rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7276

# **Abstract**

This paper develops a structural approach for modeling how respondents answer survey questions and uses it to estimate the proportion of respondents who are reticent in answering corruption questions, as well as the extent to which reticent behavior biases down conventional estimates of corruption. The context is a common two-step survey question, first inquiring whether a government official visited a business, and then asking about bribery if a visit was acknowledged. Reticence is a concern for both steps, since denying a visit sidesteps the bribe question. This paper considers two alternative models of how reticence affects responses to two-step questions, with differing assumptions on how reticence affects the first question about visits. Maximum-likelihood estimates are obtained for seven countries using data on interactions with tax officials. Different models work best in different countries, but cross-country comparisons are still valid because both models use the same structural parameters. On average, 40 percent of corruption questions are answered reticently, with much variation across countries. A statistic reflecting how much standard measures underestimate the proportion of all respondents who had a bribe interaction is developed. The downward bias in standard measures is highly statistically significant in all countries, varying from 12 percent in Nigeria to 90 percent in Turkey. The source of bias varies widely across countries, between denying a visit and denying a bribe after admitting a visit.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at akraay@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 12 re-estimates equation 3 but narrows down the way we define eligibility. Eligibility is no longer defined for any child being of preschool age, but it is defined for each child’s birth order. Therefore, column 1 defines eligibility relative to the first child, column 2 to the second child, and so on. If a mother only has 2 children, preschool eligibility status will only be defined for the first two columns pertaining to her first two children. Hence, the number of observations will fall as we go across the columns because the number of mothers with three children is fewer than the number of mothers with just one child.

Additional public preschool density has positive and statistically significant effects on the work participation of mothers with preschool-aged eligible first, second, and third children. The statistical significance of the third child is still robust to simultaneous inference with an FDR q- value of 0.095. The effects on the fourth child are positive but not statistically significant; on the fifth and sixth children they are negative and not statistically significant. Having better access to a private preschool does not seem to matter for work participation. We find a spurious positive and statistically significant effect for the second child; but the indicated positive effect does not stand the false discovery test.

# **_6.4 Heterogeneous effects by preschool enrollment level_**

Previous studies suggest that the effect of childcare services on maternal employment may depend on utilization rates; if the rate is already high then additional childcare services may no longer have an important effect on employment. It is important to note, however, that null findings in other contexts have 90 percent (Bauernschuster and Schlotter 2015) and 80 percent childcare utilization rates (Lundin et al. 2008). Meanwhile, average preschool enrollment rates among children aged 3- 6 per district between 2003 and 2014 in Indonesia is 15.6 percent; the 99th percentile is 49.8 percent.<sup>22</sup> As such, we may not expect preschools in Indonesia to have reached the saturation point yet.

Table 13 provides suggestive evidence on the heterogeneous effects of preschool availability on maternal employment by preschool enrollment rates using DDD and DDD-FE strategies. The sample is restricted to PODES years with preschool enrollment data,<sup>23</sup> which leads to a total of 37,547 individual-year observations (less than half of the sample in Table 2). Each strategy divides the sample into three groups: those subjected to less than 20 percent (N = 23,076),

> 22 Data obtained from National Socioeconomic Surveys (SUSENAS) in 2003, 2005, 2011, and 2014.

> 23 Preschool enrollment data coincide with PODES years in 2003, 2005, 2011, and 2014.

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1

# Universal Service Obligations in Utility Concession Contracts and the Needs, of the Poor in Argentina's Privatizations

Omar Chisari (Universidad Argentina de la Empresa, Buenos Aires, Argentina) and

## Antonio Estache

(World Bank and European Center for Applied Research in Economics--ECARE), , SITRC HG 3881.5 .W57 P63 N0.2250 Chisari, Omar 0. Universal service obligations in utility concession contracts and the needs of the poor in M. Celani contributed to Section I and M. Rossi to Section II. We are very grateful to J.L. Guasch, J.J. Laffont, P. Noumba and M. Rodriguez Pardina for comments.
in the Gini coefficient of 8pp, this gap narrowed to 1pp during this period, with the Islamic Republic of Iran experiencing an annual decrease of 1.2pp and Spain an increase of 0.4pp per year.

