rafmacalaba/datause-encoder-data huggingface dataset:
PPR Working Paper Series

||IllR|Authoor||Contact<br>paper|
|---|---|---|---|---|
|WPS294|Irreversibility. Uncertainty, and<br>Investment|Robert S. Pindyck|October 1989|N. Carolan<br>61737|
|WPS295|Developing Country Experience<br>In Trade Reform|Vinod Thomas|October 1989|S. Fallon<br>61680|
|WPS296|How Serious is the Neglect of<br>Intrahousehold Inequality?|Lawrence Haddad<br>Ravi Kanbur|November 1989|J.Sweeney<br>31021|
|WPS297|Effects of the Multi-Fibre<br>Arrangement on Developing<br>Countries' Trade: An Empirical<br>Investigation|Refik Erzan<br>Junichi Goto<br>Paula Holmes|November 1989|<br>L Tan<br>33702|
|WPS298|Evaluaticn and Validation of a Multi-<br>Region Econometric Model: A Case<br>Study of MULTIMOD: A Forward-L<br>Macroeconometric Model|Ahmad Jamshidi<br>ooking|||
|WPS299|The External Effects of Public<br>Sector Deficits|Carlos Alfredo Rodriguez|November 1989|<br>R. Luz<br>61588|
|WPS300|How the 1981-83 Chilean Banking<br>Crisis was Handled|Mauricio Larrain|||
|WPS301|Myths of the West: Lessons from<br>Developed Countries for Developm<br>Finance|Collin Mayer<br>ent|November 1989|<br>WDR Office<br>31393|
|WPS302|Improving Support Services for<br>Rural Schools: A Management<br>Perspective|Sherry Keith|||
|WPS303|Is Undernutrition Responsive to<br>Changes in Incomes?|Martin Ravallion|November 1989|<br>C. Spooner<br>30464|
|WPS304|The New Political Economy:<br>Positive Economics and Negative<br>Politics|Merlee S. Grindle|||
|WPS305|Between Public and Private: A<br>Review of Non-Governmental<br>Organization Involvement in World<br>Bank Projects|Lawrence F. Salmen<br>A. Paige Eaves|||
|WPS306|A Method for Macroecononr.ic<br>Consistency in Current and<br>Constant Prices|Ali Khadr<br>Klaus Schmidt-Hebbel|||
estimates<sup>2</sup> for 2003 are presented in Annex Table 1. We also use the income distribution data available from the World Development Indicators to compute the cumulative density function of per capita income for each country. For 74 of the 83 countries in the original sample, we have data for the fraction of income/consumption accounted for by individuals in each quintile. This data can be used to calculate the cumulative density function for income so that at any given level of income we know the fraction of the country’s population that is below this income level. We then estimate the fraction of population that is below these thresholds for countries in our sample to estimate the number of undernourished. The weighted average of each countries undernourished population is multiplied by each regions population to produce regional estimates.

As an example let us consider the case of Uganda. Using Annex Table 1 we assume 1810 calories per person per day is the minimum dietary norm. Now using our regression estimates of the relationship between calorie consumption and income, we find that one will require an annual income of $131 (PPP adjusted) to buy 1810 calories in Uganda and using income distribution data we find that 34 percent of the population were undernourished as they were below this threshold. In order to create regional aggregates, Uganda’s 34 percent would be weighted by its 2007 population alongside the 28 other countries from Sub-Saharan Africa and the resulting number for Sub-Saharan Africa would be applied to the population of the entire region in 2007.

> 2 There is significant variation in the dietary requirement across regions because they reflect the age distribution and stature of the population that reside in particular regions.
To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and export so that the goods and services markets clear. Second, total investment equals total saving, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.

The model is calibrated with GTAP version 7.0 data. However, not all data needed for the model are available in the GTAP database. For example, biofuels are not a proper sector in the original GTAP 7.0; therefore, we modified the database in a way that allowed us to introduce biofuels sectors in our CGE model. For this purpose, we collected detailed information on production, consumption and trade, a total of seven new biofuel sectors, which have been created by splitting existing GTAP sectors. The Splitcom<sup>14</sup> software was used to process the splits and keep the global social accounting matrix balanced. The land data are based on the GTAP 7.0 database and were derived analogously to Lee et al. (2008).

