rafmacalaba/datause-encoder-data huggingface dataset:
behavior. Surveys assess trends in economic and social status in order to identify links between adjustment policies and changes in socioeconomi **c** onditions of particular groups (Serageldin, 1989). Changes in socioeconomic conditions in turn affect the macro and micro demand for education. Household and village surveys also collect information on educational attainment, schooling expenses, and school character and location. The Social Dimensions of Adjustment (SDA) Program is undertaking similar household and community surveys in a number of African countries. The SDA program was launched in 1987 as a joint venture between the UNDP Regional Programme for Africa, the African Development Bank, and the World Bank.

# C Ouestions for further research

4.09 In conclusion, reliable evidence on the effect of adjustment policies on the education sector is limited. There appears to be a causal link between adjustment and education, but the nature of the link varies widely and is poorly understood. For example, evidence of reductions in public expenditures during adjustment is pervasive, but there is considerable uncertainty regarding the effects of declining public financing on education. Explanatory models are poorly specified, and they rely on a weak database. As a result, it is difficult to determine causality, i.e., to isolate the specific effects of adjustment policies. Furthermore, the implications of changes in financing for education quality and equity are unclear.

4.10 Further research and arialysis that focuses on process monitoring is critical. For example, the database can be improved by monitoring key education indicators during the adjustment process. Research should seek to complement existing comparative statics assessments with analyses of the dynamics of sector adjustment. Particular questions for further research that have important implications for the design and implementation of adjustment programs are given below. But even in the absence of precise answers, evidence suggests probable links between adjustment measures and the education sector. Progress is evident in the design of World Bank adjustment lending, but muc'- more needs to be done to address education sector issujes.

* How do macroeconomic policies, particularly changes in public expenditure levels, affect the supply of education? How do changes in supply in turn affect coverage, quality, and equity in the education sector?

* How is contraction in the education sector managed-what are the dynamics of budget cuts among different subsectors of education? What political forces promote or impede adjustments? If resource transfers from higher to primary education are widely recommended, why are they not implemented? How, if at all, can adjustment lending affect institutional bottlenecks?

* Is education protected or vulnerable relative to other sectors during periods of fiscal retrenchment, increasing debt, or civil service reform? Does education vulnerability vary by region or income level? If differences exist, can they be explained by differences in the adjustment process between country groups?

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Figure 3: Regional investment growth prospects A. Five-year ahead investment growth B. Actual and forecast investment growth forecasts <!-- Start of picture text --> Percent Percent<br>EAP EAP excl. China ECA LAC SAR<br>16 7<br>2011-21 2022-30<br>14 6<br>12 5<br>10<br>4<br>8<br>3<br>6<br>2<br>4<br>1<br>2<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 EAP ECA LAC SAR<br><!-- End of picture text --> Sources: Consensus Economics; World Bank.

Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; SAR = South Asia.

A. Figure shows the five-year-ahead forecasts for investment growth as of the year shown on the x-axis. Sample includes data for six economies in EAP (China, Indonesia, Malaysia, the Philippines, Thailand, and Vietnam), seven in ECA (Bulgaria, Croatia, Hungary, Poland, Romania, Russia, and Ukraine), six in LAC (Argentina, Brazil, Chile, Colombia, Mexico, and Peru) and one in SAR (India).

B. Geometric mean of actual investment growth in 2011-21 and of current-year to eight-year-ahead consensus forecasts for investment growth for 2022-30, as of September 2022. Includes 6 economies each in EAP, ECA, and LAC and 1 economy in SAR.
Table S6. Model With Interaction Between Inequality and Initial Income (Interaction with Income in 1970)

