rafmacalaba/datause-encoder-data huggingface dataset:
**Table 46. Importance of IPA services: Business setup assistance**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0315|0.0351|||0.0516|0.0828|||
||(0.0560)|(0.0547)|||(0.115)|(0.120)|||
|Export % of sales|0.00134*|0.000737|0.00145*|0.000808|0.00157|0.000977|0.00183|0.00109|
||(0.000741)|(0.000729)|(0.000747)|(0.000740)|(0.00153)|(0.00161)|(0.00159)|(0.00168)|
|Share of sourcing via imports|0.00188**|0.00192**|0.00163**|0.00166**|0.00419***|0.00426**|0.00385**|0.00390**|
||(0.000756)|(0.000763)|(0.000768)|(0.000771)|(0.00159)|(0.00171)|(0.00164)|(0.00174)|
|High-income source country|-0.194**|-0.129|-0.184**|-0.117|-0.416**|-0.269|-0.395**|-0.249|
||(0.0782)|(0.0815)|(0.0810)|(0.0818)|(0.170)|(0.187)|(0.175)|(0.189)|
|>250 employees|0.0147|-0.000197|0.0120|0.000322|0.0238|-0.000257|0.0208|0.00282|
||(0.0711)|(0.0722)|(0.0721)|(0.0733)|(0.146)|(0.156)|(0.150)|(0.161)|
|Cumulative inv. >US$10M|-0.0651|0.0269|-0.0610|0.0346|-0.111|0.0509|-0.103|0.0697|
||(0.0658)|(0.0639)|(0.0667)|(0.0648)|(0.134)|(0.137)|(0.138)|(0.142)|
|Years in country|-0.00100|-0.00196|-0.00104|-0.00228|-0.00189|-0.00401|-0.00219|-0.00477|
||(0.00251)|(0.00242)|(0.00252)|(0.00240)|(0.00510)|(0.00506)|(0.00517)|(0.00509)|
|Level of foreign ownership (%)|-0.000904|9.20e-05|-0.00106|-9.90e-05|-0.00172|0.000613|-0.00222|-6.90e-05|
||(0.00126)|(0.00128)|(0.00123)|(0.00122)|(0.00274)|(0.00295)|(0.00272)|(0.00288)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.087|0.031|0.105|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Assistance in setting up of business (e.g. registration requirements and entry permits)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

63
# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|The Effect of Pure Market Buybacks|3|
|3.|Simple Menus:<br>Relend or Exit|6|
|4.|Cost of Debt Reduction|9|
|5.|Underfunded Menus|13|
|6.|Heterogeneous Banks|15|
|7.|Evaluation of the Mexico 1990 Deal|17|
||The Deal<br>Evaluation<br>Analysis|18<br>19<br>20|
|8.|Concluding Remarks|23|
|Appe|ndix 1: The Case with Multilateral<sup>Loans</sup>|25|
|Appe|ndix 2: The Debt Value Curve<sup>and the Debt Reduction/</sup><br>New Money Frontier|27|
|Refe|rences|28|
and outputs and by market imperfections. As a result, it appears that even poorer households begin to catch up. The households that have to rely on the utilization of own labor on the household farm and are not endowed with traction power nor with a link to employment opportunities in the prospering coastal regions have fewer opportunities for adjustment. In fact the only option for them is to farm rice more intensively and to shift into the labor intensive other livestock production.

# **6. Conclusions**

In this study, we employed an innovative village equilibrium model, which fully accounts for nonseparability of household production and consumption response. This allowed us to analyze the impact of trade liberalization on agricultural supply response and off-farm employment, while simultaneously accounting for household consumption decisions. The village model is used to analyze the impact of trade liberalization, which was quantified through macro level shocks to the Chinese economy obtained from GTAP model simulations. We analyzed the impact of price changes and labor demand, the two major pathways through which international trade affects households.

Our full liberalization benchmark shows results that are well in line with the findings of national level studies and with the results of the household analysis in Huang _et al._ (2003). Analyzing the impact of changes in agricultural input and output prices, we find an increased village supply of rice and other livestock. As the cash constraint is lifted in the wake of rising incomes following liberalization, the households invest the proceeds in the capital intensive activity of livestock production. The increased supply of rice is the result of more complex interactions, however, as some household groups increase rice production, while others reduce rice production.

