rafmacalaba/datause-encoder-data huggingface dataset:
Table 12: on women’s outcomes

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Saved money during<br>past 12 months|Saved money via<br>formal institution|Saved money via<br>informal institution|Could pay 20000 CFA in case<br>of urgent matter|
|**Full sample**|||||
|ITT estimate|0.081**|0.079*|0.070*|0.108***|
||(0.038)|(0.042)|(0.040)|(0.033)|
|Observations|1963|1967|1967|1967|
|_R_<sup>2</sup>|0.060|0.074|0.041|0.052|
|Control mean|0.588|0.317|0.405|0.404|
|**Have child aged 0-2**|||||
|ITT estimate|0.079*|0.088*|0.071|0.100***|
||(0.044)|(0.045)|(0.047)|(0.033)|
|Observations|1124|1127|1127|1127|
|_R_<sup>2</sup>|0.072|0.093|0.057|0.043|
|Control mean|0.583|0.306|0.412|0.387|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 13: Effects on child development

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Gross motor score|Fine motor score|Language score|Total child development score|
|**Full sample**|||||
|ITT estimate|1.545***|2.748***|0.160|4.391***|
||(0.498)|(0.640)|(0.528)|(1.343)|
|Observations|1618|1618|1618|1618|
|_R_<sup>2</sup>|0.481|0.325|0.561|0.636|
|Control mean|43.524|20.689|34.816|99.029|
|Max score|52|54|46|152|
|**Have child aged 0-2**|||||
|ITT estimate|1.939***|1.318**|0.832|3.895***|
||(0.496)|(0.499)|(0.714)|(1.312)|
|Observations|717|717|717|717|
|_R_<sup>2</sup>|0.534|0.257|0.538|0.636|
|Control mean|39.139|16.959|26.584|82.682|
|Max score|52|41|46|139|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

40
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# **References:**

Avramovic, Dragoslav, et al. 1964. _Economic Growth and External Debt._ Baltimore: Johns Hopkins Press.

Baltagi, Badi h. 1995. _Econometric Analysis of Panel Data._ New York: John Wiley & Sons.

Brewer, Thomas L and Pietra Rivoli. 1990. "Politics and Perceived Country Creditworthiness in International Banking", _Journal of Money, Credit and Banking,_ 22(3): 357-369.

Chamberlain, Gary. 1980. "Analysis of covariance with qualitative data", _Review of Economic Studies_ 47:225-238.

Cline, William R. 1984. _International Debt: Systemic Risk and Policy Response._ Institute for International Economics.

Dhrymes, Phoebus J. 1986. "Limited Dependent Variables", ppl568-1631 in Zvi Griliches and Michael Intriligator ed. _Handbook of Econometrics Volume III._ New York: North-Holland.

Dym, Steven. 1997. "Credit Risk Analysis for Developing Country Bond Portfolios". _The Journal of Portfolio Management._ 23(2):99-103.

Feder, Gershon and Richard E Just. 1977. "A Study of Debt servicing capacity applying logit analysis". _Journal of Development Economics 4:25-38._ Feder, Gershon, Richard Just and Knud Ross. 1981. "Projecting Debt Servicing Capacity of Developing Countries". _Journal of Financial and Quantitative Analysis_ 26(5):651669.

Hajivassiliou, Vassilis A. 1994. "A Simulation Estimation Analysis of the External Debt Crises of Developing Countries", _Journal of Applied Econometrics,_ 9:109-131.

Hajivassiliou, Vassilis A. 1989. "Do the Secondary Markets Believe in Life After Debt?", _World Bank Policy, Planning, and Research Working Paper No. 252._ The World Bank. International Monetary Fund. February 1998. _Official Financing for Developing Countries._ Policy Development and Review Department: IMF.

Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobat6n. 1999a. "Governance Matters", World Bank, mimeo.

Kaufmnann, Daniel, Aart Kraay and Pablo Zoido-Lobat6n. 1999b. "Aggregating Governance Indicators", World Bank, mimeo.

