rafmacalaba/datause-encoder-data huggingface dataset:
# 5. **References**

Acemoglu, D. and F. Zilibotti. (1997). "Was Prometheus Unbound by Chance? Risk, Diversification and Growth," _Journal of Political Economy,_ 105(4), August, 709-51.

Aghion, P., A. Banerjee, and T. Piketty. (1999). "Dualism and Macroeconomic Volatility," _Quarterly Journal of Economics,_ 114, November, 1359-97.

Aghion, Philippe, P. Bacchetta, and A. Banerjee. (2000). "Capital Markets and the Instability of Open Economies" in _The Asian Crisis: Causes, Contagion, and Consequences,_ P.R. Agenor, M. Miller, D. Vines, and A. Weber, eds. (Cambridge University Press, Cambridge, England.) Bachetta, P. and R. Camninal. (2000). "Do Capital Market Imperfections Exacerbate Output Fluctuations?," _European Economic Review,_ 44 (1), 449-468.

Beck, T., R. Levine, and N. Loayza. (2000a). "Financial Intermediation and Growth: Causality and Causes," _Journal of Monetary Economics,_ forthcoming.

Beck, T., R. Levine, and N. Loayza. (2000b). "Finance and the Sources of Growth," _Journal of Financial Economics,_ forthcoming.

Bencivenga, V. R. and B. D. Smith. (1991). "Financial Intermediation and Endogenous Growth," _Review of Economic Studies,_ 195-209.

Bernanke, B. and M. Gertler. (1989). "Agency Costs, Net Worth, and Business Fluctuations," _American Economic Review,_ 79 (1), 14-38.

Bernanke, B. and M. Gertler. (1990). "Financial Ragility and Economic Performance," _Quarterly Journal of Economics,_ 105 (1), 87-114.

Boyd, J. H. and E. C. Prescott. (1986). "Financial Intermediary Coalitions, " _Journal of Economic Theory,_ 38 (2), 211-232.

Carlin, W. and C. Mayer. (1999). "Finance, Investment and Growth," unpublished manuscript.

Demirguc-Kunt, A. and V. Maksimovic. (1998). "Law, Finance, and Firm Growth," _Journal of Finance,_ December, 53(6), 2107-37.

19
and Miguel (2012). In all cases we estimated ordinary least squares with standard errors clustered at the village level.

# **7.1 Observed Behavior in Games**

Estimates of the program effects on observed behavior in our laboratory games are shown in Table 4. The table is split into an upper and lower panel. In the upper panel we report the control-group means and average treatment effects on the treated (ATTs) from each of the four games-based measures of pro-sociality (donation to the needy, contribution to the public good, trust and trustworthiness), and measures of two possible confounders (risk attitudes and the discount rate). In the lower panel we report the mean effect of the program across all four of the social capital measures. There is a very consistent pattern across all four of these measures and their mean effect. In all cases the point estimate of the program is actually negative (showing a slight reduction in the pro-sociality in the treated communities) but the coefficients are very close to and statistically indistinguishable from zero. The following discussion more detail about these results.

Column (1) in the upper panel shows the ATT on the amount donated to the needy family. Subjects in both the treated and control communities contributed 1.55 pounds, on average, a little over half of their endowment.<sup>12</sup> The point estimate of the ATT suggests that persons in treated communities actually contributed slightly less than did those in the control communities but the coefficient is very small and statistically indistinguishable from zero. Column (2) shows the ATT of CDF programming on propensity to contribute to public goods in our laboratory game. The results show that on average about 76 percent of subjects

> 12This is a very high give rate compared to standard dictator games. In Engel’s meta analysis the give rate was only about 28 percent of the available pot (Engel, 2010). We speculate that the reason for the large give rate we observed was our telling the subjects that the money would be given to a needy family.

