rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8. Estimated Guarantee Claims on the Budget (CZK bn)**

|**Guarantees**|**Default**|**Total**|**1999**|**2000**|**2001**|**2002**|**2003**|**1999-**|**1999-**|
|---|---|---|---|---|---|---|---|---|---|
|**out-standing**|**risk**|**Claim**||||||**2003**|**2030**|
|**in 1998**||||||||||
|284.8'|**Avg.**38%|107.4|**3.3**|**4.9**|5.4|**33.3**|**3.7**|_50.5_|97.8|

This amount excludes most recent loan guarantee commitments in the amount of CZK17.6 bn that are expected to be implemented in 1999.

_Another source of future claims on the budget is Konsolidacni Banka._ Konsolidacni Banka experienced about CZK 14.4bn loss in 1998, which will be covered by state bonds issue. Assuming no new programs, the analysis of Konsolidacni Banka's asset portfolio indicates that its future losses and potential claims on the state budget are likely to stabilize in the neighborhood of about CZK 6bn annually in 1999-2001. However, new government programs that require further borrowing by Konsolidacni Banka without generating adequate revenues will further increase Konsolidacni Banka's debt service and, thus, losses.

Without further privatization revenues, National Property Fund will need to further borrow to meet its commitment vis-a-vis Ceska Financni, Ceska Inkasni, environmental recovery and railway development, and to cover principle repayments for its obligations.<sup>t5</sup> To meet its obligations, analysis of the National Property Fund's commitments, excluding those vis-a-vis Konsolidacni Banka, suggests that the Fund will annually need about CZK 15 bn during 19992003 and about CZK 33 bn in 2004.

In the medium to long run, off-budget financing of government activities, guarantees and other contingent liabilities, surface as increases in government debt. In the Czech Republic, the expected increase in public debt by the amount of hidden public liabilities estimated around 12.7 percent of 1998 GDP (see table 7) is significant but not disastrous. What appears as disastrous is the dynamic in the rise of the hidden public liabilities. Clearly, the levels of new guarantees issued and new government programs entrusted for financing to Konsolidacni Banka are not 17
the of a on Measuring Effect Community-level Program Women’s Empowerment Outcomes: Evidence from India Eeshani Kandpal, Kathy Baylis and Mary Arends-Kuenning

**JEL Codes:** D13; I24; J16; O15; O17 **Keywords:** Women’s empowerment; rural community-level interventions; impact evaluation; India **Sector: GEN, ARD, POV**

Kandpal: World Bank, 1818 H Street NW, Washington DC 20433 (e-mail: ekandpal@worldbank.org). Baylis: Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, 1301 W. Gregory Drive, MC-710, Urbana, Illinois 61801 (e-mail: baylis@illinois.edu). Arends-Kuenning: Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, 1301 W. Gregory Drive, MC-710, Urbana, Illinois 61801 (e-mail: marends@illinois.edu). We are grateful to Yusuke Kuwayama, Jed Friedman, Nolan Miller, Alex Winter-Nelson, Don Fullerton, Craig McIntosh, Alain de Janvry, Thomas Walker, Chris Barrett, Phil Garcia, Laura Schechter, Carl Nelson, Catalina Londo ̃no, Sahan Dissanayake, Ben Wood, and participants of the NEUDC 2010, PAA 2011, AAEA 2012, and AEA 2012 conferences. We thank Sumita Kandpal, Geeta Gairola, Basanti Pathak, Preeti Thapliyal, Ravi Mehta and the entire _Mahila Samakhya_ Uttarakhand family for their support. Kandpal acknowledges with gratitude the financial support provided by the Goodman Fellowship and the Due Ferber International Research Award of the WGGP, the College of ACES AYRE Fellowship, and the Survey Research Laboratory’s Seymour Sudman Dissertation Award. Baylis thanks the University of Illinois Research Board for the Arnold O. Beckman award.