# **3. Simulation Results**

We first consider a range of globally uniform carbon taxes on all fossil fuels, starting from US$10/tCO2 up to US$100/tCO2. The taxes are introduced in 2012 and kept fixed in nominal

> scenarios and the baseline. Any value assumed for AEEI is common for both baseline and the carbon tax policy scenarios. Moreover, the role of AEEI would be significant only in the long-run; 50-100 years (e.g., models presented in Edenhofer et al. 2006,2010).  The version of the model used in this analysis considers a short time horizon, 10 years (2010-2020). Interested readers are referred to existing literature such as Edenhofer et al. (2006,2010), Kaufmann (2004) and  Edenhofer et al. (2005) for more discussion on AEEI.

> 14 This software and a detailed description can be freely downloaded at <u>http://www.monash.edu.au/policy/splitcom.htm.</u>

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- 11 -

24. According to thi analyses of Sang-Woo Nam and David Ortmeyer the structure of the household wealth portfolio during the middle 1970s was **as** follows: Money 5-6 percent Time/savings Deposits 12-14.5 percent Bonds under 1 percent Equities 8-11 percent UNM (curb) Loans 0.75-2 percent Land 40-50 percent Dwellings 20-25 percent Portfolio adjustments during the period were very slow because of the underdeveloped credit markets and the high transaction costs of altering portfolios. Figure 3 shows the behavior of the household portfolio over the period 1964-1975. Real estate assets make up over 70 percent of household wealth during the period, see Figure 3. The share of housing and real estate assets matches the shifts in financial policies which are reflected in the ratio M2/GNP of Figure 1.

Figure 3 KOREA: HOUSEHOLD PORTFOLIO <!-- Start of picture text --> 1964 - 1975<br>100 -<br>90 Housing<br>80-<br>70-<br>c 60<br>. so50 Land<br>o 40<br>E<br>C) 30 Other FinanciaL Assets<br>20-<br>10 Time and Savings Deposits<br>l1 a a a3 s a o ° °<br>O - I oney- ,<br>1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975<br>+ Deposits o Fin Assets a Housing<br><!-- End of picture text --> <!-- Start of picture text --> a Money x Land<br><!-- End of picture text -->
In addition, the sharp contrast in the importance of the public sector in the coastal and inland regions may also lead to the spatial disparities. In central and western China, more than 60% of the industrial production is carried out by public enterprises, which are often characterized by low profit, weak service, high pollution, and lack of forward/backward linkages to other industries. A large stateowned sector is often associated with low growth rates (Phillips and Shen, 2005). The coastal regions are less dependent on the public sector, where in general, less than 20% of the total industrial production is carried out by public enterprises. It would be more effective for the authorities to focus on the fundamentals and improve the investment climates to attract private investors.

# **4. CONCLUSIONS**

In 1988-1997, the spatial distribution of activities in China was conditioned by two major factors: openness and industrialization. Economic development is polarized in the coastal provinces. Some of these coastal provinces have reached the 3<sup>rd</sup> stage of development. The empirical analysis, to some extent, confirms the famous bell curve of regional inequality that accompanies industrial development: in most of the developed coastal provinces, the proportion of hightech industries in the total economy has increased; and some labor intensive activities have begun to de-concentrate.

However, the more remarkable result is that the relative diffusion of the labor intensive activities has taken place mainly within the coastal region, from the more developed coastal provinces to the less developed coastal provinces. It does not significantly modify the major trends of the industrial location and specialization of the inland provinces.

The widening regional disparities between the coastal and inland regions have become a major concern of the Chinese authorities. As indicated by Brun, Combes and Renard (2002), because of this concern, the central government gave priority to public investment in the inland provinces (in particular, the western provinces) in its 10<sup>th</sup> five-year plan, 2001-2005. Today’s policy choice largely conditions China’s future capacity for development, which would be conditioned by the growth of the vast inland market and the slowing-down of the widening regional disparities.

# **REFERENCES:**

Ades A. F., Glaeser E. L., 1995, «Trade and circuses: explaining urban giants », _Quarterly Journal of Economics,_ 110, 195-227.

Alonso-Villar O., 2001, « Large metropolises in the Third World: an explanation», _Urban Studies_ , 38, 8, 1359-1371.