||(1)|(2)|(3)|
|---|---|---|---|
|Inequality Variable is:|Gini<br>Brueckner et al. (20|15)<br>Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|||Panel A: Dependent Variable is ln(yit)||
|Inequalityit|25.80***<br>(5.60)|43.87***<br>(7.07)|31.50***<br>(5.55)|
|Inequalityit|-3.95***|-6.70***|-4.64***|
|*lnyi1970|(0.82)|(1.06)|(0.82)|
|ln(yit-1)|0.74***<br>(0.07)|0.74***<br>(0.03)|0.72***<br>(0.06)|
|Kleibergen Paap F-Statistic|77|67|63|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|494|706|706|
|||Panel B: Dependent Variable is Δln(yit)||
|ΔInequalityit|21.26***<br>(4.39)|55.64***<br>(10.16)|41.98***<br>(7.01)|
|ΔInequalityit|-3.26***|-8.71***|-6.31***|
|*ln(yi1970)|(0.65)|(1.55)|(1.04)|
|Δln(yit-1)|0.32***<br>(0.07)|0.22***<br>(0.09)|0.19***<br>(0.08)|
|Kleibergen Paap F-Statistic|55|42|47|
|Country FE|No|No|No|
|Time FE|Yes|Yes|Yes|
|Observations|369|602|602|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

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We are not aware of any study that provides serious estimates of TFP growth for the world as a whole, that we could use to construct estimates of China's past productivity growth performance relative to the rest of the world. All we have is the very crude development accounting exercise that we did in Section 3 of the paper. If we take this at face value, we find that productivity growth in China (measured as the residual from a simple Cobb-Douglas production function) grew at 0.7 percent per year between 1980 and 2000, while productivity growth in an aggregate of the rest of the world was zero. These are of course very naive estimates of productivity growth, but they do support the basic premise that productivity growth in China has been faster than in the rest of the world. Moreover, the estimates for China are in fact quite low, mostly reflecting the fact that we are using growth rates of per capita GDP from the Penn World Tables which makes fairly substantial -- and somewhat arbitrary -- downward adjustments to China's growth rate.

It is also informative to look at how China's productivity growth rates compare with industrial countries where we have careful estimates of TFP growth. For the United States, for example, Jorgenson and Stiroh (2000) estimate a TFP growth rate of 0.6 percent per year between 1959 and 1998, albeit with a modest acceleration to around 1 percent per year during the second half of the 1990s. Somewhat higher estimates of US TFP growth during the 1990s are provided by Basu, Fernald, and Shapiro (2001) who argue for a productivity growth rate of 2 percent per year during the 1990s (adjustment costs mean Solow residual understates TFP growth). For the UK, Basu, Fernald, and Oulton (2003) find TFP growth rates around 1 percent per year between 1980 and 2000. This suggests that reasonable estimates of TFP growth rates in China in the 1.5 to 2 percent per year range may well be somewhat higher than those in industrial countries.

Our second main point is that productivity growth in China is likely to be significantly higher than pure TFP growth because of sectoral reallocation of factors or production. For example, Kraay (1996) estimates that TFP growth for China was 1.8 percent per year over the period 1978-1994, but that overall productivity grew by 3.2 percent per year, with the difference of 1.5 percent per year capturing intersectoral factor reallocation. Here we present a simplified accounting framework that can be used to measure these efficiency gains from reallocation, and use it to speculate as to the scope 24
operation and Development, and official government publications of each country, among others;

- _educ1_ = government spending on education as percentage of GDP from the WDI database;

- _educ2_ = people with completed secondary and tertiary education as percentage of population aged 15 and over from Barro and Lee (2010);

- _rd_ = a country’s expenditures on research and development as percentage of GDP from the WDI database;

- _investment_ = measure of investment freedom in natural logarithms from the Heritage Foundation <mark>and serves as a proxy for investment promotion.</mark> The score ranges from 0 to 100 (highest investment freedom) and measures the ability of individuals and firms to <mark>move their resources in and out of specific activities both internally and across the country’s borders</mark> . <mark>This variable is mainly based on official government publications of each country on capital flows and foreign investment;</mark>

- _trade1_ = a country’s share of exports of goods and services as percentage of GDP from the WDI database;

- _trade2_ = measure of trade freedom in logarithms from the Heritage Foundation, which is a composite measure of the trade-weighted average applied tariff rate and non-tariff barriers with scores ranging from 0 to 100 (highest trade freedom), reflecting the absence of trade protectionism. The measure is based on various underlying sources including data from World Bank, the World Trade Organization, and the Economist Intelligence Unit, among others;

- _business_ = measure of business freedom in natural logarithms from the Heritage Foundation as an outcome-based indicator of a country’s institutional development. It is a measure reflecting the ability to start, operate, and close a business with scores ranging from 0 to 100 (highest business freedom). The measure mainly relies on the World Bank’s Doing Business study;

- _hhi =_ HHI of sector concentration to capture competition in a domestic firm’s sector. The HHI of sector concentration is defined as the sum of squares of firm’s output share by sector. If only one firm operates in a sector, the HHI would be 1. A lower HHI reflects a higher sectoral diversity. This measure includes both domestic and foreign firms;

18
a homogeneous behavior of countries while CAMU and WAMU countries are subject to asymmetric shocks due to differences in their economic structure.

