rafmacalaba/datause-encoder-data huggingface dataset:
|els|timated using<br>rd rate across<br>ralization is a<br>ther variables<br>s clustered at<br>(8)<br>Lib<br>0.140***<br>(0.029)<br>-1.584***<br>(0.056)<br>0.015<br>(0.019)<br>-0.041<br>(0.030)<br>-0.552***<br>(0.054)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|---|---|
|cial Chann|ressions are es<br> baseline haza<br>P. Trade libe<br>ner (1995). O<br>tandard error<br>tively.<br>(7)<br>All<br>0.127***<br>(0.025)<br>-1.033***<br>(0.051)<br>0.061***<br>(0.013)<br>0.038**<br>(0.019)<br>-0.634***<br>(0.043)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|Non-Finan|ime _t_0. All reg<br>ow for diferent <br>vided by its GD<br> Sachs and War<br>tios. Robust s<br>% levels, respec<br>(6)<br>Lib<br>0.140***<br>(0.026)<br>-1.685***<br>(0.054)<br>-0.287**<br>(0.129)<br>0.033**<br>(0.016)<br>0.038<br>(0.037)<br>-0.270**<br>(0.120)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|ternative|at started at t<br>ations also all<br>d imports di<br> database by <br> not hazard ra<br>%, 5%, and 1<br>(5)<br>All<br>0.168***<br>(0.027)<br>-1.192***<br>(0.050)<br>0.252***<br>(0.093)<br>-0.020*<br>(0.012)<br>-0.014<br>(0.027)<br>-0.155*<br>(0.080)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|rvival - Al|rld market th<br>efects. Estim<br>y’s exports an<br>d the previous<br>efcients and <br>nce at the 10<br>(4)<br>Lib<br>0.146***<br>(0.026)<br>-1.692***<br>(0.054)<br>-0.021<br>(0.025)<br>0.048<br>(0.037)<br>-0.288**<br>(0.120)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|Export Su|ntry _c_ to the wo<br>and time fxed <br> sum of countr<br>8), who update<br> We report co<br>tistical signifca<br>(3)<br>All<br>0.157***<br>(0.024)<br>-1.179***<br>(0.047)<br>0.098***<br>(0.019)<br>-0.022<br>(0.026)<br>-0.145*<br>(0.078)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|ong-Term|ct _k_ from cou<br>ting-country <br> defned as the<br>d Welch (200<br>4) of Table 3. <br>** denote sta<br>(2)<br>Lib<br>0.145***<br>(0.026)<br>-1.689***<br>(0.054)<br>-0.327**<br>(0.130)<br>0.040**<br>(0.016)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|ion and L|spell of produ<br>ntrol for expor<br>e openness is <br> Wacziarg an<br> to column (<br>s. *, **, and *<br>(1)<br>All<br>0.172***<br>(0.027)<br>-1.197***<br>(0.051)<br>0.241***<br>(0.093)<br>-0.020*<br>(0.012)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|Table 5: Stock Market Liberalizat|The dependent variable is the hazard rate for an export <br>the Cox Proportional Hazard Model (Cox, 1972) and co<br>products by defning product _k_ as strata variable. Trad<br>dummy variable based on trade liberalization dates from<br>are defned in Table 3. Full set of controls corresponds<br>(exporting country)*time (_c ∗t_0) level are in parenthese<br>StM liberalization x Distance to comp. adv.<br>StM liberalization<br>Distance to comparative advantage<br>GDP pc x Distance to comp. adv.<br>Trade openness x Distance to comp. adv.<br>Trade openness<br>Trade liberalization x Distance to comp. adv.<br>Trade liberalization<br>Full set of controls<br>Exporting country FE<br>Time FE<br>Product stratifcation<br>Observations|

41
**Figure 19: Jordan’s total export value in Manufacturing, by main trade partner**

<!-- Start of picture text --> Jordan's Manufacturing Exports<br>by Main Partner<br>China EU India<br>3000<br>JC JC JC<br>2000<br>1000<br>0<br>Saudi Arabia United Arab Emirates United States<br>3000<br>2000 JC JC JC<br>1000<br>0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>Year<br>Export Value in M US$<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). The vertical red dotted line splits the periods before and after the implementation of the JC.