Amiti M., Javorcik B.S., 2004, « Trade costs and location of foreign firms in China », Economist Forum – Annual Bank Conference on Development Economics; Communication to the International Conference on China’s economic geography and regional development _,_ Hong Kong, 2003.

Amiti M., Wen M., 2001, « Spatial distribution of manufacturing in China », in Lloyd, P., Zhang X. (Eds), _Modeling the Chinese Economy_ , Edward Elgar, London, 135-148.

19
are needed.<sup>4</sup> For instance, we need to know how slow travel is in developing cities beyond the anecdotal evidence offered by disgruntled travelers. Equally important objects of interest are the differences between cities, between different parts of the same city, and across times of day within the same city.<sup>5</sup> We hope that our results, methodology, and data sources can help guide policy and future research on urban transportation in developing countries. We devote much of the last section of our paper to providing such guidance.

Second, there is a popular view that urbanization and economic development lead to ever larger cities and increased rates of motorization. According to this view, these two features will eventually lead to complete gridlock. We do find evidence of congestion in the largest Indian cities and a strong association between congestion and household access to motorized vehicles. However, economic development also brings about better travel infrastructure which facilitates uncongested mobility. In fact, indicators of urban economic development such as faster recent population growth, higher income levels, and higher motorization rates are generally associated with better overall mobility despite worse congestion.

Third, urban transportation in developing countries is prioritized for massive investments. For instance, transportation is the largest sector of lending by the World Bank and represents more than 20% of its net commitments as of 2016.<sup>6</sup> Among the many problems that these investments are trying to remedy, the lack of urban land devoted to the roadway is widely perceived to be a chief cause behind slow mobility and urban congestion. Providing an assessment of the determinants of mobility to guide policy is thus fundamental. For instance, we evidence that better is associated with a more find suggestive mobility regular grid network and more primary roads.

Fourth, the approach we develop here is an important stepping stone towards measur4In richer countries, much of our knowledge stems from representative surveys of household travel behavior. These surveys nonetheless have clear limitations, including a lack of precision in what travelers report. They are also prohibitively expensive to carry out broadly in developing countries. For the us, the Bureau of Transportation Statistics reports a cost per household of perhaps $300 to produce the National Household Transportation Survey or about $40 million in total (see `http://onlinepubs.trb.org/onlinepubs/reports/nhts.pdf` . Accessed, January 22, 2018.) 5Several software and data services such as Inrix and TomTom propose popular measures of congestion for a large sample of world cities. These services do not make the details of their methodology public. It seems that they monitor either specific roads or average traffic speed. We show below that measures of average speed are problematic and perform poorly.

6 `http://pubdocs.worldbank.org/en/801011473440949738/WBAR16-FY16-Lending-Data.pdf` . Accessed, January 23, 2017.

2
# **Figure 7 Composition of China’s 4 Trillion Yuan Stimulus Package** , 2009

<!-- Start of picture text --> housing<br>recovery<br>10%<br>construction<br>rural<br>25%<br>infrastructure<br>9%<br>innovations<br>and economic<br>restructuring<br>9%<br>key<br>emission infrastructure<br>reduction and 38%<br>environmental<br>social<br>protection<br>development<br>5%<br>4%<br><!-- End of picture text --> _Source:_ National Development and Reform Committee (2009).

