rafmacalaba/datause-encoder-data huggingface dataset:
developing world. I achieve this by using an extensive database covering manufacturing, - agriculture, and services trade for 190 countries over the period 1990 2015, with domestic and international trade flows.

The main results show that the trade creation effects of South Asia’s trade agreements are large, positive, and significant in general. Also, these effects of FTAs vary substantially among countries in the region, sectors, and between final and intermediate goods. Results also suggest that intra-regional agreements like SAFTA have had stronger trade creation effects contributing to the goal of regional integration. And the inclusion of domestic trade flows proves to be key in understanding these results and in comparing them to the average effects of international trade agreements. The case of the large effects on agricultural goods within the regions is of particular importance for food security and development.

In conclusion, this paper shows that the trade policy implemented in South Asia in the previous decades has been a success given the trade agreements that have been put in place. But at the same time, this paper uncovers the existence of clear missing opportunities in the region. Intra-regional effects are strong, in fact, stronger than the world average, but they need to be understood in the context of a relatively low level of trade flows and results suggest the possibility of the further deepening of the intra-regional trade agreements to benefit those countries with low effects and to further stimulate regional integration.

It seems many countries would benefit more from deepening existing trade agreements than from signing agreements with new partners. Many countries in South Asia do not have trade agreements with the rest of the world. This points towards clear missing opportunities for the region.

Preferential trade agreements have increased in number in recent years and extended their reach well beyond tariff reduction, covering investment, competition, and intellectual property rights issues (Mattoo et al., 2017). This means that South Asia loses in relative terms since other country pairs reduce their trade barriers. South Asia has the opportunity and needs to catch up. In particular, there is a need to work on the backward integration of the countries in the region with the rest of the world. Imports of intermediate goods from other countries have the potential to improve efficiency and quality, help strengthen exports, and support job creation in South Asia. Trade agreements present an 19
yielded insignificant results. However, as before, this may be due to the few crises relative to observations.

# Conclusions:

The paper provides some evidence in favor of an asset view of speculative attacks and the importance of the second moments of monetary aggregates in predicting crises. In the regressions for both the Krugman- Rotemberg target zone model and the Calvo-Mendoza approach, the stock of money relative to reserves appears very significant and of the predicted sign in most specifications. The results for the drift and variance terms for the innovations in velocity are less consistently supportive of the first model with only the GARCH specifications for Europe and the overall sample generating the predicted signs and borderline significance. These results cannot be seen as strong evidence in favor of the target zone framework or as offering much confidence in the elusive measure of proximity to crisis that it theoretically offers. The variance of reserves to the money aggregates suggested by the Calvo-Mendoza approach, however interpreted, appears more significantly and may contribute additional explanatory power to models seeking to predict crises.

# References

Andersen, R. and J. Lund (1997) "Estimating Continuous Time Stochastic Volatility Models of the Short Term Interest Rate," _Journal of Econometrics,_ Vol. 77, pp. 343-377.

Bilson, J, "Leading indicators of Currency Devaluations," _Columbia Journal of World Business,_ Vol 14 (Winter 1979) pp. 62-76.

Bolllerlev, T. R.Engle and D. Nelson,(1993) "ARCH Models," in R.Engle and D, 16
**<u>Table 1: Program characteristics by village</u>**

|||**Ce**<br>**N**<br>|**rtificates issu**<br>**o**<br>|**ed 12 months**<br>|**before surve**<br>|**y?**<br>**Yes**<br>||
|---|---|---|---|---|---|---|---|
|District (_Woreda_) name|Goz|amin|Ene|may|Mach|akel|Gozamin|
|Village (_Kebele_) name|Kebi|Wolkie|Telma|S. Debir|Amanuel|D. Elias|A. Gulit|
|_Kebele_area in ha|630|2670|1964|2560|4373|1790|2172|
|No of households|1094|1050|1464|1275|1151|906|890|
|Program introduced|May 2005|Sep. 2006|Oct. 2003|Jun. 2005|Feb. 2004|Feb. 2004|Feb. 2003|
|Competed registration|Dec. 2005|NC|Aug. 2006|Dec. 2006|Jun. 2004|Jul. 2004|May 2005|
|Start of certificate distribution|Aug. 2006|NS|Sep. 2006|NS|Feb. 2005|Feb. 2005|Jun. 2005|
|No. of LAC members|15|20|21|35|14|14|18|
|Training days to LAC members|3|5|8|8|9|4|6|
|Number of village meetings|4|4|6|2|5|3|3|

**Source:** Own computation from AAU/Gothenburg/WB Survey NC=Not completed at the time of the survey. NS=Not started at the time of the survey.