Lee, Suk Hun. 1993. "Relative importance of political instability and economic variables on perceived country creditworthiness". _Journal of International Business Studies_ 24(4):801-812.

McFadden, Daniel, Richard Eckaus, Gershon Feder, Vassilis Hajivassiliou and Stephen O'Connell. 1985. "Is There Life after Debt? An Econometric Analysis of the Creditworthiness of Developing Countries" pp 179-209 in Gordon w. Smith and John f. Cuddington ed. _International Debt and the Developing Countries._ Washington, DC: World Bank.

Reuss, Konrad. 1996. "Trends in Sovereign Ratings". _The World of Banking._ 15(1):1214.

Ul Haque, Nadeem, Manmohan S. Kumar, Nelson Mark and Donald J Mathieson. 1996. "The Economic Content of Indicators of Developing Country Creditworthiness", IMF _StaffPapers_ 13(4): December 1996.

30
17 Table 1 shows the correlation coefficient between the different openness measures. They are not highly correlated, with correlation coefficients ranging from a high of 0.37 for MNTB and BMP to a low of -0.21 for the DOLLAR index and TARIFF. The difficulty of choosing a measure of openness is demonstrated by Figure 1, which shows the average of each measure for seven groups of low and middle-income countries. According to the DOLLAR index, trade distortions were highest in West Africa, followed by East Africa, and were lowest in South and East Asia. As the figure shows, regional rankings change dramatically when other measures are used.

Table 1 Correlation coefficients between different openness measures

||DOLLAR|NTB|TARIFF|BMP|
|---|---|---|---|---|
|DOLLAR|1.00||||
|NTB|0.08|1.00|||
|TARIFT|-0.21|0.23|1.00||
|_BMP_|0.21|0.37|0.08|1.00|

Figure 1 Measures of openess (1990) <!-- Start of picture text --> IN<br>140<br>~~~~~~~Li. M 1.<br>Al... A.Ma Add . Afi.<br>Umen Ddahide *menNTBh 3m.nTE *mmn BWl<br>Source: Dollar (1992) and Prichett (1991).<br><!-- End of picture text -->
# **<u>Table 1.2. Medium-Term and Long-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

|**Panel A: 1994**|<br>**-2004**||<br>|<br>**2004**||
|---|---|---|---|---|---|
|||**Poorest tercile**|**Middle tercile**|**Richest tercile**|**Total**|
||Poorest tercile|21.7|12.2|7.4|41.3|
|||_(0.6)_|_(0.5)_|_(0.4)_|_(0.7)_|
||Middle tercile|8.5|14.3|9.5|32.3|
|**1994**||_(0.4)_|_(0.5)_|_(0.4)_|_(0.7)_|
||Richest tercile|3.7|9.1|13.6|26.3|
|||_(0.3)_|_(0.4)_|_(0.5)_|_(0.6)_|
||**Total**|35.4|36.9|27.7|100|
|||_(0.7)_|_(0.7)_|_(0.7)_||
|**Panel B: 2004**|**-2015**|||**2015**||
|||**Poorest tercile**|**Middle tercile**|**Richest tercile**|**Total**|
||Poorest tercile|18.1|10.6|6.3|35.0|
|||_(0.7)_|_(0.5)_|_(0.4)_|_(0.8)_|
||Middle tercile|11.0|13.2|10.8|35.0|
|**2004**||_(0.5)_|_(0.6)_|_(0.5)_|_(0.8)_|
||Richest tercile|4.7|9.6|15.7|29.9|
|||_(0.4)_|_(0.5)_|_(0.6)_|_(0.8)_|
||**Total**|34.1|32.6|33.3|100|
|||_(0.8)_|_(0.8)_|_(0.8)_||
|**Panel C: 1994**|**-2015**|||**2015**||
|||**Poorest tercile**|**Middle tercile**|**Richest tercile**|**Total**|
||Poorest tercile|19.9|14.7|8.5|43.1|
|||_(0.8)_|_(0.7)_|_(0.6)_|_(1.0)_|
||Middle tercile|8.0|13.0|11.3|32.3|
|**1994**||_(0.6)_|_(0.7)_|_(0.6)_|_(0.9)_|
||Richest tercile|5.2|7.6|11.7|24.5|
|||_(0.5)_|_(0.5)_|_(0.6)_|_(0.9)_|
||**Total**|33.7|36.8|29.5|100|
|||_(1.0)_|_(1.0)_|_(1.0)_||

