rafmacalaba/datause-encoder-data huggingface dataset:
the GDP per capita after the 1990s and on. Figure 5 showed that the productivity growth rate has been more or less constant during the sample period. Thus, current calibration method is a reasonable one regarding productivity growth. In sum, the under-prediction of the Pred ~~7~~ 0 using the steady-state cum status-quo approach calibration method seems to be mainly due to the assumptions of the constant rates of investment, working-age population, and labor force participation. Observing the ”Pred ~~8~~ 0” simulation, we get similar results, although the fitting performance improves over the ”Pred ~~7~~ 0” simulation. In contrast, the 1990 prediction, which uses the 1980s data, fits the data very closely during the 17-year period (1991-2007), and then the model over-predicts the GDP per capita after 2008 with increasing gap. The main reason behind the good fit for the 1991-2007 period is that there were no clear trends for the investment rate, though being subject to fluctuations, so that the capital-deepening effects are well captured during this period. However, the over-prediction of the ”Pred ~~9~~ 0” for the 2008-2014 period seems to be caused by various reasons: (i) the gradual slowdown of human capital accumulation, (ii) decreasing investment rate, particularly after 2005, (iii) the stagnation of working-age population share after 2000, (iv) the sudden stagnation of productivity after 2010, which can be confirmed by Table 1 and Figures 3 to 7. Comparing the above patterns of predictions across Pred ~~7~~ 0, Pred ~~8~~ 0, and Pred ~~9~~ 0, we learn that the prediction performance of the LTGM would be good when the economy grows in the stabilized environments, but the LTGM tends to under-predict when the parameters of investment rate, working-age population share, and labor force participation rate are actively changing. The LTGM may over-predict the growth when the economy is near the final phase of transitional growth (and/or subject to negative productivity shocks).

# **4.4 Calibration 2: Time-varying Parameter Embedded Simulation Approach**

Another way of using the LTGM is to evaluate the expected changes of income growth in response to the different parameters of growth. For this exercise, we categorize the six parameters of calibration of the LTGM in the following manner. The rates of productivity growth and human capital growth are the determinants of the steady-state growth fundamentals. Thus, we call these two growth rates as ”fundamental parameters.” The changes of the rest of the 25
states. In most cases, these lockdown measures were in force for about 5-8 weeks, and ‘restricted the movement of residents and led to the closure of business operations, and the closure of regional borders linking lockdown areas with the rest of the country.’ (Amare et al. (2021), pp. 6-7). As shown in Amare et al. (2021), in addition to the effects caused by the nationwide restrictions, the above-mentioned state-level lockdown measures also had an adverse impact on affected households’ well-being. Therefore, school-age children living in states affected by these additional measures are expected to show a higher risk of school drop-out. To test for this hypothesis, we followed Amare et al. (2021) by constructing an indicator for such augmented lockdown measures (labeled as _AugmLockdown_ in our specifications below), which takes value one if the person lives in the states reported above, and zero otherwise.

# **2.2.4 Measuring Climate Shocks: Drought and Flood**

Households’ livelihoods and decision to invest in children’s education be affected may by the concomitant occurrence of climate shocks. Given that Nigeria is partially located in the drought-prone western Sahel region, such events are quite regular. As documented by the reports of the International Federation of the Red Cross and Red Crescent Societies (IFRC),<sup>12</sup> some states in Nigeria were severely hit by floods in September 2019 and October 2020. If a drought or flood occurs simultaneously with COVID-19 outbreak and its related lockdown measures, such shocks may confound the true impact of temporary school closure due to COVID-19 on school attendance. Therefore, we control for drought We measure drought in 2019 (before the outbreak of COVID-19 pandemic and school closure) and 2020 (during COVID-19 and before school reopening) using the Standardised Precipitation-Evapotranspiration Index (SPEI), which captures the severity of drought according to its intensity and duration.<sup>13</sup> Since the lean period in Nigeria covers the period from April to August,<sup>14</sup> we calculate the average SPEI index between April and

> 12 `https://adore.ifrc.org/Download.aspx?FileId=261150` and `https://adore.ifrc.org/Download.aspx?FileId=352740`

> 13SPEI data are available at `https://spei.csic.es/`

> 14 `http://www.fao.org/giews/countrybrief/country.jsp?code=NGA`

15
# Multidimensional Connectivity: Benefits, Risks, and Policy Implications for Europe and Central Asia<sup>1</sup>

