rafmacalaba/datause-encoder-data huggingface dataset:
behavioral patterns in terms of promotion rates and other indicators, and number of workers and initial unemployment rates by skill level (i.e., educational achievement). The elasticities include those in production, trade, consumption, and in the different MDG functions. The application of MAMS to Yemen covers MDGs 2 (primary education), 4 (under-five mortality) and 7 (water and sanitation access).<sup>9</sup> The elasticities for the MDG functions are informed by Sánchez and Sbrana (2009) and Sbrana (2009) for education and water and sanitation, respectively. However, rather than using the exact point estimates from the econometric partial equilibrium analysis, we use the relative importance of the determinants in choosing the (general equilibrium) elasticities. In addition, the MAMS elasticities were adjusted in order to generate plausible trends under baseline conditions—and this procedure was, in fact, entirely used to define plausible elasticity values for MDG 4 in view of a lack of empirical studies and data to better inform the definition of these elasticities. Reflecting these adjustments, Table 3.3 shows the determinants in the MAMS functions that define MDG outcomes and the corresponding elasticities used in the model.<sup>10</sup> Table 3.3: Elasticities for the determinants of MDGs

|MDG and student behaviour<br>indicator|Per student<br>or per capita<br>service<br>delivery|Per-capita<br>household<br>consumption|Wage<br>premium|Public infra-<br>structure|Other<br>MDGs(1)|
|---|---|---|---|---|---|
|Basic education(grades 1-6)||||||
|Firstgrade net intake rate|1.563|0.195|0.004|0.781|-0.031|
|Promotion rate|0.466|0.039|0.001|0.155|-0.004|
|Continuation rate(2)|0.733|0.105|0.001|0.105|-0.02|
|Under-five mortalityrate|-0.865|-0.087||-0.087|-0.084|
|Access to safe water|0.261|0.01||0.01|-0.084|
|Access to basic sanitation|1.201|0.12||0.12|-0.105|

(1) Refers to MDG 4 for education and MDG 7w and 7s for health.

(2) To the post-base cycle (grades 7-9) among students who were promoted from grade 6. Source: Authors' estimates based on Sánchez and Sbrana (2009) and Sbrana (2009).

> 9 Data were insufficient to include MDG 5 (maternal mortality).

> 10 Sensitivity analysis shows that the overall qualitative results do not change as a result of changes in elasticities within plausible ranges.

18
<!-- Start of picture text --> . Bangladesh: Employment<br>3<br>s<br>x<br>5=<br>26 Urban<br>5<br>s<br>37<br>a P<br>£ Higher education<br>or<br>6<br>2<br>[s)<br>£<br>ey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Employment<br>N<br>oI<br>% Higher education<br>zo<br>5<br>c<br>g<br>2<br>3°<br>= Urban<br>oo5o<br>a<br>aa]<br>=<br>ue<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Employment<br>g<br>§<br>n<br>x<br>2on ; ;<br>io]c Higher education<br>2<br>2<br>a :<br>& Urban<br>cy<br>ao<br>2<br>o<br>£<br>toy<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Employment<br>8<br>“ nN<br>:<br>x}<br>5" . .<br>s Higher education<br>oO<br>3<br>So<br>Q<br>£ Urban<br>4<br>[s)<br>©<br>Mey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> 50 DSSI<br>St81j<br>& t<br>3 ;<br>3) 40 t<br>51<br>=<br>330<br>&<br>2 '<br>8<br>Ge5°20 1'<br>2<br>E10<br>Z<br>0 L<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= Debt restructurings with private creditors<br>= Debt restructurings with Paris Club<br>@ Debt restructurings with Chinese creditors<br><!-- End of picture text -->
**Figure 3. Average law and order and investment profile indexes, 2010-13.**

