rafmacalaba/datause-encoder-data huggingface dataset:
|Observatio<br>555<br>547<br>541<br>547|541|541<br>968<br>948<br>933<br>953|933<br>933|
|---|---|---|---|
|ns||||
|F‐stat<br>4.510|4.568|19.85<br>5.330|5.268<br>15.76|
|_Note: P‐values are in parenthesis displayed below the estimated coe_<br>_regression control for gender‐state fixed effects. Control variables_|_fficients. *_<br>_of column_|_(**, ***) indicates statistical significance at the ten‐percent_<br>_(2) include all baseline controls that were significant deter_|_(five‐percent, one‐percent) level. All_<br>_minants of attrition and of selection_|
|_between receiving “training and grant” vs “training, but no grant”. I_<br>_food consumption, formal bank account, formal loans, informal loans_<br>_of buildings at baseline, and conflict exposure between baseline and_<br>_to the closest KCB bank branch that might correlate with other geog_<br>_average land gradient and their respective interactions with selection_<br>_Column (13) reports tests for coefficient equality between estimates_<br>_parenthesis._|_n particular_<br>_, education_<br>_endline. Co_<br>_raphic char_<br>_to the orig_<br>_from males_|_, these are age, marital status, employment status, business_<br>_level, literacy level, numeracy level, household size, number o_<br>_lumn (3) also controls for geographic features since the estim_<br>_acteristics. . Geography controls include distance to the clos_<br>_inal treatment group and the interaction between conflict ex_<br>_and females in column (6) and (12). Displayed are difference_|_ownership, food consumption, non‐_<br>_f children, number of rooms, number_<br>_ation strategy relies on the distance_<br>_est city, distance to the closest road,_<br>_posure and original treatment group._<br>_s of coefficient p‐values of the test in_|

47
# VI. **Conclusion**

By way of summary, and based on the fix provided by the newer quasi-experimental research on the economics of education, investment in education behaves in a more or less similar manner as investment in physical capital. In advanced industrial countries, the returns to human and physical capital tend to be equated at the margin.

At the same time, we should point to a major research gap, which is the marriage between the micro and the macro evidence on the returns to education. Whereas at the micro case, as amply demonstrated above, it is established beyond any reasonable doubt that there are tangible and measurable returns to investment in education, such evidence is not as consistent and forthcoming in the macro literature (see, for example, Pritchett 2001; and Psacharopoulos 2000 and Krueger and Lindahl 1998 for a different perspective).

More research on the social benefits of schooling is needed. For developing countries, there is a need for more evidence on the impact of education on earnings using quasiexperimental design. There are more opportunities today for this type of research. Moreover, this research needs to be used to create programs that promote more investment and reform financing mechanisms.

# **References**

- Acemoglu, D. and J. Angrist. 2000. "How Large Are Human Capital Extemalities? Evidence from Compulsory Schooling Laws." MIT (mimeo).

- Appleton, S. 2000. "Education and Health at the Household Level in Sub-Saharan Africa." Harvard University Center for International Development Working Paper No. 33.

- Appleton, S and Balihuta, A. 1996. "Education and Agricultural Productivity: Evidence from Uganda." Centre for the Study of African Economies Working Paper No. WPS/96-5.

- Arai, M. and C. Kjellstrom. 1999. "Returns to Human Capital in Sweden," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Ashenfelter, 0. and A.B. Krueger. 1994. "Estimates of the Economic Return to Schooling from a New Sample of Twins." _American Economic Review_ 84(5): 1157-1173.

- Ashenfelter, 0. and C.E. Rouse. 1998. "Income, Schooling, and Ability: Evidence from a New Sample of Twins." _Quarterly Journal of Economics_ 113: 253-284.

- Asplund, R. 1999. "Earnings and Human Capital: Evidence for Finland," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

_5_
al., 2013). As it is an iterative process for local populations and local administrations, the importance of continuous awareness raising efforts for both parties seems warranted (Berner, 2001). Successful experience in affecting the local budget decisions and implementation may improve the trust in and credibility of local administrations. In turn, an interested and actively engaged citizenry provide incentives for local administrations to work transparently and gain efficiency through open exchange with the service.