David Gould<sup>a</sup> , Dror Y. Kenett<sup>b</sup> , Georgi Panterov<sup>c</sup>

> a World Bank, Email: dgould@worldbank.org

> b World Bank, Email: dkenett@worldbank.org

> c World Bank, Email: gpanterov@worldbank.org

Keywords: Economic growth, Economic connectivity, Trade, Globalization, Europe and Central Asia, Multilayer networks JEL: C54, F43, G15, G28, O47

> 1 Views and opinions expressed are those of the authors only and do not necessarily represent the views of the World Bank or any institution with which they are affiliated. We thank Ginestra Bianconi, Angel Bogoev, Michael Danziger, Dobrina Gogova, Shlomo Havlin, Aaditya Mattoo, Alessandro Spelta, Ananthanarayan Sainarayan, Hans Timmer, Xin Yuan, Tlek Zeinullayev for their useful comments, feedback, and support with data processing and analysis.

1
80

# **Table A5 Dependent Variable: Growth Rate of Real GDP Regressions with Institutions**

|**Years : 1960 - 2000**|**Reg A5.1**|**Reg A5.2**|**Reg A5.3**|**Reg A5.4**|**Reg A5.5**|**Reg A5.6**|**Reg A5.7**|**Reg A5.8**|**Reg A5.9 **|**Reg A5.10**|**Reg A5.11**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2128*****|**0.2425*****|**0.2434*****|**0.2547*****|**0.2411*****|**0.2419*****|**0.2399*****|**0.2401*****|**0.2517*****|**0.2376*****|**0.2391*****|
|**Growth of Labor**|**0.5694*****|**0.3898*****|**0.4290*****|**0.3891*****|**0.3974*****|**0.4002*****|**0.4038*****|**0.4436*****|**0.4024*****|**0.4121*****|**0.4163*****|
|**Initial GDP (1960)**|**-3.8747*** **|**-3.9271*** **|**-3.7960*** **|**-3.7156*** **|**-4.0433*** **|**-3.9622*** **|**-4.0523*** **|**-3.9262*** **|**-3.8621*** **|**-4.1495*** **|**-4.1011*****|
|**Institutions**||**3.9258*****|**3.1138*****|**4.0278*****|**3.4075*****|**4.0090*****|**4.0586*****|**3.2284*****|**4.1619*****|**3.5735*****|**4.1571*****|
|**HC15**|**4.3613****|**2.4353**|**2.5856**|**2.3433**|**2.2624**|**2.3275**||||||
|**Sq of HC15**|**-1.0208****|**-0.4398**|**-0.5081**|**-0.4026**|**-0.4420**|**-0.4039**||||||
|**HC25**|||||||**2.2012**|**2.3613**|**2.0960**|**1.9131**|**2.0787**|
|**Sq of HC25**|||||||**-0.3826**|**-0.4542**|**-0.3392**|**-0.3571**|**-0.3398**|
|**Log (Educational**<br>**Quality)**|**0.2933**|**0.6307**|**0.7476***|**0.6395**|**0.6416**|**0.6423**|**0.6514**|**0.7640***|**0.6606**|**0.6744**|**0.6662**|
|**Log (Patents)**|**0.1909*****|**0.0645**|||**0.0739**|**0.0567**|**0.0618**|||**0.0703**|**0.0526**|
|**Log (Journals)**|||**0.1226*****|||||**0.1232****||||
|**Royalties per**<br>**capita**||||**0.000001***|||||**0.000002***|||
|**Phones**|**0.0033*****|**-0.0003**|**0.0001**|**-0.0002**|||**-0.0003**|**0.0001**|**-0.0003**|||
|**Computers**<br>**Internet Users**|||||**0.0014**|**-0.0014**||||**0.0012**|**-0.0016**|
|**Constant**|**-3.6660***|**-4.7680** **|**-5.4597*****|**-4.8398****|**-4.2867****|**-4.7880****|**-4.6085** **|**-5.2881*****|**-4.6688****|**-4.0900****|**-4.6414****|
|**R squared**|**0.6137**|**0.8113**|**0.8263**|**0.8111**|**0.8155**|**0.8118**|**0.8105**|**0.8261**|**0.8102**|**0.8111**|**0.8111**|
|**No. of Countries**<br>*, **, *** denotes significan<br>Robust standard errors ob|**85**<br>ce at the 90, 9<br>tained using W|**74**<br>5 and 99 perc<br>hite's correct|**74**<br>ent confidence<br>ion for heteros|**74**<br>level respecti<br>cedasticity.|**72**<br>vely|**74**|**73**|**73**|**73**|**73**|**73**|
Figure 11. Recessions and recoveries: duration, amplitude and cumulative loss <!-- Start of picture text --> Duration<br>(in quarters)<br>8<br>Recessions<br>Recoveries<br>6<br>**<br>4<br>2<br>0<br>Full Sample Advanced Economies Emerging Market Economies<br>Amplitude<br>(in percent)<br>8<br>Recessions<br>6<br>Recoveries<br>4 ***<br>2<br>0<br>-2<br>-4 ***<br>-6<br>Full Sample Advanced Economies Emerging Market Economies<br>Cumulative loss from recessions<br>(in percent)<br>0<br>-2<br>-4<br>***<br>-6<br>-8<br>-10<br>Full Sample Advanced Economies Emerging Market Economies<br>Source: Claessens et al (2012a).<br><!-- End of picture text --> Note: All the statistics except for those relating to duration correspond to sample medians. For duration, the means are shown. The duration of a recession is the number of quarters that have elapsed between the peak and the trough. The duration of a recovery is the time taken to attain the level of output reached at the previous peak. The amplitude of a recession is the decline in output from peak to trough. The amplitude of a recovery is the one-year change in output after the trough. The cumulative loss combines information about the duration and the amplitude to measure the overall cost of a recession. ***, ** and * imply significance at the 1%, 5% and 10% levels, respectively. Significance refers to the difference between advanced economies and EMEs.

