rafmacalaba/data-use-mentions-extended huggingface dataset:
Results from both BIA exercises are staggering. Liberian households make private contributions towards public education that generally exceed the public transfers implicit in the provision of public education (Figure 2a). This is in line with the aggregate contributions towards all educational spending reported for that year by World Bank (2010), which at US$ 27 million more than double the government‘s US$12.2 million financing. From a distributive point of view, unitary private spending towards public education increases by levels of wealth while unitary public transfers hardly change among wealth quintiles. This results in a distribution of _net_ public benefits that is increasingly negative, showing a progressive distribution for the ̳wrong reasons‘: progressivity is achieved because of increasing private contributions from households as their wealth increases, rather than by increasing public transfers to poorer households.

This picture is even more dramatic when considering the distribution of educational opportunities (Figure 2b). Once quintiles are computed from the distribution of attending school probabilities among children age 6-15, it becomes clear that children in the bottom two opportunity quintiles (with an expected probability of attending lower than 52 percent) rarely attend school. This leads to a very small average group gross unitary public spending and unitary private spending in these two quintiles. Children in the third quintile, with an estimated probability of attending school given their circumstances of 52-71 percent, appear to contribute slightly more than the previous groups. Households from the top two quintiles of the distribution of attending probabilities (71-85 percent and 85-100 percent, respectively) clearly contribute privately towards school attendance, more so in the case of the top quintile.

22
# **1. Introduction**

The subject of development economics has evolved from a largely theoretic discipline focused on the process of economic transformation in developing countries to a multi-faceted field in which there may be no single big idea (Lindauer and Pritchett, 2002), but in which there is active debate about the role of macro versus micro approaches (e.g. Rodrik, 2008; Bardhan, 2013), and in which there is so much data and empirical work that there is debate as to whether there has been enough emphasis on theory (e.g. Mookherjee, 2005, Banerjee, 2005).

How is this state of the field reflected in what is taught as development economics in developing countries? The answer to this question is important for several reasons. First, the next generation of policy makers responsible for implementing key development policies are likely to have their views of what policies they should pursue heavily influenced by what they have been taught. Second, the answer is instructive about the ease at which ideas and knowledge flow from the research frontier, which is of more general interest for development. Third, what is taught as development economics around the world should be of interest to researchers producing development research, and to professors teaching the subject who may wish to benchmark their own courses against what is done elsewhere. Finally, it is important for determining the extent to which development economics education is meeting the key learning goals of economics education (Allgood et al, 2015).

We collected original data on what is taught as development economics and how it is taught through a combination of surveys of instructors and standardized coding and analysis of course syllabi and examinations.<sup>1</sup> We were able to collect this for 145 undergraduate courses in 54 developing countries, and 81 masters courses in 34 developing countries. We benchmark these courses against undergraduate development economics classes taught at top-20-ranked U.S. economics departments, and at a selection of other U.S. economics departments.

>
, S. Khemani, S. Shah. 2005. Decentralization and service delivery. World Bank Policy Research Working Paper 3603. World Bank, Washington DC.

_2._ Ahmad, M. Albino-War and R. Singh. 2005. “Subnational public financial management: institutions and macroeconomic considerations.” IMF Working Paper Series 05/108. International Monetary Fund. Washington DC.

3. Adenikinju, A. 2005. “Analysis of the Cost of Infrastructure Failures in a Developing Economy: The Case of the Electricity Sector in Nigeria.” AERC Research Paper 148. African Economic Research Consortium, Nairobi, with the Centre for Economics and Allied Research, Univeristy of Ibadan.

4. Armstrong, M. and D. and E.M. Sappington. 2006. “Regulation, Competition and Liberalization.” _Journal of Economic Literature_ 44(2): 325–66.

5. ---------. 2007. “Recent Developments in the Theory of Regulation.” _Handbook of Industrial Organization._ Elsevier Publishing.

6. Auriol and Blanc. 2009. “Capture and corruption in public utilities: The cases of water and electricity in Sub-Saharan Africa.” _Utilities Policy._ 17 (2): 203-216.

7. Ayee, J. T. Søreide, J. Shukla and T. Le. 2010. “The Governance of Gold in Ghana”. Chapter in World Bank volume, forthcoming.

8. Benitez, D. 2010. “Bundling Tasks and Contracts: The Case of Public-Private Partnerships.” Processed.

9. Bertero, E., and L. Rondi, 2000. “Financial Pressure and the Behavior of Public Enterprises under Soft and Hard Budget Constraints: Evidence from Italian Panel Data.” _Journal of Public Economics_ 75: 73–78.

10. Besley, T. 2006. “ _Principled Agents? The Political Economy of Good Government.”_ Lindahl Lectures. Oxford University Press.