**Note** : Estimation results are obtained based on total household income per capita. Linearized standard errors of cell percentages are in parentheses. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th round of the RLMS, 3719 panel individuals from the 13th and 24th round of the RLMS and 2478 panel individuals from the 5th and 24th round of the RLMS

51
# **Figure 5: Results for TFP**

## **TFP**

<!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>0<br>PMGE<br>PMGE-IV<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>GFCF FCS RAIL LOCO COACH GOODS CCAP RPASS RFRT VEHP VEHG PORTS SAA APASS TEL FTEL ELEC<br>Productivity Growth Impact<br><!-- End of picture text -->

### **Infrastructure Measure**

<!-- Start of picture text --> Aggregate Railways Roads Ports Air Tele- Elect-<br>Economic Tran. coms ricity<br>Infrastr.<br><!-- End of picture text --> 48
about 1-2% of all food shipments are subject to physical examination by FDA, and a fraction of these are subject to sampling (Buzby et al., 2009; Baylis et al., 2009).

The FDA relies on a system of alerts for particularly sensitive categories of products in order to help it save and allocate inspection resources. Alerts are issued when the FDA determines that there is a particular risk associated with a product, producer/exporter, country or region of origin. In most circumstances, alerts determine that firms and products identified are subject to detention without physical examination (DWPE). In this case, the FDA automatically detains the concerned products until it is demonstrated that the violation has been remedied. Therefore the burden of proof falls on the importer or shipper, or the manufacturer/grower of the product. As noted by Baylis _et al._ (2009) alerts are strikingly rarely changed: three quarters of alerts in place in 2009 had been in place for more than 10 years, and a significant portion of them (one quarter of all alerts) for more than 20 years.

Alerts appear to be decided on similar legal basis to refusals, e.g. the standard of “appearance”.

According to the FDA, alerts are triggered by historical violations at the following levels: commodities; manufacturers/shippers; growers; importers; geographic area; and countries of origin.<sup>8</sup> Sources of information come from FDA’s own field offices, but also foreign inspections and evidence from other countries.

# **3 Methodology and Data**

As the above discussion demonstrates, US border authorities exercise broad discretions when implementing the import refusals regime. As previously noted by Baylis et al. (2009), and as suggested by the FDA itself, there is a strong possibility of path dependence: the authorities might look at historical

> 8 Presentation by Domenic Veneziano, Director, FDA Division of Import Operations at the Food & Agriculture Border Gateway Summit January 16, 2008. <u>http://www.michigan.gov/documents/mda/FDA_importproc_224440_7.pdf</u> accessed 22 November 2011.