<!-- Start of picture text --> 5.5<br>5 Latvia Czech republic<br>4.5 Moldova Slovenia Croatia Poland<br>Lithuania<br>4 Ukraine Hungary Estonia<br>Belarus Romania Slovakia<br>3.5 Turkey<br>3<br>6 7 8 9 10 11<br>Investment profile<br>Law and order<br><!-- End of picture text --> Source: authors' elaboration from UNCTAD, ICRG It is also worth mentioning the _investment profile_ variable included in the analysis, which reflects contract viability (expropriation risk), profit repatriation policies, and likelihood of payment delays. The coefficient for this variable is only significant when macroeconomic variables are not included in the analysis (see specification 6), probably because strong institutions tend to come hand in hand with better macroeconomic management. Interestingly, in Latin America and the Caribbean, over the period 1991-2010, the investment profile was the most significant variable for foreign direct investment attraction, while law and order was not (see Sánchez-Martín et al., 2014). Countries in which there had been notable cases of expropriation during the past decade (República Bolivariana de Venezuela, Argentina, Bolivia) saw their FDI inflows sharply decline. As reflected in section 3, the fact that Turkey at the moment not only seems to lag behind in terms of rule of law, but also in terms of protection of investors, suggested that it may just be the other side of the same coin. Thus, strengthening the investor protection framework could potentially help reap some FDI gains, although in this case the empirical evidence is mixed.

Finally, as evidenced by the firm level analysis, the existence of highly significant and positive knowledge spillovers in Turkey calls for policies aimed both at FDI attraction and also at facilitating 26
> <sup>34</sup>

# **Table 6b:  Two stage least squares estimates of Table 1a**

**First stage regressions**

||Continuo|us years of comp<br>(1975)|etitive elections|Average|Newspaper Circ<br>2000)|ulation (1975-|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_) , with<br>GDP/capita|Level 2000<br>(_IV_), no<br>GDP/capita|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_) , with<br>GDP/capita|Level 2000<br>(_IV_), no<br>GDP/capita|
|British legal origin|3.6<br>(_.60_)|5.65<br>(_.07_)|5.03<br>(_.40_)|-4.39<br>(_.92_)|52.47<br>(_.39_)|41.90<br>(_.46_)|
|Scandinavian legal|24.2|22.56|33.58|331.91|292.44|336.92|
|origin|(_.006_)|(_.002_)|(_0.00_)|(_0.00_)|(_0.00_)|(_0.00_)|
|German legal|-1.23|.33|10.81|216.77|211.61|264.96|
|origin|(_.91_)|(_.97_)|(_.24_)|(_0.00_)|(_.001_)|(_0.00_)|
|British colonial|-5.51|5.27|1.47|-10.73|-45.24|-59.2|
|heritage|(_.54_)|(_.17_)|(_.22_)|(_.83_)|(_.48_)|(_.33_)|
|French colonial|-16.55|-4.52|-8.39|-103.91|-42.21|-122.61|
|heritage|(_.055_)|(_.33_)|(_.07_)|(_.002_)|(_.06_)|(_0.00_)|
|Spanish colonial|-14.73|-4.52|-7.0|-33.68|-44.85|-57.30|
|heritage|(_.09_)|(_.33_)|(_.14_)|(_.33_)|(_.13_)|(_.08_)|
|_F_-statistic on|1.88|3.9|1.91|8.66|1.43|14.01|
|instruments|||||||
|**Second stage regre**|**ssions**||||||
||Continuo|us years of comp<br>(1975)|etitive elections|Average|Newspaper Circ<br>2000)|ulation (1975-|
|Political variable|.003|.02|.04|.0006|.003|.003|
||(_0.05_)|(_.20_)|(_0.016_)|(_0.00_)|(_.20_)|(_0.00_)|
|British legal origin|-.009|.05|-.16|-.003|-.05|.099|
||(_.68_)|(_.79_)|(_.51_)|(_.84_)|(._69_)|(_.15_)|
|Scandinavian legal|-.09|-.41|-1.07|-.21|-.98|-.70|
|origin|(_.11_)|(_.32_)|(_.11_)|(_.002_)|(_.26_)|(_.05_)|
|German legal|.008|.58|.32|.12|.19|-.03|
|<br>origin|(_.79_)|(_.003_)|(_.41_)|(_.03_)|(_.79_)|(_.94_)|
|Hansen_J_-statistic<br>(Chi-squared_p_-<br>value)|.07|.02|.17|.83|.41|.55|
|_N_|66|71|79|78|92|104|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Instruments for the fourth and eighth regressions are three dummy variables indicating whether a country has British, French or Spanish colonial origins. Specifications are as in Table 2, with the addition of the three legal origin variables. Constants and other conditioning coefficients not reported.
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _10_ covariates have been improved by the previous modeling step. This process can be repeated iteratively until all elements have been updated several times and new updates do not improve subsequent predictions any further. For each food item _d_ ˆ _∈_ 1 _, . . . , D_ , in this example _D_ = 3, this involves a chain of regression functions _fd,i_ , where _i ∈_ 1 _, . . . , I_ indexes the updating iteration. The iterative sequence of regressions involved in updating the imputations is referred to as a regression chain because the predictions made by the regression fitted in the previous step, feed into the inputs of the next regression model.