Evidently, information sources for raising awareness are crucial in reaching various demographic and social groups. Television as a channel to deliver information about local budgets seems, in the Kyrgyz context, to be most promising as about 94 percent of respondents in the LiK survey indicated television as a main source of news and information. Newspapers are only used as a source of information by a quarter of respondents, and are thus less effective in targeting the population. About 60 percent of people also use informal sources of information by talking to neighbors, friends and colleagues.

This quantitative study brings new evidence and highlights important policy related aspects of citizen interest and participation in local budgets in rural Kyrgyz Republic by using a novel and multi-topic household and individual survey. However, the study does not go beyond descriptive statistics and determinants of citizen proactivity. It also does not look into mechanisms and intensity of civic engagement and participation, and cannot speculate that a higher participation leads to better local development outcomes. The Kyrgyz Republic has been an unusual country in the Central Asia region that went far ahead with governance and fiscal decentralization and encouraged large participatorybased rural projects supported by development institutions (such as Village Investment Project, supported by the World Bank). These developments and advancement of research infrastructure, such as the Life in Kyrgyzstan panel survey, may shed in the future more light on how a bottom-up governance system responsive to citizens may work more efficiently.

21
18

- Bourguignon, François, Francisco Ferreira and Marta Menéndez. 2003. “Inequality of Outcomes and Inequality of Opportunities in Brazil.” Washington, D.C.: World Bank Policy Research Working Paper Series 3174.

- Bowles, Samuel and Herbert Gintis. 1996. “Efficient Redistribution: New Rules for Markets, States and Communities.” _Politics & Society_ , **24** (4): 307-342.

- Card, David. 2001. “Estimating the Returns to Schooling: progress on some persistent econometric problems.” _Econometrica_ **69** (5): 1127-1160.

- Cogneau, Denis and Jeremie Gignoux. 2005. “Earnings Inequality and Educational Mobility in Brazil over Two Decades.” Paris: DIAL. Processed.

Cohen, G.A. 1989. “On the Currency of Egalitarian Justice.” _Ethics_ **99** :906-944.

- Crosby, Alfred. 1986. _Ecological Imperialism: The Biological Expansion of Europe 9001900_ . New York: Cambridge University Press.

- Drèze, Jean and Nicholas Stern. 1987. “The Theory of Cost-Benefit Analysis.” In Auerbach and Feldstein (eds.) _Handbook of Public Economics_ . North Holland: Elsevier Science Publishers.

- Dunn, Christopher. 2003. “Intergenerational Earnings Mobility in Brazil and Its Determinants.” University of Michigan, Processed.

- Dworkin, Ronald. 1981. “What is Equality? Part 2: Equality of Resources.” _Philosophy and Public Affairs_ 10(3):185–246.

- Engerman, Stanley, and Kenneth Sokoloff. 1997. “Factor Endowments, Institutions, and Differential Paths of Growth among New World Economies: A View from Economic Historians of the United States.” In Stephen Haber, eds., _How Latin America Fell Behind._ Stanford, C.A.: Stanford University Press.

- Fehr, Ernst, and Simon Gachter. 2000. “Cooperation and Punishment in Public Goods Experiments.” _American Economic Review_ 90:980–994.

- Fernández, Raquel and Jordi Gali. 1999. “To Each According to...? Markets, Tournaments, and the Matching Problem with Borrowing Constraints.” _Review of Economic Studies_ **66** (4):799-824.

- Ferreira, Francisco H. G. 2001. “Education for the Masses? The Interaction between Wealth, Educational and Political Inequalities.” _Economics of Transition_ **9** (2):533–52.

- Forbes, Kristin J. 2000. “A Reassessment of the Relationship between Inequality and Growth.” _American Economic Review_ **90** (4): 869–87.
8 position with the CBE, especially in 2007 and 2008. During the capital outflow periods 2009 and 2011, the banking sector reduced its net credit position with the CBE.

