rafmacalaba/datause-encoder-data huggingface dataset:
|P101434|2008|2013|12/9/2012|9 Closed|Niger|
|---|---|---|---|---|---|
|P101434|2008|2013|4/24/2013|10 Closed|Niger|
|P101434|2008|2014|11/26/2013|11 Closed|Niger|
|P101434|2008|2014|6/29/2014|12 Closed|Niger|
|P101434|2008|2015|1/21/2015|13 Closed|Niger|
|P101434|2008|2016|8/3/2015|14 Closed|Niger|
|P101434|2008|2016|3/24/2016|15 Closed|Niger|
|P101434|2008|2017|12/26/2016|16 Closed|Niger|
|P101439|2006|2008||0 Closed|Moldova|
|P101439|2006|2009||0 Closed|Moldova|
|P101439|2006|2010||0 Closed|Moldova|
|P101445|2006|2008||0 Closed|Djibouti|
|P101445|2006|2009||0 Closed|Djibouti|
|P101445|2006|2020|9/24/2019|1 Closed|Djibouti|
|P101446|2008|2008||0 Closed|Mongolia|
|P101446|2008|2009|1/23/2009|1 Closed|Mongolia|
|P101446|2008|2010|6/22/2010|2 Closed|Mongolia|
|P101446|2008|2011|2/26/2011|3 Closed|Mongolia|
|P101446|2008|2012|12/19/2011|4 Closed|Mongolia|
|P101446|2008|2013|7/1/2012|5 Closed|Mongolia|
|P101446|2008|2013|1/2/2013|6 Closed|Mongolia|
|P101446|2008|2013|6/25/2013|7 Closed|Mongolia|
|P101451|2006|2008|8/28/2007|1 Closed|Ghana|
|P101451|2006|2009|8/14/2008|2 Closed|Ghana|
|P101451|2006|2009|12/21/2008|3 Closed|Ghana|
|P101451|2006|2010|9/4/2009|4 Closed|Ghana|
|P101453|2008|2008||0 Closed|Yemen,Republic of|
|P101453|2008|2009|11/21/2008|1 Closed|Yemen,Republic of|
|P101453|2008|2010|11/11/2009|2 Closed|Yemen,Republic of|
|P101453|2008|2010|4/7/2010|3 Closed|Yemen,Republic of|
|P101453|2008|2011|4/7/2010|3 Closed|Yemen,Republic of|
|P101453|2008|2012|8/4/2011|4 Closed|Yemen,Republic of|
|P101456|2007|2007|12/22/2006|1 Closed|Liberia|
|P101456|2007|2007|3/25/2007|2 Closed|Liberia|
|P101456|2007|2008|11/29/2007|3 Closed|Liberia|
|P101456|2007|2008|5/29/2008|4 Closed|Liberia|
|P101456|2007|2009|12/16/2008|5 Closed|Liberia|
|P101456|2007|2009|6/24/2009|6 Closed|Liberia|
|P101456|2007|2010|12/8/2009|7 Closed|Liberia|
|P101456|2007|2010|6/10/2010|8 Closed|Liberia|
|P101456|2007|2011|6/10/2010|8 Closed|Liberia|
|P101456|2007|2020|1/3/2020|9 Closed|Liberia|
|P101468|2007|2009||0 Closed|Georgia|
|P101468|2007|2010|8/6/2009|1 Closed|Georgia|
|P101468|2007|2011|3/29/2011|2 Closed|Georgia|
|P101468|2007|2012|7/5/2011|3 Closed|Georgia|
|P101468|2007|2020|1/13/2020|4 Closed|Georgia|
26 are taxed at the same rate. This symmetric treatment of costs and benefits makes the tax on marginal investments zero.>/ It is therefore seen that the tax-deductibility of principal and interest in China embodies part of the provisiors of the real plus financial cash flow tax ("r + f base") but without including loan proceeds as part of the tax base. While the replacement of budgetary grants with bank loans has been a step consistent with enterprise reform, this generous treatment of amortization and interest on the latter provides very substantial incentives to investment. This is anomalous in general but particularly so at times when ozer-investment by enterprises is considered to be a serious problem.

Most loans in China are amortized over five years. Table 3.2 provides information on gross fixed investment by State-owned units for the years<sup>1982</sup> to 1986, together with the amount of domestic bank loans involved in such investment. It is also known that interest rates on fixed investment lending were increased in 1985 by about 3 percent to somewhat over 10 percent for five year funds. The left-hand side of the last column in the table presents figures on amortization of those loans by State enterprises in 1986, on the assumption that interest rates were 7 percent for loans extended from 1981 through 1984 and 10 percent thereafter. It calculates amortization in 1986 to be Rmb 23.92 billion.

