rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix A: Estimate and Characteristics of Importer Transparency Index (ITI)

As in the text, the ITI index in HSW is the first principal factor, combining 11 variables linearly; i.e. 3 variables on predictability of customs administration (time spread for import, standard deviation of irregular payments, and favoritism), 4 variables of predictability and the simplification of trade policy (percentages of bound tariff lines, tariff dispersion, hidden barriers, and E-readiness), and 4 variables on simplification of customs administration (clearance time of imports, numbers of agencies involved in import, numbers of documents required for import, and irregular payments). The variables are largely taken from surveys, such as Global Competitiveness Report and Doing Business of the World Bank, The table below summarizes the weights of the components for the first principle factor, together with their data source.

Table: Weights of the Components

|Indicator|Source|Estimated Weight|
|---|---|---|
|Percentage Tariff Unbound|MAcMAP (2007)|0.054|
|Tariff Dispersion|MacMAP (2007)|0.017|
|Std. Irregular Payments|Global Competitiveness Report (2005)|0.183|
|Import Time Spread|Logistics Percepeption Index (2007)|0.050|
|Lack of E-Readiness|UN Governemt E-Readiness (2007)|0.102|
|Clearance Time for Import|Doing Business (2007)|0.225|
|Number of Import Documents|Doing Business (2007)|0.048|
|Number of Agencies|Logistics Perception Index (2007)|0.064|
|Favoritism|Global Competitiveness Report (2005)|0.119|
|Irregular Payments|Global Competitiveness Report (2005)|0.158|
|Hidden Trade Barriers|Global Competitiveness Report (2005)|0.195|

(Source) Helbel, Shepherd and Wilson (2008) Contribution of the first factor is 66 percent. As all the scoring weights are positive, the first factor can be naturally interpreted as the general tendency, commonly involving in the components. Larger weights are assigned to clearance time for imports, hidden trade barriers, irregular payments, and their standard deviation. Among them, clearance time 33
Figure 1. Overview of the Commitment to Equity Methodology <!-- Start of picture text --> TAXES<br>TRANSFERS MARKET INCOME<br>(SUBSTRACTS<br>(ADDS TO INCOME)<br>Wages and salaries, income from FROM INCOME)<br>capital, private transfers; before<br>WHO government taxes, social security<br>RECEIVES? contributions and transfers.<br>INCOME TAXES<br>& PENSION<br>CONTRIBUTIONS<br>NET MARKET INCOME<br>DIRECT<br>CASH WHO PAYS?<br>TRANSFERS<br>HOW MUCH?<br>HOW MUCH? DISPOSAL INCOME<br>INDIRECT<br>INDIRECT<br>TAXES<br>SUBSIDIES<br>CONSUMABLE INCOME<br>IN-KIND<br>CO-PAYMENTS<br>TRANSFERS<br>AND USER FEES<br>(HEALTH,<br>EDUCATION)<br>FINAL INCOME<br><!-- End of picture text --> _Source:_ Lustig (2018).

How do gender norms and roles play out in this framework? Broadly three types of activities create exposure to fiscal policies: as individuals earn income, as individuals become consumers, and as individuals provide unpaid labor to the household or become investors in household care (Figure 2).

10
Several studies examine concentration of ownership and find that it plays an important part, with majority private ownership having mostly positive effects on TFP. The overall positive effect is again driven primarily by foreign owned firms. The effect of majority domestic private ownership tends to be positive as well, but it tends to be smaller in magnitude. As before, the effect is found to be positive in Ukraine but negative in Russia. Overall, we hence find qualified support for the hypothesis that concentrated private ownership tends to increase efficiency more than dispersed ownership.