**Figure 3: Growth of the bottom 40% vs. that of the mean, circa 2008‐13**

<!-- Start of picture text --> Congo, Dem. Rep.<br>China<br>Ecuador<br>Cambodia<br>Slovak Republic<br>Vietnam<br>Indonesia<br>Iran, Islamic Rep. India<br>Argentina Cameroon<br>United States<br>Spain<br>United Kingdom<br>Latvia Luxembourg<br>Ireland<br>Croatia<br>Greece<br>-10 -5 0 5 10<br>Growth in the mean (%)<br>10<br>5<br>0<br>Growth of the bottom 40%<br>-5<br>-10<br><!-- End of picture text -->

**Source:** World Bank ‐ GSPD 2016. Own elaboration.

In sum, the shared prosperity trends for our sample of countries with available data for 2008‐13 are positive, both in terms of the headline indicator of growth in the average income of the poorest 40% ( ��� ) and in terms of the shared prosperity premium ( ��� ��� ). ��� was positive in 60 of the 83 countries, accounting for 89% of the population covered in this sample of countries, despite the fact that this period was marked by a global financial crisis, the magnitude of which had not been seen since the Great Depression of the 1930s. Nevertheless, the fact that 11% of the population in the sample lived in countries where average incomes for the bottom four deciles fell is obviously a source of concern and reinforces the need for micro‐ and macroeconomic policies that protect the most vulnerable during downturns, as well as promote income growth for the poor in the long run.

# **4. Policies that explore the efficient redistribution space**

The intent behind Kaushik Basu’s (2000) proposal of the quintile growth rate as a development objective, as well as of the subsequent adoption of expanded versions of this “shared prosperity” objective by the World Bank and the United Nations, was to re‐shape policy. The implicit presumption is that a focus on growth at the bottom of the distribution would lead to materially different policy choices than the ‘old’ objective of (distribution neutral) growth in the mean.

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The Mexican Pension Annuity Market

# **Figure 6: Impact of SAR-02 Reform on IV Premium Growth**

<!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br>-100<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Premiums (lhs) Growth (%) (rhs)<br><!-- End of picture text -->

**Source** : CNSF

**Table 20: Monthly Disability Benefit Comparison Under 1973 and 1997 Laws** **<u>(MXN)</u>**

|||Yea|1973 Law<br>rs ofcontributi|ons||1997 Law|
|---|---|---|---|---|---|---|
|Average salary|5|10|15|20|25||
|1,500|1,409|1,418|1,431|1,449|1,476|1,551|
|3,000|1,415|1,433|1,459|1,750|2,066|1,551|
|4,500|1,422|1,449|1,709|2,236|2,786|1,575|
|6,000|1,428|1,464|1,902|2,666|3,462|2,100|
|7,500|1,434|1,480|2,157|3,149|4,180|2,625|
|9,000|1,441|1,495|2,443|3,656|4,917|3,150|
|10,500|1,447|1,511|2,850|4,266|5,736|3,675|
|12,000|1,611|1,684|3,257|4,875|6,556|4,200|
|13,500|1,802|1,894|3,665|5,485|7,375|4,725|

**Source** : Fernandez Reyes (2004)

The comparison exercise reported in the previous table suggests that on average, the large majority of individuals (the shaded area) would prefer the benefits under the LSS-97. Only individuals with more than 15 years of contributions and more than twice the minimum salary (the average salary in Mexico is between four and five times the minimum salary) would prefer benefits under the LSS-73 the relative generosity of which was increased by the 2001 and 2002 reforms.

Several reasons can be attributed to the seeming irrational behavior of insured individuals that caused the annualized average decrease of 28 percent in premium since 2001 displayed in Figure 6.

