rafmacalaba/datause-encoder-data huggingface dataset:
19

|**Main**<br>**gov't**<br>**institution**<br>**responsible**|Ministry<br>Education|of|The<br>Early<br>Childhood<br>Commission<br>(ECC) within the<br>Ministry<br>of<br>Education, Youth,<br>and Information|Bureau for Early<br>Childhood<br>Education (BECE)<br>in the Ministry of<br>Education|The<br>School<br>Education<br>Department (SED)|
|---|---|---|---|---|---|
|**Is it Fee Free?**|YES||YES|NO|YES|
|**Is**<br>**it**<br>**Compulsory?**|NO||NO|NO|YES|

_Sources_ : Jamil et al., 2019; Jones et al., 2011; Oxford Policy Management, 2019; Rossiter et al., 2018; World Bank EdStats

# **Results**

The comparative analysis of the four focus countries is organized around the five categories in the analytical framework presented earlier: (1) transnational influence, (2) actor power, (3) ideas, (4) political contexts, and (5) issue characteristics. Where data are available, we highlight the extent to which political prioritization of ECE changed before and after the start of the pandemic in each focus country.

# **Transnational influence**

Transnational influence refers to the role of international organizations in encouraging national governments to prioritize ECE in response to the pandemic through the promotion of _global norms_ and the provision of _technical and financial resources_ . As lockdowns began and schools closed around the world, the international community quickly mobilized to raise awareness about the needs of young children and potential strategies to support families, such as distance learning approaches. Global efforts to raise the visibility of ECD included rapid research, position
-4-

# Proxies and Shortcut Measures of Output Levels in Manufacturing'

The need for measures of comparative performance by industry of origin is amply demonstrated by the frequency with which proxies for such estimates are used. Thus the American Productivity Center and the Asian Productivity Organisation regularly provide the "equilibrium" exchange rate comparisons shown in table 1.2. They use 1975 exchange rates because in their view, exchange cross-rates were ir -^ etter "equilibrium" then than in later years. This is, of course, an a. sted hypothesis, unless we conduct exercises of the present type.

Other economists have manipulated real expenditure levels to produce proxy estimates of real output levels by sector (see bottom half of table 1.2). They usually do this by treating final exper'itures PPPs as if they were PPPs for value added in analogous production sectors. Thus Simon Kuznets (1972) used OEEC and ECLA real expenditure studies to derive estimates of real o:etput for agriculture and industry. Jones (1976) used some of the Kravis, Kenessey, Heston and Summers (1975) expenditure PPPs to estimate manufacturing output lcvais, A.D. Roy (1982) used the same procedure with Kravis, Heston and Summers (1978), and S. Prais (1981) followed a more detailed procedure, using about half of the expenditure items listed in Kravis, Kenessey, Heston and Summers (1975) to dt ive a weighted average PPP for manufacturing. Klodt (1984), Jorgenson, Kuroda and Nishimizu (1986), and D.J. Roy (1987) are the latest in this tradition.

Proxy procedures of this type need to be crosschecked with independent estimates by industry of origin such as we present here. Until this is done for a reasonable sample of countries, one must be sceptical about such proxies. As we demonstrate later, we feel that they are not valid in the case of Mexico, and the results of Van Oostatroom and Maddison (1985) showed that such a procedure was misleading, when applied to agriculture.

Finally, at the bottom of table 1.2. we list three short-cut estimates using limited information for representative commodities as a substitute for more detailed and comprehensive estimates. Here again the validity of such short-cut methods needs to be tested against more refined evidence of the type we present in the following chapters, and the studies listed in table 1.3.