The existing privatization studies also provide information about the effect of employee (insider) ownership on efficiency. There has been a major debate about whether employee ownership and control are associated with lower or higher efficiency and excessive use of labor (labor hoarding).<sup>18</sup> We have found seven studies that examine the effect of employee ownership on TFP. Six estimates from both CEE and CIS countries are statistically insignificant and one (Estonia) shows a positive effect of employee ownership on TFP. These results are different from those of D-M who find the overall effect of employee ownership on performance to be insignificant in CEE and negative in CIS. One reason for this discrepancy may be the aforementioned limited overlap between our and D-M studies in this area. Moreover, D-M report that “the results for managers and workers show a considerable degree of sensitivity to how selection bias is handled”, while we focus on studies that handle the issue of selection. Finally, D-M recalculate some estimates (e.g., in their Table 1) for the sake of comparability across studies, while we present the effects as reported in the original studies.

Two studies distinguish between privatized SOEs and newly created private firms. Klara Sabirianova, Svejnar, and Katherine Terrell (2005) use 1992-2000 firm-level data for almost all industrial firms in the Czech Republic and Russia and find that foreign start-ups are less efficient than existing foreign owned firms, but more efficient than domestic start-ups, which are in turn more efficient than existing domestic firms. This study hence suggests that new firms tend to be more efficient than firms privatized to domestic owners. Using 2002 and 2005 firm-level data from 26 transition economies, Commander and Svejnar (2007) find that domestic start up firms are less efficient than

> 18 In addition to our discussion above, see Manuel Hinds (1990), John Earle and Estrin (1996), and Josef Brada (1996).

21
two related literatures: global macroeconomic modelling and estimates of aid requirements. Part 3.9 provides a brief summary.

# 3.2 **EmipIrical** Estimation of the Aid-Growth Relationship

Empirical analysis of the impact of aid on growth has typically regressed real growth of income on aid inflows (perhaps aggregated with other capital inflows, or perhaps disaggregated into types of aid, e.g. grant or non-grant), usually with some additional regressors included (e.g. change in TOT, domestic savings rate and various dummies).' The results from selected regressions of growth on aid are shown in Table 3.1. As can be seen, there is little agreement: both positive and negative significant relationships have been reported.

Table **3.1** Results of **selected** aid-grwth stuies

|Study<br>Data<br>Coeffic|ient|
|---|---|
|Papanek, 1973)<br>Pooled: 1950s and 1960s<br>0.39<br>;(5.80|).|
|Voi-odas (1973)<br>-<br>~<br>: -Pooled:<br>22 LDCs(none SSA)<br>.01<br>-.iioca_:S_<br>_S:A_<br>----<br>=<br>- 0.:<br>(0.20|)|
|*DowImgandt<br>H<br>e<br>**1**93--<br>-Aiiame<br>)<br>e A<br>0.43||
|>lShibly (4<br>r<br>.<br>;<br>**1**4<br>Tim-series:Sa<br>-1 2|)|
|Gupta -and<br>sl<br>C93)<br>:<br>Cross-sectio:c52LDCs<br>0.18||
|Mosley,-Hudson and<br>-Aggregat<br>sectioi::<br>-1960-7:5 LCs5<br>-0._1D_<br>~<br>~<br>~<br>~~-.0<br>=<br>-<br>-:<br>2<br>**:**<br>:'<br>-:-<br>-<br>:<br>.<br>**.**..<br>--<br>:,<br>.'0.3-<br><~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-<br>>**-**<br>-a; -:<br>->**-**<br>- ---;-.5<br>----- -- -<br>.<br>~~~~~~~~~~~~~~~~~~~~~~~~0<br>-0j,',<br>:<br>~~~~~-:<br>.-.-<br>:--19<br>06-s;i<br>32)<br>0.01|-<br>80;B:<br>.-<br>'<br>|

- ote coefficiden is thaton aid variable, the definition of whiib res between studies. Figures inparentheses are t-statistiCS.

- sa-, * . ...... ~~~~~~~~~~~~~~~~~~. .. **.** - s -.-. ; ., . .... .........................Source shown intbe.-tpas

However, the lack of unanimity need not concem us, since the equations on which these 26
Policy Research Working Paper 6052

# **Abstract**

This paper analyzes the distributional effect of public spending in Zambia using the most recent data from the 2010 Living Conditions Monitoring Survey. The analysis focuses on both the “traditional” social sectors, such as education and public healthcare, as well as other spending areas less thoroughly studied, such as agricultural support programs. Ultimately, this benefit incidence analysis addresses the extent to which spending is pro-poor and progressive; that is, it primarily benefits the poor and does so at an increasing rate as welfare levels decrease.

