rafmacalaba/datause-encoder-data huggingface dataset:
# **3. The Data**

Our study is primarily based on the World Bank Enterprise Surveys (ES), a newly available collection of firm-level datasets for developing countries as well as a limited number of high-income countries.<sup>6</sup> Questionnaires are administered within a framework of common guidelines in the design and implementation.<sup>7</sup> The dataset is assembled using the _core survey_ , which is a module of identical questions included in all questionnaires, thereby enabling cross-country analyses.

While in principle, the dataset contains information for approximately 80,000 firms in more than 110 countries, complete data for the variables used in our estimations are available for a subsample of approximately 56,000 firms in 90 countries in seven different regions surveyed during the period 2000-2006. For some countries more than one survey is available.<sup>8</sup> (See Table A1 in the Appendix for a complete list of countries and surveys available, as well as the number of observations in each country.) The median sample size is 350 firms, with several large countries having substantially larger samples (Brazil, China, India, Turkey and Vietnam have samples over 1,500). The sample of firms in each country is stratified by size, sector and location.<sup>9</sup> Because of this stratification, large enterprises are in general over-sampled in the ESs compared to their share in the number of firms, but not in terms of their contribution to GDP. The unit of analysis is the “Establishment” in the manufacturing and service sectors.<sup>10</sup> Most firms are registered with local authorities, although they may be only in partial compliance with labor and tax authorities. Unlike most

> 6  The terms Enterprise Surveys includes surveys that have been implemented regionally under other names, such as includes BEEPS, Investment Climate Surveys and RPED surveys (World Bank 2004b and World Bank 2007).

> 7 From <u>http://rru.worldbank.org/InvestmentClimate/Methodology.aspx</u>

> 8 While efforts are shifting to building a panel dataset, most repeat surveys have been additional cross-sections. There are approximately 2,000 firms that enter twice in the dataset.

> 9 From <u>http://www.enterprisesurveys.org/Methodology/default.aspx#weights: ES have been conducted</u> following simple random sampling or random stratified sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group

> 10 In Europe and Central Asia, the unit is the “firm.” In all other regions it is the plant or establishment.  As over 90 percent are single-plant firms, the distinction is not likely to affect the results.

8
**Figure 8. LCOEs along with economic lives of electricity generation technologies**

<!-- Start of picture text --> 35 Year<br><!-- End of picture text --> <!-- Start of picture text --> 30 year<br><!-- End of picture text -->

# **_4.5   Impacts of the capacity availability factor on LCOE_**

Capacity availability factors represent the quality of resources (inputs) available to produce electricity. Since fossil- and nuclear- fuels are storable, they are available throughout the year. Therefore, they have very high capacity factors. On the other hand, renewable energy resources (solar, wind, run of river hydro) are not storable; their capacity factors are relatively smaller. Table 5 provides capacity factors from IEA (2015), IRENA (2018), NREL (2019), and Lazard (2019).

Figure 9 reflects the sensitivity of LCOEs with capacity availability factors. An increase in the capacity availability factor decreases LCOEs. Like in the case of economic life, renewable energy technologies are more sensitive to their capacity availability factors than fossil fuel-based electricity technologies. The latter are more susceptible to fuel prices, as discussed earlier. For one percentage point increase in capacity availability factors, LCOEs of solar PV and wind both onshore and offshore decrease by about 3%, whereas LCOEs of gas-based technologies decrease by 0.2%. Geothermal, coal, nuclear, and biomass exhibit 1% reduction in their LCOEs for each percentage point increase in their capacity availability factors. The corresponding value for CSP and hydro is about 2%.

28
optimum inequality and should stick to it.<sup>9</sup> Perhaps not surprisingly the debate on the relationship between inequality and growth became more quiescent after such inconclusiveness (and added complexity).<sup>10</sup> Recently there have been some successes in getting a better handle on the relationship between inequality and growth. Voitchovsky (2005) and Marrero and Rodriguez (2012, 2013) argue that inequality ultimately consists of different components, some of which may be bad for growth, and others good. Once this is acknowledged, the earlier inconclusive results with “sign-switching” become more intelligible. Since different components may have opposite effects on growth and we fail to measure which one is more important at a given time and place, it should come as no surprise that the effect of "total inequality" on growth can vary. This approach gets away from looking at how a single inequality measure is associated with a single growth measure (“one-on-one” approach) to a “two (inequality measures) – on—one (growth measure)” approach.

