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

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- Banerjee, A., Duflo, E., Glennerster, R. and Kinnan, C., (2010). The miracle of microfinance? Evidence from a Ramdomized Evaluation. MIT. Mimeo.

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|||TopF|iveTr<br>(B|ading<br>ased|Partnersfor <br>onTradeInte|East A<br>nsity I|sian<sup>Ec</sup><br>ndex)|<sup>onomi</sup>|<sup>es</sup>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Thailand|**LAO**<br>|**CAM**|**SGP**|**VNM**|**MYS**||**LAO**<br>|**CAM**<br>|**VNM**|**SGP**|**IDN**|
||153.8|16.3|5.1|3.1|**2.7**||**61**<br>|**.**|**4.5**|**4.6**|**3.7**|
|Vietnam|**JPN**|**AUS**|**PHL**|**IDN**|**SGP**|X||**AM**|**IDN**|**PHL**|**JPN**|
||**4.1**|**3.8**|3.4|2.7|**2.6**|_|32.E|30.E|**7.2**|**3.8**|**3.3**|

Note: Only trading relationships with an index of<sup>1.0or aboveare includedin table.</sup> 1Source: Compiled by authors based on data<sup>in</sup> Tables<sup>S and 6.</sup> The above analysis using trade share revealed that<sup>the trade of East Asian</sup> countries with the U.S. and EU is quite significant.<sup>This conclusionis supportedby the</sup> intensity index in the case of the U.S., but not<sup>the EU.Althoughthe U.S.appears among</sup> the top five trading partners in 1998-2000<sup>foronly Cambodia and HongKong,there are</sup> in fact 11 East Asian countries that have an<sup>intensity indexof 1 or abovewith the U.S.</sup> (see Table 4.5). On the other hand, the intensity<sup>indices withthe EUare all well below 1</sup> (in fact, most are below 0.5). Thus, the intensity<sup>index confirmsan above-normal trading</sup> relationship with the U.S., but not with the<sup>EU.</sup> During 1998-2000, Japan was among the top five<sup>trading partnersfor seven East</sup> Asian countries, including China, Indonesia,<sup>the Philippines,and most of thenewer</sup> ASEAN countries. It is interesting to note that<sup>the intensity indicesof severalof these</sup> countries with Japan are nearly the same in 1998-2000<sup>as in1995-97,but Japan's</sup> position among their top five partners has<sup>droppedindicatinga relativeweakening of</sup> Japan's trade relationship with these countries<sup>relativeto their other trading partners. 32</sup> This indicates that Japan has declined slightly<sup>as a trading partnerfor someEast Asian</sup> countries, which is consistent with the findings<sup>inour earlier trade shareanalysis.3</sup> Nevertheless, the indices of East Asian countries<sup>with Japanas a partnerare allI or</sup> above (except for Cambodia) and many are<sup>2 or higher indicating a still strong trading</sup> relationship.

The newer ASEAN countries, in 1998-2000,<sup>aremore often among thetop five</sup> trading partners of the East Asian countries<sup>than they were in1995-97.</sup> This could be attributed to the effect on their trade of membership<sup>in ASEAN during the latter half of</sup> the 1990s.

Figure 4.4 below compares trade intensity<sup>indices of selected regionalgroupsin</sup> Asia, Europe and North America.

15
# **References:**

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- **Clerides, Sofronis K., Saul Lach, and James R. Tybout** , “Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico, and Morocco,” _The Quarterly Journal of Economics_ , 1998, _113_ (3), pp. 903–947.

