rafmacalaba/datause-encoder-data huggingface dataset:
- Mills, Evan (2002) “”The $230-billion Global Lighting Energy Bill,” in _Proceedings of the 5th International Conference on Energy-Efficient Lighting_ .

- National Bureau of Statistics (2009) _Tanzania Statistical Abstract 2006_ , Dar es Salaam: National Bureau of Statistics.

- National Bureau of Statistics and Ministry of Industry, Trade and Marketing and Confederation of Tanzanian Industries (2010) _Annual Survey of Industrial Production and Performance, 2008: Statistical Report_ , Dar es Salaam: National Bureau of Statistics.

- National Research Council (2003) _Cities Transformed: Demographic Change and its Implications in the Developing World_ , Washington, DC: The National Academies Press.

- Persits, Feliks, Thomas Ahlbrandt, Michele Tuttle, Ronald Charpentier, Michael Brownfield, and Kenneth Takahashi (2002) “Map Showing Geology, Oil and Gas Fields, and Geologic Provinces of Africa, version 2,” Open File Report 97-470A, version 2.0, United States Geological Survey.

- Pholo Bala, Alain (2009) “Gates, hubs and urban primacy in Sub-Saharan Africa,” CORE Discussion Papers 2009039, Universit ́e catholique de Louvain, Center for Operations Research and Econometrics (CORE).

- du Plessis, H. W., A. T. Visser, and P. C. Curtayne (1990) “Fuel Consumption of vehicles as Affected by Road-Surface Characteristics,” in Wolfgang E. Meyer and Jean Reichert eds. _Surface characteristics of roadways: international research and technologies_ , Philadelphia: American Society for Testing and Materials, pp. 480– 496.

- Teravaninthorn, Supee and Gael Raballand (2009) _Transport prices and costs in Africa: a review of the main international corridors_ , Washington, DC: World Bank.

- United Nations (2008) _World Urbanization Prospects: The 2007 Revision_ , New York: United Nations.

- World Bank (2007a) _A Decade of Action in Transport: An Evaluation of World Bank Assistance to the Transport Sector, 1995-2005_ , Washington, DC: World Bank.

- (2007b) “An Overview of China’s Transport Sector - 2007,” EASTE work-

- ing paper 15, Transport, Energy and Mining Sector Unit, Sustainable Development Department, East Asia and Pacific Region, The World Bank.

27
may have important measurement error, leading to attenuation bias in the estimated coecients. This should be considered for the quantitative interpretation of the results below.

Data on nominal and real eective exchange rate at monthly and annual frequencies come from the International Monetary Fund 2008, and data on the development of foreign exchange derivatives in dierent countries, measured by the ratio of average daily turnover to total GDP and total exports come from the Bank of International Settlements (1998, 2001, 2004, and 2007) Triennial Survey of Central Banks. Since this survey is conducted only every three years, the average value across available years is considered as representative country value. Data on a country's degree of nancial development come from Beck et al. (2000) (2006 revision).

The correlation between the annual growth of dierent international good prices and the growth of the nominal exchange rates of the countries in our sample was computed as described in section 2, using the data detailed above. This procedure yields a matrix of estimated correlations where each row corresponds to a 4-digit SITC good, each column corresponds to a country, and each entry is the estimated time-series correlation between the growth rate of the price of the good described in the row and the country described in the column. The distribution of the estimated correlations is shown in Figure 2. It shows that while the correlations cover a broad set of values, the mass of the distribution is shifted to the negative side. There are 57484 correlations in the database across 129 countries and 749 commodities with available data. Since our sample covers at most 17 observations, most of the estimated correlations are not statistically signicant, in fact only 22 and 32 percent of the estimated correlations are signicantly dierent from zero at the 10 and 20 percent level, respectively. This introduces an additional source of noise that should make even more dicult nding any signicant result.

