rafmacalaba/datause-encoder-data huggingface dataset:
# **The specific role and value of participatory approaches for achieving accountability**

**remains unclear.** Olken (2007) used randomized controlled trial methodology to examine two different approaches to accountability and found that participatory mechanisms such as anonymous feedback forms or accountability meetings had negligible impact whereas the increase in government audits led to an 8 percent decrease in missing expenditures (cited in McGee and Gaventa, 2010). In contrast, other studies argue for the value of citizen monitoring and advocacy but use less rigorous approaches (Deininger and Mpuga 2005, Sing and Vutukuru 2020, Caseley 2003).

# **Indicators**

**_Accountability_** was measured using two approaches based on RCT (Olken 2007). The first focused on participatory monitoring methods, including counting the _frequency of accountability meetings and the submission of anonymous feedback forms_ . The second counted the _frequency of government audits_ in terms of the probability that a particular entity would be audited. The resulting effect on corruption in this case was assessed in terms of the amount of missing expenditures.

# **_3.3 The link between accountability and human development outcomes_**

**Emerging evidence suggests there is a link between accountability and higher-level human development outcomes.** The analysis conducted by Fukuda-Parr et al (2011) on the relationship between the OBI and various development outcomes also included tests for various sub-indices constructed from specific subsets of questions in recognition that “some aspects of budget transparency are more important for enabling positive development outcomes than others” (p.18). For example, sub-indices were created for the strength of the legislative branch, supreme audit institution and citizen participation, and in some cases, these sub-indices were stronger predictors than the overall OBI score. The results showed that accountability, even when controlling for GDP per capita and region, was more strongly associated with health development outcomes than the OBI.

# **_Indicators_**

- **_Accountability_** was measured by the Supreme Audit Institution Strength Index (FukudaPar et al. 2011). This index reflects the straight average of country scores for a subset of questions in the Open Budget Survey.

- **_Participation_** was measured by Fukuda-Par et al. (2011) through the Citizen Participation Index that reflects the straight average of country scores for a subset of questions in the Open Budget Survey about opportunities for citizens to participate in the budget process.

**Studies examining the outcomes of community monitoring for improved service delivery show some effects on improved human development outcomes over time.** For example, the randomized field experiment conducted by Bjorkman and Svensson (2009) in Uganda for 50 communities showed positive effects related to the weight of infants receiving services by health providers and a marked decrease (33%) in under-5 mortality just one year after the first round of baseline community meetings.

11
foreign funding – its volatility in periods of crisis. The mean growth rates of FOREIGN LIABILITIES AND PRIVATE CREDIT foreign were much higher in the case of ECA countries, relative to Non-ECA countries, reflecting the greater reliance of the ECA region on foreign funding and the key role played by cross-border finance in ECA’s pre-crisis credit boom. Interestingly, the standard deviation of FOREIGN LIABILITIES in the ECA and non-ECA regions are similar, revealing that countries in the two regions are potentially exposed to the same volatility in periods of crisis, but ECA countries are much more exposed, given their greater reliance on foreign funding. This result will be further explored in Section V.

# _B. Sample Split Variables_

One of the objectives of this paper is to understand the factors that drive the response of PRIVATE CREDIT to other PVAR variables in ECA countries and other emerging countries, and identify the differences across regions. In doing so, we use various variables hypothesized to explain the difference to split the sample further. These variables are the loan-to-deposit ratio (LDR), the share of foreign ownership of the banking system (FOREIGN OWNERSHIP), and the share of foreign liabilities in total bank liabilities (FOREIGN FUNDING). To gauge the impact of these variables, we split the sample based on their pre-crisis values, right before the banking system weaknesses in ECA became apparent. Table 2B reports the pre-crisis country averages for our three sample-split variables broken for ECA and non-ECA countries.<sup>3</sup> Appendix Table A2 reports individual country-level data.