1
# _Political Economy Effects in Recipient Countries_

The likelihood of an importing country removing trade preferences is highly correlated with the growth rate of the exports of the beneficiary country (Ozden and Reinhardt (2003). In other words, countries are likely to be excluded when they start to export ‘too much’—i.e., once they actually start to benefit from GSP programs. Ozden and Reinhardt (2003) compare the export performances of US GSP beneficiaries with those of countries that were removed from eligibility (‘graduated’). Their results suggest that countries that were removed from the GSP outperform those remaining eligible for GSP treatment. Graduates tend to have higher export/GDP and industrial exports/GDP ratios, as well as higher growth rates. They hypothesize that this finding may be due to graduating countries undertaking own reforms and making more market access concessions themselves.<sup>27</sup> In a follow up paper, Ozden and Reinhardt (2005), they explore whether there is any evidence that there is a causal relationship between preferences and a more protectionist trade policy stance in recipient countries. They find that US GSP recipients implement more protectionist trade policies compared to countries removed from the GSP program. They note that preferences can decrease the incentives for domestic exporters to mobilize in favor of more liberal trade policies. Since own trade policies are more important for developing countries’ growth prospects than barriers in export markets, this perverse incentive effect of unilateral preferences may be quite damaging.

This line of argument has not gone unchallenged. It can be argued that _if_ a (transitional) SDT regime is required to help developing countries to successfully liberalize their economies, Ozden and Reinhardt may be finding evidence that preference programs are working as intended (or designed). Clearly for some countries—the successful ones that were “graduated”—GSP may have played a role in generating the initial export expansion and then breaking a domestic political deadlock that precluded opening up the economy. Major exporters such as Hong Kong, Taiwan, South Korea and

> 27 The potential detrimental incentive effects of preferences on recipient’s own trade policies were noted by Hudec (1987).

24
Table 9: Effect of aid quality on income growth

|VARIABLES||||∆y|||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Fish in 07|-0.511***|-0.375***|-0.375***|||-0.367***|-0.367***|-0.419***|
||(0.139)|(0.116)|(0.116)|||(0.115)|(0.111)|(0.123)|
|IDIOSHOCK||**-0.152**|**-0.177**|**-0.137**|**-0.0818**|**-0.139**|**-0.138**|**-0.183**|
|||**(0.103)**|**(0.125)**|**(0.108)**|**(0.125)**|**(0.104)**|**(0.106)**|**(0.113)**|
|AGGSHOCK|**-0.257***|**-0.248****|**-0.264****|**-0.253***|**-0.277**|**-0.225****|**-0.192***|**-0.343*****|
||**(0.138)**|**(0.102)**|**(0.108)**|**(0.138)**|**(0.170)**|**(0.103)**|**(0.107)**|**(0.117)**|
|AGGSHOCK * IDIOSHOCK|||0.0856||||||
||||(0.194)||||||
|D_bad house in 09|0.136|0.0500|0.0495|-0.0188|0.0405|0.0514|0.0195|0.0248|
||(0.107)|(0.0766)|(0.0769)|(0.0864)|(0.0812)|(0.0765)|(0.0800)|(0.0804)|
|Ln (number of NGOs in 07)|0.159|0.351***|0.350***|0.396***|0.442***||||
||(0.160)|(0.109)|(0.109)|(0.133)|(0.141)||||
|Ln (number of aid projects in 07)||||||0.385***<br>(0.116)|||
|Ln (total housing aid in 07)|||||||0.182***||
||||||||(0.0651)||
|Imputed market shadow wage in 07||||||||-0.0420<br>(0.0849)|
|Constant|-0.767|-1.554***|-1.549***|-1.820***|-2.160***|-1.990***|-2.073***|0.0515|
||(0.552)|(0.426)|(0.427)|(0.473)|(0.485)|(0.514)|(0.657)|(1.084)|
|Observations|282|505|505|377<br>Supp & Fish|260<br> <br>Supp & Fish in<br>07=1 & Boat|505|506|467|
|Sample|Original|Supp|Supp|in 07=1|aid in 07=1|Supp|Supp|Supp|
|Covariates are included|Y|Y|Y|Y|Y|Y|Y|Y|
|Sampling heterogeneity is controlled||Y|Y|Y|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|Kec|Kec|Kec|Kec|
|R-squared|0.182|0.223|0.223|0.247|0.285|0.228|0.230|0.206|

*** p<0.01, ** p<0.05, * p<0.1; Robust standard errors clustered at the village level are in parentheses.  Control variables include boat aid in 09, boat ower and captain pre-tsunami, D_large boat pre-tsunami, and Ln (distance to BA).  The inverse probability of remaining in the sample ("stayers") is used as a weight in Eq. (5) to account for attrition bias.  Sampling heterogeneity is controlled by including _1[s_orig=1]._