Voitchovsky (2005) evaluates inequality among the poor (the 50/10 ratio) and inequality among the rich (the 90/50 ratio) as two separate drivers of total inequality.<sup>11</sup> She concludes that bottom inequality is bad for growth while top inequality is good. It is hypothesized that bottom inequality is bad for growth because it implies higher levels of poverty which, in the presence of credit constraints, make it difficult for the poor to acquire education.<sup>12</sup> It might also lead to greater crime and social instability. On the other hand, a positive impact of top inequality on growth is regarded as supporting a classic theoretical argument that links higher inequality to higher savings which finance growth-enhancing investments. It seems reasonable that this argument applies mostly to top income inequality, as opposed to total- or bottom inequality, as a large share of aggregate savings are made by the rich. In this way, Voitchovsky basically reconciled three very common theories that linked inequality and growth and were often presented as alternatives: creditconstraints, political instability, and marginal propensity to save by the rich. According to

> 9 Banerjee-Duflo model was of a median voter hypothesis type.

> 10 A useful meta-study of the inequality and growth literature is by de Dominicis, de Groot and Florax (2006). 11 Voitchovsky (2005) uses standardized micro data from 81 surveys covering 21 rich countries, in a dynamic panel setting with five-year intervals coinciding broadly with survey data availability.

> 12 See also the recent study by Ravallion (2012) who directly compares the effects of initial poverty and inequality levels on future growth.

6
**Figure 6. Size, Progressivity, and Marginal Contributions of Education and Health to Inequality Reduction** Panel a. Size and Poverty Reduction Effect

<!-- Start of picture text --> Kakwani Marginal contribution to redistributive effect<br>0.70 0.025<br>0.60<br>0.020<br>0.50<br>0.015<br>0.40<br>0.30<br>0.010<br>0.20<br>0.005<br>0.10<br>0.00 0.000<br>Social Public Upper Lower Primary Pre School Tertiary<br>Security Health Secondary Secondary Level<br>Health Education<br>Kakwani<br>Redistributive effect<br>unequalizing <-------> equalizing<br>Regresive <-----------------------> Progressive<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

# Direct and Indirect Taxes

**The progressivity of the Personal Income Tax (PIT) is evident as more affluent individuals contribute a more significant portion of the total collected amount; indirect taxes, such as VAT, have a negligible effect on reducing inequality, but they contribute to a significant increase in poverty.** Direct taxes, such as personal income tax, are progressive and have a significant marginal contribution to poverty reduction. About 59% of the PIT revenues originate from the top quintile compared to 3% from the bottom quintile (figure 7 panel a). However, the overall impact of social security contributions is regressive, with ownaccount workers' social insurance contributions (SIC) having the most significant negative impact. The sum of all non-pension social security contributions is an increase of 3.5 percentage points in poverty when using the AROP poverty line. Among all taxes, the VAT contributes the most to rising poverty (7.3 percentage points), primarily due to its large size. This is mainly because VAT represents a relatively high proportion of income for people in lower-income deciles. Tobacco excises are regressive, and the direct effects of fuel taxes are progressive, but the marginal impact on poverty and inequality is negligible (figure 7 panels b and c). Personal Income tax, VAT, and oil derivative taxes are progressive and neutral.

27
Choi, S., D. Furceri, P. Loungani, S. Mishra, and M. Poplawski-Ribeiro. 2018. “Oil prices and Inflation Dynamics: Evidence from Advanced and Developing economies.” Journal of International Money and Finance 82: 71-96.

Clarida, R., J. Gali, and M. Gertler. 1999. “The Science of Monetary Policy: A New Keynesian Perspective.” Journal of Economic Literature 37 (4): 1661-1707.

Ciccarelli, M. and Mojon, B., 2010. “Global inflation.” The Review of Economics and Statistics 92 (3): 524-35.

Del Negro, M., S. Eusepi, M. P. Giannoni, A. M. Sbordone, A. Tambalotti, M. Cocci, and M. Linder. 2013. “The FRBNY DSGE Model.” Federal Reserve Board of New York Staff Report 647.

Del Negro, M., A. Gleich, S. Goyal, A. Johnson, and A. Tambalotti. 2022. “Drivers of Inflation: The New York Fed DSGE Model’s Perspective,” Federal Reserve Bank of New York Liberty Street Economics, March 1, 2022.