25
72

**Table** **<u>30 Causes for an Underfunding (self-reported)</u>**

|Cause|Pan<br>PF (i<br>2003|el A<br>n %)<br>2002|Pa<br>Collective<br>2003|nel B<br>–Inst. (in %)<br>2002|
|---|---|---|---|---|
|Losses on|||||
|Securities|59%|50%|59%|46%|
|Insufficient|||||
|Returns on Investments|4%|30%|22%|20%|
|Insufficient|||||
|Actuarial Funding|22%|9%|4%|5%|
|Losses on Other|||||
|Investments|9%|4%|4%|26%|
|Other|6%|8%|11%|3%|
|Total|100%|100%|100%|100%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.20, T25 and T26 2: Bundesamt für Sozialversicherung (2003), p.22 sqq., T26 and T27 The same causes also apply to “collective–institutions” (c.f. Panel B). Nevertheless, this administrative form needs a closer examination. Table 17 shows that 113 “collective– institutions” out of 126 ( ≈ 90% ) hand over their risks inherent in old age provision (at least part of them) to a life insurance company. As mentioned before, insurance companies are basically not allowed to have any funding shortage. Thus, how can the figures in Table 29 be explained? For the few “collective–institutions” that handle their risks autonomously, the same reasons as above apply. However, for the _semi– and non–autonomous institutions_ , three more reasons have to be considered:

1. Since _semi–autonomous institutions_ manage at least the accumulation of retirement capital themselves, they are subject to market risks.

2. In the case of _non–autonomous institutions_ , all risks are covered by an insurer. Nevertheless, some non–autonomous “collective–institutions” may offer “separate investment accounts” to their affiliated employers. As a direct consequence of these “separate investment accounts”, employers bear part of the investment risks.

3. The insurance company may refuse to guarantee the minimum interest requirement.

# **_5.4.4  Measures to Correct the Funding Gap_**

This subsection discusses some measures that have been taken to remedy the financial situation of pension funds.<sup>68</sup> To show the variability of actions across different pension funds and years, “collective institutions” are discussed separately, and two successive years are explored.

68 Note that these measures have been reported by the pension funds.
**Figure 1: Children’s Time-Use and Age, and Maternal education**

<!-- Start of picture text --> Children's time Use: Play Children's time Use: Housework<br>Rural Pakistan Rural Pakistan<br>5 10 15 5 10 15<br>Age of Child Age of Child<br>Mother Not Educated Mother Educated Mother Not Educated Mother Educated<br>Children's time Use: Paid-Work Children's time Use: Study/Preparation<br>Rural Pakistan Rural Pakistan<br>5 10 15 5 10 15<br>Age of Child Age of Child<br>Mother Not Educated Mother Educated Mother Not Educated Mother Educated<br>300 200<br>250<br>150<br>200<br>100<br>150<br>Minutes Playing<br>Minutes Housework<br>50<br>100<br>50 0<br>40 160<br>140<br>30<br>120<br>20<br>100<br>Minutes Paid-Work 10<br>Minutes Study/Preparation 80<br>0 60<br><!-- End of picture text --> _Notes:_ The figures show non-parameteric graphs relating child time-use with age for the sample of mothers with some education relative to those with no education. To focus on the relative differences between these groups, the vertical axis are different across the graphs. Child time use is based on a time allocation module completed for every child through the LEAPS survey in 2003.

38
# **1. Introduction**

The relationship between inequality and growth, with the causality going from high or low inequality to high or low growth, is probably one of the most important in economics. It has recently acquired added relevance because of the slowdown of growth in rich countries and simultaneously rising inequality. The relationship however has been extensively researched empirically in the 1980s and 1990s with interest declining afterwards.<sup>2</sup> Unfortunately, the results ultimately proved to be inconclusive as the relationship was found weak and of uncertain sign.