More specifically, LDR is the loan-to-deposit ratio calculated as the ratio of PRIVATE CREDIT to DEPOSITS, defined as above from the IFS. LDR proxies the extent to which domestic bank credit is funded by domestic deposits. An LDR in excess of 100 percent implies that credit is financed with other sources of funding such as parent funding, unsecured wholesale funding in international or domestic markets, or other instruments such as covered bonds. Some of these

> 3 We use 2007:Q4 values for LDR and FOREIGN FUNDING. We use the closest available observation for FOREIGN OWNERSHIP, which is 2008 for most countries.

10
Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant stunting (0-6 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g) | = 3. ——342_____<br>Mother has short stature (height < 145cm)} 9 ie —i_§_<br>Mother has low body mass index (BMI < 18.5) $ —2___ —12____<br>Attended at least 4 antenatal visits } 9 Ops<br>Skilled birth delivery} = =—*——— Ce<br>Child is currently breastfed} =—--—— —-—_—_<br>Child is currently fed solid or semi-solid foods 9 a a<br>Child is currently fed other milk & 7 -—<br>Mother reads & plays with child] <= 7 a<br>Mother handwashes regularly 12 —— —-<br>Household is food secure; —*— —+—<br>HH consumed protein rich foods in past week] 9" —— ——<br>HH consumed vitamin A rich foods in past week a2 7 Tr<br>Underwent neonatal screening 2 —.— st<br>SkillAg e d post-natappropri a lte carevaccine for “restatus; .= a>_—e >—-_—e———-<br>Improved drinking water] 9" -*—— og4<br>Improved sanitation (toilet is water sealed) | 7 —-—- —-—-<br>>75% of HHs in municipality have improved sanitation s. —-— —-—<br>Improved cooking fuel & no garbage burn} $ —_ ><br>HH member smokes}3 ~*~ —<br>Functioning refrigerator in household} 3 —— ae<br>Female —- +<br>Single birth ——— ——<br>Urban —_<br>Total HH size (N)jo +<br>Total children under five in HH (N)j.Q ——<br>Total HH income per month (1 = 1000 PHP) (log) s ><br>In poorest quintile of total HH income per capita] 6 ——<br>Mother has high school or tertiary education} % ——<br>Mother is married oe<br>Mom wasa teenager at time of birth ——<br>Mother hasa job or business >t<br>0 1 2 3 4 50 1 2 3 4 5<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. Unadjusted regression models do not control for socio-economic factors.
# ANNEX m: **COUNTRY** TYPOLOGY

This annex documents the country typology adopted for the comparative analysis of PER policy conclusions related to education cost and financing issues. The typology essentially classifies countries according to the basic policy-related determinants of the supply of public resources to the education sector. At this stage, it is limited to the sample countries covered in the present PER study. It is also static, as it relies on country data for one single benchri;ark year. Ultimately, its usefulness should benefit from a more comprehensive coverage, as well as from the incorporation of patterns of recent changes in key variables.

## I. The Methodological Framework

From a resource availability perspective, the absolute level of govermnent spending on education (SR) can be seen as the product of three basic factors: the _level of GNP_ (Y), that is, the total supply of national resources; the efficiency of _public resource mobilization_ (PRM), as reflected by the ratio of government revenue to GNP; and _sector resource allocation (SRA),_ that is, the part of government expenditure absorbed in the education sector. In formula:

## SR = Y * PRM*SRA.

For crosscountry comparison, the relative level of sector resources (SRM) is being defined in terms of GNP, and therefore:

## SRM = PRM * SRA.

Sector resource availability thus depends on two policy-related parameters: resource mobilization and resource allocation. In the PER context, the former concerns the country team's macroeconcmist, or public finance expert, while the latter is the major focus of interaction between the macroeconomist and the sector specialist. It thus seems relevant, in the context of this review. to classify countries according to their respective PRM and SRA levels.