**Table 4 – Change in CBE foreign currency assets (in Billion US$)**

|||Int. Res.<br>& For.<br>Assets<sup>1</sup>|Change<br>in CBE<br>IR & FA<sup>2</sup>|Current<br>Account<sup>3</sup>|Capital<br>Account<sup>4</sup>|
|---|---|---|---|---|---|
||Mar|17.6|2.6|1.0|1.6|
|2005|Jun|19.2|1.6|0.1|1.5|
||Sept|22.0|2.8|0.2|2.6|
||Dec|23.4|1.4|0.8|0.6|
||Mar|23.7|0.3|0.9|-0.6|
|2006|Jun|23.2|-0.5|-0.1|-0.4|
||Sept|25.9|2.7|1.3|1.4|
||Dec|32.0|6.1|0.4|5.7|
||Mar|36.4|4.4|1.1|3.3|
|2007|Jun|38.5|2.1|-0.6|2.7|
||Sept|38.9|0.4|-0.1|0.5|
||Dec|43.2|4.3|-0.2|4.5|
||Mar|47.5|4.3|0.7|3.6|
|2008|Jun|48.5|1.0|0.5|0.5|
||Sept|41.9|-6.6|-1.0|-5.6|
||Dec|34.9|-7.0|-1.5|-5.5|
||Mar|32.1|-2.8|-0.9|-1.9|
|2009|Jun|31.7|-0.4|-1.0|0.6|
||Sept|35.6|3.9|-0.5|4.4|
||Dec|36.0|0.4|-0.8|1.2|
||Mar|39.3|3.3|-1.3|4.6|
|2010|Jun|40.7|1.4|-1.7|3.1|
||Sept|45.7|5.0|-0.8|5.8|
||Dec|43.5|-2.2|-0.6|-1.6|
||Mar|30.5|-13.0|-1.0|-12.0|
|2011|Jun|26.5|-4.0|-0.4|-3.6|
||Sept|24.5|-2.0|-2.2|0.2|
||Dec|18.5|-6.0|-1.8|-4.2|

Source: Authors’ calculations based on CBE monthly reports. Notes: 1. International reserves + foreign assets of the CBE.

2. Change in (1) 3. Current account from the CBE monthly reports 4. (2) – (3)
**Other Revenue** : Total government revenue minus tax revenues. For central government. Source: Calculated by author.

**Total Government expenditure** : Total central government expenditure: economic plus social, plus general public services, plus defense, plus other expenditure. Source: GFS.

**Budget balance** : Before 2001, it is calculated as total government revenue plus grants minus total expenditure minus net lending. After 2001, it is total revenue plus grants minus total expenditure. For central government. Source: Calculated by author.

# **Productive expenditure** :

General public services expenditure Defense expenditure Educational expenditure Health expenditure Housing expenditure Transportation and communication expenditure Source: Calculated by author.

# **Unproductive expenditure** :

Social security and welfare expenditure Expenditure on recreation Expenditure on fuel and energy Expenditure on agriculture, forestry, fishing and hunting Expenditure on mining, mineral resources, manufacturing, and construction Expenditure on other economic affairs and services Source: Calculated by author.

# **Other expenditure (for productive versus unproductive classification)** :

Public order and safety Other expenditures Source: Calculated by author.

# **“Core” spending:**

General public services expenditure Educational expenditure Health expenditure Housing expenditure Transportation and communication expenditure Expenditure on fuel and energy Source: Calculated by author.

28
We base our assumption on the following facts. First, while Romania is the third largest country in CEE with a territory of 238,000 km<sup>2</sup> , its rail and road networks are among the least extensive in transition countries hindering development of national distribution systems. Romania’s rail network, which is the main means of internal transport for passengers and freight, covers 14,217 km of which only 35% is electrified. Its rolling stock is in urgent need of replacement. According to information from the World Development Indicators, the railway density in Romania in 2004 is about 4.7 km per 100 km<sup>2</sup> of land area and falls behind that in Hungary (8.9 km per 100 km<sup>2</sup> ), Poland ( 6.4 km per 100 km<sup>2</sup> ) and Croatia (4.9 km per 100 km<sup>2</sup> ). The road infrastructure of Romania also lags behind that of western Balkan states such as Croatia, or Serbia and Montenegro. Up until 2002 Romania’s public roads covered only 73,260 km, which amounted to about 30.7 km per 100 km<sup>2</sup> of land area. Less than one-quarter of roads were designated as modern and only 113 km were motorways. In the following years, the coverage of public road network increased to 78,000 km yet most of them still need almost constant repair (EIU country profile, 2003, 2006). In terms of total road density, there was about 86 km road per 100 km<sup>2</sup> land area in Romania, which was much less than 178 km per 100 km<sup>2</sup> in Hungary and 138 km per 100 km<sup>2</sup> in Poland (the World Development Indicators).