We assess the relative importance of the increase in manufacturing exports to Jordan’s main trade partners relative to other sectors. We consider total manufacturing and nonmanufacturing exports to EU and countries in Figure 19, the main group of Jordanian manufacturing importers. Figure 20 reports Jordan’s total manufacturing and non-manufacturing exports to Eu and countries in Figure 19.

# **Figure 20: Jordan’s total export value in non-manufacturing and manufacturing to main trade partners.**

20 | P a g e
Khwaja’s paper is also of interest because it shows that variation in project characteristics explains at least half the variation in maintenance. In other words, even in communities where community characteristics predict collective action problems, good project design can increase maintenance. This is an important finding because it shows that even when wholesale reform that fully addresses the political economy equilibrium is not possible, some progress can be made by changing conditions within a given equilibrium.

While this does not directly apply to public sector workers, it is a reminder of the fact that whether elite capture is a problem is context-specific and depends on the cost and benefits of elite participation in a program — as argued by Khwaja (2009) the costs to efficient program implementation of circumventing elites can be significant.

In the context of public service delivery, Rasul and Rogger (forthcoming) make an important contribution estimating the impact of variation in management practices on the quality of public services in Nigeria. Based on a data set of 4,700 projects (essentially construction projects, such as boreholes, buildings, dams and roads) across 63 organizations, the authors link management practices in different agencies to the likelihood that a project will be completed (including measurements of the quality of the project). While their data are not experimental, they argue that their estimates of the impact of management practices on public services can be considered as the coefficients in a production function because they can condition on many project and implementing agency characteristics and because they compare the same project type (i.e. training, infrastructure, etc.) across different ministries.

Their findings are striking: for the same project type, they find that the percentage of projects never started varies from 11 to 95 percent, those fully completed varies from 3 to 89 percent and projects rated satisfactory from 25 to 100 percent. Similarly, they find that – against the backdrop of a civil service which is widely considered corrupt and in which 31 percent of the projects considered were reported to have some corruption issues – a one standard deviation increase in management practices (specifically giving bureaucrats more autonomy) is associated with a 17 percent increase in project completion rates. Moreover, there is no interaction between the level of corruption associated with a project and management practices. In other words, even for a given corruption level, a change in management practices can have positive effects. Conversely, curtailing autonomy (an often advocated counter corruption measure) is associated with lower project completion.

12
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of infrastructure in Africa, including the 15 countries of the Economic Community of West African States (ECOWAS). The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

The purpose of this regional report is to present the key AICD findings for the ECOWAS community. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag in the data. The period covered by AICD runs from 2001 to 2006. Most of the technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today‘s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis is based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the regional situation. Nevertheless, some specifically regional issues—particularly of the regulatory and institutional variety—may not have been explicitly addressed in the national data collection effort.

Fourth, while water resource management is an important aspect of regional integration in Africa, this report does not explore water resource issues. The reason is that the AICD project did not cover water resources per se, but rather the specific water resource needs associated with the power, irrigation, and water supply sectors.

## Why infrastructure matters

ICT infrastructure has made a significant contribution to growth performance in ECOWAS. The regional group has 15 member states: Benin, Burkina Faso, Cape Verde, Côte d‘Ivoire, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo. For the period 10
4 country by the Bank and Fund in the eyes of other donors, and thus has become so iething of a prerequisite for purely bilateral aid flows.