- Here we discuss shortcut procedures for manufacturing only. There is also a substantial literature on shortcuts for comparative levels of GDP as a whole: see Kravis, Heston and Summers (1978b), Summers and Heston (1984) for regression methods using ICP benchmarks; Beckerman (1966), Ehrlich (1967), and ECE (1980) for the physical indicators approach. For critical comments on rlternative shortcut procedures, see Ahmad (1980), Beckerman (1984) and Marer (1985).
|**Table 8. Correlates of Being More Like the top-20 U.S.**|**Schools**<br>Similarity<br>in Topics|Share of<br>Course<br>toEmpirics|Use<br>Poor<br>Economics|Use<br>Todaro and<br>Smith|Homework requires<br>using statistical<br>software|
|---|---|---|---|---|---|
|Log Per Capita Income (PPP)|0.044***|-0.013|0.272|-0.592**|-0.581**|
||(0.015)|(0.017)|(0.173)|(0.234)|(0.227)|
|Mean Years of Tertiary Education of Population 15+|0.026|0.022|2.844***|0.992|2.489***|
||(0.041)|(0.045)|(1.043)|(0.679)|(0.811)|
|Gov't Expenditure % of GDP|-0.004***|-0.001|-0.102***|0.012|0.003|
||(0.001)|(0.001)|(0.030)|(0.017)|(0.017)|
|Instructor has Google Scholar Profile|0.054**|-0.016|0.171|-0.171|0.420|
||(0.026)|(0.040)|(0.664)|(0.394)|(0.557)|
|Instructor not listed for Course|-0.089**|-0.070***|-0.413|-1.144**|-0.590|
||(0.033)|(0.019)|(0.514)|(0.470)|(0.482)|
|R-squared or Pseudo-R-squared|0.33|0.13|0.01|0.12|0.04|
|Sample Size|91|81|92|92|92|
|Notes:<br>Robust standard errors in parentheses, clustered at the coun<br>*, **, and *** indicate significance at the 10, 5, and 1 perc|try level.<br>ent levels respecti|vely.||||
|Columns 1 and 2 are OLS regression coefficients, Column|s 3, 4 and 5 margin|al effects from|probit estimati|on.||

34
# **Figure A5: Contribution of EM7 (G7) and the next three largest EM (AM) to EM (AM) output**

<!-- Start of picture text --> Percent<br>90<br>80<br>70<br>60<br>EM AM<br>50<br>40<br>30<br>20<br>10<br>0<br>EM7/G7 SAU/KOR POL/AUS THA/ESP<br><!-- End of picture text --> _Note:_ The blue bars show the EM output share of EM7 and those of the next three largest EM: Saudi Arabia (SAU), Poland (POL), and Thailand (THA). The red bars show the AM output share of G7 and those of the next three largest AM: Korea Republic (KOR), Australia (AUS), and Spain (ESP).

29
- Birkie, Seyoum Eshetu, Paolo Trucco, and Pablo Fernandez Campos (2017). “Effectiveness of resilience capabilities in mitigating disruptions: leveraging on supply chain structural complexity”. _Supply Chain Management_ 22, 179–192.

- Burke, Marshall, Solomon M Hsiang, and Edward Miguel (2015). “Global non-linear effect of temperature on economic production”. _Nature_ 527, 235–239.

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- Burke, Marshall, Mustafa Zahid, et al. (2024). _Are We Adapting to Climate Change?_

- Carleton, Tamma et al. (2022). “Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits*”. _The Quarterly Journal of Economics_ 137, 2037–2105.

- Cevik, Serhan and Fedor Miryugin (2022). _Rogue Waves: Climate Change and Firm Performance_ .

- Chong, Alberto et al. (2014). “Letter Grading Government Efficiency”. _Journal of the European Economic Association_ 12, 277–299.

- Dell, Melissa, Benjamin F. Jones, and Benjamin A. Olken (2012). “Temperature Shocks and Economic Growth: Evidence from the Last Half Century”. _American Economic Journal: Macroeconomics_ 4, 66–95.

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- _Enterprise Survey_ (2023). Tech. rep. url http://www.enterprisesurveys.org. World Bank.