While recent studies mainly use the panel smooth transition regression (PSTR) method (Kremer, Bick, & Nautz, 2013; Ibarra & Trupkin, 2016; Ndoricimpa, 2017), this paper fills this analytical gap through the analysis of cointegration relationships at the regional and country levels by using panel and country data sets. In this paper, we investigate, through non-linear models combined with cointegration relationship, the threshold level of inflation above which inflation becomes harmful to economic growth for member states of the WAMU and CAMU regions. Models are based on results from Khan and Senhadji (2001) on thresholds, and Faria and Carneiro (2001) on cointegration relationships in the growth-inflation nexus. At the regional level and in each monetary union, we use panel data to analyze a homogeneous cointegration relationship between macroeconomic variables, and the Kao & Chiang (2000) cointegration approach is used to perform this analysis. To show the heterogeneity of this relationship, we use the Stock & Watson (1993) cointegration approach at the country level, and we derive an aggregate regional inflation threshold. We also use ARDL models to check the robustness of the analyses. Results from this study are expected to inform policy makers in CAMU and WAMU countries on the appropriate level of inflation and shed light on the suitability of the 3 percent inflation rate criterion within unions.

Overall, these results show that based on the current inflation thresholds, there are opportunities to increase the current thresholds that are used by the regional central bank in each currency union. By considering a homogeneous cointegration relationship, the inflation threshold above which price increases negatively affect growth would be 5 percent in the CAMU and 2 percent in the WAMU. However, based on the country-level analyses, it could be concluded that it is possible to increase the threshold levels used by both regional central banks: 5.4-5.6 percent in the CAMU, well above the 3 percent adopted in this currency union, and 4.3-4.5 percent in the WAMU, well above the conservative 2 percent being used by the regional central bank of this currency union.

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(see Berger et al, 2004 and Beck, 2008 for surveys). Finally, we include a measure of Financial Freedom from the Heritage Foundation, with higher values indicating fewer restrictions imposed on banks and less direct government involvement in the financial system. Given the sample size, we have countries at all levels of economic and financial development, as well as a wide variation in economic growth, bank concentration and financial freedom.

The correlations in Panel C of Table 2 confirm some of the differences between Islamic and conventional banks discussed above. Specifically, in the large sample, we find the Islamic banks rely less on non-deposit funding, have lower overhead costs and cost-income ratios, have higher ROAs and capital-asset ratios, are smaller, have higher fixed assets ratios and lower noninterest earning asset ratios. In the smaller sample, however, we find that Islamic banks have a higher share of fee-based income, rely more on non-deposit funding, and have higher loandeposit ratios, lower nonperforming loans, higher overhead costs, lower z-score, higher ROAs and higher equity asset ratios. As in the large sample, Islamic banks are smaller and have higher fixed asset and lower non-interest earning asset ratios. The different correlations can be explained by the very different country samples, which underlines the importance of (i) controlling for country factors and (ii) checking the robustness of our findings for a sample of countries with both Islamic and conventional banks to thus better control for confounding country factors.

# **4. Comparing Islamic and Conventional Banks**

Table 3 Panel A reports the regression results for the larger sample of 141 countries and 2,956 banks. Overall, the results suggest that once the bank and country controls are included, Islamic banks are more efficient than conventional banks and have higher capitalization ratios, but that they are not significantly more or less stable, do not have significantly different business models and have similar asset quality. . Specifically, the results in columns (1) to (3) show that there are no significant differences in business orientation between Islamic and conventional banks, as the Islamic bank dummy enters insignificantly (at the 5% level) in the regressions of fee income, non-deposit funding and loan-deposit ratios. On the other hand, Islamic banks have significantly lower operating costs and cost-income ratios than conventional banks (columns 4 and 5). Specifically, we find a 6.4 percentage point difference in the cost-income ratio between 14
40

# Refarence-s

- Balassa, B., 1987. "China's Economic Reforms in a Comparative Perspective." Journal of Comparative Economics. 11:410-426.