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**Figure 4: Social spending and taxation by income quintile, as a percentage of GDP per capita.**

<!-- Start of picture text --> Argentina Bolivia<br>25% Other Social 25% Other Social<br>15% Spending 15% Spending<br>Cash Transfers Cash Transfers<br>5% 5%<br>-5% Health -5% Health<br>-15% -15%<br>Education Education<br>-25% -25%<br>-35% Other Taxes -35% Other Taxes<br>-45% -45%<br>Vat/Sales Tax Vat/Sales Tax<br>-55% -55%<br>-65% Income Tax -65% Income Tax<br>I II III IV V I II III IV V<br>Colombia Honduras<br>25% Other Social 25% Other Social<br>15% Spending 15% Spending<br>Cash Transfers Cash Transfers<br>5% 5%<br>-5% Health -5% Health<br>-15% -15%<br>Education Education<br>-25% -25%<br>-35% Other Taxes -35% Other Taxes<br>-45% -45%<br>Vat/Sales Tax Vat/Sales Tax<br>-55% -55%<br>-65% Income Tax -65% Income Tax<br>I II III IV V I II III IV V<br>Mexico Nicaragua<br>25% Other Social 25% Other Social<br>15% Spending 15% Spending<br>Cash Transfers Cash Transfers<br>5% 5%<br>-5% Health -5% Health<br>-15% -15%<br>Education Education<br>-25% -25%<br>-35% Other Taxes -35% Other Taxes<br>-45% -45%<br>Vat/Sales Tax Vat/Sales Tax<br>-55% -55%<br>-65% Income Tax -65% Income Tax<br>I II III IV V I II III IV V<br>capita capita<br>Amount per capita as % of GDP per Amount per capita as % of GDP per<br>capita capita<br>Amount per capita as % of GDP per Amount per capita as % of GDP per<br>capita capita<br>Amount per capita as % of GDP per amount per capita as % of GDP per<br><!-- End of picture text --> 10
To make the analysis clearer and in line with the descriptive evidence, we sum up bilateral trade values over supplying sectors in a goods (including also primary sectors) and a services aggregates. To allow for adjustments over time in trade flows (Piermartini and Yotov, 2016), we use data from six years in four-year intervals (1995, 1999, 2003, 2007, 2011, 2015). We report robustness checks by (i) leaving the dependent variable at the sector level (see Table A1 in the Appendix) and allowing for sector-specific multilateral resistances; and (ii) using the full 1995-2015 yearly panel of country pairs. For each specification and supplying sector, we report the estimates for the private market next to the ones for the government one to ease comparison. Coefficients in bold are significantly different (at the 10% level) across the two markets.

# **5.1 Gravity results**

Tables 1 and 2 report the PPML gravity estimates for goods and services, respectively. In columns (1) and (2), we use the ‘pooled’ specification – controlling for exporter-year and importer-year fixed effects (see eq (7)). The large and significant coefficient on the ‘same country’ dummy ( _SMCTY_ variable) gives a strong indication of home bias, especially in government procurement. All other trade cost variables (except for distance) are switched off for same-country pairs, so that the omitted category for the ‘same country’ variable includes country-pairs that have, for instance, no contiguous border, a different official language and are no EU members. For trade in goods (Table 1), the estimates in column (1) imply that, relative to the omitted category, government procurement from local suppliers is around 30 times higher. In private markets, local sales are 23 times higher than international sales. As expected, the border effect is much larger in services, and significantly higher in government procurement than in private markets (Table 2). The estimates in column (1) suggest that government purchases of local services are almost a thousand times the purchases of services from abroad. The disproportionately large border effect in government procurement is only an indication of protectionist trade policies (e.g., ‘buy-national’ policies). Other characteristics that are specific to government contracts and that are not protectionist might still end up favouring local over foreign firms. In the U.S. for instance, set-aside policies that provide preferences for certain categories of firms (e.g., small and medium sized firms, veteran- or Native-owned firms) can indirectly exclude foreign bidders from competition.