24
management and bureaucratic effectiveness: evidence from the ghanaian civil service<sup>_⇤_</sup> imran rasul daniel rogger martin j.williams<sup>_†_</sup> _JEL Classification: J33, O20._

> _⇤_ We gratefully acknowledge financial support from the International Growth Centre [1-VCS-VGHA-VXXXX33301] and the World Bank’s i2i trust fund financed by UKAID. We thank Julien Labonne, Anandi Mani, Arup Nath, Simon Quinn, Ra↵aella Sadun, Itay Saporta-Eksten, Chris Woodru↵and seminar participants at Oxford, Ohio State, PMRC, and the EEA Meetings for valuable comments. Jane Adjabeng, Mohammed Abubakari, Julius Adu-Ntim, Temilola Akinrinade, Sandra Boatemaa, Eugene Ekyem, Paula Fiorini, Margherita Fornasari, Jacob Hagan-Mensah, Allan Kasapa, Kpadam Opuni, Owura Simprii-Duncan, and Liah Yecalo-Tecle provided excellent research assistance, and we are grateful to Ghana’s Head of Civil Service, Nana Agyekum-Dwamena, members of the project steering committee, and the dozens of Civil Servants who dedicated their time and energy to the project. This study was approved by UCL’s Research Ethics Committee. All errors remain our own.

> _†_ Rasul: University College London and the Institute for Fiscal Studies [i.rasul@ucl.ac.uk]; Rogger: World Bank Research Department [drogger@worldbank.org]; Williams: Blavatnik School of Government, University of Oxford [martin.williams@bsg.ox.ac.uk].
addition to these two sources, other institutions, many times government departments in producer countries, either gather their own statistics or collaborate in the gathering of data with UNODC and/or ONDCP.

Established in 1997, UNODC has become the main source for data on illegal drug markets. It employs about 500 staff members worldwide and has 21 field offices located in the main producer countries, as well as in those countries used as traffic corridors. The mandate of UNODC is to assist member countries in their struggle against illegal drugs, crime, and terrorism.<sup>10</sup> UNODC relies on voluntary contributions – mainly from just a few countries – for almost 90% of its budget.<sup>11</sup> UNODC works jointly with the respective government institutions in the producer countries to undertake the “Coca Cultivation Survey” each year. Through the Illicit Crop Monitoring Programme (ICMP), UNODC uses the interpretation and processing of satellite images to monitor illegal crops in producer countries: coca in the three Andean producer countries and opium poppy in South and East Asian countries.<sup>12</sup> Also, using surveys and studies on yields, this institution produces an estimate of potential cocaine production, gathers prices of intermediate goods such as dry coca leaf and coca base, and collects other crucial statistics such as eradication measures, drug shipments seizures, and the number of cocaine processing

> 10 More information on the mandate of UNODC, as well as its main goals, can be obtained at: http://www.unodc.org/unodc/en/about.html

> 11 Jensema and Thoumi (2004) argue that UNODC’s large proportion of earmarked funding from a few donor countries biases the type of projects where the funds are spent, hampers its policy evaluation efforts as criticisms can easily translate into a fund shortage, and prevents the organization from experimenting with programs that are not in line with the donor countries’ position on illegal drug issues. Available at: http://www.drug-policy.org/documents/Thoumi_Jensema_paper

> 12 The analysis of these images includes a number of corrections for cloud cover, spraying, dates of acquisition, etc. For a detailed explanation the reader is referred to the methodological description available in the Survey Reports for each of the Andean countries available at: www.unodc.org/unodc/en/crop_monitoring.html).

6
measures that have been previously utilized in the literature. The first is a Bayesian version of the Roll (1984) transaction cost measure: <!-- Start of picture text --> cov( ri t , , ri t , 1) if cov( ri t , , ri t , 1) 0;<br><!-- End of picture text --> It is based on the model _ri t_ , _ci t_ , _qi t_ , _i t_ , where _qi t_ , is a trade direction indicator, _ci t_ , is the transaction cost measure and _i t_ , is an error term for stock _i_ at time _t_ . Equation (1) can be derived under the assumption that buyer- and seller-initiated trades are equally likely. The Bayesian estimation of this cost measure using the Gibbs sampler is described in detail in Hasbrouck (2006).<sup>11</sup> The second measure is the Amihud illiquidity ratio, calculated as: where _Di t_ , is the number of days in month _t_ for stock _i, dvoli t_ , is the dollar volume in day _d_ , and _ri d_ , is the daily return. While the bid-ask spread captures the cost of executing a small trade, the _Illiq_ variable is akin to Kyle‟s lambda and is meant to capture the price impact of a trade. I adjust this measure as in Acharya and Pedersen (2005) to make it stationary and to remove outliers: <!-- Start of picture text --> min 0.25 0.30 Illiqi t , Mt 1,30<br><!-- End of picture text --> where _Mt_ 1 is the ratio of the value-weighted market portfolio at the end of the month _t_ -1 to that of the market portfolio in July of 1962.