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Lucas, Robert (2004), ‘The industrial revolution: Past and future’, in _The Region: Annual Report of the Federal Reserve Bank of Minneapolis_ , pp. 5–20 Marrero, Gustavo A., and Juan G. Rodríguez (2013), `Inequality of opportunity and growth’, _Journal of Development Economics_ , 104: 107-122 Milanovic, Branko (2015), `Global inequality of opportunity: How much of our income is determined by where we live?’, _Review of Economics and Statistics_ , 97(2): 452-460 Milanovic, Branko (2016), _Global Inequality: A New Approach for the Age of Globalization_ , Cambridge, MA: Harvard University Press Milanovic, Branko (2023), ` The three eras of global inequality, 1820-2020 with the focus on the past thirty years’, _mimeo_ <mark>Mookherjee, Dilip and Debraj Ray (2003), `Persistent inequality’,</mark> _<mark>The Review of Economic Studies</mark>_ <mark>, 70(2): 369-393</mark> Neidhöfer, Guido, Joaquín Serrano, and Leonardo Gasparini (2018), `Educational inequality and intergenerational mobility in Latin America: A new database’, _Journal of Development Economics_ , 134: 329-349 Novokmet, Filip, Thomas Piketty, and Gabriel Zucman (2018), `From Soviets to oligarchs: Inequality and property in Russia 1905-2016’, _Journal of Economic Inequality_ , 16: 189–223 Piketty, Thomas, Gilles Postel-Vinay, and Jean-Laurent Rosenthal (2006), `Wealth concentration in a developing economy: Paris and France, 1807–1994’, _American Economic Review_ , 96(1): 236256 Rao, Vijayendra and Michael Walton (2004), `Culture and public action: Relationality, equality of agency, and development’, in Vijayendra Rao and Michael Walton (eds.) _Culture and Public Action_ Palo Alto: Stanford University Press, pp. 3-36 Ravallion, Marti <mark>n (2014), ‘Income inequality in the developing world’,</mark> _<mark>Science</mark>_ <mark>344(6186): 851855</mark> Ravallion, Martin (2018), `Inequality and Globalization: A Review Essay’, _Journal of Economic Literature_ , 56(2): 620–642 Ravallion, Martin and Shaohua Chen (2022), `Is that really a Kuznets curve? Turning points for income inequality in China’, _Journal of Economic Inequality_ , 20:749–776 Ray, Debraj and Garance Genicot (2023), `Measuring upward mobility’, _American Economic Review_ , forthcoming.

Roemer, John (1993), `A pragmatic approach to responsibility for the egalitarian planner’, _Philosophy & Public Affairs_ , 20, 146-166 Roemer, John (1998), _Equality of Opportunity_ Cambridge, MA: _Harvard University Press_ 11
# _Objectives of This Paper_

This paper updates the early empirical literature on economic growth in transition economies, with particular focus on fiscal policy variables (fiscal balance as well as the size of government and selected expenditure components). It expands the panel data sample, relative to the existing literature, by some 7 to 10 years of additional data, representing a doubling of existing sample sizes. It utilizes a parsimonious model drawn from Fischer and Sahay (2000) of economic growth as a function of initial conditions, stabilization, liberalization and structural reform, consistent with existing empirical literature.

Allowing for the possibility that the coefficient estimates of the Fischer-Sahay model may vary over time and by quality of governance, as explained in the next section, this note expands the earlier literature by focusing on potential sources of nonlinearities: First, it explores the possibility that the key drivers of economic growth may be shifting in relative importance over time. Second, it allows for the possibility that the determinants of economic growth may vary in significance and importance, depending on the country-specific quality of governance or public sector institutions. The correlates of growth that may depend on the underlying institutional quality include such variables as public investments. Third, it allows for the possibility that there are measurable thresholds in the impact of the size of government on economic growth.

# **3. Empirical Framework: Baseline Model and Extensions**

In general, as previously discussed, three broad conclusions on the underlying determinants of economic growth in transition emerge from the first decade of empirical analysis (see, for example, Havrylyshyn 2001; Campos and Coricell 2002; World Bank 2002). These studies draw from both cross-sectional studies and panel data analyses and conclude that the following matter significantly for economic growth: (i) macroeconomic stabilization; (ii) market liberalization and structural reforms; and (iii) initial conditions. The specific measures and indexes of macroeconomic stabilization (fiscal balance, inflation, or some other measure), reform, and initial conditions have tended to vary from one study to another, but the broad findings have tended to be robust.

# _Baseline model_

This note utilizes a parsimonious regression model drawn from Fischer and Sahay (2000) reflecting the findings of the first decade of empirical research. In particular, our baseline specification is of the following general form: where _GRi,t_ is the real GDP per capita growth rate; _WDi,t_ is a war dummy or an index of armed conflict; _INFi,t_ is the natural log of inflation; _FISi,t_ is the fiscal balance in percent of GDP, with positive values denoting fiscal surpluses; and _EBSMi,t_ and _EBLIBi,t_ are the 9
are centrally recruited, hired and fired, assigned and reassigned and paid by the Teacher Service Commission, a subsidiary of the Ministry of Education located in Nairobi. At the local level, school management committees, consisting of parents, the head teacher, and district education board officials, have official governing authority for each school.