71
detrended series: ~ T where yi;t = yi;t � t � T<sup><u>1</u></sup> Pk=1<sup>k</sup> is the detrended series for yi;t: � � Technical e¢ ciency is typically measured using technical e¢ ciency scores from a Stochastic Frontier Analysis (SFA). This is the approach we adopt. The SFA approach relies on a reduced-form relationship between inputs and outputs. The country with the highest health output after controlling for inputs is the most e¢ cient, and the e¢ ciency level of the other countries is measured with respect to the most e¢ cient country.<sup>21</sup> In particular, we compute technical e¢ ciency scores in the health sector using a parsimonious version of the model estimated in Greene (2005). The outcome of interest is life expectancy, and the input is health spending per capita. The model is: where � t’s are year ...xed e¤ects, vi;t � N (0; �v), and ui;t = jUi;tj � Exp(0; �u): The controls are population density and OECD membership.<sup>22</sup> The parameters are estimated by maximum likelihood. The estimates of vi;t � ui;t are translated into an estimate of ui;t using the standard Jondrow, Materov, Lovell, and Schmidt (1982) formula. Technical e¢ ciency then is simply: ^ where ui;t is the maximum likelihood estimate of ui;t: Table 4 presents the estimation results. The coe¢ cients follow the same pattern noticed in prior work using di¤erent data. The asymmetry parameter � = �u=�v is also within the range of variation reported previously. Estimated mean e¢ ciency is 86.48.

Following Baltagi, Demetriades, and Law (2009), who study the e¤ect of ...nancial openness on banking sector development, we introduce MTEF regional penetration as external

> 21The SFA was inspired by Farrell (1957), who de...ned technical e¢ ciency as the ability to produce the maximum possible output from a given set of inputs, and measured it as the di¤erence between maximum attainable output and observed output. Ine¢ ciencies might arise from waste or because the most coste¤ective set of programs is not implemented.

> 22Greene (2005) also includes education spending per capita, as an input, and controls for government voice and accountability, government e¤ectiveness, share of government ...nancing, the Gini coe¢ cient, and GDP per capita.

12
PERU: The IMR is derived from the 1977-78 National Fertility Survey and the 1986 Demographic and Health Survey. Life expectancy at birth estimates are from Coale-Demeny model life tables selected on the basis of the 1981 census.

**SAINT KITTS AND** NEVIS: The IMR and CDR are estimated from government sources (Statistics Office, Planning Unit). The IMR is obtained through linear interpolation. Life expectancy at birth is estimated from Coale-Demeny model life tables selected on the basis of the official CDR and the age-sex distribution.

**SAINT LUCIA:** The IMR estimate is based on interpolation of official vital registration data, as published in PVSR. Life expectancy at birth is estimated from Coale-Demeny model life tables, selected on the basis of official CDR and age-sex distribution of the population.