Focussing more closely on the projections, a strikin- anomaly in the PFPs is the inconsistency between the projections for growth and investment rates. A standard growth accounting framework would lead one to expect that if a country is predicted to undergo more rapid growth over a specified period, then _ceters paribus_ that country should be expected to have a higher rate of investment over thaf period. However, there is little comlation between the projected increased in investment and the projected growth rates by country. Overall, the projected increase in growth far exceeded the projected increase in investment. One is therefore left to wonder whether the increased productivity of existing capital and future investment could be expected to be the main sources of increased growth over the PFP period. In the short run, increased utilization of existing capacity can be the source but in the medium to long run this can not be the main source of growth.

The primary concern of this paper is with the realism of the supply response assumed in PFPs. In particular, it deals with the possibility that PFP projections could display excessive optmism about the medium term outlook. There are a number of reasons why this might happen. First, the issue of government "ownershl;' of the PFP program has given rise to concern. Involvement of the government in PFP formulation was very low in the initial stages. It has improved with later PFPs but there is still a tendency for gove..mer.:3 to simply sign off' on a document written for them by the
variables included in gravity equations, such as distance, language, colonial ties. Since our research exploits the time series variation in the trade and aid data to investigate the relationship, we incorporate both time fixed effects as well as time-varying bilateral fixed effects. The time dimension of the time-varying bilateral fixed effects is an average over five years. As we use yearly aggregate trade flows, a time variation by year would not be feasible. In other words, for every pair of countries we have three time-varying (for the years 1990-1994, 1995-1999, and 2000-2005) bilateral fixed effects.

We are now able to postulate that bilateral imports _tijt_ are a function of the countryspecific variables and our baseline empirical specification therefore takes the following form: _αijT_ denotes all bilateral 5-years fixed effects between country _i_ and _j_ . _Yit_ and _Yjt_ measure the GDP in both countries in year _t_ . The importer’s applied tariff is  1   _ijt_  . The aid for trade facilitation flows received in year _t_ by the importer is denoted as _ITFAit_ , whereas _ETFAjt_ measures the aid received by the exporter. These aid flows represent the sum of aid received from bilateral donors.<sup>28</sup> _γt_ stands for the year fixed effects and _εijt_ is the residual. The following section presents the estimation results for the model developed above and conducts several robustness tests in order to test the validity of our results.

# **_IV. Estimation Results_**

In this section, we consider several different permutations of our main specification. The first step is simply to establish whether aid for trade facilitation is related to trade flows. The second step considers whether this relationship is different for different country groups, in particular focusing on the tied-aid issue. The third question relates to lagged effects of aid. Finally, we consider how the relationship between aid for trade facilitation and trade might vary with the type of aid flow.

## **IV.1. Main Specification and Disaggregations**

Table 1 shows estimation results using bilateral non-zero trade data and several different permutations of countries receiving aid-for-trade facilitation flows over the years 1990 to 2005. In the first two columns, trade between all 167 exporters and 172 importers in our sample is considered. The common variables, such as GDP and tariff, have the expected signs and magnitude. The two variables that capture that amount of aid for trade facilitation received ( _ln_itfa_ : aid received by importers and _ln_etfa_ : aid received by

> 28 We exclude multilateral aid flows as it is rather difficult to contribute multilateral aid flows to certain countries. In addition, multilateral A4TF amounted to less than one sixth of all A4TF in all years.

10
# Table of Contents

# ABSTRACT

|I.<br>Introduction ............................................|1|
|---|---|
|II.<br>Existing Fossil Fuel Pricing Regimes and World Subsidies ...................|3|
|III.<br>Empirical Frcmework .........................................|13|
|IV.<br>Implications for Greenhouse Gas Emissions ...........................|16|
|V.<br>Summary and Conclusions .......................................|21|
|REFERENCES ...|3|
|List of Tables||
|Table 1:<br>Carbon Dioxide Emissions from Fossil Fuel Combustion .................|<br>4|
|Table 2:<br>Total Subsidies (mission US$) .<br>..................................|7|
|Table 3:<br>Subsidies to Coal (billion US$) ..........<br>......................|10|
|Table 4:<br>Subsidies to Natural Gas (billion US$) ............................|11|
|Table 5:<br>Subsidies to Petroleum Products (billion US$) .......................|12|
|Table 6:<br>C02 Emissions Reductions .<br>..................................|19|