The third measure used in this paper is the Pastor and Stambaugh (2003) reversal gamma:

> 11 The Gibbs estimate is obtained from Joel Hasbrouck‟s website:

> http://pages.stern.nyu.edu/~jhasbrou/Research/GibbsEstimates2006/Liquidityestimates2006.htm.

10
<!-- Start of picture text --> — Policy Stringency Mobility - transit stations —— Mobility - workplaces<br>100 20<br>e<br>80 2<br>: ° =<br>ke)£[=©<br>>D 60 5<br>2 —_—_—_—; -20 S©<br>a 2<br>2D 40 =<br>5 8<br>ES =<br>fe) -40 2<br>20 8<br>o)<br>0 -60<br>Jan20) Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21<br><!-- End of picture text -->
# 3) **HOW INTEGRATED IS TRADE IN THE SADC REGION?**

Table 1 and Table 2 present the share of SADC trade in regional exports and imports from 1980 to 2003. African trade data are notoriously problematic with different datasets providing different values of trade flows (Yeats 1990). Concerns about data quality are particularly relevant for intra-African trade flows. Nonetheless, various insights emerge from the data.

Intra-SADC trade grew significantly from 1980, but has halted in recent years. For example, the share of SADC exports destined for the region more than tripled to 9.9 percent from 1990 to 1995, but then rose very gradually to 12.1 percent in 2008 (based on sample excluding Angola, DRC, Madagascar and Seychelles for which earlier data are not available) (Table 2). These trends are corroborated by the gravity model estimates (column 1 of Table 9 in Appendix 1) which imply no significant change in intra-SADC trade over the 1991 to 2005 period (see the insignificant coefficient on the intra-SADC dummy variable).

Dependence on the region for trade divides countries into two groups. Malawi, Mozambique, Zambia, and Zimbabwe depend heavily upon SADC, particularly for imports. These countries source upwards of 50 percent of their imports from other SADC countries and sell more than 20 percent of their exports to the region. The remaining countries in SADC maintain much stronger trade relationships with the rest of the world (ROW). For example, intra-regional trade makes up approximately 10 percent of Mauritian exports and imports. SACU sources only 5.6 percent of its imports from the region. SADC accounts for a much higher percentage (10.5) of SACU exports, which leads to large trade imbalances between SACU and the rest of SADC.

Substantial asymmetries in trade flows persist. SACU trade, which is predominantly made up of South African trade flows, dominates intraregional trade flows. Between 60 to 70 percent of SADC exports to the region are sold to SACU (Table 1), while 80 to 90 percent of SADC (excluding SACU countries) imports from the region are purchased from SACU (Table 2). The region is therefore more dependent on South Africa as a source of imports than as a market for exports.

7
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute more**

<!-- Start of picture text --> b. Potential contribution of infrastructure to annual per capita<br>economic growth in Central African countries, in percentage points<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Roads Power ICT<br>Percentage points<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in Central African countries, in percentage points, 2001–05 <!-- Start of picture text --> 4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Roads Power ICT<br>Percentage points<br><!-- End of picture text --> _Source:_ Calderón 2009.

# The state of the CAR’s infrastructure

The CAR’s 4.5 million people sparsely populate its 623,000 square kilometers (km2) (figure 2a). Civil conflict resulted in significant displacement, including rapid and hazardous urbanization centered on Bangui, where economic activity is concentrated and 20 percent of the CAR’s population lives. Secondary cities remain relatively small, with fewer than 120,000 inhabitants. At around 7.1 inhabitants per km2, the CAR’s low population density imposes a big challenge to financing the development of infrastructure assets. One solution in the ICT sector is to adapt low-cost technologies, such as WiMax;<sup>1</sup> meanwhile, water supply services can be expanded via standposts and protected wells.

Poverty incidence in the CAR is one of the highest in the continent: more than two-thirds of the population live below the poverty line. Poverty is higher in rural areas and in regions affected by the civil conflict (figure 2b). In the southwest of the country, poverty occurrence is lower, in particular in Bangui, where most of the economic activity and infrastructure is concentrated. In 2007 the Participatory Poverty Analysis identified poor governance, insecurity, and lack of economic opportunities connected to recent conflicts as the three main causes of poverty in the CAR.