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- **and** , “Exporting and Plant-Level Efficiency Gains: Its in the Measure,” _Journal of Political_

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29
on average across<sup>_i_</sup> ) due to a decrease in tariff rates (i.e., if<sup>ln1 </sup><sup>_tj_0</sup> ), we would expect to have a negative and significant<sup>__</sup> estimate. The log initial population of _i_ , _j_ the<sup>_ith_</sup> largest city<sup>ln</sup><sup>_p_</sup> 1985 has been included in the regression to capture the convergence effects among small cities because a small city may grow faster than a larger city (where a larger city is not necessarily the largest city). Hence, the coefficient in front of the log initial population<sup>__</sup> also has an expected negative sign.<sup>12</sup> We employ two alternative estimation methods: Ordinary Least Squares (OLS) and Two Stage Least Squares (TSLS). Although OLS is our benchmark method, we employ TSLS to consider possible endogeneity issues. These issues arise because it may be the case that, for instance, tariff policy and urban concentration are reflections of a deeper causal variable, such as institutions. In this view, economic and political institutions would have a causal effect on urban concentration and trade (and other) policies, which would causally affect urban concentration. Although it is difficult to deny the purchase of such arguments when one is concerned with levels (cross-section), given the slow rate of change in and persistence of institutions over time commonly found in the recent empirical literature (Acemoglu et al. 2001, 2011), one would expect that these concerns would not be valid in differences (time series). In fact, ET show that in the sample used here, there exists neither a clear nor a robust relationship between institutional changes and changes in trade policy.

However, given that trade policy is a choice variable and therefore endogenous, there remains a need for a source of exogenous variation in the trade policies of the 1980s

> 12One important detail is that the _i_ th largest city in the pre-liberalization period may be the _i_ th largest city in the post-liberalization period. This does not present a problem for our analysis because we
- 30 -

In our view, implementation depends not only on a set of technocratic measures that can take place by the acts--decrees and orders of civil servants--but also on the capacity to manage the political support for these measures. The upfront identification of winners and losers from the various reforms has to be an integral part of the feasibility of structural adjustment programs. There is a diverse array of subgroups and interests within the government itself that will react differently to various reforms. For example, public entexprise managers would oppose reform if it involves closing down or retrenchment. Ministries charged with revenue collection and fiscal balances would respond favorably if a reform package added significantly to available budget resources. Civil servants might respond negatively to pay scale reform and cut back in employment levels, but they might be deterred from opposition if a reform lessened or removed legal restrictions on their private economic activities. In Nigeria, in the early years of adjustment program, the military government imposed pay cuts on the military itself, thereby establishing a measure of fairness and credibility for the overall program .' But subsequently, the distribution of gains and losses from the adjustment programs as among the military, the political class and the rest of the society had tumed out to be unfair and perceived so widely.

Private sector constituencies might also behave differently. The more competitive industrial concerns, crippled by shortages of foreign exchange and import restrictions, might support trade reform as they did in many of the sample countries: export-oriented farmers that are direct beneficiaries of devaluation, and producers of domestic food crops respond favorably to pricing and marketing reforms. The case studies report that in six ont of seven countries, both export and food production rose in response to successful devaluation and liberaization of prices and marketing. Farmers in general benefitted from shift of resources from urban consumers to rural producers. Outher poorer urban groups, which had been hit hard by budget cut-backs, could have been supportive if specially targeted programs had provided them assistance at the time such cut-backs were

**22 The discussion is drawa from Gnllhali, I, "The Political Economy of Reform in Sub-Saharan Africa, EDI Policy Seminar Report No. 8., Washington, D.C., 1988.**
Table 5 Export Participation and Market Density Share of Land

||Model 1|Model 2|Model 3|
|---|---|---|---|
|IV – Linear Model||||
|Coefcient|0.30|0.18|0.18|
|Standard error<br>|(0_._052)<sup>_∗∗∗_</sup>|(0_._07)<sup>_∗∗∗_</sup>|(0_._07)<sup>_∗∗∗_</sup>|
|_R_<sup>2</sup>|0.13|0.21|0.21|
|IV – Tobit||||
|Marginal efect|0_._31|0_._18|0_._18|
|Standard error|(0_._040)<sup>_∗∗∗_</sup>|(0_._052)<sup>_∗∗∗_</sup>|(0_._050)<sup>_∗∗∗_</sup>|
|Observations|6554|6554|6554|
|Export Price Index|No|Yes|Yes|
|Food Markets|No|No|Yes|
|Household characteristics|Yes|Yes|Yes|
|District characteristics|No|Yes|Yes|

Note: Export participation is measured with the share of land devoted to export crops (coffee, tea, cotton, pineapples, passion fruit). The first panel shows results from 2SLS regressions of export participation on export market density. The second panel shows MLE results from a Tobit specification with endogenous variables (using the control function approach). Standard errors (within parenthesis) are cluster-corrected. Based on the Uganda National Household Survey 1999/2000.