Second, the distribution system in Romania is underdeveloped as very few professional distributors are in operation. Foreign retails find it difficult to find distributors with the required skills and capital base (EIU 2004, 2006). Third, one of the global retailers confirmed that the company does not use a centralized procurement system in Romania and that each outlet independently sources goods for sale. This suggests that individual stores are more likely to source locally than nationally.

The underdevelopment of the transportation infrastructure and the distribution sector would limit retailers’ ability to source products across regions. The potential spillover from global retail chains to the supplying sectors would, therefore, be constrained by regional boundaries. As we recognize that regional characteristics may affect the entry decision of global retail chains, we will also use instrumental variable approach in our analysis.

Our second identifying assumption is that entry of a global retail chain into the region should affect some manufacturing sectors but not others. More specifically, we believe that sectors supplying consumer products to supermarkets, as opposed to sectors supplying industrial inputs, should be affected. As food products are the most popular goods sold in all formats of 12
|Per capita revenue after transfer|75|75|
|---|---|---|
|Per capita net income|975|525|
|(income+transfer)|||

Case 2: Region A Reduces Its Per Capita Tax Base to $900

||Region A<br>($)|Region B<br>($)|
|---|---|---|
|Per capita income|900|500|
|Per capila local tax revenue|90|50|
|Per capila transfer|-20|20|
|Per capita revenue after transfer|70|70|
|Per capita net income<br>(income +transfer)|880|520|

# 3.4. Sources of **Data Required for Calculation**

][n selecting formulas for equalization transfer, a major consideration is the availability of data. In many developing countries, data constraints force the central governmen **t** o adopt relatively simple models with fewr variables. This section briefly discusses the possible channels through which data could be collected for the purpose of calculating equalization transfers.

'The central government agency in charge equalization transfer can use a variety of data sources. In most countries, the easiest source is the statistics provided by the central government's statistical agency. In addition to the central statistical agency, line ministries can often provide more detailed statistics on need indicators such as demographic composition, land areas, student enrolment rates, health indicators, length and quality of roads, electricity consumption, number of policemen, etc. Data on local tax bases are often supplied by local tax authorities.20 In cases where the central and local governments share the same tax bases and the center is responsible for tax collection, tax base data can be easily obtained from the central tax authorities. In cases where local tax bases differ from central tax bases, local tax authorities should be required to provide annual tax base figures to the central government agency in charge of

## 20 Hen: "local governments" refers to subnational governments in general.

45
The structural characteristics of the countries are captured in the following variables. Trade openness is measured as the (log) of the ratio of total trade to GDP. Financial development corresponds to the (log) of the ratio of the private credit provided by banks and other financial institutions to GDP, obtained from Beck, Demirguc-Kunt, and Levine (2000). When not available we used data on the domestic credit to private sector (as a fraction of GDP) from WDI. Openness in capital account transactions is captured by the Ito and Chinn (2002) index.<sup>9</sup> The index is such that a higher value indicates a higher degree of openness. The index of labor market flexibility is calculated from data in World Bank (2003) and is a weighted average of three indicators --flexibility of hiring, conditions of employment, and flexibility of firing-- as in Botero et al. (2004). For this paper, the original index was rescaled to range between 0 and 1, with higher values indicating more flexible labor markets. Finally, the index measuring the ease of firm entry was calculated from data in World Bank (2003) and O’Driscoll, Feulner, and O’Grady (2003) and is a weighted average of four indicators –registration procedures, cost of registration, days to registration, and burden entry regulations—as in Chang, Kaltani, and Loayza (2005). This index also ranges from 0 to 1, with higher values indicating less restricted firm entry.