Eickmeier, S. and B. Hofmann, 2022. “What Drives Inflation? Disentangling Demand and Supply Factors.” BIS Working Papers 1047, Bank for International Settlements.

Feldkircher, M. and P. L. Siklos. 2019. “Global Inflation Dynamics and Inflation Expectations.” International Review of Economics and Finance 64: 217-241.

Forbes, K.J., I. Hjortsoe, and T. Nenova. 2018. “The Shocks Matter: Improving Our Estimates of Exchange Rate Pass-Through.” Journal of International Economics 114 (September): 255-275.

Forbes, K. 2019. “Inflation Dynamics: Dead, Dormant, or Determined Abroad?” Brookings Papers on Economic Activity 2019 (Fall): 257-319. Fry, R. and A. Pagan. 2011. “Sign Restrictions in Structural Vector Autoregressions: A Critical Review.” Journal of Economic Literature 49(4): 938-960.

Gali, J. and T. Monacelli. 2005. “Monetary Policy and Exchange Rate Volatility in a Small Open Economy.” The Review of Economic Studies 72(3): 707-34.

Gambetti, L., E. Pappa, and F. Canova. 2008. “The Structural Dynamics of U.S. Output ‐ and Inflation: What Explains the Changes?” Journal of Money, Credit and Banking 40(2 3): 369-88.

Gertler, M. and P. Karadi. 2015. “Monetary Policy Surprises, Credit Costs, and Economic Activity.” American Economic Journal: Macroeconomics 7 (1): 44-76.

Ha, J., M.A. Kose, and F. Ohnsorge. 2023. “One-stop Source: A Global Database of Inflation.” Journal of International Money and Finance vol. 137(C).

Hamilton, J. D. 2011. “Historical Oil Shocks.” NBER Working Paper 16790, National Bureau of Economic Research, Cambridge, MA.

Hou, K., D.C. Mountain, and T. Wu. 2016. “Oil price Shocks and Their Transmission Mechanism in an Oil-exporting Economy: A VAR Analysis Informed by a DSGE Model.” Journal of International Money and Finance 68: 21-49.

Hristov, N., O. Hülsewig, and T. Wollmershäuser. 2012. “Loan Supply Shocks during the Financial Crisis: Evidence for the Euro Area.” Journal of International Money and Finance 31 (3): 569-92.

21
has not been systematically studied. The only research we are aware of that examines the link between international migration and source country fertility is Fargues (2007). His analysis is based on fertility behavior in three source countries, namely Morocco, Turkey and Egypt. Migration from Morocco and Turkey over the period 1960-2000 was essentially to the low-fertility countries of Western Europe while that of Egypt was essentially to the high-fertility countries of the Persian Gulf. Fargues shows that fertility rates in these countries were a¤ected by the rates prevailing in their migrants’ host countries, with rates declining in Morocco and Turkey and increasing in Egypt. He also ...nds that the degree to which the demographic transition has been attained increases with migration rates across regions of Morocco and Turkey and decreases with migration rates across regions of Egypt.

Fargues posits that the impact of host countries’fertility rates on those in migrants’home countries is due to the transfer of behavioral norms from host to source country. However, he does not subject his hypothesis to rigorous testing or consider alternative ones.<sup>4</sup> This paper provides a rigorous econometric analysis of the relationship between international migration and source country fertility. The impact of the former on the latter may have a number of causes, one of which is the transfer of host country fertility norms. These causes are examined theoretically and the hypotheses derived from the model are tested empirically. The econometric analysis is based on a new database of international bilateral migration for the year 2000 (Parsons et al. 2007) that covers all countries and territories. We ...nd that fertility in migrants’ home countries decreases (increases) in the case where it is higher (lower) than fertility in the host countries.

# 3 Theory

As South-North migration can a¤ect fertility decisions in the South through multiple channels, assessing the e¤ect of di¤usions of norms requires controlling for the other mechanisms at work. A stylized theoretical model is helpful to derive testable predictions.