Depending on several factors, such as the values at which the missing data values in the first regression equation are initialized, the order in which the algorithm cycles through the data, random elements of modeling (for example, taking bootstrap samples or using stochastic methods to generate synthetic training data), the result after _I_ iterations will each time be different. Hence, the process is repeated _M_ times, thus simulating a function space indexed by _f_<sup>ˆ</sup> _d,i,m_ . Because the properties of simulated values for missing data change throughout the chain, the chained equations method allows to find various prediction models beyond what could be estimated with observed data alone. More details are provided in the appendix B.B1.

An ensemble predictor is finally constructed by averaging the _M_ prediction results from the regression model at the tail of the simulation chain _f_<sup>ˆ</sup> _d,I,m_ . In particular, after generating _M_ imputations, the final imputation for a price element _x_ ˆ _t ∈_ ( _A, B, C_ ) is generated by calculating the ensemble average Since there are stochastic elements to the iterative algorithm, each prediction at iteration _I_ will be generated from a different prediction model. Increasing _M_ improves both the stability of the stochastic result as well as the accuracy of the ensemble prediction. This is similar to bootstrap aggregating, a prediction improvement technique central to the Random Forest algorithm of Breiman (2001), in which multiple random simple base learners are combined to improve stability and accuracy by canceling out random prediction errors. The key difference is that bootstrap aggregation produces multiple learners by taking random draws of the training data, while the randomization in learners at iteration _I_ result from different stochastic simulations of chained model predictions, see section B.B2 for more details on the derivation of the stochastic ensemble predictor.

# _C. The regression specification_

The methods are applied to each country individually, so the desired flexibility of the prediction models varies. Moreover, the properties of the data change throughout the simulation, thus the desired flexibility of the model should change
We next gather macroeconomic and other country information, including variables related to macroeconomic policies and country-level indicators related to their economic and financial development. The country variables used are mainly from the World Bank’s World Development Indicators, except for the indexes on financial liberalization and institutional environment (see Appendix Table 1 for details). Additionally, we collect information from Worldscope on firm characteristics, including information on balance sheet and income statements, for all listed firms in the local markets.

# **3. Methodology**

To investigate the macroeconomic and microeconomic factors related to the use of international equity markets, we start from earlier work at the country level (Claessens et al., 2006), where we show that a number of country fundamentals matter for the aggregate international stock market activity. We employ these country fundamentals here as well. Specifically we use: GDP in U.S. dollars, GDP per capita, inflation, fiscal surplus, and trade openness (exports and imports relative to GDP). For the institutional environment, we use indexes on the country’s degree of financial openness, law and order, and degree of investor protection.<sup>10</sup> For the microeconomic factors, we work with a standard set of firm characteristics used in the literatures on internationalization and going public decisions. The ones we employ can be thought to fall under four headings: size, growth, performance, and international activity. As proxy for firm size, we use total assets (we also used number of employees and total sales revenue, which led to similar results). As growth characteristics we employ growth in total sales revenue. For performance we use return on assets. As a proxy for firm’s international real activity we employ the share of foreign sales over total sales. Finally, we also use the sector in which the firm is mainly active.<sup>11</sup>

> 10 We also used some other country level variables, including the development of the local financial markets and the degree of cross-border equity flows. Including these variables does not change the qualitative results for the country variables we do include and report here. At the same time, these variables may be endogenous to internationalization, so there are reasons for not using them.

