rafmacalaba/datause-encoder-data huggingface dataset:
that reform-minded executives in developing countries use IMF support to help overcome opposition to potentially unpopular policies. The idea is that after the executive reaches an agreement with the IMF, failure to achieve reform is more costly because rejection of those policies is also seen as a rejection of the IMF, and all domestics politicians and interest groups might find it costly to reject the IMF.<sup>12</sup> Vreeland hypothesizes that an executive’s likelihood of using IMF agreements in this way depends on the checks and balances she faces. Those facing no veto players ( _i.e._ , dictators) would have no need for IMF support. Those facing too many are unlikely to overcome opposition, despite IMF support. Thus the IMF would be unwilling to enter into agreements with them. In some intermediate range, IMF agreements should be more prevalent because they are more likely to achieve the desired objective of overcoming the opposition of veto players. We assume that World Bank adjustment loans could serve a similar purpose, and the parallel to potentially ‘strong’ and ‘weak’ reformers discussed above should be clear.

To operationalize these concepts, we use data on the number of checks and balances stipulated in country constitutions as reported in Beck _et al._ (2001). We include the number of checks and the squared number of checks in the selection equations that follow to test whether Vreeland’s hypotheses are valid for our dataset. We further hypothesize that a country’s borrowing needs might affect the likelihood of receiving World Bank adjustment loans non-linearly. It is common for developing countries to apply proceeds from new World Bank loans to repay old ones. For countries with a relatively large stock of World Bank debt, this sort of evergreening would be more likely. Beyond some point, however, debt accumulation might become problematic, making future agreements less likely. For countries with relatively little past borrowing, predictions are more difficult. This could reflect a preference for self-reliance, and thus we would expect little future borrowing. Or demand for loans could be cyclical, and thus lending would decline during up-cycles and increase during subsequent down-cycles. We try a variety of variables measuring countries’ past and current indebtedness and overall fiscal health to test these hypotheses.

> 12 “The IMF may restrict access to loans, it may preclude debt rescheduling with creditors who require an IMF arrangement to be in good standing, and decreased investment may result if investors take cues from the IMF.” Vreeland (2004), p. 2.

11
<!-- Start of picture text --> Growth in Resource and Non-Resource Exports<br>4500 150%<br>4000 \ /- 100%<br>3500 een<br>50%<br><53000 ‘i % . a i”<br>=<br>E 20 \ ‘ verte 0%<br>E 200 \, . “50%<br>“ \ \<br>- 1500 100%<br>1000<br>500 “150%<br>° PeAAR aes<br>ARR AR RRR RRR RAR AA<br>Resource Nor-Resource = = = Growthin Resource<br><!-- End of picture text --> <!-- Start of picture text --> so Resource Exports and Growth in Non-Resource soos<br>60.00%<br>2000<br>40.00%<br>:<br>= 10 20.00%<br>*E 1000 0.00%<br>v<br>20.0%<br>300<br>-20,00%<br>a o / \ 60.00%<br>BSS134392988 RESRR388838RRR RRR<br>—Cunies ——Mali ——Nigee ——Growthin Nor-Resouce (Guniea Mali, Niger)<br><!-- End of picture text -->
direct investors. The British Virgin Islands in the case of China also play a similar role.<sup>13</sup> Similar to the situation for FDI, a large proportion of portfolio investment is channeled via Hong Kong (China) and Mauritius, as shown in Table 3b. More generally, the geographical investment patterns highlights that much of the investment in China is coming from other Asian economies, whereas it is investors from the advanced economies that are most prominent in India. In part, this disparity reflects the differential impact of geography on FDI versus portfolio investment; it might be attributed to the large ethnic Chinese emigrant communities in Asia that are a natural source of investment flows to China.

To summarize, the current state of the international financial integration of China and India has several striking features. First, their international balance sheets are highly asymmetric – with official reserves dominating the asset side, and equity liabilities highly important for both countries (FDI for China, portfolio equity for India). Second, the absolute level of non-reserve foreign assets is very low. In terms of global impact, these countries’ global holdings of foreign assets and liabilities are relatively small, with the important exception of the official reserves category. Third, the net foreign asset positions of these countries are more positive than might be expected for countries at their level of development.