The 20 country-good pairs with the most positive and negative correlations are reported in Table 1. The commodities are distributed across many countries. For instance, the 20 commoditiescountries with the most negative correlations are distributed across 12 countries and 18 commodities, and the 20 with the largest positive correlation are distributed across 15 countries and 17 commodities. In each group, the commodities cover a wide variety of industries. Figure 3 shows that this variation is a robust pattern of the data. The average correlation across commodities within countries (Panel A) uctuates between -0.4 and 0.4. As expected from the histogram, the average correlation is negative in most countries, but there is a sizable number of countries where it is positive and there is no individual country that stands as an outlier. The same applies to the distribution of average correlations across countries within commodities (Panel B). This suggests, as it is indeed the case, that the results reported below are robust to dropping any particular commodity and country.

15
in the same industry matters, but only for the quality of goods exported by Russian firms to developed country markets. The estimated coefficients are highly statistically significant. This is true both when the FDI proxy enters by itself as well as in the full specification. The result holds for both measures of export quality. A one-standard-deviation increase in the FDI presence in the same sector is associated with a 5% and 7% increase in the quality of exports when the quality is proxied with unit values and the Khandelwal et al. (2013) measure, respectively.

Table 3 distinguishes between exports to neighboring countries and other destinations.<sup>13</sup> When I consider unit values, the results are confirmed, suggesting that foreign presence in the input-supplying sector is positively related to the exports by Russian firms. The estimated coefficients are highly statistically significant in all four specifications, but the magnitudes are twice as large when exports to non-neighbors are considered. Regarding the alternative measure of export quality, the effect of interest is statistically significant only when non-neighboring countries are being considered.

The ORBIS database contains information on the nationality of investors, which allows me to classify FDI as originating from developed countries versus the rest.<sup>14</sup> Germany, the United States and the United Kingdom are the top three source countries of FDI accounting for about 226, 126 and 104 foreign affiliates, respectively. This translates into about 21%, 12% and 10% of foreign affiliates within the grouping, respectively. They are followed by the Netherlands and Finland. The presence of FDI originating from non-developed countries is much smaller. In this group, China, Turkey and Belarus are the largest investors each with about 30 affiliates. They are followed by the Democratic People’s Republic of Korea and Poland, with 25 affiliates each.

> 13The neighboring countries include: Azerbaijan, Belarus, China, Estonia, Finland, Georgia, Kazakhstan, the Democratic People’s Republic of Korea, Latvia, Lithuania, Mongolia, Norway, Poland, and Ukraine. 14Although there is no one widely accepted definition of developed countries, I use the following list, based on the World Bank classification from the early 2010s, including: Austria, Australia, Belgium, Canada, Denmark, France, Finland, Germany, Greece, Iceland, Ireland, Italy, Israel, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, the Republic of Korea, Spain, Sweden, Switzerland, United Kingdom, and United States.

11
and ‐0.56 respectively. The median district growth rate is also negative for Electrical and leather manufacturing industries.

# 5 Determinants of Employment Growth in Ghana

## 5.1 Key local determinants of employment

In our analysis below, we explore the extent of agglomeration economies on employment growth in Ghana, following Glaeser (1992) and Combes (2000), amongst many others. The previous section suggested some important trends in Ghanaian manufacturing‐ especially the growing dominance of Greater Accra and the decline in particularly wood and furniture, which were two industries not concentrated in Accra. We seek to explain these patterns through the lens of agglomeration economies. We test for localization and urbanization economies using several measures. These are specialization, competition (localization economies) and population density, average plant size and diversity (urbanization economies).

We also add in human capital in the district‐industry in the 2003 NIC, using the proportion of skilled workers in each industry‐district to test for human capital externalities. We use distance from Accra and Kumasi as a proxy for transport costs to larger markets, and, in the case of Accra, to export markets. We use the level of imports in the industry to control for external competition, which is assumed to affect all industries equally, after controlling for transport costs proxied by distance.

Combes (2000) argued that employment growth as well competition, diversity, average size and specialization within _zones d’emploi_ ‐industries should all be normalized using the level for the country as a whole in each industry, which he terms normalization, and which allows comparison across sectors. This was undertaken in the original Glaeser et al (1992) paper for some but not all variables, which Combes (2000) remedied. We now describe each of the urbanization and localization economies variables and their normalizations.