- Barro, R. and X. Sala I Martin. 1989. "Economic Growth and Convergence Across the United States." Paper presented at the National Bureau of Economic Research Conference on Economic Growth, Cambridge, Massachusetts, October 6-7.

- Buchanan, J. M. 1975. "Public Finance and Public Choice." National Tax Journal 28, no. 4 (December):383-94.

- Byrd, W., and Lin Qingsong, eds. 1990. China's Rural Industrv. New York: Oxford University Press for the World Bank, forthcoming.

- Dollar, C., "Industrial Reform and Economic Efficiency in China" mimeo, June 1989.

- Johnson, D. G. 1986. "Economic Reforms in the Peoples Republic of China." Paper prepared for the 30th Anniversary Conference of the Graduate Program in Economic Development, Vanderbilt University, October 17-19.

- Lin, J. Y. 1986. "The Household Responsibility System in China's Agricultural Reform: A Theoretical and Empirical Study." Paper prepared for the 20th Anniversary Conference of the Graduate ProZram in Economic Development, Vanderbilt University, October 15-17.

- Meng Xin. 1990. "The Rural Labor Market." In W. Byrd and Lin Qingsong, eds., China's Rural Industry. New York: Oxford University Press for the World Bank.

- Mueller, D. C. 1976. "Public Choice: A Survey. "Journal of Economic Literature 14, no. 2 (June):395-433.

- Ouchi, W. 1982. TheorX Z: How American Business Can Meet the JaRanese Challenge. New York: Avon.

- State Statistical Bureau of China. Various years. Statistical Yearbooks of China.

- Svejnar, J. 1990. "Productive Efficiency and Employment." In W. Byrd and Lin Qingsong, eds., China's Rural Industry. New York: Oxford University Press for the World Bank.

- U.S. Government. 1983. U.S. County and City Data Book. 1983. Washington, D.C.: U.S. Government Printing Office.
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Figure 19. Tariffs Imposed by Vietnam (LHS) and faced by Vietnam (RHS) in trade with RCEP members in 2020 and 2035 <!-- Start of picture text --> Crops<br>Non metallic minerals Food and beverages<br>Meat products<br>Animal products Petroleum, coal products<br>Animal products<br>Wearing apparel and leather Chemical, rubber, plastics<br>Motor vehicles and parts<br>Textiles<br>Wood and paper products<br>Non metallic minerals<br>Other extraction<br>Wearing apparel and leather<br>Textiles<br>Chemical, rubber, plastics<br>Other manufactures<br>Other transport equipment<br>Meat products<br>Other extraction<br>Metals<br>Crops<br>Basic pharmaceuticals<br>Fossil fuels Electrical equipment and...<br>Total manufacturing<br>Total natural resources Total natural resources<br>Total agriculture<br>Total Total<br>0.0 5.0 0.0 5.0 10.0<br>2020 2035 2020 2035<br><!-- End of picture text --> Note: LHS = left-hand side; NTBs = non-tariff barriers; RHS = right-hand side. Source: RCEP Official schedule (https://www.dfat.gov.au/trade/agreements/not-yet-in-force/rcep/rcep-text) trade weighted tariffs. Trade from WITS (2020). Source: World Bank staff calculations using ENVISAGE model The service sectors have the highest level of initial non-tariff measures (NTMs), both as imposed and faced by Vietnam within the RCEP, as shown in Figure 20 and Figure 21 (according to the data from Cadot et al. (2018) for good and Hoekman and Shepherd (2019) for services). Among service sectors, public administration and services (with Vietnam imposing an initial level of NTM of 46 percent and facing an initial level of 73 percent) is highest, followed by trade, tourist services and communication, which had the highest NTMs in 2020. In the goods sector, the highest level of NTMs imposed by Vietnam is recorded in wood and paper products (23 percent in 2020), while the highest NTMs faced by Vietnam are in meat products (17 percent). Under the shock of NTM reduction, the RCEP scenario, we assumed a reduction of 35 percent in agricultural goods, and 25 percent in manufacturing, and services goods, between RCEP member countries, and a 10 percent reduction in NTMs between RCEP members and the rest 25
higher returns and that there is a growing divergence between the most profitable of those firms and the other high-skill firms. We also see a large divergence between the ROIC in high skilled versus low skilled industries when we split industries by RMAN, CPS, or NRCOG. However, a concern with these estimates is that in high skilled industries, intangible capital is being mis-measured so as to reduce total invested capital, thereby inflating ROIC numbers. Indeed when we split industries by their Intangible Capital/Assets ratio into Low ICAP industries (Intangible capital/Assets ratio is less than median) and High ICAP industries(Intangible capital/Assets ratio is greater than or equal to median), we see the divergence to exist mainly in High ICAP industries. A detailed definition of ICAP is provided in the following section.