> 11 We also employed a number of other variables, such as the growth in the number of employees, assets per employee, research per employee, research over revenue, property, plant, and equipment growth, price-

8
# **6. Effects on son and fertility preferences of couples by housing ownership status**

A last question to consider is whether groups that do not have substantial assets or wealth exhibit the differential effects by religion, sibling composition and time that have been associated with the change in Islamic inheritance law. If son preference among Muslims is truly influenced by the Islamic inheritance exclusion rule, the effects observed among couples who have little or no estate should be small or insignificant.

In Indonesia, housing, land and durable goods are the most important assets in an individual’s estate (Cammack 1999b). The 2007 DHS collects information on the ownership status of the dwelling unit and on the possession of durable goods, but not on land ownership or income. Land ownership can only be imperfectly inferred for men and women who worked in agriculture during the 12 months preceding the survey (25 and 39 percent of the sample, respectively). Only they were asked whether they worked on their own or someone else’s land. Wealth quintiles are included in the dataset and are reported for all observations. However, they are constructed based on an index that aggregates household possessions, dwelling characteristics, source of drinking water and other measures. The elements of the index are possibly endogenous to the sex composition and size of the family.<sup>15</sup> In order to maximize sample size and minimize potential endogeneity problems, I use the indicator of housing ownership in the analysis. Virtually all couples provide information on the tenancy status of their dwelling (99.6 percent of the sample). Although this indicator does not represent the economic conditions that prevailed when fertility decisions were made, it is a reasonable proxy. I compare the effects of the inheritance exclusion rule between couples who own and do not own housing. I follow the same specification described for the couple-level analysis of son and fertility preferences and fertility outcomes.

> 15 Some durable goods, for instance tools or vehicles, may be a function of the total number of sons and may be used for production. Moreover, children may contribute to household wealth.

24
- 35 -

gender interventions were introduced and for which PCRs were available had failed to adequately provide measurements to assess the impact of the project on female enrollment rates.

# B. Approaches in the **Ongoing** Projects for Which Results are not Available

Since the mid-1980s the Bank has paid greater attention to gender issues in higher education projects. Increasing numbers of projects acknowledged gender issues in higher education and a significant number of them introduced interventions to overcome the barriers affecting women's participation in tertiary education. Many of these projects are recent and the outcomes are not yet available. However, the approaches currently being followed deserve to be discussed.

_Increase the_ **_Qaliy_** _of Education at the Secondary Education Level:_ It is logical to think that the quality and kind of education received at the secondary level will affect postsecondary educational enrollment. In many developing countries, the irrelevancy and poor quality of educational materials affect girls' enrollment at the primary and secondary levels (Herz, et al. 1991). Therefore, in some cases where quality of basic education is particularly low for girls, improvement of basic educational opportunities (i.e. by increasing opportunities in math and science classes), may ultimately increase women's access to higher education. In fact, many projects addressed the low quality of female education at the lower levels. In Mali the Government would revise curriculum to emphasize the basics. This would overcome parents' initial reluctance, and is expected to produce more literate and numerate graduates who will be more productive and better able to adapt to the demands of an evolving labor market or continuing into the higher levels.

A project in China, for example, is focusing on increasing the quality of basic education in order to increase women's access to further educational opportunities. Introduced provisions include: (a) publicity and heightened public awareness of the importance to society of education girls; (b) strengthening measures to make education compulsory; (c) improving conditions for rural teachers to retain teachers parents trust to educate their daughters; and (d) targeting provisions of cash to poor families that need to cover school-related expenditures. The project expects a more effective delivery of educational services will result in higher school attendance,
<!-- Start of picture text -->
|<br>World 24<br>14 19 |<br>23<br>12<br>AFR 7 |<br>“<br>EAP 32<br>27<br>*e<br>ECA 16 19 |<br>27<br>LAC 22<br>MNA World Aggregate = 22%<br>SAR<br>0 5 10 15 20 25 30 35 40<br>Percentage of firms led by women in the region by size of firm<br>Small (5-19 employees) NNN, Medium (20-99 employees)<br>ME Large (>=100 employees)<br><!-- End of picture text -->
- Barth, E. and Roed, M. 1999. "The Return to Human Capital in Norway," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Becker, G.S. 1964. _Human Capital: A Theoretical and Empirical Analysis._ New York: National Bureau of Economic Research.

- Becker, G.S. and B.R. Chiswick. 1966. "Education and the Distribution of Earnings." _American Economic Review 56:_ 358-369.