*This paper is an extension of Larsen and Shah (1992c), "World Fossil Fuel Subsidies and Global Carbon Emissions," Policy Research Working Paper Series No. 1002, the World Bank, Washington, D.C., and explores ideas suggested by Lawrence H. Summners and Andrew Steer. The paper was supervised by Anwar Shah, Principal Economist, PRDPE, World Bank, and financed by a grant from the World Bank Research Commnittee, RPO 677-28. It has benefitted from discussions with Ramon Lopez and Gordon Hughes, comments by Anthony A. Churchill, Mahan Monasinghe and Robert P. Taylor, and able research assistance from Isidro Soloaga.
Table 7 Basic Productivity Regression

||Simple D|iference|Diference-in-|Diferences|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Age of household head|0.006<br>0.006|0.007<br>0.006|0.014<br>0.008*|0.010<br>0.007|
|Age Squared|–8.17E–05|–9.21E–05|–1.57E–04|–1.23E–04|
||–6.18E–05|–6.12E–05|–0.77E–04**|–0.75E–04|
|Male household head|0.128|0.132|0.093|0.087|
||0.040***|0.040***|0.047**|0.047*|
|Family Size|0.103|0.078|-0.035|0.008|
||0.031***|0.031**|0.036|0.036|
|Share of males|–0.003|–0.024|–0.030|–0.034|
||0.087|0.086|0.106|0.105|
|Farm type|–0.102|–0.059|0.124|0.059|
||0.038***|0.039|0.043***|0.044|
|Size cotton plot|–0.290<br>0.023***|–0.306<br>0.023***|||
|Relative plot size|||–0.343|–0.377|
||||0.024***|0.024***|
|Livestock|0.133|0.092|–0.064|–0.060|
||0.032***|0.033***|0.038*|0.040|
|Harrows||–0.067||–0.064|
|||0.046||0.053|
|Ploughs||0.102||0.032|
|||0.019***||0.027|
|Basal fertilizer||0.075||–0.705|
|||0.108||0.256***|
|Top-dressing fertilizer||0.084<br>0.105||–0.616<br>0.281**|
|Cotton price|–0.080|–0.101|0.117|0.070|
||0.054|0.053*|0.064*|0.062|
|_F_ <sup>1</sup>|–0.126|–0.115|–0.512|–0.469|
||0.053**|0.053**|0.059***|0.058***|
|_F_ <sup>2</sup>|0.108|0.147|0.159|0.190|
||0.031***|0.032***|0.039***|0.039***|
|Constant|6.414|6.434|–1.312|–0.958|
||0.259***|0.255***|0.317***|0.309***|
|Observations|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.11|0.12|0.11|0.14|

Note: Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Variables included in **x** are the age and age squared of the household head, a dummy for households where the head is male, the log of the total household size, the share of males in the household, a dummy for farms with total area smaller than 1 ha (farm type), the log size in ha. of the cotton plot, the relative sizes of the cotton and maize plots, a dummy for livestock raising households, and harrows, ploughs, basal fertilizer and top-dressing fertilizer in physical units.

31
industry and firm level data to explore the positive impact of finance on growth. More recently, Oliver, Levine and Warusawitharana (2014) find that external finance supports productivity growth in their sample of European countries.