AGGSHOCK=Bad50(=1[Fraction of bad aid boats>0.5]) IDIOSHOCK=D_have a bad aid boat in 09
48 van Doorslaer et al. (2006b; 2007) come closest to our work, but the poverty line in van Doorslaer et al. (2006b) is the old international poverty line, and in any case the number of surveys analyzed is 11 compared to our 600+ surveys.<sup>48</sup> Therefore, instead of comparing our financial protection results to published numbers, we perform external and internal consistency checks.<sup>49</sup> For the former, we perform three checks to determine the following:

1. Whether the welfare aggregate is aligned with the welfare aggregate reported in PovcalNet. Data sets are flagged if the absolute value of the relative difference between the measure of welfare derived from the survey is more than 10% apart from the value reported in PovcalNet (in log terms).

2. Whether the poverty headcount is aligned with the poverty headcount reported in PovcalNet. Data sets are flagged if the poverty headcount derived from the survey is more than 10 percentage points apart from the value reported in PovcalNet.

3. Whether the budget share of health expenditures derived from the survey is aligned with the aggregate budget share of health out-of-pocket expenditures. Data sets are flagged if the health budget share derived from the survey data is more than 5 percentage points apart from the aggregate budget share for health payments. The aggregate budget share for health is constructed by taking the ratio between aggregate out-of-pocket expenditures expressed in local currency units (in nominal terms for the year of the survey), and aggregate consumption, also expressed in nominal local currency units. The information for aggregate out-of-pocket expenditures is extracted from the WHO Global Health Expenditure Database

> 48 The data in Wagstaff et al. (2018a; 2018b) are not a potential comparator source either, as 80% of the datapoints there come from a preliminary version of the 2018 HEFPI database, the rest coming from WHO. 49 These checks were refined during the collaborative studies with WHO staff (Wagstaff et al. 2018a; Wagstaff et al. 2018b).
<!-- Start of picture text --> o 45 degree line<br>0,”<br>He)<br>es “<br>2 ; a<br>fe}Sy 7 4<br>3 6<br>=) &o,<br>8 gp<br>Oo o Cg<br>ae BaD Oo<br>g Co gro<br>= fo) 5 SSO<br>Pa 77 °2<br>g<br>s<br>>' ris /<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>“I<br>a<br>°<br>gE<br>aon<br>2o4<br>ed<br>weeSSsoD _<br>3?Soor<br>2005 2010 2015<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> a<br>8<br>4<br>:<br>3<br>r<br>a<br>©<br>a<br>2005 2010 2015<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> g<br>=<br>=}<br>2<br>cL<br>xaeeo<br>5} a!<br>iy a<br>S28<br>oaroF<br>Bees2a iar}<br>2<br>2005 2010 2015<br>Year<br><!-- End of picture text -->
within the confidence interval. This might be explained by the relatively longer period between rounds and smaller sample size. However, the difference is only 11 percent in relative terms for the conditional upward mobility. Thus, I think that these results confirm that the methodology also works well in other contexts.

# **5. An application for LAC**

This section presents mobility results for the whole LAC region using the approach described in Section 2. To this end, I use the SEDLAC database of LAC household surveys and I focus on intragenerational mobility measures for the whole region, as well as for 17 LAC countries for which data are available between 2004 and 2015. These years coincide with a period of sustained economic growth and marked poverty reduction (Cord et al. 2017, Vakis et al. 2016).

Figure 9 shows two conditional types of transitions: (i) the proportion of poor who escape poverty (defined as those with income lower than $4 PPP per day); and (ii) the proportion of nonpoor who fall into poverty. As the figure suggests, LAC has experienced a dramatic mobility in the last 11 years. There is considerably more upward than downward mobility: 43 percent of the initial poor escaped poverty by 2015, while about 10 percent of those whose initial incomes were higher than the poverty line fell into poverty during the last 11 years.

These trends vary across countries. For instance, less than a quarter of the original poor escaped poverty in Guatemala and Honduras during the last 11 years, which compares with a significantly higher conditional upward mobility of more than 70 percent in Uruguay and Chile. More important, despite the strong poverty reduction in the last decade in LAC, downward income mobility is particularly worrying in some countries in the region. For instance, more than 10 percent of the initial non-poor fell into poverty in Nicaragua, Mexico, Honduras, and Guatemala.