In examining the determinants of employment growth, we use a normalized measure of employment growth in an industry‐district between the 2003 and 2014 firm censuses as our dependent variable, following Combes (2000), Where d is the district and i is the industry. As Combes (2000) notes, the normalization is done because the aim is to explain why employment growth in an industry in a particular district is higher than the national growth rate in that industry, and not to explain growth in that district‐industry.

The obvious measure of the extent of agglomeration economies is the extent to which employment density impacts employment growth. In our analysis employment density is the density of total employment (actually persons engaged‐ which includes those recorded in the firm census as working proprietors, unpaid workers or apprentices) in each district, where empd is total persons engaged across all industries in the district and the area of each district is obtained from the IPUMS 2000 population census data.

11
**strong, that a participation of. foreign investors** is not **essential** in **order to obtain an adequate** price **for SOEs.**

Table 3 FOREIGN DIRECT INVESTMENT FROM PRIVATIZATIONS22 (in US$ Million and percent)

||198a|1989|1990|1991|1992|1988-92|
|---|---|---|---|---|---|---|
|Europe&Central**Asa**|||||||
|**-fDlf**rom privatizations|33.2|**61**6.6|586.6|1,930.0|2,704.8|6,869.2|
|**-asshareof**total**FOI (%)**|2.38|22.06|17.84|36.37|47.87|31.86|
|**Latin America & Caribbean**|||||||
|-FDI from privatizations|213.7|157.3|2,136.3|3,299.9|2,311.8|8,119.0|
|**-as**shareof total FDI**(%M**|2.67|2.20|27.65|26.85|16.78|16.43|
|**Eet Asia& Pacific**|||||||
|-FDI from privatizations|0 0|**0.0**|0.0|75.0|301.7|376.7|
|**-as**share**of**total**FDI**(%)|0.00|0.00|0.00|0.58|1.95|**0.68**|
|South**Asia**|||||||
|-FDl from privatizations|0.0|0.1|10.6|4.2|37.0|51.9|
|**-as**share**ot total**FDI**(%**|0.00|0.04|3.64|1.18|**8.81**|3.39|
|Sub-Saharan**Africa**(a)|||||||
|-FDI from privatizations|0.0|13.8|38.1|2.8|44.0|98.5|
|-as share**of**total**FOI (%)**<br>|0.00|0.56|5.70|0.15|3.44|1.37|
|North Africa&Middle**East**|||||||
|-FDI from privotizations<br>|**0.0**<br>|1.0|0.0|3.2|22.5|26.7|
|-as share**of**total FDI%%)|0.00|0.06|0.00|0.45|1.09|0.38|
|TOTAL|||||||
|-FDl from privatizations|246.9|787.8|2770.6|5314.9|5421.8|14,542.0|
|-as share of total FDI (%)|1.25|3.38|11.54|15.67|14.17|10.44|

_So,rceforPFD data: DMW,_ **_Balanc ofPayments_** _Yearbook 1992_ Note: Foreign dired invcsment data for 1992 ae World Bank and DM staff esrimate. (a) excludLng South Africa

# **V. Privatization Exoeriences at the** Countra **Level**

For the period 1988-92, privatization transactions were recorded for a total of 72 nations, of which 47 were developing countries. Several countries, however, only show one or two transactions, therefore not indicating the existence of an extensive privatization prograr aiong those 29 developing countries with numerous privatization transactions, the experiences differ substantially in terms of size, sectoral focus. and foreign participation.

In terms of sales volume, Mexico clearly leads the developing world with a total of almost $21 billion during 1988-92, about $5 billion of which came from foreign investors. The three largest sellers, Mexico, Argentina, and Brazil, accounted for 60 percent of total privatization revenues among developing countries. With respect to numbers of 22 A country breakdown on FDI resulting from privatizations and its share in total FDI is provided in the appendix in Tables A-3 and A-4.