However, a number of recent papers revisit the finance-growth relationship and find more nuanced effects. This paper builds on this newer literature on productivity and finance that examines the possible negative relationship between productivity and/ or growth and finance in very large financial systems. Gazdar and Cherif (2015) find that finance is more likely to support growth in MENA countries in good institutional contexts. Demetriades and Law (2006) find that financial development has larger effects on GDP per capita when the financial system is imbedded within a sound institutional framework. Barajas et al (2013) find that the beneficial effect of financial deepening on growth varies across countries; lower income countries benefit less because their regulatory and supervisory systems are less developed. Aizenman, Yinjarak and Park (2015) find a nonlinear effect of finance and growth and an uneven effect across sectors; they compare East Asia and Latin America, concluding that both the quantity and quality of finance matter. When the financial system channels resources to productive sectors of the real economy, then growth in finance and real output are positively correlated. Arcand et al (2012) find that there is a threshold beyond which finance does not have a positive impact on growth. Shen and Lee (2005) show that stock market development has a positive effect on growth but banking development does not; they find that the conditioning variables of financial liberalization, high income level and good shareholder protection mitigate the negative effects of banking development on growth. Cecchetti and Kharroubi (2012, 2015) find that the impact of finance on growth is nonlinear (very large financial sectors can have a negative relationship with growth) and that a fast growing financial sector has a negative impact on aggregate productivity growth. In their theoretical model, this is because low productivity (and high collateral) projects are more likely to be financed and also because the financial sector may take away human resources from more productive sectors. Their empirical work covers advanced economies.

Few papers have differentiated between the impact of credit going to households versus firms. Yet in recent times, there has been increasing attention given to credit to households as there have been fast increases in this type of credit (for example in European and US credit markets); moreover much of credit to households is mortgage credit a type of credit that 4
**Figure 7. Decomposition of wage structure effects, by percentile in 2011**

<!-- Start of picture text --> 0.80<br>Total<br>Explained<br>0.60 Spec. Error<br>0.40<br>0.20<br>0.00<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>‐0.20<br>‐0.40<br>0.80<br>Experience<br>Education<br>Occupation<br>0.60<br>Industry<br>Other<br>0.40<br>0.20<br>0.00<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>‐0.20<br>‐0.40<br><!-- End of picture text --> _Note:_ Each category includes: Experience: potential experience, potential experience squared; Education: secondary, technical, vocational, university; Firm: public or semipublic, foreign owned or co-owned, firm size; Other: changed occupation but not place of work, changed occupation and place of work, changed place of work.

# **4.3. Decomposition results over time**

The comparison of the RIF decomposition for 1996, 2002, and 2011 shows that important variations in the wage structure occurred in the Russian Federation since the transition to a market economy in 1992 (for a more detailed discussion see Calvo et al., 2015). The general conclusions are maintained, since the largest gap is always observed in the middle of the wage distribution. However, over time, there have been changes in the importance of the wage structure and the composition effect, as well as of the covariates along the wage distribution. For example, the importance of the composition effect 23
Analogously to Table 5, we give a breakdown by demographic group in Table 6. Again significant impacts on wage employment are confined to women and those under 30, though significant impacts of the voucher and training on the self-employment of men and workers over 30 do emerge.

Recall that some workers in the control sample were more likely to be employed in temporary employment programs in the baseline survey (Table 2). Thus our results so far might over-state the employment gains from the program. To address this concern, we use a double difference ("difference-in-difference") approach in which we net-out the baseline differences when calculating mean impacts. Table 7 gives these estimates. The effect on private employment holds in the double difference estimates. And again there are no significant effects on other outcomes. The close correspondence between the double- difference and single-difference results is consistent with success in randomizing the assignment.

Turning to administrative records of the Ministry of Labor, we found that take up of the wage subsidy by firms hiring a worker with a voucher was very low. Indeed, only three of the workers in the treatment group who were hired by private firms were in fact registered by their new employer. (And one firm registered all three workers.) We offer an explanation for this finding in the next section.

# VI. **Interpretations**

Some features of our results require care in interpretation, and in drawing conclusions about the effects of scaling up the program. Although employment improved amongst voucher recipients, there are no signs of an impact on their incomes, at least by 18 months. Here it appears that voucher recipients took up private sector jobs in the expectation of a higher and/or more stable stream of incomes in the future. Given that opportunities for workfare jobs were _15_