20
ucated workers in almost 50 percent: the share of medium and high educated workers are 5 percentage points and 2 percentage points above that in domestic fi rms, respectively<sup>25</sup> . Table 3 reports the same results for non-manufacturing. Again, part of the differences in the workforce composition between domestic and foreign fi rms are due to region, sector, size of the foreign fi rms. However, as reported in column (2) of these tables, even after controlling for these characteristics, the proportion of medium educated and high educated workers is 6 percentage points and 9 percentage points above that in domestic fi rms.

These cross-sectional fi ndings may at least in part be driven by selection of foreign investment into certain fi rms or sectors with a more skilled workforce. Table A3 in the Appendix shows that at the time of the acquisition, foreigners may choose among domestic fi rms operating in the same two-digit sector with very different workforce compositions<sup>26</sup> . By estimating the same model within two-digit sectors of activity I rule out the second hypothesis of sector selection. Table A4 in the Appendix shows that foreign fi rms have a more educated workforce within almost all two-digit sectors<sup>27</sup> .

# 3.1. The Sample of Foreign Acquisitions

One way to control for the unobservable characteristics that might explain the differences between foreign and domestic fi rms is by comparing the period before and after the acquisition for those fi rms that are acquired by foreigners. This sample, by construction, controls for part of the unobserved fi rm heterogeneity since it is the same fi rm changing ownership, and several things remain constant before and after the acquisition. For example, with information on acquisitions it is possible to know whether foreigners increase the demand for education or if they buy fi rms with the closest structure possible to theirs. To disentangle these effects, the following model is estimated for the sample of acquisitions and foreign

> 25To allow for differentiated impacts on the labor market outcomes according to the percentage of foreign capital, I estimate the same model with minority, majority and full foreign ownership dummies. Foreign fi rms have a more educated workforce for all groups and results are not statistically different between majority and full ownership.

> 26Table A3 in the Apendix computes the means and standard deviations for the proportions of high educated workers within each sector of activity for the domestic fi rms in the sample. The heterogeneity in the workforce composition also holds within a regions and sectors of activity.

> 27Sectors where the differences between foreign and domestic fi rms are highest include the wholesale and retail trade, transport and communication as well as real estate (all non-manufacturing sectors).

12
**Table 14: Composition analysis – Krishnaratne, White, & Carpenter’s (2013) sample of materials provision studies**

||**Conn 2014**|**Glewwe et al.**<br>**2014**|**Kremer, Brannen, &**<br>**Glennerster 2013**|**McEwan 2014**|**Murnane & Ganimian**<br>**2014**|
|---|---|---|---|---|---|
|Banerjee et al. (2007) - India|-|Computers &<br>electronic games|Computer-assisted<br>learning|Instructional materials/<br>Teacher training/Class<br>size or<br>composition/Contract or<br>volunteer teachers|Computer-assisted learning|
|Barrera-Osorio (2009) - Colombia|-|Computers &<br>electronic games|Technology|Computers or technology|Computers in schools|
|Glewwe et al. (2004) - Kenya|School supplies|Flipcharts|Flipcharts|Instructional materials<br>Instructional|Flipcharts|
|He et al. (2008) - India|-|-|-|materials/Computers or<br>technology|Scripted lessons|
|Lai et al. (2012) - China|-|-|-|Computers or<br>technology/Teacher<br>training|-|
|Tan et al. (1999) - Philippines|-|Textbooks/School<br>meals|-|Instructional materials/<br>Teacher training/<br>Treatments with food,<br>beverages, and/or<br>micronutrients/<br>Treatments that modify<br>school management or<br>supervision|-|
|Percentage of studies included in<br>review|17%|67%|50%|100%|67%|
|Percentage of RCT studies<br>included in review|17%|67%|50%|100%|67%|

35
plausible assumptions, the optimal risk financing structure takes the form as depicted in Figure 7. Full self-retention is optimal for small losses in Layer I. Partial self-retention and contingent debt are optimal in Layer II. In Layer III, insurance, contingent debt and selfretention are jointly used. Finally, because of the borrowing constraint, the loss in the upper layer IV is partly covered using insurance and the remaining loss is self-retained.