The results indicate that overall public education spending in Zambia is neither pro-poor nor progressive, but while this is true for the system as a whole it is not true for all of its parts. The net unitary benefits of primary and secondary education are clearly both propoor and progressive. However, their progressivity is ultimately outweighed by the extreme concentration of tertiary education benefits among the wealthiest members of Zambian society. Health spending is also regressive and not pro-poor. Although unitary net benefits are slightly progressive, unequal access remains the key constraint. In contrast, the benefits of agricultural-input subsidy programs follow a somewhat progressive pattern (for each beneficiary in the top quintile there are almost two beneficiaries in the poorest quintile) but clearly suffer from targeting problems. Consequently, without better-designed and more conscientiously implemented targeting mechanisms, public spending on health, education, and fertilizers will not be able to further the government’s larger objectives for pro-poor and progressive development policy.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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:// econ.worldbank.org. The author may be contacted at jcuesta@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
Policy Research Working Paper 8337

# **Abstract**

India is one of the fastest growing major economies. However, at 14 percent of gross domestic product, its logistics costs are high relative to the 8 to 10 percent that is typical of most advanced economies. High logistics costs and poor logistics performance impact the competitiveness of the economy on multiple levels: (1) firms deliver less competitive goods and services; (2) consumers pay more than peers for goods; and (3) the cost of achieving improvements in gross domestic product is excessive. The development of a national transport and logistics network to facilitate competitiveness and sustainable development and uplift rural regions will play an increasingly important role in shaping spatial organization in emerging economies. An element that is absent, yet critically important for national logistics issues in emerging economies, is sufficiently detailed freightflow analysis to facilitate targeted infrastructure investments and enable transformational change to improve national logistics performance. This paper presents the results of a disaggregated macroscopic freight demand analysis developed for India through a hybrid approach, calibrating the modeled input-output matrix and resulting freight flows with data where available. Data was obtained from multiple sources, such as agricultural statistics, national enterprise surveys, a financial performance database of Indian companies, population statistics, and transportation statistics from rail, inland waterways transport, highways, and ports. The model provides evidence for decision making on several levels. Aggregating freight flows enables planners to identify gaps in critical infrastructure and logistics chains. Disaggregated flows support decisions on the location of logistics clusters, maximizing the potential of multimodal transport systems, and designing the distribution and storage networks that underpin the economy.

This paper is a product of the Transport and Digital Development Global Practice. 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://econ.worldbank.org. The authors may be contacted at baritua@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
locations despite the presence of strong forces of economic geography that tend to generate or magnify the gaps.

India is not the only large developing economy where regional disparities in industrial development constitute an active policy issue. In various forms and to different degrees regional equity in industrial development seems to be a policy issue in countries such as China and Brazil as well. We therefore hope that our results would inform prospective studies in that broader context. To the extent that regional inequality in industrial development is a legitimate policy concern, we hope the paper contributes to better understanding of the business environment as an instrument of policy intervention. The notion seems too common in development circles that better performing locations or regions must have better business environment on the average. Our paper suggests that this is not necessarily true unless one defines business environment widely enough to subsume all aspects of the economic environment of producers including the region’s economic geography. This would certainly make the concept of business environment tautological and redundant. If we define it to include only those aspects of the economic environment that government policy can influence in the medium to the short run, then it is not necessarily the case that the most productive locations have the best policy environment. The case for improving the business environment of relatively backward regions through policy reforms need not thus be that those policies are necessarily worse than those in more advanced locations. It can be that, in the presence of sources of cost advantages no one controls, it makes sense for governments to compensate for them by acting on factors that they can influence.