# **III. The Domestic Financial Sector and International Financial Integration**

To probe the extent to which the stylized facts above can be explained by developments and policies related to the domestic financial sectors in China and India, we very succinctly summarize the trends in three interrelated aspects of the financial sector: financial liberalization and exchange rate policies, the evolution (and state) of the domestic financial sector, and patterns in savings and investment. A much more detailed, but still brief account is provided in Appendices I and II, complementing the description in this section.

As becomes evident when summarizing their evolution, these factors are fundamentally related to cross-border asset trade and the international balance sheets. This section highlights the sharp changes in the domestic financial sector in each country since the early 1990s, the expected changes in the years to come, and their interaction with the international financial integration. We conduct the analysis by turning to the particular developments in the financial sectors of each country.

# **III.a. China**

China has adopted a gradualist approach to financial liberalization, including the capital account. During the 1980s and 1990s, the main focus was on promoting inward direct investment flows (that is, FDI), which led to a surge of direct investment in China in the 1990s. Investment by foreigners in China’s stock markets has been permitted since 1992 through multiple share classes, but access is still restricted and a heavy overhang of stateowned shares limits its attractiveness. Debt inflows have been especially restricted, as

> 13 See World Bank (2002) and Xiao (2004).

8
In our application, the proximity indicator is used as a measure of how close the capabilities of workers in a sector are from those required for full efficiency in other sectors; in the PS literature, it is used as a measure of a country’s capabilities, also considering other inputs. The proximity indicator follows the disaggregation of the CGE database. For the commodities that it covers, we use the Comtrade export database. However, proximity data are also needed for sectors not covered by Comtrade. Non-Comtrade commodities are made up of services and a subset of goods (the latter including utilities and construction); all tend to be relatively non-traded. Given that, export data may not exist or be relevant to RCA measurement, we instead use GTAP value-added data. Given that our dataset has a one-to-one mapping between activities (production sectors) and commodities (outputs), it is straightforward to define RCA and proximity indicators using export data for one commodity subset and VA data for another commodity subset.

The computed raw proximity data between all pairs of commodities are shown in Table 4.<sup>17</sup> Further, before the CGE implementation of these raw proximities, scaling was needed to generate empirically valid representations of labor market segmentation.<sup>18</sup> This is part of the broader challenge of testing and improving the validity of CGE models in manners that consider the context of different types of applications.

17 A model with multiple labor categories would require multiple proximity matrices to permit proximity data to differ by labor category (for example, classified by educational attainment, skill, or occupation). For example, IT and human resources staff may have very small distances between sectors; other skilled tasks may be highly specialized. 18 In our application in Section 3, the scaled proximity parameter, ���,�, used in the model is derived from the original raw proximity parameter using the following formula: ���,� = ��� ���� ��� ���� ���,� + � ∙�1 −���,� �, where � is a factor-specific scaling parameter. The higher the value of the latter, the higher the scaled proximity. To ensure that 0 ≤���,� ≤1, the scaling ��� ��� ���� parameter should satisfy the following restriction: −���,� ��1 −���,� �≤� ≤1. Note that �<sup>����</sup> lim→�<sup>���</sup> ���<sup>�,�</sup> + ����� ∙�1 −������,� ��= 1. If the non-scaled proximities are too low, simulated factor reallocation falls short of what is observed in practice; to make reallocation easier, the value of the scaling parameter should be within the following range: 0 < �<sup>����</sup> ≤1. Given current uncertainty about its value, it may be seen as the exogenous shift parameter that is used in the non-base simulations to explore the implications of different degrees of sensitivity of efficiency for potentially relocated labor to proximity.

16
aggregate level, but one cannot assert with confidence that it is a leading indicator, because of its probable endogeneity. Better governance indicators reduce the likelihood of growth decelerations, but they are not closely associated with more frequent accelerations.