Lastly, an important point of context is that, on average, private primary schools in Kenya dramatically outperform public primary schools on national standardized tests. Bold et al. (2011) show that for the period 1998 to 2005, this performance gap was equivalent to roughly 1.5 standard deviations of the pupil level distribution of scores on the Kenya Certificate of Primary Education exam, administered to pupils in grade eight. This gap has widened slightly in the wake of FPE. While the distribution of scores has higher variance in the private sector, the private distribution exhibits first-order stochastic dominance vis-a-vis public schools, suggesting that they perform better across the board and that differences are not driven solely by elite and expensive academies in Nairobi.

# **Data**

We draw on two types of data: household survey data, which underlies the core of our analysis, and school-level administrative data. Both data sets are nationally representative and span the period before and after the enactment of FPE.

## **Household survey data**

The analysis of education expenditure and enrollment is based on two consecutive, nationallyrepresentative, cross-sectional, household surveys conducted by the Kenya National Bureau of Statistics. The first is the 1997 Welfare Monitoring Survey (WMS), which includes a sample of – – 10,874 households including 13,639 children of primary-school age interviewed roughly five years prior to the introduction of FPE (National Bureau of Statistics 1997). The second round of data is taken from the 2006 Kenya Integrated Household Budget Survey (KIHBS), spanning 13,212 households (with 14,610 children of primary-school age), interviewed three academic years after FPE had been implemented (National Bureau of Statistics 2006).

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change in inequality of opportunity has occurred. Consider, for example, two extreme cases with the same reduction in D-index, where the reduction in one case is entirely due to an improvement in the distribution of circumstances and in another case attributable to improvements in achievement for one or more groups. Each of these scenarios has very different implications for how the education system in the country has worked and how future education policy should be shaped.

To account for these effects in the overall change in inequality of opportunity, we use a methodology that is now fairly standard (and very much in the same spirit as the standard growthinequality decomposition of poverty changes), which consists of decomposing changes in inequality into two general components: coverage and composition effects. The coverage effect refers to changes in group specific coverage rates while the composition effect refers to changes in the distribution of circumstances between the two periods. The purpose of the exercise is to purge our estimates of the composition effect.

In general, the change in inequality can be expressed as: 2 1, ∆= DD −DD where the sub-indices 1 and 2 correspond to the first and second periods respectively. By adding and subtracting 2 1] –the counterfactual that corresponds to the value of the inequality index in the second period if distribution of circumstances were on average that of the first period – cc we obtain DD = EE[DD |XX 2 1). cc cc The first component of the expression corresponds to the coverage effect and the second component ∆= (DD −DD ) + (DD −DD

to the composition effect.

The decomposition can be performed either parametrically or non-parametricall <mark>y. The parametric decomposition consists in obtaining the coefficients of a discrete choice model for 2009 and computing the predicted probabilities</mark> for 2012 <mark>using the 2009 coefficients (</mark> Barros et al., 2010). <mark>Doing so, we can estimate D</mark> using the same procedure described in section 2.1. W <mark>e use</mark> Ñopo (2008) matching approach to perform the non-parametric decomposition. This is done by cc resampling without replacement all students in PISA 2009, and matching each observation to one synthetic student in PISA 2012 averaging all students in the comparison group with exactly the same circumstances. Doing this we preserve the empirical distribution of circumstances for those students in the 2009 round of PISA.

Under non-parametric matching we can re-write ∆ as: 2 1 2 1 1 2 2 1 1 2)<sup>cc</sup> ], where 1 and ∆= [(DD2 correspond to the specific sets of the distribution of circumstances for the first and −DDcc)|XX ∩XX ] + [(DDcc −DD )|XX ∩XX ] + [(DD −DD )|(XX ∩XX second periods respectively; 1 2 corresponds to those sets of the distribution of circumstances we can find for both the 1XX XX<sup>st</sup> and 2<sup>nd</sup> periods; and 1 2)<sup>cc</sup> denotes the sets that are outside of the XX ∩XX common support of circumstances. (XX ∩XX

16