22
# **Figure 13. Expenditure efficiency in small states and other economies**

_Small states spend more, relative to GDP, on social development and infrastructure investment than other EMDEs, but do not achieve superior outcomes. Several factors, including weak governance and implementation challenges, contribute to inefficiencies in public projects. Small states, particularly commodity exporters, spend significant amounts on subsidies, especially for energy consumption. Digital connectivity is particularly poor in small states in EAP and SSA, but significantly better in small states in LAC than in other EMDEs._

## **A. Healthcare, education expenditures and human development outcomes, 2011-21**

## **B. Spending efficiency, 2010-20**

<!-- Start of picture text --> Percent of GDP Health expenditure HDI score Score Small states<br>Education expenditure EMDEs excl. small states<br>8 0.8<br>HDI (RHS) Advanced economies<br>1.0<br>6 0.6<br>0.8<br>4 0.4<br>0.6<br>2 0.2 0.4<br>0.2<br>0 0.0<br>Small states EMDEs excl. EMDEs excl. 0.0<br>SS SS and LICs Education Health Investment<br>C. Energy subsidies, 2022 D. Digital connectivity, 2019<br>Percent of GDP Petroleum Index, 100 = best<br>4 Natural gas 60<br>Electricity EMDEs excl. small states<br>3<br>40<br>2<br>1<br>20<br>0<br>Small Small Small EMDEs<br>states states: states: excl. small 0<br>Commodity Commodity states LAC small EAP small SSA small<br>exporters importers states states states<br><!-- End of picture text --> _Sources_ : Black et al. (2023); Cantu-Bazaldua (2021); GFS (database); Herrera et al. (forthcoming); WDI (database); World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LICs = Low-Income Countries; SS = Small states; SSA = SubSaharan Africa.

A. Panel shows the Human Development Index (HDI) score for 2021 and average health and education expenditures as a percent of GDP for 30 small states and 104 EMDEs excluding small states for the period 2011-21.

B. Bars show average efficiency score for up to five different methodologies for 35 small states for health, 6 small states for education, and 8 small states infrastructure investment for the period 2010-20 from Herrera et al. (forthcoming). Sample includes 32 advanced economies and up to 111 EMDEs excluding small states.

44
**Figure 18. Progressivity of In-Kind Transfers in Albania (Kakwani Index)**

## **Figure 19. Marginal Contribution of In-Kind Transfers in Albania (Only Redistributive Effects)**

<!-- Start of picture text --> 0.6 0.035<br>0.5<br>0.025<br>0.4<br>0.015<br>0.3<br>0.2 0.005<br>0.1<br>-0.005<br>0<br>-0.015<br>-0.025<br>-0.035<br>Source: Own estimates using the Albania HBS 2015. Source: Own estimates using the Albania HBS 2015.<br><!-- End of picture text -->

# **VII. Additional Applications for Policy Making**

**Beyond providing a static view of the distributional impact of the fiscal system, the model allows for policy simulations** . This section presents a policy-relevant scenario of a reduction in the top bracket of the Personal Income Tax (PIT), from 23 percent to 18 percent, as noted in the Government of Albania’s 4-year program objectives (announced in 2017).

**Importantly, as with the majority of household surveys around the world, individuals with top incomes in Albania are not fully captured in the HBS** . Therefore, the full extent of the policy impact cannot be evaluated, and results can be interpreted as a lower bound.<sup>42</sup> Analyses from other countries are exploring the use of alternative data sets, such as credit data in Indonesia,<sup>43</sup> to complement the household survey and capture the top of the income distribution.

**The reduction in PIT, as measured in this paper, has a very small (simulated) impact on inequality.** The impact of the reduction in PIT on poverty and inequality across the different income concepts is shown in Table 4. As expected, the simulated PIT reduction is inequality-increasing, but the effect is very small, as shown by very small increases in the Gini index (measured using disposable, net, consumable and final).

> 42 Since the PIT is a progressive instrument, in the case of no-underreporting of labor incomes at the top, a rising PIT would have a disproportionately larger impact on richer households.

> 43 Ongoing work in Indonesia, as referenced in World Bank (2015) and World Bank (2018), is using the Central Bank’s consumer credit database on mortgages, vehicle loans and others, to estimate top income households and combined, this with the household survey data to get a more complete distribution.