Our findings should be qualified by a major data limitation that we have pointed out in the paper. This is that our data come from the most industrially advanced cities in India. In terms of regions, each state is represented in the World Bank Investment Climate Survey by the top three or four industrial cities in that state. We are therefore comparing locations across the top end of the size distribution of cities and urban centers. It is possible that as we move down the distribution the relative importance of geography and business environment shifts from what is reported here. It is difficult to say if these 34
||in both the public and private systems, but we cannot<br>analyze expenditure differences between individuals<br>(Households with at least one member going to public<br>schools and another to private school include some<br>10.5% of all households with at least one member in<br>public school under Scenario1, and 17.1% under<br>Scenario 2).||
|---|---|---|
|4|Even after these assumptions there are still students<br>for which we do not have schooling information for<br>2009.<br>(2.5% of subjects age 7-31 do not have schooling<br>information for 2009)|**Main assumption**: All subjects for whom we do not have<br>complete information about schooling, school level or type<br>of school are assumed not to be attending school.|
|5|Official data on per-student public expenditure for<br>colleges reported by the Ministry of Education refers<br>only to teachers‘ colleges, excluding trade colleges,<br>technical schools, agricultural colleges or nursing and<br>paramedical schools.<br>(In 2009 combined public spending on technical<br>colleges and agricultural colleges was 3% higher than<br>spending on teachers colleges; the number of students,<br>however,is not available.)|**Main assumption:**Public expenditure per student in all<br>colleges is the same as the expenditure per student in<br>teacher colleges.|
|6|Outlier observations<br>(0.1% of households)|**Main assumption:**Expenditures on education larger than<br>the mean log-expenditure plus three times the standard<br>deviation (considering only positive values of expenditure)<br>are considered unreliable and are excluded from the<br>analysis.|

_Source: Authors_

# **4.3. Allocation of Healthcare Benefits**

LCMS VI collects health information for 2010, the year the survey was collected (see Appendix 3). However, 2008 is the most recent year for available official information on the number of public healthcare beneficiaries (patients/visits) and for government expenditures on: (i) Health Posts and Clinics; (ii) Provincial Hospitals; and (iii) National and Specialized Hospitals. Ideally, the BIA would obtain from the survey data each member‘s number of visits to public health facilities during the year and then combine that information with official expenditure reports. However, in practice this is not possible. Table 9 describes the assumptions that were necessary in order to resolve a number of inconsistencies in matching the official administrative data with the household-level information recorded in the LCMS.

19
**Table 6**

**Parameter Estimates for Panel Specification with Simple Dummy, Nairobi**

||**Central**|**Coastal**|**Lake**|**Northern**|**Southern**|**National**|
|---|---|---|---|---|---|---|
||-0.02***|-0.02***|-0.00|-0.02***|-0.05**|-0.02***|
|_μ_|(8.38)|(3.20)|(0.68)|(7.54)|(4.66)|(5.33)|
|(<br>E<br>−<br>i<br>)|0.13***|0.23***|0.20***|0.13***|0.12**|0.16***|
|pt−1<br>pt−1<br>|(8.26)|(5.76)|(4.93)|(19.82)|(7.02)|(9.23)|
|Δ<br>E|0.21***|0.12|0.26***|0.33***|0.21***|0.21***|
|pt|(6.90)|(1.52)|(5.23)|(21.13)|(4.52)|(6.75)|
|Δ<br>F|0.33***|0.01|0.32***|0.26***|0.09|0.20***|
|pt|(8.00)|(0.11)|(5.01)|(14.92)|(1.55)|(4.37)|
|Δ<br>I|2.78***|0.77|1.70***|2.40***|1.25***|1.68***|
|pt|(67.95)|(1.09)|(4.33)|(7.81)|(7.30)|(6.61)|
|I|-2.78***|-3.49***|-3.83***|-2.75***|-2.07***|-3.09***|
|BAN|(15.77)|(6.93)|(17.23)|(11.13)|(2.75)|(12.72)|
|C~~(~~<sup>2πt</sup><br>)|0.04***|-0.07***|0.03***|0.04***|0.05***|-0.05***|
|os<br>12<br>|(15.43)|(4.30)|(7.14)|(2.64)|(9.68)|(8.20)|
|Si<sup>2πt</sup><br>|-0.01|-0.01|-0.03***|0.02***|-0.03**|-0.01**|
|n~~(~~<br>12<br>)|(0.81)|(1.62)|(4.63)|(4.54)|(2.35)|(2.32)|
|NDVI|-0.23***|-0.38***|-0.19***|-0.48***|-0.65***|-0.31***|
|t|(8.13)|(2.95)|(2.66)|(4.64)|(6.30)|(7.42)|
|_R-square_|0.28|0.22|0.24|0.32|0.18|0.20|