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adopters who have not withdrawn – the first signs of desertion started almost immediately, indicating that for a number of farmers NTX adoption was short-lived. From time of adoption until 2005, only one-quarter of adopters produced snowpeas uninterruptedly.<sup>21</sup> Two additional features emerging from the figures are noteworthy: in the adoption process, virtually no new adoption is observed after 1995; and the withdrawal survivor function depicts a gradual but persistent withdrawal process, with generally higher rates from the late 1980s on.<sup>22</sup> <!-- Start of picture text --> Figure 1a: Adoption Survivor Function, Calendar Time Figure 1b: Withdrawal Survivor Function, Calendar Time<br>1<br>.75<br>.5<br>.25<br>0<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>year year<br>1<br>.75<br>.5<br>.25<br>% households not yet adopted % households not yet withdrawn<br>0<br><!-- End of picture text --> Figures 2a and 2b present the events from a slightly different angle. While the above survival functions show the percent of the population that has yet to undergo the event of interest (adoption, or withdrawal among the sample of adopters), these empirical hazard functions give the likelihood of adopting (withdrawing) in each time period (starting from the first year “at risk” of adoption or withdrawal<sup>23</sup> ) _conditional_ on not having adopted (withdrawn) by the previous time period. The hazard functions suggest an initial acceleration of adoption and then a strong and consistent downward trend in adoption, showing that if farmers did not adopt early, they became less and less likely to do so in the years ahead. The withdrawal hazard function in Figure 2b, on the other

> 21 Here and in the subsequent analysis, “uninterrupted” is defined as not having stopped producing snowpeas for more than 2 years. Thus, in calculating the spell in the withdrawal model, a household is assumed to change state, i.e. withdraw, only when stopped producing for more than 2 years.

> 22Exceptions are the 1991-1992, 1996-1997, and (to a lesser extent) 2001-2002 seasons, in which more pronounced drops in survival are detected, likely because of some “heaping” effects due to recall bias. 23 Note again that while “calendar time” is the same for all households, “household time” is not.

18
lower numbers who were invited to enroll). It reports results from regressions of the dependent variable listed in the row (e.g., an indicator for male) on an indicator for treatment assignment and on fixed effects specific to each childcare-center-by-age-group draft. The third column shows robust standard errors. There are very small differences between treatment and control.

|**Table 1: Balanc**|**e of Contr**<br>|**ols**<br>|Rbt|
|---|---|---|---|
||Mean<br>among<br>Control|Regression-<br>Adjusted<br>Difference|ous<br>Standard<br>Errors|
|Male Child|0.52|0.01|0.02|
|White Child|0.29|0.04|0.02|
|Black Child|0.13|-0.02|0.01|
|Mixed Race Child|0.54|-0.02|0.02|
|Other Race Child|0.03|0.00|0.01|
|Birthweight (in kilos)|3.19|0.02|0.03|
|Birth Height (in cms)|49.26|0.26|0.20|
|Planned Birth|0.34|-0.02|0.02|
|First Born|0.42|-0.02|0.02|
|Age of the Mother at Birth|20.30|0.07|0.23|
|Prenatal Care|0.96|0.00|0.01|
|Natural Birth Delivery|0.69|-0.02|0.02|
|Premature Birth|0.12|0.02|0.02|
|Breastfed up to 6 Months|0.78|-0.04|0.02|
|Household per Capita Income|521.81|-54.32|35.86|
|Household Size|4.71|0.22|0.21|
|Age of Caregiver|29.83|-0.25|0.44|
|Caregiver can Read and Write|0.96|0.02|0.01|
|Caregiver Finished Middle School|0.68|0.03|0.02|
|Caregiver Graduated from High School|0.32|0.04|0.02|
|_Notes_: The table investigates whether the trea<br> <br>Caregiver has a College Degree|tment and co<br> <br>0.01|ntrol children ar<br> <br>0.01|e comparable<br> <br>0.00|

_Notes_ : The table investigates whether the treatment and control children are comparable in terms of predetermined characteristics. The first column shows means among the control children. The second column reports the coefficient on the treatment indicator from regressions of the dependent variable listed in the row on the treatment indicator and on dummies specific for childcare center × age group. The third column shows robust standard errors. _N_ = 2,113.