- Gasbarro, Federica and Jonatan Pinkse (2016). “Corporate Adaptation Behaviour to Deal With Climate Change: The Influence of Firm-Specific Interpretations of Physical Climate Impacts”. _Corporate Social Responsibility and Environmental Management_ 23, 179–192.

- Graff Zivin, Joshua and Matthew Neidell (2012). “The impact of pollution on worker productivity”. _American Economic Review_ 102, 3652–3673.

27
Ellerman, A.Denny and Barbara K. Buchner. 2007. The European Union Emissions Trading Scheme: origins, allocation, and early results. Review of Environmental Economics and Policy 1(1), 66-87.

- Ellerman, A. Denny, Barbara K. Buchner and Carlo Carraro. 2007. Allocation in the European Emissions Trading Scheme: Rights, Rents and Fairness. Cambridge: Cambridge University Press

Europa. 2007. Emission Trading Scheme (EU ETS). Available on the Internet at: http:\ec.europa.eu.

Halvorsen, Robert and Tim R. Smith. 1986. Substitution possibilities for unpriced natural resources: restricted cost functions for the Canadian metal mining industry. The Review of Economics and Statistics 68(3), 398-405.

International Energy Agency. 2009. Energy, prices, and taxes (quarterly). Available on the Internet at <u>http://www.iea.org/Textbase/stats/index.asp</u> .

Larson, Donald F., Philippe Ambrosi, Ariel Dinar, Shaikh Mahfuzur Rahman and Rebecca Entler. Forthcoming. Carbon markets, institutions, policies and research. The International Review of Environmental and Resource Economics.

Morrison, Catherine. 1988. Quasi–fixed inputs in U. S. and Japanese manufacturing: A generalized Leontief restricted cost function approach _._ The Review of Economics and Statistics 70(2), 275– 287.

Platts Energy Service. 2009. Custom Energy Market Data. Available on the Internet at <u>http://www.platts.com/Analytic%20Solutions/Custom/.</u> United Kindgom. 2009. Average prices of fuels purchased by the major UK power producers. Department of Business Enterprise and Regulatory Reform. Available on the Internet at http://www.berr.gov.uk/whatwedo/energy/statistics/source/prices/page47818.html Watanabe, Rie and Guy Robinson. 2005. The European Union Emissions Trading Scheme (EU ETS). Climate Policy 5(1), 10-14.

20
in 2007, what was their probability of remaining in poverty? Although these concepts are closely linked, both merit detailed treatment.

# 5.1. Poverty transitions

Figure 1 shows the evolution of the joint probabilities of all four poverty transition categories over consecutive survey rounds. The Appendix contains the corresponding transition matrices. The upper-left graph gives the bounds on the probability of being in poverty in both survey rounds, which is how we define chronic poverty. This joint probability may also be interpreted as the share of the population that is in chronic poverty. First, we can observe that this share decreases, from somewhere between 18–36 percent for 2004–2007 to a number in the range of 3–11 percent for the 2014–2016 interval. Conversely, the upper-right graph shows that the probability of not being poor in both rounds increases from 48–60 percent to 81–86 percent respectively. These are impressive positive developments consistent with the general picture of the Malaysian economy in this period, i.e., of fast growth that was broadly shared. Nevertheless, we can also observe that the decrease in the fraction of the population that is chronically poor has slowed recently compared to the 2007–2014 period.

Regarding movements into and out of poverty, we observe that bounds have narrowed over time, but also that the share of the population escaping poverty has likely become smaller as part of the total: the range goes from 3–16 percent in 2004–2007 to 3–9 percent for the most recent survey-pair (lowerleft graph in Figure 1). This is perhaps not surprising given that the headcount poverty rate has steadily declined: there are fewer and fewer people who could potentially escape poverty. At the same time, when it comes to downward mobility, the impression is that this has also become less likely over time (lowerright graph).<sup>23</sup> These findings are consistent with the general picture of steadily declining poverty in Malaysia over the reference period. Controlling for initial poverty status can offer additional nuance to these observations, and we turn next to an examination of such conditional probabilities.