**<u>Table 8: Conditional probability of growth acceleration and deceleration at the aggregate level</u>**

|**Variable**|**Depend**<br>**growt**<br>|**ent varia**<br>**h accelerat**<br>|**ble: frequ**<br>**ion per c**<br>|**ency of**<br>**ountry**<br>|**Dependent v**<br> <br>|**ariable: f**<br>|**requency**<br>|**of growth**<br>|**deceleratio**<br>|**n per cou**<br>|**ntry**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Model<br>1|Model<br>2|Model<br>3|Model<br>4|Model<br>5<br>Model<br>6|Model<br>7|Model<br>8|Model<br>9|Model<br>10|Model<br>11|Model<br>12|
||.110|||.047|-.253||||||-.233|
|Ln investment|(1.70)|||(.72)|(-3.23)||||||(-1.97)|
|||.056||.061||||||||
|Voice (governance indicator)||(1.87)||(1.98)||||||||
||||.082|.082||||||||
|Resource rich country|||(1.77)|(1.73)||||||||
||||||-.078||||||-.036|
|Ln ODA per capita|||||(-2.17)||||||(-.84)|
|Ln imports||||||-.145<br>(-2.40)|||||.020<br>(.21)|
|Political stability (governance<br>indicator)|||||||-.068<br>(-2.08)|||||
|Government effectivenness<br>(governance indicator)||||||||-.127<br>(-2.63)||||
|Rule of law (governance indicator)|||||||||-.115<br>(-2.60)|||
|Control of corruption (governance<br>indicator)||||||||||-.132<br>(-2.68)||
|R2|.06|.08|.06|.16|.10<br>.19|.12|.09|.14|.14|.15|.21|
|N|45|44|45|44|45<br>45|45|44|44|44|44|44|
|Note: t-test in parentheses.<br>Source:Arbache andPage (20|07b).|||||||||||

Table 9 shows fixed-effect logistical models that predict the presence of a growth acceleration or deceleration at the country level. Increases in savings, foreign direct investment, and consumption increase the odds of a growth acceleration, whereas government consumption and major conflicts reduce the odds. In the deceleration regression, increases in savings, investment, FDI, and trade reduce the odds of a growth deceleration, whereas inflation and minor conflicts increase the odds of collapse.<sup>28</sup> PT Only savings, FDI, and conflict appear in both regressions. These results suggest that policies aimed at attaining sustained growth and preventing growth collapses need to focus on ways to increase savings, attract foreign investments, and reduce conflicts.

> 28 Random-effect models, including dummies for oil-producing countries, and landlocked and resource-rich countries returned statistically insignificant results. Hausmann tests suggest that fixed-effect estimates are preferable to random effect.

22
Table 1. Determinants of migration flows <!-- Start of picture text --> (1) (2) (3) (4) (5)<br><!-- End of picture text -->

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|Heck with|Heck w/o|Poisson|IV|
|Lagged diasp|0.620|0.660|0.699|0.703|0.761|
||(34.35)***|(47.97)***|(43.91)***|(16.20)***|(10.92)***|
|Col links|0.331|0.219|0.127|-0.312|-0.051|
||(2.45)**|(2.03)**|(1.10)|(1.65)*|(0.26)|
|language|0.388<br>(5.20)***|0.477<br>(6.71)***|0.496<br>(6.48)***|0.298<br>(2.53)**|0.234<br>(2.27)**|
|Log(dist)|-0.408<br>(9.04)***|-0.501<br>(12.04)**|-0.448<br>(10.69)***|-0.337<br>(3.28)***|-0.259<br>(2.84)***|
|Schengen|0.168|0.257|0.277|0.061|0.160|
||(1.19)|(2.00)|(2.02)**|(0.23)|(1.11)|
|Constant|3.750|2.785|2.365|3.461|2.365|
||(6.92)***|(4.82)***|(4.02)***|(3.06)***|(2.69)|
|Observations|3608|5610|5760|5374|3486|
|Mills ratio|-|0.908<br>(7.60)***|1.19<br>(9.35)***|-<br>-|-<br>-|

<!-- Start of picture text --> OLS Heck with Heck w/o Poisson IV<br>Lagged diasp 0.620 0.660 0.699 0.703 0.761<br>(34.35)*** (47.97)*** (43.91)*** (16.20)*** (10.92)***<br>Col links 0.331 0.219 0.127 -0.312 -0.051<br>(2.45)** (2.03)** (1.10) (1.65)* (0.26)<br>language 0.388 0.477 0.496 0.298 0.234<br>(5.20)*** (6.71)*** (6.48)*** (2.53)** (2.27)**<br>Log(dist) -0.408 -0.501 -0.448 -0.337 -0.259<br>(9.04)*** (12.04)** (10.69)*** (3.28)*** (2.84)***<br>Schengen 0.168 0.257 0.277 0.061 0.160<br>(1.19) (2.00) (2.02)** (0.23) (1.11)<br>Constant 3.750 2.785 2.365 3.461 2.365<br><!-- End of picture text --> <!-- Start of picture text --> (6.92)*** (4.82)*** (4.02)*** (3.06)*** (2.69)<br>Observations 3608 5610 5760 5374 3486<br>Mills ratio - 0.908 1.19 - -<br>- -<br>(7.60)*** (9.35)***<br><!-- End of picture text --> 16
# **Table 10. OLS Regressions – Percentage of Labor Income**