8
probability-based resilience measure. Upton et al. (2016) apply this approach to food security using a household panel from Northern Kenya. These approaches are consistent with a longstanding literature on vulnerability and poverty dynamics (as noted by Béné et al., 2012 or Constas et al. 2013). However, the measures developed by Cissé and Barrett (2018) are more relevant for analyses based on long panels than for the short panels typically collected for impact evaluations of specific programs.<sup>25</sup> When precise information on shocks is available, resilience can be proxied more directly by observing the degree to which households can mitigate the effect of shocks on welfare outcomes, along with the types of coping strategies they use (Constas et al., 2013). In the context of impact evaluations, another approach is to analyze how interventions affect households’ ability to manage shocks, which can be analyzed by assessing the heterogeneity of program impacts by exposures to climatic shock measures (Macours et al., 2012; Asfaw et al., 2017; Béné et al., 2017; Christian et al., 2019). This is the main approach used in this paper. Specifically, we measure the effect of exogenous climatic shocks on household consumption, and then analyze whether cash transfers mitigate the negative effect of shocks based on an interaction term between shocks and transfers. Exogenous covariate weather shocks are obtained from satellite rainfall data as described in sections 2.2 and 3.2. Similar to (1), the DID specification taking shocks into account can be written as follows, for households with P s s ≤ C C C : ssii F 0 1T 2S 3T ∗ S + εTT (3) ,ii,vv ii,vv vv ii,vv vv ii where F yy = ββ is the First Difference of the outcome of interest (as in + ββ + ββ + ββ equation (2)) for household ,ii,vv 2015,ii,vv 2012,ii,vv in village-hamlet , T is equal to 1 when household belongs to a YY = YY −YY village assigned to cash transfer treatment, and S ii,vv is an indicator variable equal to 1 in case of ii vv ii covariate shock in at follow-up (2015). Finally, vv3 captures the additional (mitigating) effect of vv the transfers for households affected by shocks.<sup>26</sup> In this specification, 1 captures the average vv ββ effect of the transfers, 2 the effect of shocks, and 3 households’ capacity to mitigate the effect ββ of shocks. We test whether the combined effect 1 3 is significantly different from 0 to assess ββ ββ ββ + ββ

> 25 Measuring resilience in practice is challenging given that the concept evokes long time windows and multiple welfare dimensions (Béné et al., 2016; Béné et al., 2017; see also Carter and Barrett, 2006, for a discussion similar challenges related to measuring poverty dynamics).

> 26 As such, the specification can be characterized as a triple-difference specification. As explained in section 3.1 for equation (1), we also estimate a single-difference specification for outcomes only available at follow-up.

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Table 5: Growth and Conflict in the Cross-section

**Panel A: Country Level, Penn World Table GDP per Capita Growth**

|sample<br>num|ber of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|4535|2.69|6.35|-42.36|115.43|
|civil war countries|4107|1.73|7.69|-64.61|90.03|
|civil war countries during peace|3163|2.03|7.17|-39.11|90.03|
|violent countries during violence|673|0.29|10.19|-64.61|52.07|

# **Panel B: Country Level, Worldbank GDP per Capita Growth**

|sample<br>num|ber of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|4244|2.52|5.53|-45.83|142.07|
|civil war countries|3832|1.78|7.64|-65.03|182.36|
|civil war countries during peace|3224|2.14|7.20|-34.96|182.36|
|violent countries during violence|608|-0.14|9.44|-65.03|50.03|

**Panel C: Country Level, Night Light per Capita Growth**

|sample|number of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|2055|6.01|44.40|-76.12|1641.00|
|civil war countries|1869|6.04|30.97|-63.94|564.25|
|civil war countries during peace|1558|6.62|32.30|-54.99|564.25|
|violent countries during violence|311|3.13|23.01|-63.94|116.33|