However, whether the supply of public resources for education is high or low by international standards bears little relation to the sector's actual financing needs. Government spending on education may represent a substantial proportion of GNP, perhaps at the limit of sustainable resource mobilization and sector allocation, and still not meet the sector's overall financing requirement. Short of a comprehensive accounting framework of sector financing needs, an international comparison of public spending on education therefore still ought to incorporate some comparative notion of sectoral output performance. To this end, countries have been further distinguished according to their primary enrollment rate (PE), which was selected in view of the high priority of achieving universal primary education among sector objectives, as well as the large amount of education financing absorbed by this subsector (around 40 percent on average in developing countries). However, since PE. in addition to sector resource mobilization parameters, also 67
_25_ All V's which are included in the participation equation _(a,, O'_ but excluded from the performance equation,( 8v=0 ) are legitimate instruments. The V's provide a source of variation in participation that is exogenous to performance. On the other hand, neither the Z's nor the W's are valid instruments: they directly affect both participation and performance.

To choose appropriate instruments, we need a positive model for partcipation. Hypotheses based upon the larger study of these water projects (Narayan 1994) (as well as theoretical literature on the determinants of participation) generated a set of equations to estimate the effects of participation (Appendix table 1.3 shows the ful "first stage" regressions). We identify four variables as legitimate instruments: "extent to which participation was a project goal"; "percent of investment costs bome by users"; 'beneficiares overall net benefits of participation"; and "extent to which organization is based on local collectives." We hypothesize each of these phenomena may directly affect participation, but should have no independent, direct effect on project performance after controlling for participation.

In table 7, the OLS and IV results are compared-for both the bivariate and the limited multivariate case'<sup>5</sup> . The estimated impact of participation increases with IV estimates. For instance, when extent to which participation was a project goal" is used as an instrument, the bivariate impact rises from .63 to .70; the multivariate rises from .28 to .34. Similar results-the IV producing a higher and statistically significant estimate-are observed for each of '5 The full multivariate case loses too many degrees of freedom. So while the results are empirically similar they are less precise.
# **Figure 1: FTS and DAC Data on Emergency Aid**

<!-- Start of picture text --> year<br>Total Emergency Aid from DAC<br>Total Emergency Aid from DAC, excluding complex emergencies<br>Total Emergency Aid from FTS<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>3000<br>2000<br>2000 US$ Millions 1000<br>Total Emergency Aid<br>0<br><!-- End of picture text --> 19
inheritance and the consequences of the HIV/AIDS epidemic. But, the situation of everwidowed women of often young ages who have remarried and so are no longer identified as ―widows,‖ or have been in some other way absorbed into male headed households along with their dependent children has to my knowledge received scant attention.

This paper has used available data to examine the individual welfare of women who have experienced the shock of widowhood in Mali. The welfare of their dependent children has also been examined.

A review of the situation of women in Mali suggests that they may be generally more vulnerable and less able to mitigate and cope with downside risk than men. And women may be exceptionally vulnerable to the loss of a husband.

An analysis of household consumption data on female headed households indicates that allowing for their smaller household size ─ and hence, potential scale economies ─ they have lower living standards than male headed households in both rural and urban areas only before controlling for marital status. Households with female heads who are also widows are significantly poorer than all other households.

Finally, the paper turns to an examination of individual welfare indicators from the 2006 Mali DHS. I first investigate whether selection into widowhood ─ whereby widows are poorer prior to widowhood not because of it ─ is a concern in this setting. The tests do not suggest that selection bias is an issue. The analysis then confirms that widows who are not necessarily household heads experience lower levels of individual welfare indicators. It also reveals that relative to other women, the vulnerability and lower welfare of women previously widowed or divorced persists through remarriage. Furthermore, these detrimental effects are passed on to children ─ and possibly more so to daughters ─ suggesting an intergenerational transmission of poverty stemming from widowhood and (less so) divorces.

Yet a review of social safety nets implemented by government and donors reveals that there is little systematic attempt to reach widows or their dependent children. Although it may be tricky to target widows and it should be kept in mind that the same conditions that create inequality within households also constrain the ability to target women per se with interventions, more thought, resources and effort need to go into helping these vulnerable groups.