The main mechanisms we envisage here are the following. By a¤ecting the expected return to higher education, migration prospects impact on adults’ human capital investments, which in turn, determine the opportunity cost of raising children. As a more educated child has a higher probability to emigrate to a rich country, expectations about o¤spring migration impact on the ’quantity-quality’tradeo¤. Through remittances, past migrations impact on adults’income and a¤ects the demand for

> 4Ebanks et al. (1975) for Barbados and Lee and Farber (1985) for Korea compute the impact of migration on fertility in the home country. However, their calculations are unrelated to the impact of migration on fertility behavior back home. Rather, they calculate what the fertility in the home country would have been had migrants stayed home by assuming that migrants’fertility rates are equal to those of observably similar non-migrants.

7
# Once NEET, Always NEET? A Synthetic Panel Approach to Analyze the Moroccan Labor Market

_Federica Alfani Fabio Clementi Michele Fabiani_ The World Bank University of Macerata University of Macerata Washington DC, USA Macerata, Italy Macerata, Italy _Vasco Molini_<sup>∗</sup> The World Bank Rabat, Morocco _Enzo Valentini_ University of Macerata Macerata, Italy

**Keywords:** synthetic panel, NEET, youth employment, gender equality, Morocco. **JEL Classification:** C23, E24, J13, O55.

> ∗ Corresponding author. The authors acknowledge financial support from the World Bank. We thank colleagues from the Haut Commissariat au Plan of Morocco (HCP) for their excellent data preparation.
As noted above, the investment portfolio was divided into two components in 2005: the Beta portfolio and the Alpha portfolio. At the same time, the traditional asset allocation decision based on benchmarks was abandoned in favor of a risk budgeting methodology. The strategic asset allocation is expected to be much more diversified in the future in risk terms, meaning that the share of total risk arising from equities will be lowered in favor of other sources of risk, especially alternative asset classes. This is similar to the pattern adopted by public pension funds in Canada and other countries (Vittas et al 2007).

ATP started using interest rate swaps in the fourth quarter of 2001 to attain a more efficient hedging of its pension liabilities (Table 5). Initially, the purchase of interest rate swaps was limited to a potential gain of 10 billion DKK from a 1 percentage point fall in interest rates, but this limit was raised in subsequent annual reviews. Full hedging of pension liabilities has been achieved, assuming that the hedges are enforceable. In fact, the mission of the new department that specializes in hedging pension liabilities is to maintain full hedging after tax. In addition, the maturities of liabilities and swaps are almost completely matched in order to reduce second-order risks.

The bulk of the hedging portfolio consists of receiver swaps in EUR. While the pension liabilities are marked-to-market based on DKK interest rates, ATP also engages in additional hedging activities aiming at covering the interest rate spread risk between EUR and DKK interest rates. The principal amount of interest rate swaps reached 112 percent of total pension liabilities, including the bonus equalization fund, up from 67 percent in 2005.<sup>10</sup>

**<u>Table 5: Pension Liabilities and Interest Rate Swaps, 2001-2006</u>**

|DKK billion|2001|2002|2003|2004|2005|2006|
|---|---|---|---|---|---|---|
|Total Pension Liabilities|245.3|241.5|260.8|304.5|359.0|362.7|
|Interest Rate Swaps|65.1|157.8|191.8|217.7|241.6|407.7|
|IRS/TPL(%)|26.5|65.3|73.5|71.5|67.3|112.4|
|||Source: AT|P||||

Despite the heavy use of interest rate swaps and the adoption of a policy that resembles a liability-driven investment (LDI) approach, most of the assets continue to be invested in foreign and domestic bonds (68 percent in 2006). Listed equities represent 25 percent and alternative assets 7 percent. However, ATP invests in equity futures and options, which implies a higher exposure to equities than indicated by the balance sheet data.

# **8. Investment Performance**

Investment performance is monitored by reference to benchmark portfolios that are constructed from published indices. The domestic market index is customized to limit exposure to large groups. The 10 largest equity exposures accounted for more than 50 percent of the total equity portfolio in the late 1990s (more than 20 percent of total assets), while exposure to the largest 3 mortgage credit institutions accounted for 30 10 For a brief discussion of ATP policies in the context of the growing use of derivatives by Danish pension institutions to hedge their embedded options, see Ladekarl et al (2007).

