL CHILD ASSESSMENT To address the first aim of validating our assessments by examining their psychometric properties, we measured: 1) the internal consistency of the measures using Cronbach’s alpha; 2) IRR using Cohen’s kappa, Krippendorff’s alpha, and percent agreement; 3) construct validity by examining the correlations between the measures; and 4) convergent validity by examining associations with known covariates in bivariate regressions. For our second aim, to better understand the relationships between baseline measures of mother tongue and LOI receptive vocabulary and scores on subsequent vocabulary assessments, we estimated a series of ordinary least squares (OLS) regression models to examine the associations between baseline agestandardized receptive vocabulary scores in all languages (English, Swahili, and Luo) and English receptive vocabulary at follow-up. We repeated this analysis for follow-up measures of child expressive vocabulary as well as Swahili and Luo receptive vocabulary; we present these results as supplemental analyses. Our final aim was to examine the association between caregiver literacy and child vocabulary at two time points. We used OLS regression to examine the association between baseline caregiver literacy in Luo and English and child English and Luo receptive and expressive vocabulary scores at follow-up. To test whether the relationship between baseline mother tongue and LOI receptive vocabulary scores and follow-up LOI receptive vocabulary varied with caregiver literacy, we estimated OLS regression models that included both caregiver literacy and baseline child receptive vocabulary (in English, Luo, and Swahili). All regressions used age-adjusted _z-_ scores for child vocabulary, and standard errors were adjusted for household clustering. All statistical analyses were conducted using Stata 14.2.

# **Descriptive Statistics**

**Results**

11
**Table 7: Determinants of participation in off-farm activities: multivariate probit estimation**

||**Nonfarm self-**<br>**employment**|**Nonfarm wage**<br>**labor**|**Farm wage**<br>**labor**|
|---|---|---|---|
|Female headed household|-0.141*|-0.214***|0.036|
||(-1.929)|(-2.722)|(0.485)|
|Age of household head in years|-0.016*<br>|-0.005<br>|-0.016*<br>|
||(-1.799)|(-0.545)|(-1.695)|
|Age of household head squared|0.000<br>|-0.000<br>|0.000<br>|
||(1.021)|(-0.153)|(0.458)|
|Number of years of schooling of head|0.086***|-0.011|-0.059**|
||(3.777)|(-0.445)|(-2.305)|
|Number of years of schooling of head squared|<br>-0.011***|<br>0.008***|<br>-0.000|
||(-3.928)|(2.817)|(-0.129)|
|Number of children (0-14)|0.024|-0.004|0.089***|
||(1.388)|(-0.224)|(4.792)|
|Number of female adults|-0.010|0.102***|0.280***|
||(-0.321)|(2.944)|(7.873)|
|Number of male adults|0.051|0.265***|0.138***|
||(1.548)|(8.332)|(3.646)|
|Head was born in the village of residence|-0.124*|0.041|0.074|
||(-1.749)|(0.549)|(1.041)|
|Head was displaced|0.107*|-0.032|0.059|
||(1.808)|(-0.529)|(0.995)|
|Log total land owned in hectares|0.097***|-0.039*|-0.209***|
||(4.020)|(-1.737)|(-9.289)|
|Sale value of household assets in USD1000|0.529**|1.169***|-10.199***|
||(2.264)|(4.899)|(-7.152)|
|Sale value of livestock in USD1000|<br>0.003|<br>-0.298***|<br>-0.920***|
||(0.030)|(-2.706)|(-5.655)|
|Remittances and other transfer income in USD1000|-0.075|-0.307**|0.173|
||(-0.831)|(-2.450)|(1.363)|
|Total amount borrowed in USD1000|0.956**|0.257|-1.154**|
||(2.437)|(1.036)|(-2.156)|
|Constrained in the semi-formal credit market|-0.220***|-0.039|0.141**|
||(-3.828)|(-0.603)|(2.321)|
|Time to the nearest market in hours|-0.122**|-0.040|0.074|
||(-2.470)|(-0.827)|(1.244)|
|Time to the nearest main road in hours|-0.034|-0.053|0.052|
||(-0.694)|(-1.200)|(0.991)|
|Constant|-0.066|-1.244***|-0.539*|
||(-0.234)|(-4.127)|(-1.806)|
|Log pseudolikelihood||-4,560.40||
|Wald chi2 test||1,921.435***||
|Error term correlation between columns 1 and 2,_ρ_21||-0.103||
|Error term correlation between columns 1 and 3,_ρ_31||-0.203||
|Error term correlation between columns 2 and 3,_ρ_32||-0.295||
|Likelihood ratio test of_ρ_21=_ρ_31=_ρ_32= 0: chi2(3)||125.534***||
|Number of observations||3,235||

**Note:** District dummies included, but not reported. Robust t-stats adjusted for clustering at the village level in parenthesis: * significant at 10%; ** at 5%; *** at 1% ***.