- Bedi, A. 1997. "The Importance of School Quality as Determinant of Earnings in a Developing Country: Evidence from Honduras." _International Journal of Educational Development_ 17(4): 427-437.

- Behrman, J.R. and M.R. Rosenzweig. 1999. "'Ability' Bias in Schooling Retums and Twins: A Test and New Estimates." _Economics of Education Review_ 18(2): 159-167.

- Belli, P. and M.A. Ayadi. 1998. "Returns to Investment in Education: The Case of Nicaragua." World Bank (mimeo).

- Bennell, P. 1996. "Rates of Return to Education: Does the Conventional Pattem Prevail in subSaharan Africa?" _World Development_ 24(1): 183-199.

- Bevc, M. 1993. "Rates of Return to Investment in Education in Former Yugoslavia in the 1970s and 1980s by Region." _Economics of Education Review_ 12(4): 325-43.

- Brunello, G., S. Comi, and C. Lucifora. 1999. "Returns to Education in Italy: A Review of the Applied Literature." in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Card, D. 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems." _Econometrica_ 69(5): 1127-1160.

- Chin, A. 2001. "The Returns to School Quality When School Quality is Very Low: Evidence from Operation Blackboard in India." (mimeo).

- Christensen, J.J. and N. Westergard-Nielsen. 1999. "Wages and Human Capital: The Danish Evidence," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe,_ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Cohen, B. and W.J. House. 1994. "Education, Experience and Earnings in the Labor Market of a Developing Economy: The Case of Urban Khartoum." _World Development_ 22(10): 1549-1565.

6
_Table 2: Impact on Technology Take-up_

||Tried new tec<br>by the D|h introduced<br>A (yes=1)|Live|stock|Tried new tec<br>Cr|h on: (yes=1)<br>op|Farming|Method|
|---|---|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|Men|Women|
|round 3|0.090***|0.088***|0.058*|0.098***|0.100***|0.106***|0.086***|0.082***|
||(0.029)|(0.032)|(0.030)|(0.034)|(0.028)|(0.031)|(0.025)|(0.027)|
|FIF*round 2|-0.027|-0.031|-0.042*|-0.019|-0.018|-0.002|-0.024|-0.003|
||(0.030)|(0.034)|(0.022)|(0.025)|(0.030)|(0.031)|(0.025)|(0.024)|
|FIF*round 3|0.055*|0.044*|0.069**|0.056*|0.037|0.056**|0.021|0.048**|
||(0.032)|(0.025)|(0.029)|(0.028)|(0.030)|(0.024)|(0.021)|(0.022)|
|WIF*round2|-0.032|0.032|-0.041|0.028|-0.061|0.010|-0.073|0.026|
||(0.060)|(0.062)|(0.044)|(0.042)|(0.054)|(0.051)|(0.044)|(0.058)|
|WIF*round3|-0.049|-0.044|-0.026|-0.022|-0.052|-0.060|-0.123***|-0.032|
||(0.058)|(0.037)|(0.042)|(0.040)|(0.052)|(0.040)|(0.044)|(0.038)|
|WIF*FIF*round2|0.070|0.083|0.073|0.025|0.076|0.010|0.094*|0.009|
||(0.077)|(0.071)|(0.063)|(0.066)|(0.069)|(0.057)|(0.055)|(0.061)|
|WIF*FIF*round3|-0.006|0.075|0.100|0.085|0.016|0.046|0.105*|0.026|
||(0.090)|(0.069)|(0.079)|(0.073)|(0.079)|(0.063)|(0.056)|(0.063)|
|Observations|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.040|0.028|0.053|0.040|0.036|0.030|0.042|0.030|
|Estimates: FIF2 + WIF2 + FIF_WIF2|0.011|0.084|-0.009|0.034|-0.003|0.017|-0.002|0.032|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.849|0.205|0.851|0.430|0.947|0.762|0.963|0.555|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.000|0.075|0.143|0.12|0.002|0.042|0.004|0.042|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.997|0.208|0.043**|0.058*|0.980|0.447|0.947|0.439|
|Control Mean(BL)|0.363|0.211|0.196|0.077|0.230|0.164|0.245|0.100|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household and region. DA stands for Development Agent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

17