> 23 The lower-bound estimate for the probability of moving into poverty is even (very close to) zero. For interpretation of this extreme result, remember that we assume perfect correlation of errors between rounds for the lower-bound estimate, i.e., minimum mobility, and that we project income backwards. Consequently, in the absence of major changes in the coefficients on the household time-invariant regressors, it is to be expected that a household that is poor in round 2 is unlikely to have been non-poor in round 1. Since we add the scaled round 2 residual to fitted round 1 income, we also project backwards any idiosyncratic shock that may have happened in round 2. Hence, the result is no surprise. For the opposite probability of poverty exit, the same mechanism holds, but the lower bound does not approach zero due to generally increasing incomes.

16
# **Eference**

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**Aizenman, J. and Lee, J.** (2007), “International Reserves: Precautionary Versus Mercantilist Views, Theory and Evidence”, _Open Economies Review,_ Vol. 18, issue 2, pp. 191-214.

**Caballero, R.J. and Lorenzoni** , **G.** (2006), “Persistent Appreciations, Overshooting and Optimal Exchange Rate Intervention,” _MIT,_ Mimeo

**Calvo, G. and Reinhart, C.** (2002), “Fear Of Floating”, _The Quarterly Journal of Economics_ , _MIT Press_ , Vol. 117, Vol. 2 (May), pp. 379-408.

**Calvo, G. A** . (2000), “The Case for Hard Pegs in the Brave New World of Global Finance”, _Mimeo, University of Maryland._

**Calvo G., A. Izquierdo and R. Loo-Kung** (2005), “Relative Price Volatility under Sudden Stops: The Relevance of Balance-Sheet Effects”, _Journal of international Economics_ , June 2006, also NBER Working Paper No.11492

**Calvo G. and E. Talvi** , (2006) “Phoenix Recoveries” Mimeo IADB.

**Chang, R. and Velasco, A** . (2001), “A Model of Financial Crises in Emerging Markets”, _Quarterly Journal of Economics_ , Vol.116, pp. 489-517.

**De la Torre, A., Levy Yeyati, E. and Schmukler, S.** (2003), “Living and Dying with Hard Pegs: The Rise and Fall of Argentina’s Currency Board”, _Economía,_ Vol. 5 (2), pp. 43-99.

**Diaz Alejandro, C.** (1965), “Exchange Rate Devaluation in a Semi-Industrialized Country”, Cambridge, United States: MIT Press.

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**Edwards, S.** (1989), “Real Exchange Rates, Devaluation, and Adjustment”, Cambridge, MA: The MIT Press.

**Eichengreen, B.** (1994), “International Monetary Arrangements for the 21st Century” Washington, DC. _The Brookings Institution_ .

**Fisher, S.** (2001), "Exchange Rate Regimes: Is The Bipolar View Correct?", _Journal of Economic Perspective_ s, Vol. 15(2)(Spring), pp. 3-24.

**Frankel J.** (2005),“Contractionary Currency Crises in Developing Countries”, _IMF Staff Papers_ , Vol. 52, No.2.

35
_Table 2 Balance table of characteristics across arms. In Columns (1)-(4), standard errors are listed in parentheses below the means. Columns (5)-(7) show p-values from t-tests of the differences of the means of each variable in each group. Column (8) shows the p-value from a joint orthogonality test of treatment arms._