# **1.2 Mis-measurement of Intangible Capital**

One of the concerns with the above definition of star firms is that financial statements do not measure intangible assets accurately and the consequent underestimation of intangible capital is likely to be more important in high skilled industries. This would lead to overestimation of ROIC and biased regression estimates. The concern that conventional measures of invested capital do not properly capitalize the value of intangibles is a long standing one. Earlier attempts to address it include Peles [1971], Hirschey [1982], and Falato et al. [2013]. More recently, Peters and Taylor [2017] have produced firm-level estimates of intangible capital and shown that including intangible capital in the definition of Tobin’s q produces a superior proxy for investment opportunities. They also show that their adjustments are not sensitive to specific assumptions on the depreciation of intellectual capital. Thus, while these measures are, by construction, approximations, they are arguably the best available.

Hence, as an alternate definition of invested capital, we replace the _INTANit_ in equation (2), with the new definition of intangible capital from Peters and Taylor [2017], _ICAPit_ .

where _ICAPit_ , is defined as the sum of externally purchased intangible capital (Compustat item 11
methodologies for calculating emissions reductions, involved host countries and credit buyers, expected CERs, and power generation capacity. While information about inputs used in the projects are not available, the technology employed for each project is laid out in the baseline documentation of CDM projects and therefore implicitly in the project classifications, which are based on applied baseline technologies.

Scrutiny of the dataset shows that the CDM portfolio has grown rapidly since its inception in 2003. By December 2010, 6,977 CDM projects have been sent to UNFCCC for validation. 1,079 of these projects have been registered, 351 are in the process of review, 5,270 are in the process of validation, while 226 projects were either withdrawn or rejected by the CDM Executive Board (EB) or terminated by independent Designated Operational Entities (DOE) upon audit. (UNEP Risoe CDM/JI Pipeline Analysis and Database, 01 January 2011). Moreover, there are 49 observations with missing abatement data and 2 observations with zero abatement. The remaining 6,700 CDM projects in the pipeline are expected to reduce approximately 872.47 Million tCO2e in each year and 3.51 billion tCO2e by 2012.

Following the UNEP Risoe Center protocol, the CDM projects in the pipeline can be categorized into eight major types: (1) renewable resource based, (2) methane avoidance, coal bed/mine and cement, (3) supply-side energy efficiency, (4) demand-side energy efficiency, (5) hydrofluoro-carbon (HFC), perfluoro-carbon (PFC), and nitrous oxide (N2O) reduction, (6) fossil fuel switch, (7) forestation, and (8) transport. Except for fossil fuel switch and transport projects, each major category can be divided into several specific types. The CDM board has approved 115 different methodologies to calculate emissions reductions by these projects. The methodologies account for the technologies employed by the projects. For some projects, combinations of two or three relevant methodologies are used. However, approved methodologies can be categorized in six major groups: (1) large scale (AM), (2) large scale consolidated (ACM), (3) small scale (AMS), (4) large scale afforestation and reforestation (AR-AM), (5) large scale afforestation and reforestation consolidated (AR-ACM), and (6) small scale afforestation and reforestation (AR-AMS). Table 1 reports the number and percentage of the CDM projects in the pipeline and annual 10