11. Besley, T. and S. Coate.
**<u>│</u> 3**

for fulfilling both economic and social objectives, as well the availability and quality of the data. By augmenting the set of publicly available information on differences between plants of multinational and domestic firms, this study paints a picture that contributes to a better understanding of what multinational firms bring to advance various development outcomes, and the differences across countries.

Compiling indicators of development outcomes at the level of firms is predicated on the understanding that differences between firm outcomes are a driving force behind development impact at the level of the economy. Outcomes of foreign-owned firms are however not to be confused with aggregate development impact of international business; they are one among a number of such drivers that shift macro-economic and social outcomes of host countries. In addition to the variety of characteristics of firms presented in this work, the actual aggregate impact of foreign multinationals on host countries remains dependent on the volume of investment, local conditions, spillovers to domestic firms, and government policies.

The picture this exercise unveils is highly heterogeneous. While there appears to be no striking trade-off between competitiveness and inclusiveness of foreign multinationals, contrary to the efficiency-equity trade-off that fuels intense debates (WEF & EIB, 2017), one can observe systematic differences in how pronounced their premia are across regions and income groups. Relative to other regions, foreign MNEs established in Eastern Europe and Central Asia, for example, exhibit higher premia over domestic firms in the majority of outcomes relating to competitiveness and inclusiveness. Foreign MNEs established in Latin America stand out in terms of productivity and skills transfer while the opposite pattern is observed in Sub-Saharan Africa, where foreign multinationals stand out with respect to job expansion and wages. The mix appears to be highly specific to the type of multinationals each region attracts, including the industry and investor motivations, as well as the host economy conditions. In the Middle East and North Africa, for example, multinationals differ significantly in terms of export propensity and geographical diversification due to the concentration of FDI in
irrespective of how many appliances are covered by voluntary standards or labeling schemes (see Table 6f for the framework used for assessing EE policies, regulations and incentives).<sup>7</sup> The CIRI scores for EE reveal that, within the South Asian region, India is well ahead in terms of relevant policies, regulations and incentives as well as institutional structures for EE (See Annex III). India‘s high scores are largely due to a larger number of electric appliances being covered by mandatory energy performance standards, as well to the higher weights assigned to building codes. India has also introduced a trading scheme for EE credits—the first of its kind in South Asia. As EE initiatives get underway in other South Asian countries and more mandatory standards are introduced, the climate for diffusion of energy efficient appliances will certainly improve. However, India—due to the sheer size of its market, coupled with strong economic growth and its imperatives for energy conservation, energy security and greenhouse gas reductions—is likely to remain the most attractive market for energyefficient products in South Asia.

# **Comparative Analysis of Energy Efficiency CIRI Scores for South Asia and Other Countries**

The scores obtained in the case of EE policies, regulations and incentives in some ways provides a more robust and quantitative basis to assess where countries stand in terms of the environment to deploy energy-efficient products. This is because while the presence of voluntary standards and labeling initiatives are acknowledged with a nominal score, countries are really rewarded for the presence of mandatory standards and labeling initiatives (which carries a greater weight of 2). Further the more appliances that are covered by mandatory standards and labeling initiatives, the higher the CIRI score countries chalk up.

## **Box 3:  Key Indicators for Construction of CIRI EE**

||**Cross-cutting**|
|---|---|
|1|Existence of Law and Policy on EE|
|| Existence of EE Policy|
||
(Adhvaryu et al., 2019). A novel feature of this paper is that I use a panel data setting and household level measure of exposure to coffee price change (i.e., the fraction of household farmland allocated to coffee). Existing literature mostly relies on repeated cross-sections and/or a measure of exposure that is based on geographic locations, so that all households located in a given geographic region are assumed to be equally exposed to price shocks. However, this approach masks massive variation in households’ choice of crop portfolio within a narrowly defined geographic unit. In Ethiopia’s top coffee-producing district, for instance, the fraction of farmland allocated to coffee ranges from less than 10% for some households to 100% for others, averaging about 60%.<sup>1</sup> My approach enables me to capture variation in exposure to coffee price shocks among households within a narrowly defined geographic unit. Moreover, Ethiopia provides an ideal setting to study this problem compared to most of the settings studied in the literature. About 30% of the households live the below poverty line (World Bank Group, 2015), coffee production is the main source of livelihood for about 20% of the population (Central Statistical Agency, 2016), and there is considerable variation in the share of coffee production in household income due to large geographic variation in agro-climatic suitability to grow coffee.