There are instances of enterprises being allowed to repay loans over periods longer than five years. To assess the importance of such a provision, the calculations above were reworked with a loan term of eight years. The amortization figures for this case appear on the right-hand side of the<sup>last</sup> column in Table 3.2. The Statistical Yearbook of China, 1987 reports that profits and taxes from State enterprises equalled Rmb 134.14 billion in that year, of which<sup>Rmb</sup> 65.15 was taxes, leaving Rmb 68.99 billion as after- tax profits.This yields an effective tax rate of 49 percent = (65.15/134.14). Inclusion of amortization on existing loans in the tax base would lead to the situation described in Table 3.3.

W<sup>1</sup> These arguments are spelt out in Atkinson and Stiglitz (1980) and King (1986).
and mode choice. Looking at the reverse sense of causation (i.e., from traffic to economic outcomes), several papers point to externalities from cars (mainly congestion) as a significant bottleneck for economic activities. There is empirical evidence for the US that congestion hinders employment growth (Boarnet, 1997), lowers productivity (Fernald, 1999), shrinks output growth (Hymel, 2009) and reduces income (Jin and Rafferty, 2017).<sup>3</sup> The economic effects of congestion are found to be stronger in vehicle-intensive industries (e.g., manufacturing) relative to other industries, as documented for the US (Fernald, 1999) and the UK (Graham, 2007). These findings, however, have been challenged by a recent study, which reexamines US metropolitan area data over 30 years, and concludes that the impacts have been overestimated (Marshall and Dumbaugh, 2018).

By generating information on people, places, and activities at high spatial and temporal scales, big data has the potential to shed light on the causes and consequences of both mobility and congestion. Regarding the causes of congestion, a recent paper exploits vehicle counts from aerial imagery to document the impact of ride-hail services on congestion in New York City (Mangrum and Molnar, 2018). To our knowledge, the and of have not been assessed productivity employment consequences congestion yet using big data. As for mobility, several studies use big data to relate it to productivity and employment outcomes. For example, Toole et al. (2015) use CDR data to study the reciprocal links between employment status and individual mobility in two undisclosed European countries. In the first country, the authors focus on the closure of a large manufacturing firm and measure the reduction in the intra-urban mobility of laid-off workers inferred from changes in geo-referenced mobile phone activity. Conversely, the authors apply IA techniques in the second country to estimate aggregate unemployment rates from individual users’ mobile phone behavior. Another study uses a gravity model to relate commuting flows estimated with CDR data to within-city variations in wages and productivity in Bangladesh and Sri Lanka (Kreindler and Miyauchi, 2017). It shows that

> 3 For surveys of the economic impacts of transportation, see Redding and Turner (2015) and Berg et al. (2017) for the case of developing countries.

10
18 privatizing them. Unfortunately, hard evidence on the impacts of Singapore’s hospital corporatization initiative is not available, but the (sometimes anecdotal and selective) evidence that does exist points to a number of (largely predictable and mostly negative) consequences, including rapid increases in costs and prices charged to patients, rapid growth in technology and high-tech interventions (especially surgical procedures), and attempts by hospitals to dump highcost patients and to avoid offering services to low-income patients.

Japan and the other Tigers also offer some useful lessons on paying providers. Korea’s experience with a narrow benefit package provides an illustration of the danger of providers shifting demand from insured services whose prices are regulated to uninsured services whose prices are unregulated and highly lucrative. The social insurance systems also offer some useful lessons vis-à-vis reimbursement of insured services. The experiences of Korea and Taiwan (China) are less positive than that of Japan, which has managed to combine careful cost control with fine-tuning of profit margins on different types of care. Experiences with DRGs in Korea and Taiwan (China) have also proved useful, both pointing towards cost-savings, but in the case of Taiwan (China) drawing attention to the possible knock-on effects on service volume and total health spending. Korea and Taiwan (China) both offer important lessons for the separation of prescribing and dispensing. For example, compensation costs in both cases were substantial and in the case of Taiwan (China) were so large they offset the cost savings caused by more ‘rational’ prescribing. The risk of cost-savings never being realized because of other concessions to providers—such as allowing them to have onsite pharmacists—is also illustrated by the pilot in Taiwan (China).