24
variable for whether the individual reports saving at least some fraction of their income during the past six months, and (iii) a dummy variable indicating whether the respondent said they save more each month than they did a year ago. We construct a savings outcomes index by taking the average of these three dummy variables. All three savings outcomes are higher in the treatment group than in the control group at follow-up, although the differences are not statistically significant for the individual variables. We do, however, find a positive and significant impact of the course on the savings outcome index. Control group individuals reach on average 65 percent of all three savings outcomes, increasing to 68 percent in the treatment group.

Recall that we provided monetary incentives of 500 or 1,000 pesos ($36 or $72) to some randomly chosen treatment group individuals in order to encourage them to attend the financial literacy course. These amounts are equivalent to 5.5 and 11 percent of median monthly income in our sample. To check whether the positive effect on savings outcomes is driven by the monetary payments instead of the course itself, we add a dummy variable to estimation questions (1) and (2), indicating whether the individual received an incentive payment for course participation. Panel C of table 5 replicates the results in panel B, controlling for the monetary incentive dummy variable. The estimated impact of the course on savings outcomes is slightly smaller, but remains statistically significant.

The course included material on retirement savings and pension funds. Table 6 shows the impact of the course on retirement savings behavior and outcomes. In the control group, 57 percent of individuals have a pension fund and the course did not change this percentage in the treatment group. Pension funds are typically provided through an individual’s employer, implying that we did not necessarily expect to find an impact of the course on this outcome since acquiring a pension fund may involve switching jobs. The other variables reported in this table are based on follow-up survey questions that were only answered by individuals who have a pension fund. In order to account for possible selection bias, we fill the variables in with “0” for individuals who do not have a pension fund, so that they are defined for the complete sample, except for individuals who did not know whether they have a pension fund or not.

In terms of retirement savings behaviors, we measure whether (i) individuals choose a pension fund administrator based on fees or returns, as opposed to using the default option, relying on friends’ advice etc., (ii) they check their pension fund statement regularly and, (iii) they have calculated how much money they will need upon retirement. We construct an index of retirement saving behavior that is the average of the three behavior dummy variables. As the retirement savings outcome, we examine whether individuals report saving money for 23
# **Figure 6. Financial Performance Trend of Tamil Nadu Electricity Board**

<!-- Start of picture text --> 1000<br>0<br>1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04<br>(1000)<br>(2000)<br>(3000)<br>in Rs crores<br><!-- End of picture text --> <!-- Start of picture text --> EBIDTA Profit before subsidy<br><!-- End of picture text --> Source: Government of Tamil Nadu.

# **Figure 7. Average Annual Growth in Budget Components, 1998/99 to 2002/03**

<!-- Start of picture text --> 20% 19%<br>18%<br>18%<br>16%<br>14%<br>12% 11% 11%<br>10% 10% 10%<br>10%<br>8% 7% 7%<br>6%<br>4%<br>2%<br>0%<br>Percent<br>Tax<br>Revenue Receipts Central Transfers Own Tax Revenues Own Non Revenue Revenue Expenditure Salaries (incl GiA) Pension & Retirement Benefits Interest Expenditure Subsidies<br><!-- End of picture text --> _Source_ : Government of Tamil Nadu budget documents Note: The salary and pension growth rates of 19 percent and 26 percent still underestimate growth in real liabilities owing to a postponement of salary and pension payments in 2000/01 and 2001/02.

12
any issuance activity between 2003 and 2011. In the SDC-Orbis data, 384 firms are issuers and 1,078 are non-issuing firms.<sup>3</sup> Our analysis focuses on firm size and growth, measured by the level and growth rate of total assets, turnover, and the number of employees. Firm assets and turnover are measured in constant 2011 U.S. dollars, using the U.S. consumer price index (CPI) to discount nominal values. The analysis also examines firm profitability, and other financial indicators such as return on assets (ROA), leverage (including bank and other types of financing), and the maturity profile of liabilities.

# **3. Capital Raising Activity in the Arab World**

The available literature has documented considerable progress in the development of the financial sector in the Arab world. Nonetheless, it is essential to analyze the extent to which such developments have translated into an increased use of capital financing by the private sector. The data used here focus on capital raising activity at the transaction level of equity, bonds, and syndicated loans.