The key point of this section is not to provide an extensive analysis of mobility in the region but rather to showcase the type of new insights that a researcher can obtain by applying the technique described in the paper. Indeed, cross sectional data using the same years and data sources would not have allowed an understanding of the welfare dynamics that the technique allows disentangling as above. Using cross sectional data alone would only reveal that between 2004 and 2015, poverty in the region went down from about 40 to 24 percent; but we would not know the nature of those movements.

13
Table 3

# CORRELATION BETWEEN ENROLLMENT RATIOS AND ILLITERACY RATES: PERU, 1985-86

|ENROLLMENT<br>RATIO|TOTAL||CO|HORT GRO|UP (IN YE|ARS)||
|---|---|---|---|---|---|---|---|
|(by school<br>g-oup)||15-19|20-24|25-29|30-34|35-39|40+|
|Initial|-.596|-.538|-.552|<br>-.1'0|-.597|-. 616|-. 587|
|Primary|.694|.694|<br>.691|<br>.327|.691|.711|<br>.691|
|Secondary|-.815|-.881|-.847|<br>-.639|-.829|-.851|-.812|
|Postsecondary|-.669|-.696|-.672|<br>-.516|-.682|-.714|-.658|
|University|-.402|-.444|-.439|<br>-. 332|-.394|-.406|-.412|
|Other|-.725|-.663|-.689|<br>-.483|-.731|-.751|-.736|

- _Notes:_ l.All correlations except those marked by an asterisk are significant at the 5 percent level. All correlations are significant at the 10 percent level. The test used to determine statistical significance of the correlation is the t-statistic

t - [(n-2r)/(1-r )] . n is the number of observations, r is the computed correlation coefficient, and (n-2) is the number of degrees of freedom.

- 2.The number of observations is 23. Lima and Callao, and Loreto and Ucayali are aggregated to maintain conformity with available GDP accounts.

- 3.Enrollment Ratios are calculated as ratios of total enrollment in the department to total population. For an idea of how much agedistributions differ across department, see column 5 of Table in the Appendix.

- _Sources:_ For illiteracy rates, enrollment and population, _Peru: Compendio EstadlstLco, 1987,_ Systema Estadistico Nacional, Instituto Nacional de Estadistica.

39
**Figure 2. Reported Contribution Density (Contribution Months/Months in** EAP) (Affiliated Men and Women who Responded to the PRIESO)

<!-- Start of picture text -->
1.00<br>1.00  - 0.93<br>0.90  - 0.85<br>0.80  0  O.T7<br>0.69<br>0.70  -<br>co  0.60<br>0.50  -<br>0<br>l  0.40<br>080  -<br>0.30<br>0.20  - 0 26<br>0.10  -<br>1  2  3  4  5  6  7  8  9  10<br>g> 0.40  Deciles (by  Contribution  Density)  of 0.5-Affiliated  Men<br>1.00<br>1.00<br>0.89<br>0.90<br>0.80 -0.77<br>0.70 0.00  -0.6_ .0<br>~050  -F<br>0.30  -03<br>0.20  -0.21<br>0.10<br>0.10<br>0.00<br>OV  2  3  4  5  6  7  8  9  10<br>Deciles (by  Contnbution Density)  of Affiliated Womien<br><!-- End of picture text -->

40
rather than the degrees of intensity of such restrictions and controls.'<sup>0</sup> In practice controls can take a variety of forms ranging from direct quantitative limitation on certain transactions or associated transfers, to indirect measures - such as, withholding taxes or reserves on external assets/liabilities, intended to influence the economic incentive to engage in certain transactions. And such controls could apply to transfer of funds associated with financial transactions or the underlying transactions themselves. There exists of course no single measure of a country's level of openness, but a viable measure of a country's level of financial openness to international capital markets needs to incorporate, at least, the distinction between the severity of controls and the different types of transactions contributing to capital flows. Table 5.3 provides preliminary information on such a measure for a sample of 96 countries in 1997.