The sample of countries used in the empirical analysis is shown in Table 2. The sample includes 90 countries from different regions and income levels. The largest region in the sample is Sub-Saharan Africa with 31 countries, followed by Latin America with 20, East Asia and Pacific and with 11, Middle East and North Africa and Western Europe with 10 each, and South Asia, East and Central Europe, and North America with 4, 3, and 1 countries respectively. With respect to income, there are 36 low-income countries, 36 middle-income countries, and 18 high-income countries. The sample includes all countries where at least 15 continuous observations of both terms-of-trade and output per capita were available during the period 1974-2000 and with available measures of the structural characteristics described above. We excluded from the sample large industrial

> 9 The Ito-Chin index corresponds to the first principal components of the following 4 binary variables reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER): existence of multiple exchange rates, restrictions on current account, capital account transactions, and the existence of requirements to surrender exports proceedings.

9
20

1.8, while for China the drop was from 1.5 to 1.1 (quarterly data provide slightly different figures, but confirm the substantial decline in their elasticities in the 2000s).<sup>12</sup> Finally there is also some evidence of a structural break in the trade-income relationship for both countries between the two periods. Interestingly, the Euro Area, which accounts for a sizeable share of the world trade elasticity due to its significant import share, displayed an increasing elasticity of imports to its own income over the 2000s. Other geographical areas with decreasing trade elasticity, most notably Latin America and the Caribbean, have little weight in explaining the changing responsiveness of world trade with respect to GDP.

In the rest of this section, we therefore investigate each of the candidate explanations for the lower world trade elasticity, paying special attention to developments in China and the United States.

# **B.   Changes in the Composition of World Trade**

A first explanation is that the composition of world trade may have changed in later years towards a trade category with lower income elasticity of demand. The technical appendix shows that the trade elasticity can be decomposed into a weighted average of the elasticity of different components of trade with respect to income, where the weights are given by the share of the specific category in world trade. Hence, the decline in the world trade elasticity could be driven by a fall in the elasticity of its components and/or by an increasing share of the component with lower elasticity (i.e. by a changing composition of world trade).

To test this hypothesis, we decompose the world trade elasticity into its various components and estimate separately the elasticity of services trade and of goods trade with respect to income for the world. Results presented in Table 6 show that the main force in the decline of the world trade elasticity has been the fall of the goods trade elasticity in the 2000s, which

> 12 We ran country regressions both including and excluding the real effective exchange rate (REER) and results do not change much. In the tables, we report coefficients for the regressions without the REER.
Table 5: HRS on Ability/Effort by Self‐Reported Measures of Effort

||(1)<br>Domain 1<br>Satisfa|(2)<br>Index: Job<br>ction|(3)<br>Domain<br>Ability|(4)<br>2 Index:<br>/Effort|(5)<br>Domain 3 In<br>Stru|(6)<br>dex: Support<br>cture|(7)<br>Domain 4 In<br>Engag|(8)<br>dex: Student<br>ement|
|---|---|---|---|---|---|---|---|---|
|Treatment|‐0.580**|‐0.572**|‐0.673**|‐0.651**|‐0.277|‐0.246|‐0.0126|0.0118|
||(0.251)|(0.245)|(0.259)|(0.249)|(0.223)|(0.217)|(0.170)|(0.156)|
|Effort Median|0.480*|0.461*|0.189|0.204|0.197|0.276|0.139|0.182|
||(0.259)|(0.268)|(0.164)|(0.157)|(0.226)|(0.233)|(0.164)|(0.149)|
|Treatment X Effort|‐0.0636|‐0.0552|0.568*|0.532*|0.268|0.239|0.0843|0.0743|
|Median|||||||||
||(0.378)|(0.370)|(0.310)|(0.293)|(0.346)|(0.342)|(0.247)|(0.245)|
|Controls|N|Y|N|Y|N|Y|N|Y|
|Observations|348|348|348|348|348|348|348|348|
|R‐squared|0.061|0.087|0.071|0.103|0.023|0.054|0.008|0.036|

Standard errors in parentheses clustered by school. *** p < 0.01, ** p < 0.05, * p < 0.1.

24