**Figure 7. Optimal risk management strategy using insurance, contingent debt and selfretention, non-proportional insurance premium.**

<!-- Start of picture text --> Layer IV<br>Layer III<br>Layer II<br>Layer I<br><!-- End of picture text --> <!-- Start of picture text --> Retention<br>Contingent debt<br>Insurance<br><!-- End of picture text --> Source: Gurenko and Mahul (2003).

The insurance cost due to the variability of the loss is a key parameter in the design of the optimal risk-financing strategy. As this variability cost increases, (i) the level of self-retention increases in layers III and IV and remains constant in Layer I, (ii) the level of contingent debt increases in Layer III and is unchanged in Layer IV, and (iii) the level of insurance decreases in layers III and IV but always remains larger in Layer IV (Gurenko and Mahul 2003).

# **5. Conclusion**

This paper examines how market-based risk financing instruments could enable productive but asset-poor farmers exposed to production shocks to engage in riskier but higher return agricultural activities and thus escape poverty. It designs a conceptual framework to address the financing of agricultural production shocks using market-based risk financing instruments. Its contribution to the debate on agricultural risk management is twofold:

- It tests the business viability of a specified crop by assessing the minimum business capital requirements needed to ensure the continuity of the crop business after the occurrence of an adverse production shock and the farmer’s ability to meet it; and

- It offers an innovative conceptual approach for the design of an optimal risk management program to finance the minimum capital requirements using a combination of risk financing instruments, such as insurance, savings, and contingent debt.

19
<!-- Start of picture text --> Figure 6<br>Personnel by profession (%)<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>ECA LAC EAP<br>Lawyers Accountants Economists Administrative Temporary Lawyers Accountants Economists Administrative Temporary Lawyers Accountants Economists Administrative Temporary<br><!-- End of picture text --> Ideally, we should be able to measure the productivity per analyst working in mergers and anticompetitive conduct cases. With the available data obtained from the responses, we can proxy productivity by the ratio of mergers and anticompetitive cases per lawyer and economist. Figures 7 and 8 show this ratio for all countries<sup>11</sup> that responded the questionnaire in the ECA, EAP and LAC regions. The main characteristic of this productivity variable is its significant variance in ECA and EAP. This variance is difficult to justify and leads to discard the variable “cases per lawyer (or economist)” as a valid indicator of productivity. The main drawback of this variable lies in the huge heterogeneity of mergers and anticompetitive conduct cases. If we consider merger cases, given that their nature and complexity vary significantly, competition agencies that have to deal with many “small” mergers need to have many employees, each in charge of many, but easy-to-handle mergers. Other agencies, that need to handle only a few mergers a year may have less employees but each of which may be assigned only one or two cases per year. Thus, a comparison of a productivity indicator measured by cases per employee could be seriously mislead. In other words, when analyzing the level of activity and productivity of personnel in competition agencies, we should account for the quality dimension as mergers

> 11 Country names can not be displayed for confidentiality reasons.

11
Table 3: Differential Dropout and Attrition

||(1)<br>% Students Dropped Out<br>_b/se_|(2)<br>% Students Non-Attritted<br>_b/se_|(3)<br>% Teachers Non-Attritted<br>_b/se_|
|---|---|---|---|
|Treatment|_−_2_._841|0_._176|_−_0_._943|
||(2_._14)|(2_._00)|(2_._11)|
|Constant|34_._264_∗∗∗_|84_._833_∗∗∗_|80_._866_∗∗∗_|
||(1_._52)|(1_._45)|(1_._53)|
|Observations|337|337|337|
|R-sqr|0_._005|0_._000|0_._001|