9
|Table G6|. Graft|||
|---|---|---|---|
|Code|Table<br>R|ow|Concepts measured|
||(Appendix 1)|||
|Represe<br>|ntative sources<br>|||
|DRI|A 1.3||Corruption among public officials, effectiveness of<br>anticorrupton initiatives|
|EIU|A 1.5|10|Corruption among public officials|
|PRS|A 1.10|6|Corruption in the political system as a "threat to<br>foreign investment'|
|WDR|A 1.13|17<br>**15**|Frequency of "additional payments" to "get things done"<br>Corrupton<br>as"g@o4wgI<br>tgminif@§s"|
|Non-repr|esentative sources|||
|BERI|A 1.1|2|Mentality regarding corrupbon|
|CEER|A 1.2||Effect of corruption on "attractiveness<br>of country as<br>a place to do business"|
|FHNT|A 1.6||Perceptions of corruption in civil service, business<br>interests of policymakers|
|GALLUP<br>GCS98|||Frequency of "cases of corruption" among public officials|
|GCS97|A1.8|17|Irregular, additional payments connected with import|
||||and export permits, business licenses, exchange<br>controls, tax assessments,<br>police protection or<br>loan applications (GCS98 and GCS97 for 5<br>additional counties)|
|||24|Frequency of "irregular payments" to officials and judiciary|
|GCSA|A 1.8|17|<br>Irregular, additional payments connected with import and|
||||export permits, business licenses, exchange<br>controls, tax assessments, police protection or<br>loan applicatons|
|||66|Corruption as an obstacle to business development|
|PERC|||<br>Effect of corrupbon on business environment for foreign companies|
|WCY98|A 1.12|6|ImDroDer<br>Dractices in the oublic sDherp|

60
European Commission (2007). _European Economy – Public Finance in EMU_ . European Commission, Brussels.

Guichard, S., M. Kennedy, E. Wurzel and C. André (2007), “What Promotes Fiscal Consolidation: OECD Country Experiences”, _OECD Economics Department Working Paper_ , No. 553 Gupta, S., E. Baldacci, B. Clements, and E.R. Tiongson. (2003). “What Sustains Fiscal Consolidations in Emerging Market Countries?” IMF Working Paper No. 03/224.

Gupta, S., B. Clements, E. Baldacci, and C. Mulas-Granados. (2004). “The Persistence of Fiscal Adjustments in Developing Countries.” _Applied Economic Letters_ : 1350–4851.

Kose, A., Kurlat, S. A., Ohnsorge, F. L., Sugawara, N. (2017). “A cross-country database of fiscal space.” Policy Research working paper; no. WPS 8157. Washington, D.C.: World Bank Group.

Lavigne, R., (2011). “The political and institutional determinants of fiscal adjustment: entering and exiting fiscal distress”. _European Journal of Political Economy_ , 27, 17-35.

Mattina, T. and V. Gunnarsson. (2007). “Budget Rigidity and Expenditure Efficiency in Slovenia”, IMF Working Paper, WP/07/131.

McDermott, C.J. and R.F. Wescott. (1996). “An Empirical Analysis of Fiscal Adjustment.” IMF Staff Papers No. 43, 725–53.

Mierau, J.O., Jong-A-Pin, R., de Haan, J., (2007). “Do political variables affect fiscal policy adjustment decisions? New empirical evidence.” _Public Choice_ 133, 297-319.

Mulas-Granados, C. (2003). “The Political and Economic Determinants of Budgetary Consolidation in Europe.” _European Political Economy Review_ 1(1): 015–039.

Pelzman, S., (1992). “Voters as fiscal conservatives”. _Quarterly Journal of Economics_ . 57, 327-361.

Perotti, R. (1998). “The Political Economy of Fiscal Consolidations.” _Scandinavian Journal of Economics_ 100(1): 367–94.

Perotti, R., Strauch, R. and Hagen, J. von (1998). _Sustainable public finances_ . London: CEPR.

Persson, T. and Tabellini, G., (1999). Political economics and macroeconomic policy, ch. 22, p. 1397-1482 in Taylor, J. B. and Woodford, M. eds., Handbook of Macroeconomics, vol. 1, Part C, Elsevier.

Pinho, M. (2004). "Political models of budget deficits: a literature review," FEP Working Papers 138, Universidade do Porto, Faculdade de Economia do Porto.

Purfield, C. (2003). “Fiscal Adjustment in Transition Countries: Evidence From the 1990s.” IMF Working Paper No. 03/36.

Roubini, N. and J. Sachs. (1989). “Political and Economic Determinants of Budget Deficits in the Industrial Democracies.” _European Economic Review_ 33(5): 903–33.