The CAR’s economy is dominated by the primary sector, which accounted for 52 percent of the GDP in 2008. In the primary sector, subsistence agriculture represents 28 percent of the GDP, and livestock about 13 percent. The country’s main sources of export revenues are timber, cotton, coffee, and extractive industries. Timber has accounted for about 16 percent of export earnings, which are small overall.

> 1 Worldwide Interoperability for Microwave Access.

4
# **Key Issues & Lessons Learned**

## **Institutional Structures**

**In the diagnostic reviews, one of the most difficult issues relates to the institutional structure for financial consumer protection.** Regulation of financial consumer protection lies midway between financial sector regulation and consumer protection regulation for goods and services. Different countries have taken different approaches. While all banking supervisory agencies see their role as maintaining soundness and stability in the banking sector, some view consumer protection as an additional implied responsibility. Other banking supervisors take a different approach. They focus only on prudential supervision and view consumer protection in banking as the responsibility of the economy-wide consumer protection authorities. However in these cases, the consumer protection authority is generally responsible for a limited number of retail financial services, such as consumer credits and payments. Consumer protection in insurance, securities and private pensions falls to the financial supervisory agencies for each area. Table 2 summarizes the different approaches taken by the nine countries studied.

**Table 2: Financial Consumer Protection: Who is Responsible?**

|**Country**|**Financial Supervisory**<br>**Agency**|**Depends on Financial**<br>**Service**|
|---|---|---|
|Azerbaijan|X||
|Bulgaria||X|
|Croatia|X||
|Czech Republic|X||
|Latvia||X|
|Lithuania||X|
|Romania||X|
|Russia||X|
|Slovakia|X||

_Source_ : Diagnostic Reviews

**From a consumer’s perspective, the government’s structure can be difficult to comprehend** . For five of the nine case studies, the applicable financial consumer protection authority depends on the type of financial service (as seen in Table 2). In all nine countries, if the problem relates to securities, the consumer should go to the securities supervisory agency and for private pension issues, to the pension supervisory agency. If a consumer has a problem related to an insurance product, the consumer should approach the insurance supervisory agency, although in Russia, the consumer protection service may have responsibility. (Experts on Russian insurance do not agree who is responsible for consumer protection in insurance.) If the problem relates to a consumer credit or a payment or remittance—and a consumer protection agency is in place—the consumer should contact the consumer protection authority. Otherwise the consumer should approach the banking supervisory agency.

**The Diagnostic Reviews found that financial consumers generally had no clear idea of which agency to approach.** For problems on consumer credits, consumers would write letters to any—or all—of nine different types of institutions: the financial supervisory agency, the consumer protection agency, the finance ministry, the ministry of economy (if it supervises the consumer protection agency), members of parliament, the president’s office, the professional associations, the chambers of commerce and nongovernmental consumer organizations. Moreover consumers do generally not realize that none of the nine

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# **Box 2: Household spending on education makes up a large proportion of total spending in lowincome and lower-middle-income countries**

Household spending on education tends to account for a greater percentage of GDP in low-income countries than wealthier countries (see figure below). Comparable information on household education spending at the country level is relatively scarce. However, using country averages over four-year periods, we found that, on average, household spending on education in 2010-13 accounted for 2.6 percent of GDP in low-income countries but only 0.7 percent in high-income countries.

It also appears that household spending makes up a larger share of total education spending in low-income countries. In 2010–13, we found that average government spending on education accounted for 3.7 percent of GDP in low-income countries and 4.8 percent in high-income countries. Combining the data on household spending with those on government spending, it becomes clear that households in low-income countries provided 41 percent of all education spending compared to only about 13 percent in high-income countries.

In terms of regions, households in Africa and Latin America contribute the most to education as a share of GDP. In the four-year period from 2010 to 2013, households in Africa spent the equivalent of 2.1 percent of GDP on education. When household and government spending are combined, African countries spent the equivalent of 6.4 percent of GDP on education and Latin American countries spent 6.8 percent.

Only about 40 countries have more than 10 data points on household education spending between 2000 and 2015 in the UIS database. In this limited sample of countries, average household spending appears to have fallen during the first half of the 2000s from 1.4 percent in 2000 to 0.8 percent in 2005, but then began to rise and in 2013 accounted for about 1.2 percent of GDP.