The issuance data show that there has been a sizable expansion in equity and debt markets in the Arab region since the early 1990s. The total amounts raised in equity, bond, and syndicated loan markets, relative to the region’s GDP, have grown by factors of 8, 6, and 2, respectively, between 1991-1998 and 2007-2014 (Figure 1). In comparison to international standards, while equity and syndicated loan markets seem to perform relatively well, bond market activity still lags behind. For example, the total value of equity issuances by firms in the Arab world stood at 0.8 percent of GDP in 2007-2014, compared to 0.7 percent in Eastern Europe and Central Asia (ECA), 1.0 percent in Latin America and the Caribbean (LAC), 1.6 percent in Asia, and 1.4-1.8 percent in developed countries. On the other hand, at 1.2 percent of GDP, bond market issuances remained low compared to approximately 1.6 percent in ECA, 2.0 percent in LAC, 2.6 percent in Asia, and 5.8-7.7 percent in

> 3 Appendix Table 3 reports the number of issuing and non-issuing firm per Arab country.

8
Inland capture fisheries accounted for 6.8 percent of total fisheries production in 2001. Predominantly, inland capture fisheries are found in developing countries; China accounted for 25.4 percent of production in 2000, whilst other developing countries collectively accounted for 67.4 percent<sup>3</sup> . These included India, Bangladesh, Uganda and Indonesia (Figure 1). Kenya accounted for 2.4 percent of global inland capture fishery production in 2000.

# **Figure 1. Major inland capture fisheries producers, 2000:**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>Tonnes ('000)<br>China India Bangladesh Uganda Indonesia Russia Tanzania Egypt Cambodia Kenya<br><!-- End of picture text --> _Source: FAO (2002)._ Global demand for fish and fishery products has increased rapidly over the last 50 years from around 39 million tonnes (live weight equivalent) in 1961 to approaching 130 million tonnes in 2000 (FAO, 2002). Human consumption of fish increased from 27 million tonnes (live weight equivalent) in 1961 to 95.5 million tonnes in 1999<sup>4</sup> . Over this period, the average annual growth rate in consumption (excluding China) was 2.4 percent, whilst population only increased at 1.8 percent per annum, suggesting a significant increase in per capita consumption.

Global trade in fish and fishery products was US$55.2 billion in 2000, having increased by 8 percent since 1998. This increase was largely due to a rise in the volume of commodities traded, since the price of the major products actually declined (FAO, 2002). Major exporters include Thailand (US$4.4 billion) and China (US$3.7 billion). Collectively, developing countries account for around 50 percent of world exports.

Industrialized countries account for around 80 percent of global imports of fish and fishery products (Figure 2). Thus, the predominant trade flows are from developing countries to industrialised countries and between industrialised countries. However, while developing country exports increased at an average rate of 8.1 percent per annum through the 1990s,

> 3 Actual production in developing countries is probably greater than these recorded data due to problems with the accurately monitoring levels of capture at numerous and scattered points of landing.

> 4 The balance is accounted for by the use of fish in production of animal feed.

3
discrimination in educational attainment (Kingdon, 1998; Drez and Kingdon, 2001; Zimmerman, 2011, to name a few). One plausible explanation for the recent drop in FLFP, therefore, is that with the recent expansion of secondary education (due to sustained efforts by the central and state governments), as well as rapidly changing social norms in India, more working age young females (15-24 years) are opting to continue their education rather than join the labor force early.<sup>3</sup> If this argument is valid, it implies that the reduction in FLFP should at least be compensated by an increase in educational enrollment for young women. In other words, the sum of the ‘percentage loss’ of working age females from the labor force and the ‘percentage gain’ in schools should at least be the same or rising over the same period. We call this ‘ _sum’_ the combined participation rate (CPR). To calculate the combined participation rates, we add up the labor force participation rates and educational participation rates as provided in Table 8.<sup>4</sup>

**Table 8: Female Labor Force Participation and School Attendance**

||FLFP<br>(|Rate (%)<br>1)|% Young Females (15-24) in<br>Schools (2)|Combined Particip<br>(CPR) (1|ation Rate (%)<br>+2)|
|---|---|---|---|---|---|
||Rural<br>(R1)|Urban<br>(U1)|Rural<br>(R2)<br>Urban<br>(U2)|Rural<br>(R1+R2)|Urban<br>(U1+U2)|
|Panel I: Young Cohort (15-24)||||||
|1993-94|42.0|18.3|10.7<br>31.0|52.7|49.2|
|2004-05|38.2|19.7|17.8<br>35.2|56.1|54.9|
|2011-12|22.7|14.5|32.8<br>45.7|55.5|60.2|
|Panel II: All (15 & above)<br>1993-94|49.0|23.7|3.3<br>9.7|52.3|33.4|
|2004-05|49.4|24.4|4.9<br>9.9|54.3|34.3|
|2011-12|35.8|20.5|8.5<br>12.0|44.4|32.6|

Source: Authors’ own estimate based on NSSO data.