Sachs, J. (1989). “Social Conflict and Populist Policies in Latin America.” NBER Working Paper No. 2897.

Tabellini, G., Alesina, A., (1990). “Voting on the budget deficit.” _American Economic Review_ 80, 37-49.

- 32 -
that specific capital items may be more or less useful in smoothing consumption ex post. With the k-function having as arguments not only total wealth but _a'._ as well, the equilibrium condition (6) becomes (6') _Vfa_ = -vaa _[ raix_ + ica'r.

If (6') is the correct characterization of the equilibrium, then without prior knowledge of the association between the profit variability and ex post consumption smoothing effects xa ci for each al, it is no longer possible to know how profit level and profit variability effects will be correlated.

It is not surprising that liquid assets (financial assets and food stocks) play a role in smoothing consumption as well as provide a source of funds for purchasing variable inputs. However, evidence in Rosenzweig and Wolpin (forthcoming) based on the ICRISAT data suggests that bullocks play a predominant role in consumption smoothing among the other durable agricultural stocks. Given evident constraints on borrowing and the liquidity of bullocks, most farmers are unable to sustain profit-maximizing bullock stocks, which thus exhibit high marginal profit level returns.

5. Weather Risk, Wealth and the Riskiness and Profitability of Farm Investment Portfolios Based on the estimates of the profit function and on the actual asset portfolios a L of the household we can construct individual measures of portfolio riskiness rk, from (9), for each farm household k. There is considerable inter-household variability in r--based on survey-period averages for each household, the sample mean of r **is** .000632 and the standard deviation is .000539. At the sample mean of wealth, the mean estimate of r implies an average standard deviation in total household profits of 544 rupees at the mean standard deviation of the monsoon onset; the average coefficient of variation in profits 22
- 10 -

Table 2: Distribution of Bank-Supported VET Investments By Sector and Mode (2)

||1963-76|1977-86|
|---|---|---|
|SECTOR|||
|Agriculture|25.1|12.7|
|Commerce/Services|7.0|6.9|
|Industry|67.9|80.4|
|o Secondary|(18.6)|(13.3)|
|o Post-secondary|(20.9)|(22.7)|
|o Non-formal|(25.8)|(40.8)|
|o Teacher Training (VET)<br>-------------------------------------------------------------------|(2.6)|(3.6)|
|MODE (all sectors)|||
|Secondary|34.4|19.5|
|Post-secondary|27.5|22.7|
|Non-forn..|34.9|50.5|
|Teacher Training (VET)|3.2|4.0|
|-------------------------------------------------------------------<br>TOTAL (%)|100.0|100.0|
|TOTAL BANK LENDING ($US million)|712.2|3,126.7|

Source: Schwartz (1988, forthcoming) Thus there is indeed evide:nce that Governments and the Bank have, in the broad swing of time, invested proportionally less in secondary vocational education and training and more in non-formal modes and in university-level training.

Have these patterns been the same for countries of differing income levels? Table 3 shows the overall distribution of education sector lending between general and VET for two time periods for
of Xi and Zi in each subgroup. The details of our adaptation of the Hosmer-Lemeshow test are given in Appendix A.4. The second goodness-of-fit test we use is a Rao score test developed by Murphy (2007).<sup>10</sup> This test embeds the bivariate normal distribution within a larger family of distributions by adding more parameters to the model and checks whether the additional parameters are all zeros using the score for the additional parameters at the BP estimate.<sup>11</sup> We set both tests to reject at a 5% significance level using asymptotic chisquare critical values.<sup>12</sup> In our simulations with a bivariate normal data generating process, both tests reject about 5% of the time, as expected. The score test performs much better than the Hosmer-Lemeshow test in detecting our non-normal data-generating processes, as shown in Figure 8. The results of this comparison of the two tests agree with those of Chiburis (2010) from simulations without an endogenous regressor.