Forbes (2000), using a panel of mostly rich countries, found that higher inequality was positively associated with growth; so did Li and Zou (1998). Forbes (2000) however found that the relationship was weakened (or could turn negative) when she increased the time-length of the growth spells. Other studies before her in fact had predominantly reported on a negative association, see e.g. Persson and Tabellini (1991), Alesina and Rodrik (1994), and Perotti (1996).<sup>3</sup> In Barro (2000) and Banerjee and Duflo (2003) the baseline results were largely inconclusive. Although both studies ultimately did establish a more intricate relationship when delving deeper; Barro (2000) reports a negative correlation for a subsample of low income countries, while Banerjee and Duflo (2003) find evidence of a nonlinear relationship.

Recently, Voitchovsky (2005) and Marrero and Rodriguez (2012, 2013) have had some success by acknowledging that total inequality is built up of different components that may each have their own relationship to growth. Voitchovsky (2005) separately evaluates inequality among the poor and among the rich, and concludes that bottom inequality (that is, inequality among the poor) is bad for growth while top inequality is good. Marrero and Rodriguez (2012, 2013) measure how much may be attributed to inequality of opportunity and treat the residual inequality as a measure of inequality of efforts. In two separate applications, one to the EU and one to the US, they find that inequality of opportunity is

> 2 For excellent reviews of the inequality-growth literature, see e.g. Aghion _et al._ (1999) and Bénabou (1996a).

> 3 More recently, Benjamin _et al._ (2011) found empirical evidence of a negative relationship between inequality and subsequent growth at the village level in rural China. However, they report that the effect disappears when regressing growth on more recent observations of inequality.

2
free trade agreements (FTAs) in other parts of the world, so far there has been little systematic work that could inform the continuing and expanding negotiations in South Asia. There have been several important studies of regional South Asian trade, some completed in the mid-1990s, prior to the recent drive toward SAFTA and the formation of bilateral FTAs within South Asia.<sup>2</sup> There is a general lack of information about the structure of recent trends in trade and what they imply about the prospects for further regional integration.

This paper is part of a larger study under “South Asia’s Trade Integration: Policies and Prospects Program,” a joint initiative of the trade team of the World Bank and PREM Sector Unit on South Asia (SASPR). The main objective of this paper is to evaluate whether South Asia possesses certain fundamental conditions (defined by empirical evidence) to become a successful trading bloc. It should be noted, however, that this paper deals solely with static trade effects. Many economists have argued that the benefits of an FTA, such as SAFTA, stem from dynamic gains, such as increased foreign direct investment to smaller countries, greater political stability and cooperation, and/or enhanced credibility of reform efforts. These issues, however, are not under the purview of this paper. While this paper does not directly address the net welfare effect of trade creation and trade diversion, it relates to these effects as implied by the various conceptual propositions.

It must be noted at the outset that official accounts of South Asia’s international trade statistics are flawed by the high incidence of informal trade between India and its neighbors. Available estimates (for selected years) suggest that informal trade is even higher than officially recorded bilateral trade between India and its neighbors.<sup>3</sup> However, these estimates are based on surveys carried out prior to 1995. It should be noted that it is likely that unilateral trade reforms

> 2 Srinivasan and Canonero. (1994, 1997) and Pigato, Farah, Itakura, Jun, Martin, Murrel and Srinivasan (1997), and recent studes include Panagariya (2001) and Mukherji (2001).

> 3 Taneja (1999) for estimates of informal trade between India and its neighbors. It must be noted these estimates are based on surveys carried out incorporating important smuggling centers and extrapolated to arrive at national figures. Thus, they are prone to higher margins of error.