> 3 Several recent studies have provided evidence for this hypothesis. Rangarajan et al. (2011) estimated that 44 percent of the share of women who withdrew from the labor force during 2004-05 to 2009-10 were those who chose to engage in education. Neff et al. (2012) also argue that the decline in FLFP in rural areas during the period of 2004-05 to 200910 is partly due to the effect of education, wherein nearly a quarter of the working age group, consisting of young females (15 to 24 years) have moved into higher education. Kannan and Raveendran (2012) estimated that within the total drop in FLFP during the same period, about 27 percent of this drop may be accounted for by additional enrollment in education. Abraham (2013) also finds that FLFP among women in the age group of 16 to 25 years had declined during 2004-05 to 2009-10, mainly due to rising educational attendance. Similarly, Thomas (2012) observed that a rise in educational enrollment was the primary reason for the decline in FLFP among the younger age groups during 2004-05 to 2009-10.

> 4 We calculate combined participation rates for two sets of age groups: 15 to 24, and 15 years of age and above.

13
8 pipelines. These customers have benefited a great deal from the deregulated wholesale gas market.<sup>2</sup> Between 1988 and 1994 the average real price paid by industrial consumers and electric utilities decreased by 15 percent and 19 percent (table 1). Small end users (commercial and residential users) remain captive to local distribution companies because their annual consumption is below the eligibility threshold for open access.<sup>3</sup> These end users saw a decline of only 3 percent in the real average price they paid for natural gas deliveries between 1988 and 1994.

Table 1 Average Natural Gas Prices and Price Changes, 1988 and 1994 (1994 dollars per thousand cubic feet)

|Category|1988|1994|Percentage change|
|---|---|---|---|
|Wellhead|2.05|1.83|-11|
|City gate|3.54|3.08|-13|
|End use||||
|Residential|6.64|6.41|-3|
|Commercial|5.62|5.43|-3|
|On-system industriala|3.58|3.05|-15|
|Electric utility|2.83|2.28|-19|

a. On-system sales are sales of natural gas to the end users by a local distribution utility. _Source:_ U.S. Department of Energy 1995b.

# Natural Gas Market

In the now competitive wholesale gas market trading takes place through bilateral decentralized transactions among producers, marketers, local distribution companies, and large end users. Trading has become concentrated in spot markets organized by a number of market centers in producing regions and consumer areas. These spot markets generate efficient price signals about the market value of natural gas, instantly reacting to actual and expected changes in supply and demand.

Deregulation of the gas industry has facilitated the separation of physical and financial trading. Gas market participants minimize supply risks by balancing their demand with gas supply contracts in the short and long term. They minimize price risk by taking financial positions on their gas supply contract portfolio. As a result, two distinct markets have developed in the wholesale natural gas market in the United States: a physical gas market, where contracts for physical natural gas delivery are traded, and a financial gas market, where contracts for price risk management are traded.

> **2** Electric utilities and industrial customers purchased 73 percent and 76 percent of total gas deliveries in the wholesale market in 1995. They use local distribution companies primarily as transporters of natural gas from an upstream gas marke **t** o their consumption site. 3 Commercial consumers purchased about 23 percent of total consumption in the wholesale market, while residential customers purchased almost exclusively from local distribution companies in 1995.
- 12 -

# m. CONSTRAINTS TO FEMALE PARTICIPATION AND INTERVENTIONS

## A. Identified Constraints in the Bank Projects

The specific interventions introduced need to be assessed against the backdrop of the specific constraints to women's participation in particular countries. The important questions are: What factors inhibit gender parity at the tertiary level? Which interventions (if any) have been introduced to overcome these identified constraints? Which are the successful interventions? Are there regional variations in constraints and interventions?

The constraints on female participation in higher education vary across regions and even within countries; however, certain patterns in constraints affecting women's participation in postsecondary education exist among most regions. One obvious initial constraint shared by several countries is the small pool of students completing secondary school. It is very difficult to increase women's access to higher education without raising female enrollments at the secondary level. Even if a pool of qualified female students exists at the secondary level, a number of other constraints inhibit women's participation in higher education.

1) Societal Constraints: In some of the Bank projects social constraints have been relatively well identified (see Appendix Table 1). For example, recent projects in India identified the lack of single-sex higher education institutions, inadequate hostel facilities for women and the lack of female teachers as major obstacles inhibiting women's participation in tertiary education. Suitable interventions to overcome the identified constraints have been proposed in the projects, including provision for single-sex institutions and dorm facilities for girls and provisions for increasing female teachers. Preliminary results indicate that these gender-specific interventions may have been effective, for currently these institutions are experiencing excess demand by female students.

However, few projects identified the social constraints to women's enrollment. One such constraint is parental attitudes towards educating their daughters. Only four out of 49 projects