Data Source: MLSS 2021. Shares of longitudinal sample. Analytical sample restricted to MLSS schools observed in baseline and endline. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 4: Impact of the Intervention on Test Scores

||(1)|(2)|(3)|
|---|---|---|---|
||Math Score|English Score|Total Score|
|Treatment|12_._979_∗∗_|8_._566|10_._443_∗_|
||(6_._094)|(6_._355)|(5_._940)|
|Constant|339_._098_∗∗∗_|325_._052_∗∗∗_|305_._597_∗∗∗_|
||(9_._468)|(10_._726)|(10_._788)|
|Observations|4363|4363|4363|
|R-sqr|0_._113|0_._123|0_._151|

_Notes:_ This table looks at the impact of the school leadership on Test Scores (columns 1-3) at the student level. The outcome variables are: Math Knowledge Score (column 1); English Knowledge Score (column 2); Total Knowledge Score (column 3). Analytical sample restricted to MLSS schools observed in baseline and endline. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. First row of the table shows point estimate coefficients and statistical significance. Robust standard errors (clustered at the school level) are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

28
# **E Additional Tables**

Table E.1. Balance for Predictors of Marriage Age Across Gujarat and Synthetic Gujarat

||(1)<br>Gujarat|(2)<br>Synthetic Gujarat|
|---|---|---|
|Muslim|0.073|0.073|
|SC/ST/OBC|0.740|0.739|
|Male Household Head|0.890|0.889|
|Poor|0.282|0.281|
|Rich|0.491|0.491|
|Rural Resident|0.637|0.637|

_Note_ : This table reports means of variables that are used to predict age at marriage from 1990 to 2001 across Gujarat and Synthetic Gujarat. The variables represent the proportion of muslims, SC/ST/OBC, Male Household Heads, Poor, Rich and Rural Resident in Gujarat and Synthetic Gujarat.

41
owned firm. Skilled is also a dummy variable, measured as whether a person at least attended high school or college. Foreign× Skilled is the interaction term. Xpic′ includes individual characteristics that can affect one’s income, including gender, marital status, age and age squared, ethnic minority dummy. μi is industry fixed effect, and θc is city fixed effect.

The results are reported in Table 10. City fixed effects are not included in column (1) and (3), column (2) and (4) add city fixed effects. Foreign firms pay a significant wage premium that ranges from 3 percent to 16 percent depending on whether city fixed effects are controlled for. Skilled workers also earn a much higher monthly income than unskilled worker, individuals who at least went to high school on average make 30 percent more than those who failed to attend high school. The gap is even larger between those who went to college and those who do not, the college premium can be as high as 50 percent. Both interaction terms are also large and highly significant, foreign firm wage premium is 10 percent and 24 percent higher for those who went to high school and college respectively. This provides strong incentives for people to go to college where the presence of foreign firms increased substantially. Anecdotal evidence also abounds in the attractiveness of foreign firms to college students in 1990s and early 2000s. Being employed in a foreign firm is associated with success during that time, more than half of the top ten most attractive employers for college graduates in engineering major are foreign companies in 2008, foreign firms even make up 8 out of the 10 best employers for students in business major.<sup>1</sup> Foreign language and international trade related majors were also the most popular college major in early 2000s.

Table 10. Foreign wage premium

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Panel A: H<br>Dependent var: log<br>|igh school<br>monthly income<br>|Panel B<br>|: College<br>|
|Foreign|0.151***|0.0306***|0.163***|0.0395***|
||(0.00507)|(0.00491)|(0.00427)|(0.00415)|
|High school|0.313***<br>(0.00196)|0.282***<br>(0.00189)|||
|Foreign * High school|0.111***<br>(0.00766)|0.0993***<br>(0.00733)|||
|College|||0.535***<br>(0.00300)|0.491***<br>(0.00288)|
|Foreign * College|||0.249***<br>(0.0104)|0.235***<br>(0.00999)|
|Observations|1,388,158|1,388,158|1,388,159|1,388,159|
|Industry FE|Y|Y|Y|Y|
|City FE|N|Y|N|Y|
|Individualcharacteristics|Y|Y|Y|Y|

Note: This table reports the estimated foreign wage premium. Individual characteristics include gender, marital status, age, age square, ethnic minority dummy, as well as skill dummy. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

> 1 https://new.qq.com/omn/20190917/20190917A0KI0X00.html

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