It would be a mistake to view the health systems of Japan and the Tigers as ‘finished business’—all are likely to carry on exploring ways of financing and delivering health care more efficiently, if not also more equitably. But in the meantime, their existing arrangements and recent reform experiences all provide potentially useful lessons for East Asia’s developing countries, if not for one another.

# **References**

1. OECD Health Data [program]. Paris: OECD, 2005.

2. World Health Organization. World Health Report 2005: Make every mother and child count. Geneva: World Health Organization, 2005.

3. Lu J-FR. The Application of National Health Account Approaches in Estimating National Health Expenditures: Taiwan (China) Experience. Tao-Yuan: Department of Health Care Management, Chang Gung University, 2001.

4. O'Donnell O, Van Doorslaer E, Rannan-Eliya R, et al. Who pays for health care in Asia? EQUITAP Working Paper # 1, Erasmus University, Rotterdam and IPS, Colombo, 2005.

5. Lu JR, Hsiao WC. Does Universal Insurance Make Health Care Unaffordable? Lessons from Taiwan (China). _Health Affairs_ 2003; **22** (3 (May/June 2003)) **:** 77-88.
Policy ReseaRch WoRking PaPeR 4841

# **Abstract**

This paper presents new evidence on the causal links between changes in the business environment and firm productivity growth. It contributes to the literature in three important aspects. First, it constructs a unique database merging information from two large firm-level databases. The samples of both databases are merged on four criteria—country, sub-national location, firm size, and year—producing a panel of 22,004 firms in eight economies of Eastern Europe and the former Soviet Union: Bulgaria, Croatia, Czech Republic, Estonia,, Poland, Romania, Serbia, and Ukraine. Second, the paper addresses shortcomings of earlier studies, namely reverse causation, multicollinearity, and unreliable productivity estimates. Firm productivity growth is estimated drawing on corporate financial data from manufacturing firms included in the AMADEUS database. Changes in the business environment are estimated from the World Bank Enterprise Surveys conducted in 2002 and 2005. Multicollinearity problems in the full model regression are mitigated by constructing a set of six aggregate indicators of the business environment (using principal component analysis). The paper finds that, over the period 2001 to 2004, an increase of one standard deviation in infrastructure quality, financial development, governance, labor market flexibility, labor quality, and market competition raises the total factor productivity of the average firm by 9.8, 7.8, 3.2, 3.4, 5.8, and 3 percent, respectively. Lastly, the paper decomposes firm productivity growth and ranks the relative impact of changes in these six aspects of the business environment by country, by firm size, and by industry.

This paper is a product of the Economic Policy Division, Eastern Europe and Central Asia Department. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at panoscasero@ 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
up technologies. We are here following a soft linking approach, therefore, believe that such disaggregation is not necessary. In fact, if we can disaggregate all sectors and represent them in the CGE model, the linkage of top-down and bottom-up models may not be necessary.

# **2.2 Top-Down CGE Model**

The CGE model used in the study is a recursive dynamic model to analyze the economic effects of energy and environmental policies in China. It explicitly models the behavior of four economic agents: household, government, enterprise and the rest of the world (ROW). Production sectors are classified into 16 sectors, of which five are energy supply sectors (coal mining, oil and gas extraction, petroleum refinery, gas processing, and electric power generation). Please see Table 1 for the definitions of the sectors. A detailed description of the model is available in Appendix A.

**Table 1. Definition of sectors/commodities in the CGE model**

|**Sector Name**|**Definition or coverage**|
|---|---|
|AGRI|Agriculture, Forestry, Animal Husbandry and Fishery|
|COAL|Mining and washing of coal|
|OILNG|Extraction of petroleum and natural gas|
|MINE|Mining and processing of metal and nonmetal|
|FTPMF|Food, tobacco, textile, leather, fur, feather, timber, furniture, paper, printing|
|PETRO|Processing of petroleum, coking, processing of nuclear fuel|
|CHEMI|Manufacture of chemical products|
|NMETA|Manufacture of non-metallic mineral products|
|METAL|Smelting and processing of metals|
|OTHMF|Other manufacture|
|ELECT|Production and distribution of electric power and heat power|
|GAS|Production and distribution of gas|
|WATER|Production and distribution of tap water|
|CONST|Construction|
|TRANS|Transport, storage and postal services|
|SERVI|Other services|