35
<!-- Start of picture text --> 0.2<br>0.15<br>0.1<br>0.05<br>0<br>-0.05<br>6 6.5 7 7.5 8 8.5 9<br>Log of 1970 Per Capita GDP<br>Annual Growth Rate of Per Capita GDP, 1970-1996<br><!-- End of picture text -->

# **5.2** σ **-convergence**

This section evaluates σ-convergence for Rio Grande do Norte’s municipalities by working with a normalized vector of per-capita income<sup>7</sup> . Figure 5.2 shows crossmunicipality standard deviations of per-capita income.

The dispersion or σ-convergence of per-capita income for RN’s municipalities shows **–** cross-municipality standard deviations increased during 1975 96 (from 0.40 to 0.47). This suggests that the growth process in the municipalities is accompanied by perturbations, which increase the dispersion of per-capita income among the municipalities. These findings also indicate that municipalities with lower initial income do not grow faster than municipalities with high initial income and, furthermore, that the cross-section dispersion of per-capita income does not collapse as time passes. Hence, the pattern of growth among municipalities is very different from what Cavalcanti and de Goes (1996) found among Brazilian states, which are converging in terms of income and, furthermore, the dispersion is diminishing. However, this finding is not very surprising, as other researchers have found similar results in other regions (Quah 1993).

> 7 Over time, some municipalities show high and inexplicable variations in per-capita income, e.g., it increased ten times between 1970 and 1980 and returned to 100% of initial level in 1996. Because of this, some municipalities were excluded from the sample to calculate the σ convergence.

13
<!-- Start of picture text --> Tr"% Pdkqy<br>WPS 581<br><!-- End of picture text --> Ttds paper- a product of the Trade Policy Division, Country Economics Departnent- is part of a larger effort in PRE to measure the effectiveness of structural adjustment efforts, especially trade refonn, in developingcountries. It includes both this empirical analysis and otherresearch identifying possible causal links between policy and performance. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sheila Fallon, room. N10-035, extension 37947 (31 pages).

To measure the effectiveness of the World Bank's structural adjustment programs, Conway examines the data on actual economic performance for 75 counties for the period 1976-86.

He finds a clear association between participation in a World Bank adjustment lending program and cross-country differences in economic performance and policy. Countries that paricipated in adjustment lending programs tended to have the following characteristics, compared with countries that did not participate in such programs:

- More rapid economic growtt

- More rapid inflation

• A less negative current account balance as a percentage of GNP

- Deeper financial sectors

# * Depreciation of the real exchange rate.

The first three indicators reflect differing performance; the second three, different policy mixes. In other words, the countries have not benefited merely by increased financing at the margin but have also undertaken significantly different economic policies.

Conway speaks of the association and correlation, not causes. No components of adjustrnent lending prograns are singled out for praise or blame. The atheoretic methodology he uses does not identify causal links between bank adjustment programs and these measures, and provides no means of separating the effecLt of Bank lending fiom other factors. The adjustment lending programs were often concurrent, for example, with IMF stabilization policies, so to that extent the correlations are measures of the joint impact of the two.