**Notes:** The dependent variable is the change in the logarithm of the nominal price in market _i_ . All regressions employ a (market) fixed effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute z-statistics in parentheses, significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than typical due to clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level.

30
**Table 6: Per Capita GDP Growth and Investment Ratio of Selected Cities (1991-98)**

|City|percapita GDPgrowth<br>Investmenti|nGDP|
|---|---|---|
|Beijing|1.98|37.36|
|Changchun|13.68|25.07|
|Changsha|10.76|25.54|
|Chengdu|9.55|35.58|
|Chongqing|1.38|2.51|
|Dalian|10.03|25.11|
|Guangzhou|9.97|33.46|
|Guiyang|5.90|33.82|
|Hangzhou|10.18|23.52|
|Harbin|8.72|31.18|
|Kunming|11.73|26.07|
|Lanzhou|5.05|36.32|
|Nanchang|12.45|19.95|
|Nanjing|8.83|31.21|
|Shanghai|7.73|51.22|
|Shenzhen|9.12|31.53|
|Tianjing|8.18|39.60|
|Wuhan|9.18|38.88|
|Xian|8.73|26.36|
|Zhengzhou|10.51|37.13|

_Source_ : Lin and Song (2002) Improving the business environment is one major strand of the urban economic strategy in China. Equally important is the role of municipal and county governments in modulating and in implementing enterprise ownership reforms. Although the broad guidelines have been determined by the central government, the microstructure of the reforms and how these are being played out across urban China is in the hands of subnational governments. It is they that are setting the pace of privatization and it is the local industrial bureaus that determine how much autonomy is permitted to the corporatized state enterprises<sup>28</sup> and the governance structures being crafted to monitor their performance. CIMC (and also Huawei) is one striking example of how the autonomy extended by the Shenzhen municipality has permitted the company’s management to achieve dramatic results through horizontal mergers, innovation, and an aggressive strategy to

> 28 Corporatized enterprises are those that have sold some of their equity to the non-state sector and have been organized as registered corporate entities with traded shares that are subject to the rules of corporate law.

28
# Contents

|**1.**|**Introduction ...............................................................................................................................3**|
|---|---|
|**2.**|**The macro-econometric model MFMod-TCD ...............................................................................6**|
|**3.**|**Baseline scenario and “business as usual” behavior of the Chadian economy ...............................8**|
|i.|Oil sector and price modelling .......................................................................................................... 8|
|ii.|<br>Modelling the « standard » behavior of the public expenditures .................................................. 11|
|iii|.<br>Baseline scenario ............................................................................................................................ 14|
|**4.**|**Outturns under alternative revenue management rules ............................................................ 17**|
|i.|The CEMAC fiscal framework .......................................................................................................... 17|
|ii.|<br>Price smoothing mechanism ........................................................................................................... 22|
|iii|.<br>CEMAC+ rule ................................................................................................................................... 24|
|iv|.<br>Comparison of outcomes ................................................................................................................ 32|
|**5.**|**Conclusion ................................................................................................................................ 34**|
|Refe|rences**....................................................................................................................................... 36**|
|App|endix**......................................................................................................................................... 37**|
|A.|<br>Baseline response of the Chadian economy to an oil price shock .................................................. 37|
|B.|<br>Functioning of the implemented oil revenue management ........................................................... 40|

2