13
FIG.URE 1 COTE d'IVOIRE FINANCIAL TAX <!-- Start of picture text --> Measre 1<br>22 -<br>2-<br>1.8<br>I.e<br>0. 1.4<br>a<br>0<br>'t 1.2<br>04-<br>0.2<br>0- - -----<br>1981 IG82 1983 I1984 19as 1988 1 987 1GMa<br>EZ Cwrr Ir.dTwx<br><!-- End of picture text --> 41
- Lyons-Ruth K, Zoll D, Connell D, Grunebaum HU 1986) The depressed mother and her one-yearold infant: environment, interaction, attachment and infant development. in Maternal depression and infant disturbance. New directions for child development. eds Tronick EZ, Field T (Jossey-Bass, San Francisco), 34

- Martorell, R. 1999. “The nature of child malnutrition and its long-term implications.” Food & Nutrition Bulletin, Volume 20, Number 3, September 1999, pp. 288-292(5).

- MINSA. 2018. “Plan Nacional de Fortalecimiento de Servicios de Salud Mental Comunitaria 2017 – 2021” (RM N° 356 – 2018/MINSA) Ministerio de Salud Dirección General de Intervenciones Estratégicas en Salud Pública - Dirección de Salud Mental - Lima: Ministerio de Salud; 2018. 96 p

- Murray L, Cooper P. Effects of postnatal depression on infant development. Arch Dis Child 1997;77:99-101

- Nielsen Forman D, Videbech P, Hedegaard M, Dalby Salvig J, Secher NJ (October 2000. "Postpartum depression: identification of women at risk". BJOG 107 (10):1210–7. doi:10.1111/j.1471-0528.2000.tb11609.x. PMID 11028570.

- Parsons, C. E., Young, K. S., Rochat, T. J., Kringelbach, M. L., & Stein, A. (2012). Postnatal depression and its effects on child development: a review of evidence from low-and middle-income countries. British medical bulletin, 101(1), 57-79.

- Patel V, Rodrigues M, De Souza N. Gender, poverty and post-natal depression: a cohort study from Goa, India. Am J Psychiatry 2002; 159: 43–7.

- Paxson, Christina, and Norbert Schady. 2007. Cognitive Development among Young Children in Ecuador: The Roles of Wealth, Health, and Parenting. Journal of Human Resources 42(1): 49-84.

- Paxson, Christina, and Norbert Schady. 2010. Does Money Matter? The Effects of Cash Transfers on Child Development in Rural Ecuador. Economic Development and Cultural Change 59(1):187-229.

- <mark>Persson, P. and Rossin-Slater, M., 2018. Family ruptures, stress, and the mental health of the next generation.</mark> _<mark>American economic review</mark>_ <mark>,</mark> _<mark>108</mark>_ <mark>(4-5), pp.1214-52.</mark>

- Petterson, S. M. and A. B. Albers 2001. Effects of Poverty and Maternal Depression on Early Child Development. 72: 1794-1813.

23
42 richer parents self-select their children out of public facilities and into private schools.

When comparing the incidence of public education subsidy shares between 1978 and 1987, the paper finds that shifts have been markedly pro-poor at the secondary and university levels. The incidence of subsidies to primary schools was quite pro-poor in 1978, and has become slightly less so in 1987.

There is, however, great scope for reform, aiming to enhance the quality of education for the poor. Further public spending could be used to ensure that all schools are adequately endowed with the inputs necessary for good education. Basic public provisioning for the essential teaching materials, such as teachers' manuals, chalk, textbooks and so on, would help redress the vast quality differentials which exist between schools, and are primarily associated with private provisioning. Many have argued that education policy should be more oriented towards improving the quality of basic education. This paper's results reinforce this message from a slightly different perspective. Such a policy, achieved through public provisioning of basic teaching and learning materials, appears to be a particularly efficient way to target and transfer resources to poorer groups in Indonesia.

The health sector has also undergone significant changes in recent decades. Public policy efforts at achieving widespread provision of primary health care in rural Indonesia are reflected in the utilization data. The paper finds that there is increased recourse to some kind of medical service by all those who report bein(, ill -- whether poor or otherwise -- together with a drop in the use of practitioners of traditional medicine. The changes since 1978 are most striking for the poorest groups. Nonetheless, in 1987 it remains true that whether an illness resulted in outpatient or inpatient care is highly correlated with living standards and urban residence. The likelihood of visiting a private doctor or a hospital is lower for the poor.

The use of primary health centers in rural areas has spread and equalized over the consumption quantiles. The poorer groups used these services much more in 1987 than they did in 1978. Rich and poor now appear to