||(1)<br>T1: 7-day one-line<br>recall|(2)<br>T2: 7-day individual<br>diary|(3)<br>T3: 7-day individual<br>recall|(4)<br>T4: 7-day HH<br>bounding/ salience|(5)<br>T1vs.T2, p-value|(6)<br>T2vs.T3, p-value|(7)<br>T2 vs. T4, p-<br>value|(8)<br>p-value from joint<br>orth. test of<br>treatment arms|
|---|---|---|---|---|---|---|---|---|
|HH head is male|0.63|0.61|0.65|0.62|0.60|0.24|0.89|0.65|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|Age of HH head|53.43|54.28|54.41|53.12|0.38|0.89|0.21|0.42|
||(0.67)|(0.68)|(0.64)|(0.63)|||||
|HH head is university-level|0.23|0.28|0.25|0.29|0.05*|0.19|0.85|0.09*|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|Age of spouse|49.36|50.67|49.82|49.20|0.21|0.40|0.15|0.47|
||(0.76)|(0.73)|(0.71)|(0.70)|||||
|Spouse is university-level|0.27<br>|0.27<br>|0.27<br>|0.26<br>|0.97|0.96|0.80|0.99|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|Number of HH members|<br>4.05|<br>4.22|<br>3.94|<br>4.15|0.16|0.02**|0.55|0.10|
||(0.09)|(0.08)|(0.08)|(0.08)|||||
|_Number of in-HH members_|3.89|4.06|3.84|4.03|0.14|0.04**|0.81|0.13|
||(0.08)|(0.08)|(0.07)|(0.08)|||||
|_Number of adults in HH_|2.91|3.07|2.91|3.03|0.04**|0.04**|0.63|0.08*|
||(0.05)|(0.06)|(0.05)|(0.06)|||||
|_Number of others not living in_|||||||||
|_HH_|0.16|0.16|0.10|0.12|0.91|0.06*|0.17|0.18|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|_Number of children in HH_|<br>0.98|<br>0.99|<br>0.93|<br>1.00|0.88|0.34|0.87|0.69|
||(0.05)|(0.05)|(0.04)|(0.05)|||||
|Owns computer|0.50|0.55|0.48|0.56|0.18|0.05*|0.68|0.06*|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|HH owns the accommodation|||||||||
|where they live|0.90|0.90|0.92|0.90|0.97|0.41|0.86|0.76|
||(0.01)|(0.01)|(0.01)|(0.01)|||||
|HH<br>accommodation<br>separate|||||||||
|<br>house|0.86|0.86|0.84|0.85|0.82|0.39|0.67|0.85|
||(0.02)|(0.02)|(0.02)|(0.02)|||||
|Total annual spending on non-|||||||||
|food|26808|25862.66|26147|27528|0.59|0.88|0.30|0.79|
||(1334.45)|(1138.89)|(1412.11)|(1154.58)|||||
|Total festival food spending|1654|1586.12|1689|1659|0.41|0.23|0.39|0.68|
||(61.41)|(56.73)|(66.07)|(63.01)|||||
|Percent of sample which was|||||||||
|replacement|0.20|0.20|0.26|0.21|0.94|0.04**|0.85|0.10|
||(0.02)|(0.02)|(0.02)|(0.02)||||0.65|
|N|<br>478|<br>473|<br>477|<br>476|||||
|*** p<0.01, ** p<0.05, * p<0.1|||||||||

15
<!-- Start of picture text --> \.<br>Poverty %* | (/ | L<br>National Percentile nN po r y<br>nS BS 249/77) S A /<br>aS, (eas<br><!-- End of picture text -->

<!-- Start of picture text --> 86.0 é f S ) Sarankhi0 la;<br>* Lower Poverty Line<br><!-- End of picture text -->
SEEs benefitted from a reduction in<sup>interest payments, when part of their debt</sup> to the Central Bank was consolidated and assumed by the Treasury.<sup>Following</sup> an erosion in real wages through<sup>most of the 1980s, in 1989 wage payments rose</sup> sharply following a framework<sup>agreement with the unions stipulating a 143</sup> percent wage increase (compared<sup>to an annual inflation rate of 70 percent). No</sup> further increase in relative<sup>sales prices was realized. But sales prices were</sup> in the short run manipulated<sup>for political purposes, as highlighted in Graph</sup> 3. Before elections, SEE price<sup>increases were allowed to</sup> Ttble 2. Indicators of SEE Performance,<sup>1980-89</sup>