Figure 2 illustrates the modeling of the behavior of each production sector. We use a sixtier nested constant elasticity of substitution (CES) combination function to represent a production sector. This multi-tier CES representation provides flexibility to the model by 7
A renewed emphasis on poverty reduction began in 1990. The understanding of poverty, under the influence of Sen and others, broadened from a narrow focus on income and consumption to a more complex vision of the linkages between growth and poverty reduction. Richer countries tend to have better social indicators. From this undeniable fact, the Bank drew the conclusion that human development promotes income growth and income-poverty reduction. Not all economists share this view.<sup>26</sup> The emphasis on poverty in the 1990s was not new. What was new from the late 1980s onward was the articulation of a more complete strategy, combining growth with delivering social services (education, health and social protection) to the poor. In other words, it was the recognition that it was not growth alone that would do it—which was implicitly the view of the 1980s. At the World Bank, the change was spearheaded by the _1990 World Development Report_ on Poverty, which included the first standardized global estimates of the prevalence of poverty, and by a shift in the institution’s emphasis after the arrival of James Wolfensohn as President in 1995. There was an expansion in analytic tools and capabilities, in both data and methods, linking micro data to aggregate outcomes, and with a view toward assessing policies and programs. This was built on the rapid expansion in household survey data production, which started in the mid-1980s.

Since then, development economics—moving away from macroeconomic explanations à la Keynes or Harrod/Domar—has increasingly emphasized the microeconomic foundations of development. Researchers and policymakers have realized how crucial micro-level decisions are for economic growth. Examples are the role of women in household decision-making; the effects of the proportion of household resources controlled by women on the health and nutrition of their children; the role of microeconomics in poorly functioning land, labor, and credit markets; and the role of informal networks and institutions in dealing with market failures. The aim of this micro development literature is to understand what institutions may arise at the micro level to cope with such failures and to structure policy to provide for them.

> 26 For a dissenting view, see Sudhir Anand and Martin Ravallion, “Human Development in Poor Countries: On the Role of Private Incomes and Public Services,” _Journal of Economic Perspectives_ , Vol. 7, No. 1 (1993), pp. 133-150.

14
Wang, Dewen Yongtang Ma and Changyou Zhu. 2010. “Social Security for Rural Migrant Workers in China: Current Coverage, Institutional Design and Policy Suggestions,” in _Social Protection as Development Policy: Asian Perspectives_ , Edited by Sarah Cook and Naila Kabeer, Routledge India Originals.

Wang, H. & Huang, G. 2000. Assessing the reform models of China’s urban health insurance system. _Chinese Health Policy_ 2000: 9-11..

World Bank. 1997a. _China 2020: Development Challenges in the New Century_ , Washington DC: World Bank.

World Bank. 1997b. _Old Age Security: Pension Reform in China_ . Washington DC: World Bank World Bank. 2005. _Integration of National Product and Factor Markets: Economic Benefits and Policy Recommendations_ , Report No. 31973-CHA.

World Bank (forthcoming): A Vision for Pension Policy Reform, Human Development Sector Unit, East Asia and Pacific Region. Washington, DC: The World Bank.

Zhang, Zuoji. 2002. _Social Security Knowledge Readings for Leading Cadres_ . Beijing: China Labor and Social Security Press.