28
performing countries, the ratio of test scores relative to the leader is 300/625=0.48 in the worst‐ performing countries. This implies that a year of school in the worst‐performing countries is “worth” only about half as much as a year of school in the best‐performing countries.

- A fourth approach is suggested in Schoellman (2012), who relates estimated returns to education of immigrants in the United States to test scores in their countries of origin, and finds a strong positive relationship.  He then introduces this relationship into a model in which agents take education quality into account when choosing their investment in years of school.  His estimates suggest that the return to a year of school needs to be doubled relative to standard benchmarks to take into account the fact that in countries where school attainment is low, this reveals an endogenous response to low quality.  While this argument does not provide a direct mapping from individual country test scores, it suggests a factor‐of‐two quality adjustment which is similar to World Bank (2017) and Filmer, Rogers, Angrist, and Sabarwal (2018).

- A fifth approach can be anchored in the small literature that has jointly estimated returns to years of school and returns to cognitive ability, where cognitive ability is proxied using test scores.  For example, Hanushek and Yang (2009) estimate Mincerian regressions of log earnings on education and test scores and find a return per year of school of 10 percent, and a return per standard deviation of test scores of 10 to 20 percent (as cited in Hanushek, Ruhose and Woessmann (2015)) .  The ratio of these two estimated returns implies that one standard deviation of test scores is equivalent to 1 to 2 years of school in terms of its effects on earnings. Under the somewhat arbitrary assumption that a standard deviation of test scores in the Hanushek and Yang (2009) study is equivalent to a PISA standard deviation of 100, this would imply that the 300 PISA point gap in test scores between the worst and best performing countries is equivalent to approximately 3 to 6 years of school.  At the other extreme, Caselli (2014) cites estimates in Vogl (2014) that suggest a return to school of 7.2 percent and a return per 100 PISA points of 1.4 percent, suggesting that the 300 PISA point gap in test scores across �.�

- countries is equivalent to only about �3 �0.6 years of school. �.�

Absent a clear empirical consensus on the equivalence between test scores and years of school, the HCI calculations in this document use the mapping from test scores to quality differences suggested by Filmer, Rogers, Angrist, and Sabarwal (2018) because of its simplicity and ease of communication. The calculations are implemented using the most recently available test scores for each country. This 33
The first is a **respect for the market economy.** A precondition for a policy-based finance system is the existence of a private sector business structure and private finance that can be supplemented with policy-based finance. In Japan's case, its prewar experience as a market economy and its endeavor to establish postwar economic reform created an environment that fostered an entrepreneurial spirit among the private sector. I The second characteristic (which became particularly evident in Japan as the high growth period unfolded) is the **close relationship between the government's econoniic plans and policy-based finance.** Through policy-based finance, the priority allocation of financing was implemented in line with government policies that incorporated the will of the private sector. The precondition for this relationship was the existence of a public savings system, and a vehicle fpr allocating funds efficiently -- that is, the Fiscal Investment and Loan Program The third characteristic (particularly relevant to JDB) is a **respect for managerial independence** given the existence of a sound financial composition and management. While JDB had inherent limitations as a government-related financial institution, its neutral and fair appraisal basp enabled it to make funding decisions autonomously.

# **B. EMPIRICAL EVALUATION OF CREDIT POLICY IN JAPAN"**

A successful implementation of credit policies would show the following characteristics:

- Financial support to successful, dynamic industries would decline over time since as a result of their very success, such industries would be expected to repay their loans and achieve financial independence. Even if successful companies might continue to rely on bank loans for financing their expansion plans, they would presumably cease to receive support from government funds.

- Financial support to declining industries would probably last for longer periods. A basic objective of policy would be to smooth the adjustment process and reduce capacity in an orderly fashion in order to minimize dislocations in labor markets and regional economies. Thus, the provision of government financial support would likely delay the adjustment, but whether this would be econ,mically beneficial or not would depend on the balance between the benefits and costs involved.

- Financial support to growing firms would be more permanent and would not be affected by the fortunes of particular industrial sectors. However, successful firms would not

- This section draws on Calomiris et al (1992) and Calomiris and Himmelberg (1994a and 1994b).

14