2
**Figure 10 Fiscal risks from early termination of public-private partnership portfolio in sample countries as percentage of 2020 GDP**

a. Low scenario b. Medium scenario c. High scenario <!-- Start of picture text --> 0.30 0.30 0.30<br>0.25 0.25 0.25<br>0.20 0.20 0.20<br>0.15 0.15 0.15<br>0.10 0.10 0.10<br>0.05 0.05 0.05<br>0.00 0.00 0.00<br>2021 2022 2023 2024 2025 2021 2022 2023 2024 2025 2021 2022 2023 2024 2025<br>Albania Argentina Bhutan Brazil Bulgaria<br>Georgia Ghana Indonesia Kenya Kosovo<br>Peru Romania Solomon Islands South Africa Ukraine<br>Percent of GDP<br><!-- End of picture text -->

# _Source:_ Authors’ calculations.

_Note:_ Each data point represents the average annual maximum expected loss at the 99 percent confidence level from early termination of the PPP portfolio over the period starting at the end of 2020 and ending at the end of the corresponding year as a percentage of 2020 GDP. Figure excludes Burundi and Ethiopia, for which the highest fiscal risk estimates do not reach 0.002 percent of GDP.

Expressing the fiscal risks as a percentage of annual government revenues gives an idea of the fiscal challenge each country could face. Brazil’s fiscal risks are 2.80–8.85 percent of annual government revenues, depending on the scenario. The fiscal risks as percentage of annual government revenues reflect differences in the sizes of the economies, with a few notable exceptions (figure 11). The fiscal risks from early termination of the PPP portfolios in Ghana and Indonesia are larger relative to other countries in the sample as percentage of annual government revenues. These risks are as high as 1.96 percent in Ghana and 3.90 percent in Indonesia. Fiscal risks in Peru are much closer to those of Brazil when expressed as a percentage of government revenues than they are when are expressed as a percentage of GDP.

27
<!-- Start of picture text --> Percent of GDP —Advanced economies<br>2 —EMDEs<br>0<br>-2<br>“4<br>-6<br>8<br>-10<br>-12<br>onSessssesesagty Oo DWOTON FT O DO OD<br>Nnnnnwnnnnnanaa<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP —Advanced economies<br>130 —EMDEs<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>380 owonwnnornep Rr<br>oO5s oOo8B oOeB oOcb oOgeroO oOeereoO oO oO aoO<br>NNN NN NNN NN NSN<br><!-- End of picture text --> <!-- Start of picture text --> Index, 100 = Jan 2019<br>250 —Energy —Metals —Agriculture<br>200<br>150<br>100<br>50<br>0 a<br>NOoannodTn”ncrowrwrTy NNN<br>TYyryYyraYygegqgagqgqggqgqqg<br>crv>agewrnyaecepyaQewwpa<br>ASNCTO 87STOOTSGBGHOBOTSBON<br><!-- End of picture text -->
<!-- Start of picture text --> —e— Rural<br>—e— Urban<br>I —m— National<br>3000 || —_<br>Siao —— — —=80 74 — — Bo >ya<br>CO ——————————— 55 65 sa<br>I 83 63<br>60) i 60<br>505 | 50 4g so _ -<br>| 430 -_——oe<br>vt40 |I BBa<br>30) a<br>I 3<br>ooan |<br>l<br>ie<br>I<br>a<br>I 1995 1996.a 1997a 1998a es1999 2000 ss2001 2002 2003a<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 4. Top truncation is the more likely source of low inequality<br>Percent increase in Gini coefficient<br>2002 2003 2004<br>160<br>Top<br>truncation<br>140<br>120<br>100<br>80<br>60<br>Bottom<br>truncation<br>40<br>20<br>0<br>Source: Azerbaijan HIES data.<br>Azerbaijan Baku Non-Baku Rural Azerbaijan Baku Non-Baku Rural<br><!-- End of picture text -->

# **_Hypothesis 4_**

The Gini coefficients under hypothesis 4 are not large enough for Azerbaijan, compared to its comparators in Table 1, particularly for 2003 and 2004, to warrant that the missing households were from both the top and the bottom of the wealth distribution. Under hypothesis 4 we assumed that there was truncation both in the top and bottom wealth groups. Here we substituted the consumption per capita of half of the missing households following hypothesis 2 and the remaining half were replaced following hypothesis 3. The resulting Gini coefficients are reported in Table 10. The findings thus reinforce the conclusions under hypotheses 2 and 3 that the likely source of low inequality is upper truncation rather than lower truncation or mixed truncation.