There are potentially a number of ways to reduce the vulnerability of widows. On one front, legal reforms and measures such as inheritance laws that protect the security of widows 26
such that φ= 1 indicates the central tendency of TFP in the cross section. A value of φ above 1 indicates high TFP relative to the firms in the cross section, while a value below 1 indicates low TFP. Rearranging (3): If we assume Cobb-Douglass production technology and that the TFP index can be written φ _i_ = _e_<sup>ν</sup> _i_ , (4) is specified as, which can be transformed into a linear expression amenable to regression methods: Here, the natural logarithm of the TFP index is equal to the residual term in the econometric production function. Interpretation of the residual term in this way should be done with caution, however. Measurement error is also likely to have an effect on the size and distribution of the residuals. A more conservative conclusion is that firm level variations in TFP account for a substantial component, but not all, of the residual values.<sup>4</sup> The TFP analysis in this paper is based on cross-sectional data at the firm level. It is important to bear in mind that in a cross section collected in one year or over a relatively short interval, all firms have access to the same level of technology. Thus

> 4 Stochastic frontier analysis (SFA) attempts to isolate that component of the residual that may be attributed to efficiency variations from a “white noise” component that depends principally on measurement error. Since this method has complex statistical requirements that are often not satisfied in the data, we do not employ it in this paper. More limited results of SFA application in China data will be provided in a companion paper. For a review, see Kumbhakar and Lovell (2000).

> The authors also applied Escribano-Guasch (2005) one-step methodology by combining the two equations estimated, namely (1) production function and (2) TFP function, into a single equation, and estimating the models for both the Brazil and China data.  The results were very similar, with no instances of sign change for statistically significant parameters and very few cases where the estimated coefficients between the two approaches differed by as much as one standard error.  The authors also found that they were able to reject the hypothesis that parameter values equal zero more often and at a higher confidence level using the twostep approach presented in this paper.  The reason could be possible multicollinearity when a single equation includes a large number of independent variables, leading to higher standard errors.

4
Figure 18: Share of municipalities that can be ranked and statistically distinguished one another <!-- Start of picture text --> 50%<br>49%<br>48%<br>47%<br>46%<br>45%<br>44%<br>43%<br>42%<br>41%<br>40%<br>39%<br>38%<br>37%<br>36%<br>35%<br>34%<br>33%<br>32%<br>31%<br>30%<br>29%<br>28%<br>27%<br>26%<br>25%<br>75 80 85 90 95 97.5 99 99.5<br>Confidence Interval (%)<br>% of municipalities with significant changes<br><!-- End of picture text --> Source: Authors’ Calculation.

Tarozzi and Deaton (2007) direct their concerns to a large extent at what they term the “standard” application of the ELL approach (what we call the “optimistic” approach, above) which in the context of this study would involve applying the location effect at the EA level in the Census rather than the municipality level. In the presence of some unobserved inter-cluster correlation, it is clear that standard errors obtained under the assumption of no such correlation will be understated. ELL (2002) found that, in the case of Ecuador, the degree of understatement was negligible if the inter-cluster correlation was assumed not to extend all the way to the target population level. Here, we have allowed for inter-cluster correlation all the way to the municipality level, and have indeed imposed the highly conservative assumption that the entire inter household correlation observed at the EA level applies at the municipality level (ELL conservative approach) It is of interest to ask how far wrong we might have gone in our Minas Gerais setting if we had not taken this conservative stance, and had proceeded with the “optimistic” approach of applying the location effect entirely at the EA level. Figure 19 indicates that, indeed, confidence intervals are narrower when the location effects are applied only at the EA level. However, it is noteworthy that a hypothetical policy maker, presented with such an “optimistic” poverty map and its accompanying standard errors, would not come away with a wildly unreasonable picture of the 29
**4**

analysis using national accounts evidence on sectoral growth combined with poverty data from household surveys. Special attention is given to SSA, though the evidence we bring to the debate covers the wider developing world.

The paper begins by developing a simple conceptual framework (section 2) in which the effects of agriculture and non-agriculture on poverty are shown to arise from two principal sources: a growth effect and a participation effect. The paper then examines the direct and indirect growth effects across both sectors in sections 3 and 4, followed by an assessment of potential differences in the participation component in section 5. Section 6 synthesizes how these different effects are expected to play out in terms of poverty reduction across different groups of countries and concludes.