SURINAME: The IMR and life expectancy at birth estimates are estimated from Coale-Demeny model life tables, selected on the basis of annual vital rates and the age-sex distribution. TRINIDAD AND TOBAGO: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on the 1980 census and preliminary results of the 1987 Demographic and Health Survey. The current estimates are obtained with the U.N. medium mortality decline scenario.

URUGUAY: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N./CELADE based its estimates on data from the vital registration system through 1987 and on the 1985 census.

VENEZUELA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N./CELADE based its estimates on the 1977 World Fertility Survey and on adjusted vital registration data. Current estimates are obtained by projection with the U.N. medium mortality decline scenario.

VIRGIN! ISLANDS (U.S.): The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth estimates are derived from Coale-Demeny model life tables selected on the basis of the CDR and age-sex distribution.

# Northern America

CANADA: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth are official projections from _Population Projections for Canada 1984-2006._

**PUERTO RICO:** The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth is from _World Population Prospects 1990._ The U.N. based its estimates on the 1980 census and the official CDR. UNITED STATES OF AMERICA: The IMR is the average of vital registration estimates for 1985 to 1989 as published in _Monthly_ **_Vital_** _Statistics Report._ Life expectancy estimates are obtained from the U.S. Bureau of the Census.

16
Policy Research Working Paper 8429

# **Abstract**

Rising youth unemployment rates have been increasingly recognized as a serious challenge in developing and advanced economies, as the trend indicates a potential skills gap between the demands of the workforce and recent graduates. Effective dual education programs utilizing a combination of classroom instruction and practical skill training present an approach to developing a skilled workforce and meeting workforce demands. To evaluate the impact of the Philippine Dual Training System on labor market outcomes, this paper analyzes data from a recent survey tracking graduates from the Dual Training System and regular vocational training programs provided by technical vocational training institutes. The data analysis reveals that the Dual Training System has a significantly higher rate of return on labor market earnings compared with regular, classroom-only vocational training programs, particularly among high school graduates who did not perform well academically during basic education. The magnitude of the impact of the Dual Training System is also likely to increase in correlation with the intensity of the on-the-job component.

This paper is a product of the Education Global Practice and the Social Protection and Jobs Global Practice. 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://www.worldbank.org/ research. The authors may be contacted at tkoyama@worldbank.org or pacosta@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
areas. For these areas, either the prediction from the auxiliary data must be ignored, the sample must be ignored, or the area must be dropped from the estimation. Finally, the Fay-Herriot model does not account for uncertainty in the survey-based estimates of variance, which tends to underestimate the variance of the prediction.

Despite these limitations, the Fay-Herriot model is much simpler to apply and explain than a unit level model. It generally provides credible estimates and is a standard workhorse of small area estimation. We estimate a Fay-Herriot model by aggregating all indicators to the area level, weighting by population. The Fay-Herriot models are estimated using the Stata FHSAE command (Corral, et al, 2018). We utilize the “Chandra method”, which estimates the prediction model using GLS (Chandra et al, 2013).

# C. Household-level model

In contrast to area level models, unit level models are specified at the level of the individual unit, which in this case is the household.<sup>11</sup> Although the auxiliary data only varies at the sub-area level, predicting welfare at the household level fully utilizes the information in the sample survey on the level and variability of household welfare. In addition, unlike sub-area models, well-established software packages are available that utilize empirical best methods to estimate unit-level models with area-level mixed effects.

The EBP model can be written as follows:<sup>12</sup> (3) G(Y 1 sa 2 a 3 Xr a Where: ii ii ) = ββ XX + ββ XX + ββ + ηη + εε The EBP model can be written as follows:<sup>12</sup> Yi is the non-monetary welfare for household i, which in each country is the principal component score constructed using the loading factors in Table 1.

G(Yi) is a transformed version of welfare. Traditionally, ELL uses the log function to transform welfare, but any monotonic transformation can be used. As described in greater detail below, we use an ordered quantile normal transformation for the primary set of results.

Xsa is a set of satellite-derived indicators that vary at the sub-area (village or GN Division) level.

Xa is a set of satellite-derived indicators that vary at the area level. The areas sub-districts in Sri Lanka and districts in Tanzania. These predictors are important to include to reduce the variance of the area random effect and thereby mitigate bias in the estimated mean squared error (Elbers, Lanjouw, and Leite, 2008).