This measure referred to as Financial Openness Index is constructed using disaggregated measures of capital controls based on the classification and information contained in the IMF Annual Report on Exchange Arrangements and Exchange Restrictions (AEAER), and drawing on the coding methodology developed by Quinn and Toyoda (1997). The measure is a composite index of our coding of rules, regulations, and administrative procedures affecting capital flows (both inflows and outflows) for a total of 27 individual transactions in the current and capital accounts of the balance of payments (see Box 5.1) for each country in the sample. Thus out of 96 countries in the sample, it is interesting to note that 46 can be classified, as of 1997, as open and 10 as semi-open, with both these categories including emerging market economies mostly in the Latin America region and Eastern Europe.

10 See, for instance, Razin and Rose (1994), Alesina, Grilli and Milesi-Ferretti (1994).

9
where is firm growth from period t to t+1 and is a firm-fixed effect. Under the null hypothesis that the crisis did not improve the allocative efficiency of employment re-allocation amongst continuing firms , whereas ( 0) under the alternative hypothesis that the crisis enhanced (diminished) the importance of productivity as a determinant of firm growth.

Serial correlation in the error term, in conjunction with the presence of lagged size as an explanatory variable would render OLS estimates of the employment growth equation biased. To address this concern, we also use a Fixed Effects estimator. The fixed effects transformation is biased due to the correlation between the transformed error and the transformed explanatory variables (Nickell, 1981), but as the OLS and Fixed Effects estimators are biased in opposite directions, they provide a confidence interval within which the true parameters lie (Bond 2002).<sup>6</sup>

# **4. Data**

The Indonesian Manufacturing Census (1991-2001) collected by the Indonesian Statistical Agency, BPS (Badan Pusat Statistik), provides the empirical basis for our analysis. It contains information on all Indonesian manufacturing establishments with more than 20 employees and spans the pre- and post- crisis periods, as well as the crisis itself. It has very detailed information on employment, inputs and outputs, industrial classification, exporting, ownership, investment behavior, and the capital stock, which we measure as the replacement value of machinery and equipment at the end of the calendar year. Employment is measured as the average number of workers per day. We augmented the data with industry-level measures of

> 6 While the Difference and Systems GMM estimators developed by Arellano and Bond (1991) are in principle capable of yielding unbiased estimates, these estimators are not well suited for our data;  the difference GMM estimator is likely to result in poorly behaved estimates when variables are highly persistent as is the case with our data (for surviving firms the correlation between lnLt and lnLt-1 is 0.98 – both in crisis and non-crisis years), while the Systems GMM estimators relies on a mean stationarity assumption that is palpably undesirable in the context of a crisis (See Roodman, 2006, for a discussion). We therefore eschew this approach.

14
- _25_ -

# III. PROBLEMS AND PROSPECTS

Investing irn poverty-oriented institutional<sup>development may</sup> require alternative methods of planning,<sup>evaluating, funding, and implementing</sup> poverty reduction programs. Moreover,<sup>the approach does not necessarily</sup> produce quick, easy or cheap results.<sup>Consequently, many governments and</sup> development agerncies are skeptical<sup>about the feasibility of ID approaches as</sup> part of their proiects and programs.<sup>More specifically, objections to</sup> poverty-oriented institutional<sup>development efforts emphasize:</sup>

* the costs versus the benefits<sup>of such approaches;</sup> * the scale on which they can operate; i their compatibility with conventional<sup>project frameworks,</sup> * the degree and types of decentralization<sup>they require, and</sup> * their political feasibility.

This section of the paper will address<sup>these issues, and with the use of</sup><sup>**case**</sup> studies, it will illustrate that these<sup>criticisms may be misleading.</sup>

# COSTS AND BENEFITS

Investing in institutional interventions<sup>that seek to organize the</sup> poor and develop indigenous management<sup>capacity may entail both increased</sup> administrative costs and higher up<sup>front costs for activities such as</sup> community organizing and management<sup>training. But increased administration</sup> costs may be justified when they lead<sup>to returns on investment which are</sup> sustainable over a longer period,<sup>more equitable distribution of benefits, and</sup> improvements in non-pecuniary indicators<sup>of poor peoples living standards (for</sup> example, reductions in the incidence<sup>of dowry). Moreover, there is strong</sup> evidence to suggest that the need for<sup>financial assistance will fall overtime</sup> as a result of declining cost structures,<sup>and when early efforts are made to</sup>