I use the Ethiopian Socioeconomic Survey (ESS) data collected by the joint effort of the Ethiopian Central Statistical Agency (CSA) and the World Bank.<sup>2</sup> ESS offers nationally representative panel data (2011, 2013 and 2015 rounds) on about 4000 households and provides information on household production, consumption, labor supply, and child anthropometric measures. This data has a number of unique features compared to similar datasets used in the literature. First, it includes direct measure of household consumption expenditure disaggregated into food and non-food items. Second, the dataset includes on- and off-farm
directly affected by infrastructure breakdowns (e.g. by experiencing a power outage onsite), it may still be forced to stop production as interruptions along the supply chain stifle input supply or output demand. Such indirect effects are unlikely to be taken into account by firms in their self-reported sale losses estimates due to electricity outages but will be included in the infrastructure utilization rates.

Unfortunately, the ES do not provide information about supply chain linkages. Instead, our econometric strategy proxies supply chain disturbances using regional disruption levels. Specifically, we average blackout and water supply interruption durations for all firms in any given region and introduce this region-level variable as a separate regressor into the regression model. We also interact it with the firmspecific disruption level to distill the pure direct and indirect impacts on utilization rates.

To assess this issue systematically we analyze a model that simultaneously estimates the impact of water, electricity, and transport disruptions on firms’ utilization rates. The model is fit to a pooled dataset of 29,675 firms from 95 predominantly middle-income countries, sampled between 2007 and 2014. Estimation is conducted by weighted ordinary least squares using normalized sample design stratification weights that sum to unity for each survey wave. Standard errors clustered at the regional level to account for regressor correlation with region fixed effects and uncertainty associated with region-specific marginal effects.

As in Section 4.1, the model is based on a harmonized global panel dataset constructed from the World Bank’s Enterprise Surveys. As part of these surveys, firms report on the quality of water and electricity infrastructure, including the average monthly frequency and duration of service disruptions. Firms also report transport disruptions using a subjective ordinal scale. These firm-specific subjective measures allow us to compute regional average disruption levels for blackouts, water shortages and transport disruptions. Those region-level variables can then be used to estimate how disruptions outside any firm’s boundaries affect individual firms – that is, indirect effects. Results for this approach are presented in Table 5.

19
classifications in economic census data.

First, industrial classifications in censuses are based on points of production rather than consumption, and so may include or exclude goods and services that are not in the relevant product market. Even so, for purposes of data management, the Commission indicates a high-level industrial classification of the relevant market in each case and then provides further detail in a written description because the relevant markets in question are more specific than those covered by the high-level classification. We use this information to assign each relevant market a 6-digit industry classification from the North American Industry Classification System (NAICS), the lowest level of aggregation possible.<sup>10</sup> We define this as the _treated industry_ . We then work forward and backward from the treatment date to define a consistent industry classification that captures the same set of activities from the first to last census in our dataset, including new activities that emerge during that time period. These classifications are generally more inclusive than a 6-digit classification, but there are many multiples within 3-digit classifications.

Second, relevant markets sometimes have specific geographic boundaries. In the majority of investigations, the relevant market is national (e.g., the national market for soft drinks). In the remaining investigations, we use relevant market definitions in the disclosures to define geographic boundaries for each market as a 6-digit industry within a set of municipalities or states. Within these boundaries, we aggregate establishment-level micro-data to construct series for the treated industry (e.g., all real estate brokers in the municipalities surrounding Lake Chapala in Jalisco state).

We define a pool of _control industries_ to be all other 6-digit industries in the same 3- digit sub-sector as the treated industry. The motivation for this is that other industries in the treated industry should share common (unobserved) trends in demand and costs, while still being outside the relevant market defined by investigators. This approach to identifying controls in similar industries has been used in other studies of antitrust (Sproul, 1993; Kwoka
find a similar effect on self-employment for the ―old Europe‖ (the West, the North, and the South).

Note that our results are not directly comparable to those of Schneider et al. (2010) and Bajada and Schneider (2009) on determinants of the size of the shadow economy (although theoretical considerations are to a large extent similar, as long as factors controlled in all these studies are concerned). In fact, for 2004-2007 there is virtually no correlation between the size of the shadow economy as estimated in Schneider et al. (2010) and the share of informal (at the main job) employment, be it dependent, or own-account, or total. This is because a formal employee or a formal self-employed might contribute to the shadow economy (as defined in Schneider et al., 2010) via his/her main (formal) job or via secondary (formal or informal)

> 32 Data from Estonian Labor Force Survey (Statistics Estonia, 2010) provide partial support: during the growth period between 2003 and 2008, the rate of non-contracted employees was fairly stable near 2% of the labor force, but felt to 1% in 2009. Nikolovova et al. (2010: Figure 10) compare the dynamics of GDP growth and two (rough) proxies of the share of informal employment in the labor force for Czech R. and Slovakia, based on LFS data for 1998-2009. The overall dynamics of informality is clearly pro-cyclic, but during the 2009 recession (for Slovakia – also during the 1999 recession) in became counter-cyclic. Note that Nikolovova et al. (2010) use small establishment size or self-employment status as proxies for informal employment, because Czech and Slovak LFS (in contrast with Estonian LFS) do not provide information on employees without legal contract.