# **2 Conceptual Framework**

Let _Pi_ be any (decomposable) measure of poverty and _Yi_ per capita Gross Domestic Product (GDP) in country _i_ . The proportionate change in poverty in a country _i_ can then be seen to be identical to the GDP elasticity of poverty (defined as the proportionate change in poverty divided by the proportionate change in per capita GDP)<sup>3</sup> ,<sup>times the proportionate change in per capitaGDP</sup> ( _Yi_ ): We refer to the first multiplicative term in (1) as the _participation effect_ and the second multiplicative term as the _growth_ effect.

Not all growth processes generate an equal amount of overall growth nor an equal amount of poverty reduction (World Bank, 2000). The growth and participation effects may differ substantially across sectors. The latter has been illustrated empirically for India by Ravallion and

> **3** Note, by using GDP growth rather than mean household income change as is common in this sort of identity, we are very much focusing on the overall growth process (not simply the growth in household income).  The elasticity concept used here reflects the impact of growth on both average incomes of households and how those incomes are distributed. This is commonly referred to as the “growth elasticity of poverty” which is strictly speaking not correct.
Table 16. 1993 Nominal and Effective Rates of Transportation Protection Against African Exports of Processed Goods to the United States.

|HS<br>Code|Description|U.S. Value Added<br>Coefficient|Nominal Transport<br>Protection (9)|Effective Transport<br>Protection (%)|Difference|
|---|---|---|---|---|---|
|11|Products of the milling industry|0.22|18.4|33.0|14.6|
|14|Vegetable products|0.38|24.4|36.1|11.7|
|15|Animal and vegetable oils|0.08|9.9|56.1|46.2|
|20|Preparations of vegetables|0.41|16.1|28.2|12.1|
|23|Animal feeds|0.52|_56.1_|87.2|31.1|
|24|Cured tobacco|0.10|11.2|32.3|21.1|
|31|Fertilizers|0.29|28.4|62.9|34.5|
|32|Tanning and dying extracts|0.66|16.4|20.4|4.0|
|34|Soap and cleansing preparations|0.52|9.9|12.1|2.2|
|42|Articles of leather|0.49|6.8|9.3|2.5|
|46|Manufactures of plaiting materials|0.42|31.9|62.6|30.7|
|48|Paper and paperboard|0.43|10.6|8.3|-2.3|
|52|Fabrics of cotton|0.41|12.2|16.9|4.7|
|56|Discontinuous man-made fibers|0.35|18.5|33.6|15.1|
|57|Other textile materials|0.55|17.1|24.1|7.0|
|61|Articles of apparel and clothing|0.56|6.2|14.5|8.3|
|64|Footwear|0.48|4.3|3.2|-1.1|
|65|Headgear and parts|0.56|8.8|12.1|3.3|
|68|Articles of stone or plaster|0.53|17.5|26.6|9.1|
|69|Ceramic products|0.58|17.1|22.8|5.7|
|70|Glass and glassware|0.64|7.7|10.2|2.5|
|73|Iron and steel|0.53|7.3|12.1|4.8|
|82|Tools and cutlery|0.60|6.9|8.1|1.2|
|83|Articles of base metal|0.34|17.4|36.8|19.4|
|85|Electrical machinery|0.56|2.4|7.1|4.7|
|90|Scientific instruments|0.68|5.6|7.7|2.1|
|91|Clocks and watches|0.40|6.6|11.2|4.6|
|92|Recorders and musical instruments|0.54|20.2|34.2|14.0|
|94|Furniture and parts|0.53|10.8|18.6|7.8|
|95|Toys and Games|0.58|9.3|16.3|7.0|
||AVERAGE ALL ABOVE PRODUCTS|0.46|14.5|25.5|11.0|

Source: Value added coefficients and effective rate esimates based on data drawn from the US Department of Commerce, Census of Manufactures. Nominal transport costs computed from Department of Commerce trade tapes.

-_
<!-- Start of picture text --> Index, 100=t Previous recessions<br>120 —September 11,2001<br>—Covid-19<br>80<br>60<br>40<br>20<br>t t+10 t+20 t+30 t+40 t+50 t+60<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>10 mRange Average —2020<br>0<br>6<br>-10<br>Before During<br><!-- End of picture text -->