Xr is a set of regional dummy variables indicating super-areas for which the survey is considered to be representative. The Sri Lanka specification also includes dummies for the rural and estate sectors, while the Tanzania specification includes a rural dummy.<sup>13</sup>

> 11 See Morris (1983) for a comprehensive review of EBLUP models, and Jiang and Lahiri (2006) for a comprehensive review of their application to small area estimation.

> 12 See Molina and Rao (2010) and Tzavidis et al (2018).

> 13 Sri Lanka is unique in that the national statistics office designates certain areas as the “Estate Sector”. The estate sector mainly consists of tea plantations and is historically economically disadvantaged.

12
Using Labor Supply Elasticities To Learn About Income Inequality: The Role of Productivities versus Preferences<sup>_∗_</sup> Katy Bergstrom<sup>_†_</sup> William Dodds<sup>_‡_</sup> _Keywords: income inequality, productivity, preference, labor supply elasticity JEL Codes: D63, J22, H21, H31_

> _∗_ We would like to thank our advisors, Doug Bernheim, Raj Chetty, and Caroline Hoxby for their guidance and support on this project. We would also like to thank Jose Maria Barrero, Pascaline Dupas, Petra Persson, Alessandra Peter, Luigi Pistaferri, Juan Rios, Florian Scheuer, and Isaac Sorkin for helpful advice, as well as participants at various seminars at Stanford University for their useful comments. Finally, thanks to The Ric Weiland Graduate Fellowship in the School of Humanities and Sciences, the B.F. Haley and E.S. Shaw Fellowship for Economics, and the Arthur and Eva Karasz Fellowship for financial support.

> _†_ Development Research Group, World Bank. Email: kbergstrom@worldbank.org.

> _‡_ Charles River Associates. Email: wdodds@crai.com.
table). The difference in the number of workers between women crossovers and women non-crossovers is of the same magnitude but not statistically significant once we control for region.

The results in columns 3 and 4 of Table 2 present the differences in average monthly profits between the categories of firms. A hierarchy of profit earnings emerges: men in male-dominated sectors are the top earners and make statistically significant higher profits than all other categories. Men in male-dominated sectors, men in female-concentrated sectors and female crossovers have 116, 82 and 67 percent statistically significantly higher profits than female non-crossovers, respectively. While different in magnitude, the difference between profits for males in female-concentrated sectors and female crossovers is not statistically different.

|**Ta**|**ble 2. Business Perfo**<br>Number of e<br>|**rmance**<br>mployees<sup>18</sup><br>|Profits in the l<br>|ast month(IHS)<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||26.979*|24.798|0.608***|0.666***|
|Female Crossover [A]|(15.21)|(15.20)|(0.14)|(0.13)|
||34.515***|36.980***|0.698***|0.817***|
|Male in female-concentrated sector[B]|(8.07)|(8.23)|(0.07)|(0.07)|
||56.706***|58.726***|1.003***|1.161***|
|Male in male-dominated sector [C]|(9.27)|(9.41)|(0.08)|(0.08)|
|Constant|77.230***|130.904***|6.471***|7.038***|
||(6.11)|(22.67)|(0.06)|(0.20)|
|Observations|31,756|31,756|17,351|17,351|
|Region FE|NO|YES|NO|YES|
|Developed countries|YES|YES|YES|YES|
|Developing countries|YES|YES|YES|YES|
|Mean Female Non-Crossover|77.23|77.23|6.47|6.47|
|P-value A=B|0.61|0.42|0.50|0.25|
|P-value A=C|0.06|0.03|0.00|0.00|
|P-value B=C|0.01|0.01|0.00|0.00|
|Adjusted R-squared|0.00|0.00|0.01|0.03|
|Standard errors in parentheses *** p<0.01, ** p<|0.05, * p<0.1||||

Subgroup analysis of the business performance results for developed and developing countries is presented in Table 3. The hierarchy of profits only holds for the developing countries alone (see columns (3) and (4)). Male entrepreneurs in developed countries perform better than female entrepreneurs irrespective of whether they are in a male-dominated or female-concentrated sector. We still find that female non-crossovers have the lowest profits and that female crossovers earn higher profits than female non-crossovers in both developed and developing countries. Specifically, in developed countries, men in male dominated sectors, men in female concentrated sectors and female crossovers have, on average, 134, 130 and 50 percent higher

> 18 It should be noted that the distribution of the employees is skewed to the right. The median number of employees is just 2, for both developed and developing economies.

10