|**Variables**||
|---|---|
|Land Gini|0.4487<br>(0.3120)|
|Dummy Native Populations in Colonial Times (1535-1540)|-0.0034<br>(0.0306)|
|_Dummy_Female Household Head (1 female head of household , 0|-0.0074|
|otherwise)|(0.0175)|
|Max educ in the household in years|-0.0021<br>(0.0019)|
|Number of Children under 5 years of age|0.0261**<br>(0.0110)|
|Number of Children between 5 and 18 years of age|0.0209***<br>(0.0045)|
|Number of members 65 or older|-0.0629***<br>(0.0143)|
|Number of Members of Extended Family|-0.0035<br>(0.0056)|
|Dummy Production Problems due to Land Quality (1 Problems in the Rural|0.0602**|
|District, 0 otherwise)|(0.0251)|
|_Dummy_Quality of Access to the Municipality (1 if there is a paved road or|0.0580**|
|if the road is in good condition, 0 otherwise)|(0.0261)|
|Constant|0.1378<br>(0.2087)|
|Observations|3,141|
|R-squared|0.1099|
|Cluster by community||
|*** p<0.01, ** p<0.05, * p<0.1||

Source: CLS-WILL: First wave – community and household questionnaire.

41
_Social”_ (2006) extended social benefits to the lowest-income population. The implementation of new social programs is reflected in an increasing share of transfers<sup>6</sup> in households’ total income. In particular, transfers represented 3 percent of the total household income in 2003 while in 2011 they represented almost 10 percent<sup>7</sup> (Figure 14a). Additionally, coverage of public transfers also increased from 26 percent of households in the first quintile in 2003 to 70 percent in 2011<sup>8</sup> (Figure 14b). Rofman (2013) also shows that social transfers are a very important part of household income for households in the first decile of the income distribution and estimates that non-contributory transfers led to a reduction of 10 percent in poverty incidence, 16 percent in poverty intensity and 23 percent in poverty severity. He also analyzes the vulnerability of households and shows that the combination of a rise in unemployment to 2002 levels and a 10 percent reduction in real household income would increase the poverty rate to 26.8 percent, if the current transfers system remains in place, and to 32.2 percent if the prevailing system was that of 2002 highlighting the importance of social transfers.

<!-- Start of picture text --> Figure 14. Evolution of public transfers, 2003 and 2011<br>b) Percentage of households receiving public<br>a) Public transfers share of total income<br>transfers*<br>12% 80%<br>70%<br>10%<br>60%<br>8%<br>50%<br>6% 40%<br>30%<br>4%<br>20%<br>2% 10%<br>0%<br>0%<br>1 2 3 4 5<br>1 2 3 4 5<br>Income quintile<br>Income quintile<br>2003 2011 2003 2011<br>70%<br>9.9%<br>46%<br>26% 26% 27%<br>3.0% 2.8% 16%<br>1.8% 12%<br>0.9% 1.1% 0.5% 0.4% 0.2% 0.1% 7% 2% 4%<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011 and 2003 _Note:_ For comparability across time, all numbers in the figure are representative of urban areas (more than 5,000 inhabitants), since ECH became representative at the national level only after 2006. * Does not include non-contributory pensions The change in poverty can be better understood by analyzing the impact on the labor market and the different sources of income. Even though the unemployment rate tends to remain around 6 percent over the benchmark scenario and there are no important changes in the proportion of the inactive population, under the crisis scenario, unemployment is 1.4 percentage points higher in year

> 6 Includes government transfers different than non-contributory pensions. Specifically, the programs included are: unemployment insurance, _Hogar consituido_ and _Asignaciones Familiares_ .