30
/sup> Self-financed growth is not a new idea. Feldstein and Horioka (1980) wrote a seminal paper asking how internationally mobile capital really was. In regressing national investment rates on national savings rates for 21 OECD countries using data from 1960 to 1974, they consistently found coefficients close to 1.0 and stable over time. These results were evocative of a closed economy, suggesting capital was not as mobile across borders as might have been assumed; if it were, one would expect a close to zero correlation between national saving and national investment. This finding became enshrined as the ―Feldstein-Horioka puzzle‖ and is evocative of home biases and self-financed growth.<sup>7</sup> In a paper related to APR, Prasad, Rajan and Subramanian (2007)—PRS—examined the links between capital flows and growth in the financially globalized 1990s. The starting point of

> 5 For details and caveats, see Aizenman, Pinto and Radziwill (2004, 2007).

> 6 If the country is on average running current account surpluses, the self-financing ratio would exceed 1. 7 It would of course be a genuine puzzle only if there were no legal restrictions on capital flows across countries.  In other words, F-H were testing de facto capital mobility.  See Obstfeld and Rogoff (1996) for further discussion of the various F-H interpretations.

4
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ facility is the largest smartphone factory in the world, producing 120 million units per year.<sup>12</sup> The new smartphone production facility will be set up in the Saigon Ho-Tech Park just outside Ho Chi Minh City. Samsung currently employs 75,000 production workers and software engineers, hiring an average of 2,000 people a week for its production facilities. The company will reach 100,000 employees in Vietnam by the end of the year. Vietnam is now responsible for 1/3 of Samsung’s global output.

**Figure 4. Vietnam’s Electronics Exports and Inward FDI, 1995-2009**

<!-- Start of picture text --> 1800 60<br>1600<br>50<br>Foreign value added<br>1400<br>1200 40 Re-imported domestic<br>value added<br>1000<br>30 Indirect domestic value<br>800 added (originating from<br>domestic intermediates)<br>600 20 Direct domestic value<br>added<br>400<br>FDI Stock<br>10<br>200<br>0 0<br>1995 2000 2005 2009<br>USD Million USD Billion<br><!-- End of picture text --> _Source:_ OECD-WTO TiVA Dataset; UNCTAD FDI Database Samsung relies heavily on Korean suppliers that have co-located in Vietnam to produce intermediate inputs. Of the company’s 67 suppliers in the country, only four are Vietnamese. They include Goldsun Packaging and Printing JSC (corrugated paper packaging materials), the Thang Long Packaging Production Export–Import JSC (thin-film packaging), Viet Hung Packaging Company Ltd (corrugated paper packaging) and Nam A Company Ltd (packaging). The prominence of packing companies mentioned here is no accident. Packaging is one of the lowest value added inputs to any manufacturing operation. Fifty-three of Samsung’s
World Bank in Sierra Leone; UN in Sudan; Australia in Timor Leste; and UN in Togo may be able to provide good examples.

# **5. Conclusion**

The two main purposes of this paper are to identify (i) the key aid effectiveness challenges in FCS and (ii) good performance examples which can provide useful lessons to help address aid effectiveness challenges. The identification of challenges is the critical first step to improve aid effectiveness, and exploring strong performance by peers in critical areas is a practical way to find adaptable approaches. As both recipients and providers are key stakeholders for aid effectiveness – recognized as the delivery and management of aid – this paper provides analyses from both angles.

This paper relies on the results of the PD survey due to its country coverage, issue coverage and availability of historical data. In order to enable us to use the results for comparison, the results are standardized. While the data sources and methodology are useful, they have limitations. Therefore, the results of this paper need to be carefully interpreted and strong performance on a particular indicator by a given country/development partner many be the result of factors other than good and replicable practices.

Bearing that in mind, the results show that:

- In all regions except MNA, the performance of FCS significantly lags behind that of non-FCS, especially on indicator 3 (aid flows are aligned with national priorities (aid on budget)); indicator 7 (aid is more predictable) and indicator 5b (use of country procurement systems).

- Despite the poor FCS performance in general, several countries can provide good examples which other FCS can learn from (e.g. indicator 7 (aid is more predictable) in West Bank and Gaza).

- The comparison between 2005 and 2010 shows that FCS‘ below average performance in 2010 is matched by below average improvements over this period.

- On average, among development partners, MDBs and other MOs such as the World Bank, EU institutions and United Nations