28
Table 9: other variables from the logit regressions

|||**women**||**m**|**en**|
|---|---|---|---|---|---|
||Wife|single mother<br>.I|unmarried<br>Ino<br>kids|husbands|unmarried<br>no kids|
|_Demographics_||||||
|Age<br>2______________|0.062**<br>(0.011)|-0.055<br>(0.031)|-0.018<br>(0.062)|-0.087<br>(0.044)|0.062<br>(0.051)|
|age<sup>2</sup>|-000096**<br>(0.00013)|-0.00013<br>(0.0003)|-0.00038<br>(0.00068)|0.002<br>(0.004)|-0.0012*<br>(0.00056)|
|years of education|0.0054<br>(0.0054)|-0.014<br>(0.012)|0.036<br>(0.042)|-0.045**<br>(0.012)|-0.072*<br>(0.036)|
|_Wealth_||||||
|household labor<br>income in period 1|-2.7xlO-**<br>(4.69x10-<sup>7 </sup>)|-6.67xl 0**<br>(2.11x10-<sup>6</sup>)|-2.69xl O6<br>(8.83x10-<sup>6 </sup>)|-2.42x10 <sup>6</sup><br>(1.38x10-<sup>6</sup>)|-1.01 xl O-<br>(7.26x10-<sup>6</sup>)|
|<br>_Labor demand_||||||
|regional:||||||
|North Central|-0.28**<br>(0.099)|0.24<br>(0.28)|-0.39<br>(1.18)|-0.059<br>(0.24)|0.72<br>(0.97)|
|Pacific|-0.19*<br>(0.089)|0.36<br>(0.26)|0.011<br>(1.14)|0.5*<br>(0.22)|1.46<br>(0.95)|
|Northeast|0.032<br>(0.082)|0.035<br>(0.24)|-0.5<br>(1.01)|0.3<br>(0.21)|0.66<br>(0.88)|
|North|-0.21**<br>(0.077)|-0.025<br>(0.24)|-0.69<br>(1.08)|0.22<br>(0.2)|0.84<br>(0.91)|
|North northeast|-0.16<br>(0.091)|0.032<br>(0.27)|-1.49<br>(1.25)|0.28<br>(0.23)|1.09<br>(0.97)|
|Central|-0.081<br>(0.08)|0.33<br>(0.24)|-0.68<br>(1.08)|0.59**<br>(0.21)|1.98*<br>(0.89)|
|Gulf Central|-0.13<br>(0.098)|0.097<br>(0.27)|-0.25<br>(1.21)|0.18<br>(0.25)|1.2<br>(0.99)|
|% local labor<br>market female|0.48<br>(0.51)|3.97**<br>(1.31)|-0.66<br>(4.53)|-2.95*<br>(1.24)|-1.32<br>(0.38)|
|city size|<br>7.11xl0-<sup>9</sup><br>(1.2x10-<sup>8 </sup>)|<br>-9.04x 10-<sup>9</sup><br>(3.57x10-<sup>8</sup>)|<br>-1.5xl0-<sup>7</sup><br>(1.56xlO-<sup>7</sup>)|<br>6.15xI_0_<sup>_8_</sup><br>(3.17x10-<sup>8 </sup>)|1 .36x I0-<br>(1.28x10-<sup>7 </sup>)|
|sample size|53463|6275|448|4193|542|
|<br>log likelihood|-17018|-2439|-216|-2441|-303|
|Chi2|770**|466**|54**|380**|65**|
|(d.o.f.)|(46)|(40)|(23)|(39)|(26)|

The estimates are from Model 3, the Model that had the best fit. The coefficient values for Models I and 11 differ slightly, but the sign and magnitude of the coefficient estimates is very similar among the three models

** significant at the 1% level

* significant at the 5% level

42
**Figure 1: Product-space for Penang, 2007**

<!-- Start of picture text --> 5 10 15 20 25<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation

**Figure 2: Product-space for Malaysia, 2006**

<!-- Start of picture text --> 0 5 10 15<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br><!-- End of picture text --> Source: authors’ calculation 10
that is, average annual light growth (a measure of overall economic growth), population sizes (a measure of migration changes), and Mean Logarithmic Deviation (MLD) and Theil index as measures of spatial inequality.

The results in Table 6a and 6b indicate similar statistically insignificant effects as those in Tables 5a and 5b, respectively, even when we used average and real prices as controls; this also suggests the important role of prices in affecting our outcome variables. The same results hold for the coefficients of expectation effects. The results also hold for the coefficients of mineral prices, except for a positive and significant World Bank’s real price coefficient on welfare estimates.

Table 6c shows the post-accession impacts on districts with mineral endowments using the different outcome variables. The results indicate insignificant effects on overall economic growth, migration, and spatial inequality alternative measures regardless of the mineral prices used as controls. The findings confirm the robustness of those in Table 5a. Similar to Table 6c, Table 6d reports the post-accession impacts on districts with active mining activities. Again, the results show insignificant effects on overall economic growth, migration, and spatial inequality alternative measures, regardless of the source of mineral prices data. The results thereby reaffirm the robustness of those in Table 5b.

In order to identify the heterogeneous local economic effects of post-accession, as described in section 3, we exploited the exogenous spatial variation from the scale (small or large) of mining operations, the value (high or low) of minerals extracted, and the nature (extractive or transformative) of mining activities. The main goal is to estimate and evaluate the treatment effects across these difference exogenous variations. Therefore, we specified and estimated the following model: where ������ represents a dummy for an active large or small mining scale operations, a dummy for active high or low value minerals extracted, and a dummy for extractive or transformative mining activities. The coefficient of interest here is ��, which captures a vector of the treatment effects post-WTO accession. The rest of the variables are interpreted in the same fashion as in equations [4] and [5].

Tables 7a–7c, in Appendix II, show the estimated results. As above, columns 1-4 present the estimates based on constant USGS mineral prices, while columns 5-8 report the estimates based on the World Bank’s real prices data.

19