||1980|1984|1989|
|---|---|---|---|
|SEE PSBR (percent of GNP)<br>|6.4<br>|2.3<br>|2.4<br>|
|Total PSBR (percent of GNP)<br>|10.3<br>|6.5<br>|5.6<br>429|
|Share of SEE PSBR (percent<sup>of total)</sup>|62.3|35.4|.|
|SEE share in total public<sup>investment</sup><br>(percent)<br>|66.6<br>|51.9<br>|41.5<br>|
|SEE share in outstanding bank<sup>credit</sup><br>(percent)<br> <br>|33.0|11.5<br>|14.2<br>17|
|Profit margin<br>1/ (percent)|..|7.5|.|
|Share of wages and salaries<sup>in total</sup><br>current expenditure (percent)<br>|..|11.8<br>|14.7<br>|
|Sales per worker (in millions<sup>of TL</sup><br>at 1988 prices)|..|38.0|36.4|
|Inpercent of GNP<br> <br>||||
|Financing<br>requirements|..|8.6|6.0|
|Internally generated funds<br>|..|3.5<br>|2.7<br>09|
|Transfers from the budget and<sup>extra</sup><br>budgetary<br>funds<br>Other<br>2/|..<br>..|2.5<br>2.7|.<br>2.4|

Source : Statistical Appendix, Tables 3-7 1/ Operating surplus/sales of goods and services. 2/ Defe,red payments, change in cash balances and borrowing (domestic and external).

13
# **IV. Econometric Analysis**

# **The Data**

The data for the estimation procedures used in this paper cover the period from 1951 to 1985. The choice of the period is based on the fact that this period represents that for which we have the most reliable production data prior to the start of the 14-year conflict in 1989.

# **Econometric Models and Estimation Techniques**

In estimating the supply response for iron ore in Liberia, we adopted two approaches. The first involved the traditional approach utilizing the price-only model in the context of the Nerlove partial adjustment mechanism. The second approach follows more closely that used by Hojman (1984), which includes a wider array of independent variables. Both approaches are discussed below.

# **_Partial Adjustment Model_**

The partial adjustment model we use for the estimation of supply response relies on two principal behavioral assumptions. First, we assume that firms, the basic unit of the industry, plan for a level of output of iron ore in period t that maximizes profit (π) subject to prevailing conditions including cost. Functionally, this can be expressed as: Where: Pt = price of iron ore in period t, Q<sup>*</sup> t = the planned output in period t and C(Q<sup>*</sup> t<sup>) = cost function</sup> Further, we assume that price is exogenously determined under competitive market conditions. This is an assumption that may be more open to question since the world market for iron ore is quite complex and for a part of the period covered a substantial proportion of the iron ore trade was on the basis of long-term contracts. In addition, since many of the mines in Liberia had direct links to steel mills, it would be reasonable to assume that a substantial amount of the supplies from Liberia were valued at internal ‘transfer’ prices. However, there was still a considerable amount of iron ore that was traded on the ‘free’ market and in many cases contract prices were based on these free market prices (see, for example, Williams and Fraser, 1985) and contracts were often adjusted to reflect changes in ‘free’ market prices.

Following Williams and Fraser (1985), we specify the following exponential form for the cost function: 10
Policy Research Working Paper 8415

# **Abstract**

To what extent does unreliable electricity provision pervasive in many African countries affect job creation in the region? This paper addresses the question by assembling household and firm level data from 29 African countries along with unique project level data on foreign direct investment (FDI). Leveraging several quasi-experimental approaches, the paper shows that outages have a non-trivial negative impact on employment. The effect is driven by a reduction in employment in non-agricultural sectors and skilled jobs. Unskilled jobs are unaffected by electricity outages. The negative effect of outages on firm entry and the performance of incumbent firms are plausible channels..

This paper is a product of the Office of the Chief Economist, Africa Region. 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 author may be contacted at jmensah2@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