* A lower ratio of current government spending to GNP

The PRE Working Paper Saies disernninates the findings of work under way in the Bank's Policy, Research, and Extenal Affairs Complex. An objective of the sries is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by PRE Editorial Services
# **Working for Yourself or for Your Kids? Childcare Expansion Policy in Uzbekistan**

Dilnovoz Abdurazzakova, Chiyu Niu, Avralt-Od Purevjav JEL: J13, J22, I25, O15, D13 Dilnovoz Abdurazzakova: Central European University, Chiyu Niu: The World Bank, Avralt-Od Purevjav: The World Bank; Working paper: will be updated further.
# **Figure 6. EAP: Investment growth slowdown and investment needs**

_In 2014, virtually all EAP economies recorded investment growth below their long-term average, mainly reflecting weak private investment. A rebound of investment in 2015 helped, but investment growth remains below its long-term average in more than half of EAP economies. Long-term forecasts suggest continued weakness in investment growth, while sizable investment needs remain in infrastructure._

**A. Share of countries with weak B. Contributions to investment growth investment growth**

<!-- Start of picture text --> Percent<br>100<br>Below long-term average Contracting<br>75<br>50<br>25<br>0<br>2010 2011 2012 2013 2014 2015<br>C. Long-term investment growth<br>expectations<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>14 Public Private<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>Commodity exporters Commodity importers<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

# **D. Infrastructure investment needs , East and Southeast Asia**

<!-- Start of picture text --> Percent Percent of GDP<br>8 4<br>7<br>6 3<br>5<br>2<br>4<br>3<br>1<br>2<br>1<br>0<br>0<br>Energy Transport Telecoms Water and<br>2010 2011 2012 2013 2014 2015 2016<br>Sanitation<br><!-- End of picture text --> Sources: Bhattacharya (2012), China Economic and Industry Data Database (CEIC), Consensus Economics, General Statistics Office of Vietnam, Haver Analytics, Inderst (2016), Investment and Capital Stock database, International Monetary Fund, World Bank.

A. Share of countries in EAP region with investment growth below the long-term (1990-2008) average or negative investment growth (“contracting”).

B. Weighted averages of gross fixed capital formation growth rates in the public and private sectors, respectively, in constant 2005 U.S. dollars. The sample includes nine EAP economies.

C. Five-year ahead consensus forecasts made in the year denoted. Weighted average.

14
under the ETS-cum-RES scenario is implemented through a RES production subsidy rate of 50%<sup>9</sup> , and the emissions trading target is lowered such that the RES subsidy and the emissions price achieve the desired 10% emissions reduction relative to benchmark.

Considering that hydropower has the lowest generation cost and high competitiveness compared with other renewable energy technologies in China, it is not included in the RES policy in this study. The technologies included are solar, biomass, and wind.

Table 2. Scenarios under different policies.

|Scenario|RES subsidy rate|CO2intensity decrease|
|---|---|---|
|S0: Benchmark scenario without any policies|0|0|
|Scenario ETS: ETS policy only|0|10%|
|Scenario ETS-cum-RES: ETS policy and<br>RES policy|50%|10%|

For simplicity of presenting the results, the 30 regions are classified into three areas (eastern, central, and western) based on the regional divisions used by the National Bureau of Statistics of China. Table 3 shows the classification of regions.

Table 3. Classification of regions.

|Category|Regions|
|---|---|
||Beijing (BJ), Tianjin (TJ), Hebei (HB), Liaoning (LN), Shanghai (SH),|
|Eastern regions|Jiangsu (JS), Zhejiang (ZJ), Fujian (FJ), Shandong (SD), Guangdong (GD),<br>Hainan (HN)|
|Central regions|Shanxi (SX), Jilin (JL), Heilongjiang (HL), Anhui (AH), Jiangxi (JX), Henan<br>(HeN), Hubei (HuB), Hunan (HuN)|
||Inner Mongolia (IM), Guangxi (GX), Chongqing (CQ), Sichuan (SC),|
|Western regions|Guizhou (GZ), Yunnan (YN), Shaanxi (SaX), Gansu (GS), Qinghai (QH),<br>Ningxia (NX), Xinjiang (XJ)|

# **4. Results**

Because this is a static long-term analysis, the results shown in this section do not

> 9 The subsidy rate is determined based on data on levelized costs for power generation from various sources and current electricity generation mix. A 50% price subsidy means reduction of the long-run marginal cost of the renewable energy aggregate (excluding hydro), which is the price of electricity from those sources in the model, by 50%.

16