**Households Contribute the Largest Share of National Income to Education in Low-income Countries** _Household Education Spending as a Percentage of GDP, 2006_ – _13_

<!-- Start of picture text --> 3.0<br>2006–09 2010–13<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Income group Region<br>LIC LMIC UMIC HIC ECA (24) MNA (5) EAP (7) LCR (13) AFR (13)<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ Income groups are defined by World Bank country income group classification in 2017. Figures in parentheses record the average number of countries in each region for which data are available. LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, EAP = East Asia and the Pacific, MNA = Middle East and North Africa. There was no information for <u>South Asia.</u> 6
vote 6 percent of the outstanding voting stock or shares of any organization.” The ownership threshold for related-party exports is set at 10 percent (U.S. Census Bureau 2014). For notational convenience, related-party and intra-firm trade are hereinafter interchangeably referred to as trade.<sup>5</sup>

**Quantitative importance of arm’s-length and intra-firm trade.** Just over half (about 57 percent) of total U.S. trade is conducted at arm’s-length between unrelated firms. The share of arm’s-length trade is much lower for U.S. imports (50 percent) than exports (70 percent), for U.S. trade in capital goods (50 percent) than final goods (60 percent), and for U.S. trade with advanced economies (51 percent) than with EMDEs (64 percent). In general, a higher per capita income of a trading partner is associated with a lower share of arm’s-length trade. The share of intra-firm trade of total U.S. trade has remained broadly stable from 2002 until the global financial crisis but subsequently increased, especially for U.S. trade with EMDEs (Figure 3).

**Country composition of arm’s-length and intra-firm U.S. trade.** Geographical proximity and the North American Free Trade Agreement (NAFTA) favor intra-firm transactions with two of the United States’ largest trading partners, Mexico and Canada. About half of all U.S. exports to, and more than half of all U.S. imports from, Canada and Mexico are intra-firm transactions. Canada is the single largest destination of U.S. intra-firm exports (almost one-third of total U.S. intra-firm exports) and imports, followed by Mexico (about one-fifth of total U.S. intra-firm exports; Figure 4). More than half of U.S. imports from its main non-NAFTA trading partners (with the exception of China and Italy) are also intrafirm transactions. In contrast, U.S. exports to its main non-NAFTA trading partners are predominantly arm’s-length—53-65 percent of U.S. exports to large European Union and Asian countries (France, Germany, Japan, Korea, Netherlands, and United Kingdom; Figure 4) fit this description.

# **3 Evolution of arm’s-length and intra-firm trade since the crisis**

Global trade growth has slowed sharply since the global financial crisis, from an average of 7.6 percent during 2002-08 to an average of 4.3 percent during 2010-14. During the 2007-09 global financial crisis, global trade volumes contracted by 11 percent, as domestic demand dropped and trade finance was curtailed (Levchenko, Lewis, and Tesar 2010; Chor and Manova 2012).

> 5The two terms imply different ownership shares. Intra-firm trade is defined as trade between firms with control and ownership shares of at least 50 percent.

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<!-- Start of picture text --> State of Bahia, Brazil: RPPS Social Security Deficit<br>(% tax revenues)<br>100.0%<br>90.0%<br>80.0%<br>70.0%<br>60.0%<br><!-- End of picture text --> <!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>3%, aUy 32 2Ry a%,<br><!-- End of picture text --> <!-- Start of picture text --> Projected Pension System Deficits as % of GDP<br>2.0%<br>avs io sd -<br>eee 2 Rist is, 5<br>} ’ ~<br>\ 4<br>71 “ie<br>1<br>-2.0% +<br>aoe<br>4.0% Uy u T T T ul<br>2011 «2021. 2S «2031S «2041 = -2051 Ss 2061S 2071<br>—— 1998 Reform — — 2011 Reform ------2012 Rev 1<br><!-- End of picture text --> <!-- Start of picture text --> Projected Average Old Age Pension Benefits as % of<br>Average Wage<br>60.0% ><br>50.0%<br>40.0% - ==<br>se,<br>a<br>30.0% + SCTCT EP<br>= —— *Feetenes, **eneses<br>si ~ a ee<br>20.0% + = a =<br>10.0% +<br>O.086 4 - a - " 7" +<br>ee ee ee eee<br>—— 1998 Reform — — 2011 Reform -----+2012 Rev 1<br><!-- End of picture text -->