14
(obtained from World Development Indicators) in 1985 to measure the initial quality of the transportation infrastructure in each country in the sample. This variable allows us to control for the ease with which resources can move across the cities in a given country. Higher transportation costs associated with poorer infrastructure create incentives for the concentration of economic activity in a smaller number of cities.

_Regime Change Dummy_ **.** This is a dummy variable taking a value of 1 when dictatorship ends in a country before 1985. Following Ades and Glaeser (1995), we accept a country as a dictatorship when its Gastil index is higher than 3. Therefore, countries switch from a dictatorship to a democracy when their Gastil index decreases from above 3 (in 1970–4) to below 3 (in 1980–4). We use the Gastil index as documented by Barro and Lee (1994). This variable is important for our purposes because the literature (e.g., Ades and Glaeser 1995) has documented a significant and robust positive relationship between the urban concentration levels and dictatorships.

Estimation Results The regression results for our benchmark case are given in table 1, where the sample is the same across different regressions. The estimates of<sup>__</sup> are negative and significant using any estimation methodology for all types of tariffs (except for the tariff change in consumption goods when TSLS using the first instrument is employed).<sup>22</sup> For

> 22Changes in the tariffs for consumption goods would generally be expected to affect urban concentration differently than changes in the tariffs for intermediate and capital goods. This is because access to intermediates is more relevant for urban agglomerations where backward and forward linkages between firms matter, as in Krugman and Livas (1996).
<!-- Start of picture text --> 1<br>F i gure 2a : DB v ersus 1 0th and 9Oth Percenti l es of ES , Imports<br><!-- End of picture text --> <!-- Start of picture text -->
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the country at an earlier age. Previous work on the topic typically does not make this distinction when estimating the effects of immigrant concentration in the classroom.<sup>3</sup> Second, the paper takes advantage of some features of the Dutch primary school system and of the PRIMA dataset to identify the effect of immigrant peers on natives’ scholastic achievement. Estimates based on classroom-level peer composition reported in the literature are likely to suffer from non-random allocation of students between classrooms.<sup>4</sup> On the other hand, using grade-level peer composition can estimate peer effects imprecisely or even lead to a downward bias, if most learning spillovers occur at the classroom level (see Carrell et al. (2009) or Brodaty (2010), among others). The Dutch primary school system presents an attractive feature to tackle those issues, as the large majority of Dutch primary schools only have one classroom per grade. Although we report our main results for the full sample, we assess the robustness of our estimates in the subsample of schools with a single classroom per grade. Our identification strategy relies on small changes in immigrant concentration across grades within the same school. We present a battery of tests and robustness checks to assess the validity of our identification strategy, including balancing tests for selection on observables, but also placebo tests which suggest that our results are not driven by selection on unobservables.

Finally, this study adds to the thin literature that investigates the effects of immigrant concentration on natives’ achievement at school in early ages, as our sample consists of primary school students from age five. The focus on early ages is relevant in the specific context of the question investigated as immigrant classmates, defined as foreign-born students, have spent less time in the 3Although this is not the main focus of their paper, the only exception is Branden et al. (2016) in the Swedish context.

4One recent exception is Ballatore et al. (2015) who attempt to account for the endogeneity of classroom formation to identify the effect of immigrant classmates.

3
<!-- Start of picture text --> Low Skill, Cutoff=0<br>of<br>a2@25<br>0.05<br>0<br>“6 “4 2 0 2 4 6<br>Log Ratio<br><!-- End of picture text --> <!-- Start of picture text --> Low Skill, Cutoff=1000<br>of<br>2<br>a2A<br>0.05<br>0<br>“6 “4 2 0 2 4 6<br>Log Ratio<br><!-- End of picture text --> <!-- Start of picture text --> Low Skill, Cutoff=10000<br>o4<br>oa225<br>0.05<br>06 “4 2 0 2 4 6<br>Log Ratio<br><!-- End of picture text --> <!-- Start of picture text --> High Skill, Cutoff=0<br>o4<br>a 2 25<br>0.05<br>0<br>“6 “4 2 0 2 4 6<br>Log Ratio<br><!-- End of picture text -->