# 4 Conclusion

We have derived asymptotic results and presented simulations comparing bivariate probit and linear IV estimators of the average treatment effect of a binary treatment on a binary outcome. Our simulation results provide some practical guidance on the choice of specification in practical problems with different parameter values and the presence/absence of covariates and can help explain widely differing results depending on the specification chosen. Our findings can be summarized as four main messages for practical applications in empirical models with binary regressors and binary outcome variables:

- Researchers should expect IV and BP coeffi cients to differ substantially when treatment probabilities are low or when sample sizes are below 5000. Linear IV estimates are particularly uninformative for hypothesis testing when treatment probabilities are low,

> 10See Chiburis (2010) for corrections to several errors in Murphy (2007) and an alternative derivation of the test.

> 11Since Ti is endogenous, predicted probabilities of (yi, ti) used to calculate the test statistic are computed conditional on Xi and Zi but not Ti.

> 12Murphy (2007) recommends bootstrapping the critical value of his test, but we find that the asymptotic critical values work well enough even at small sample sizes that the time-consuming bootstrap is not necessary.

19
34

|_Countries_|_Level of Minimum_<br>_Capital Adequacy_<br>_requirements_<br>_(percentage end_<br>_1998)_|_Of which_<br>_tie_<br>_(_<br>_Tier I_|_tier I and_<br>_r 2_<br>_%)_<br>_Tier2_|_Loan Classification_<br>_Requirements -_<br>_(Number of Days_<br>_after which loan has_<br>_to be classified as_<br>_NPL)_|_P_<br>_requir_<br>_clas_<br>_p_<br>_Sub_<br>_Stan_<br>_dard_|_rovisioni_<br>_ements fo_<br>_sif ed as_<br>_erformin_<br>_Doubt_<br>_ful_|_ng_<br>_r loans_<br>_non-_<br>_g_<br>_Loss_|_Limit on Risk_<br>_Exposure:_<br>_Liquidity Ratio_<br>_(% of assets to be_<br>_held against_<br>_deposits)_|_Limit on Risk_<br>_Exposure:_<br>_Forex_<br>_(%o offorex assets to_<br>_be held)_|_Single Exposure Limit_<br>_(% of capital)_|
|---|---|---|---|---|---|---|---|---|---|---|
|_Asia & Pacific_|||||||||||
|Hong Kong|8%|4%|4%|90|At ban|k's discr|etion|25% of liabilities|Watched closely by<br>HKMA|_25%_of capital base|
|India|8%|No limit|Not<br>more<br>than<br>100% of<br>tier 1|210|10%|20 to<br>50%<br>of sec.|100<br>%<br>|Minimum cash<br>balance of 11% of<br>time, demand<br>deposits; statutory<br>25% of time,<br>|Not allowed, must<br>square daily.|Individua**l**oans: At<br>bank's discretion.<br>Corporate: 25% of<br>capital funds. Group:<br>50% of bank's capital<br>|
|||||||||demand deposits.||funds|
|Indonesia|8%|4%|4%|90|15%|50%|100<br>%|5%|Net open position<br>less than 20% of<br>capital; 25%<br>maximum exposure<br>limit for individual<br>|85%|
||||||||||currency||
|Korea|8%|No limit|Up to<br>100% of<br>tier I<br>|180|20%|75%|100<br>%|5% on demand, 2%<br>on time|20% of capital|45% of capital|
||||capital||||||||
|Malaysia|8%|8%|Not<br>liibl|180|No|50%|100<br>%|15%|No restrictions||
||||ege||||||||
|Philippines|10%|10%|<br>Not<br>eligible|180|25%|50%|100<br>%|15%|No more than 15%<br>of equity|25% of capital, plus<br>15% provided the loan is<br>adequately secured by<br>real estate mortgage or<br>the assignment of<br>readily marketable<br>bonds or other high<br>grade securities|
|Thailand|8.5%|4.25|4.25%|90|20%|50%|100<br>%|6%|Net long 20% of tier<br>I capital. Net short<br>15% of tier I capital|<br>25% of tier I capital|

Source: World Bank Data.