> 7 In this period, the mean transfer per capita in the first quintile increased from 44.36 to 293 Uruguayan pesos per month. (values in 2005 Montevideo’s prices)

> 8 In the case of _Asignaciones Familiares_ , administrative data shows that it increased its coverage in 36 percent between 2003 and 2011 passing from 402.672 to 549,295 beneficiaries during this period.( Banco de Previsión Social 2011)

14
## **e. External Validity using a second dataset**

To establish external validity of any statistical results across time and contexts beyond reasonable doubt, it would be necessary to conduct identical analysis many times (replication) and then derive bounds for the relationships. This is not possible in the timeframe of the initial analysis, but has potential for a future exercise.

Up to now, we have examined statistical learning techniques using the original dataset to create a “pseudo‐external validity”. To further explore the concept of external validity, we attempted replicating the analysis of the initial vulnerability function on a second dataset. However, the consumption methodology was incomparable so we briefly review the method here.

To answer this question, we would first employ model selection techniques to identify the model which gives the most similar results to the first dataset. In this case, there is no resampling involved, we use the second data set as the validation/testing dataset.

The first step involves fitting a regression model using the original/training dataset. Thereafter the second dataset or testing dataset is fit to the model and the MSE is calculated.

Given that the second dataset consists of _n_ observations, { ���, �� �, ���, �� �, ... ���, �� � }, we would find the predicted values, �<sup>�</sup> ��� � predicted by the regression model obtained using the training dataset and we calculate the MSE; � � MSE � � ������� � �<sup>�</sup> �����<sup>�</sup> (5.2) We would then select the model for which the test MSE is the smallest. To measure the accuracy of the model or the extent to which it gives the most similar results to the first dataset we would then calculate the bootstrap estimates, this time resampling from the second dataset.

# **5. Presentation of results from the statistical analysis with interpretation**

As noted in section 4, statistical learning can give information on how informative a model is when applied to a holdout sample. As this was an exploratory exercise, rather than presenting one methodology and set of results, the exercise begins with the q‐fold replication. One subset of the data is taken as the “training dataset” and then we assess the model fit in terms of the RMSE on the holdout sample or “testing dataset”. There are thus several different cuts of the data that have been applied to assess the information needed.

We began with the combined 2005/11 dataset, and conducted q‐fold (10 rep) cross‐validation on the shortlisted 4 models with a linear specification for the drought variable. To be specific, this meant that for the pooled dataset, we fit the model onto 9 tenths of the data, and assess the fit on the one remaining tenth. The exercise is repeated 10 times. As discussed in section 4, the models are assessed based on the average mean squared error (MSE) over all ten repetitions as defined in equation (5.1). The results are shown in table 6.1 below. There is little difference between the models, with model 4 having the lowest average MSE by a small margin.

24
manufacturing employment and establishments, respectively (Ghani, Kerr and O’Connell, 2013a).

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Our data for the unorganized sector come from the National Sample Statistics (NSS). These surveys are used for many published reports on the state of Indian businesses and government agency monitoring of the Indian economy. The typical survey collects data from over 150,000 Indian establishments. In this respect, the surveys are comparable to the Annual Survey of Manufacturing conducted in the United States, with the Indian sampling frame being about three times larger.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The surveys provide sample weights that we use to construct populationlevel estimates of employment by district. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions and policy choices. The average district size is roughly twice the size of a U.S. county (average size of ~5,500 square kilometers) and there is substantial variability in district size (standard deviation of ~5,500 square kilometers). Indian districts can be effectively considered as self-contained labor markets and, to some degree, economic units.

Our surveys record economic characteristics of plants, such as employment, output, and raw materials. Our analysis considers aggregated measures of manufacturing employment in locations. For this purpose, we sum the activity of plants up to the district or district-industry level, combining the organized and unorganized sectors and using sample weights to prepare population estimates. We use the two-digit level of the NIC system for calculating industrial specialization and diversity for districts. This level of aggregation contains 22 manufacturing industries. This level of aggregation meaningfully balances the identification of distinct industries and economic activities with not being so granular as to create exception outliers in specialization measures.

Our core sample contains 554 districts. This district count is larger than that analyzed in Ghani, Kerr and Tewari (2013), which included 429 districts, for two reasons. First, by considering the 2000 to 2010 period, we do not need to restrict the sample to districts that were surveyed in the 1980s and 1990s as in our longer panel. Second, we retain districts that record 6