“Conflict” is defined as a year in which the number of battle related deaths is higher than 0.008 per 1000 population. GDP per capita growth rate are computed using data from Penn World Tables and World Bank Dataset. Growth rate of night light per capita are computed using data from National Oceanic and Atmospheric Administration (NOAA).
|1999|0.093|||||-0.030||
|---|---|---|---|---|---|---|---|
|2000|||0.734|||||
|2001|0.129|0.226||||||
|2002||||||||
|2003|0.101||||0.385|||
|2004||0.207|0.646|-0.007||||
|2005|-0.008|||||-0.356||
|2006<br>||0.194||-0.011||||
|2007|-0.01||||0.412||0.047|
|_Change in average_|**_-0.055_**|**_0.109_**|**_0.165_**|**_0.022_**|**_0.326_**|**_-0.326_**|**_-0.224_**|
|_Industry Premium_|**_(-0.166)_**|||||||
|**Stand. Skill Wage Premium**||||||||
|**(Secondary and above)****||||||||
|1988||0.265||||||
|1990|||0.837|||||
|1991||0.273||||||
|1992||||-0.044||||
|1994|0.544*|0.245|0.868|||||
|1996|0.604|||||||
|1997|0.619|0.269|||0.091|||
|1998||||0.090|||0.299|
|1999|0.611|||||0.274||
|2000|||0.745|||||
|2001|0.647|0.258||||||
|2002||||||||
|2003|0.584||||0.324|||
|2004||0.276|0.662|0.289||||
|2005|0.607|||||0.479||
|2006||0.296||0.281||||
|2007|0.650||||0.270||0.714|
|_Change in average_|**_0.106_**|**_0.031_**|**_-0.175_**|**_0.325_**|**_0.179_**|**_0.205_**|**_0.415_**|
|_Skill Premium_|**_(0.046)_**|||||||
|**Stand. Skill Wage Premium**||||||||
|<br>**(Tertiary and above)*****||||||||
|<br>1988||0.414||||||
|1990|||1.087|||||
|1991||0.406||||||
|1992||||0.229||||
|1994|0.769*|0.381|1.248|||||
|1996|0.803|||||||
|1997|0.865|0.402|||0.204|||
|1998||||0.266|||0.281|
|1999|0.748|||||0.412||
|2000|||1.232|||||
|2001|0.730|0.378||||||
|2002||||||||
|2003|0.721||||0.455|||
|2004||0.390|1.088|0.474||||
|2005|0.694|||||0.583||

34
to limit currency fluctuations. A high degree of nominal exchange rate instability may also be of concern to policymakers to the extent that it translates into a high degree of variability in the real exchange rate and distorted relative price signals to domestic producers. Another important reason is that in (partially) dollarized economies (like Peru or Turkey for instance) large fluctuations in exchange rates can lead to banking and financial instability by inducing large portfolio shifts between domestic- and foreign-currency denominated assets. Finally, in countries where the corporate and banking sectors hold large foreign-currency liabilities, exchange rate depreciations can have significant adverse effects on their balance sheets. This was, indeed, one of the important features of the Asia crisis.<sup>26</sup> When limiting (or preventing) exchange rate fluctuations is a stated or an implicit policy target, it will be usually very difficult for the central bank to convey to the public its intention to give priority to price stability over other objectives of monetary policy in a credible and transparent manner. Private agents are likely to discount heavily public pronouncements; and the lack of credibility will translate into higher inflation expectations. Thus, the absence of (implicit or explicit) commitment to a particular level for the exchange rate-or, equivalently, giving the inflation target unambiguous priority over other policy objectives-is an important prerequisite for adopting inflation targeting. In fact, a credible commitment to an inflation targeting regime in developing economies, by enhancing macroeconomic and financial stability, may well provide a greater degree of stability to a flexible nominal exchange rate than a pegged arrangement that is subject to recurrent speculative pressures (and possibly frequent crises and forced devaluations) due to perceived inconsistencies in macroeconomic policy.

# **4.3 lransparency and Accountability**

Openness and transparency in the conduct of monetary policy are important ways to improve credibility in an inflation targeting framework. By making the central bank publicly accountable for its decisions, they raise the incentive to achieve the inflation target and enhance therefore the public's confidence in the ability of the monetary authorities to do so. And by expos26See, for instance, Alba et al. (1999). It should be noted, however, that these last two points can also be viewed as calling for adequate regulation and supervision of the domestic banking system, not necessarily as arguments in favor of a rejection of inflation targeting as a policy regime.

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