rafmacalaba/data-use-mentions-extended huggingface dataset:
22

# References

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Alonso-Borrego, C. and M. Arellano (1996): “Symmetrically Normalised Instrumental Variable Estimation Using Panel Data,” CEMFI Working Paper No. 9612, September.

Altonji, J. and L. Segal (1994): “Small Sample Bias in GMM Estimation of Covariance Structures,” NBER Technical Working Paper No. 156.

Anderson, T.W. and Hsiao, C. (1982): “Formulation and Estimation of Dynamic Models using Panel Data,” Journal ofEconometrics 18: 47-82.

Andrés, J. and I. Hernando (1997): “Does Inflation harm Economic Growth? Evidence from the OECD”, NBER Working Paper No. 6062, June.

Arellano, M. and Bond, S. (1991): “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies 58: 277-297.

Arellano, M. and Bover O. (1995): “Another Look at the Instrumental Variable Estimation of Error-Component Models, ” Journal ofEconometrics 68: 29-51.

Bailliu, J. and H. Reisen (1998): “Do Funded Pensions contribute to Higher Savings? A Cross-Country Analysis,” OECD Development Centre manuscript, Paris.

Baltagi, B. (1995): Econometric Analysis ofPanel Data, New York: Wiley.

|

Baltagi, B. and Griffin, J. (1997): “Pooled Estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline,” Journal ofEconometrics 77: 303-327.

, Barro, R. (1974): “Are Government Bonds Net Wealth?”, Journal ofPolitical Economy 82: 1095-1117.

Blundell, R. and S. Bond (1997): “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” University College London Discussion Paper 97-07.
Panel C compares household characteristics of students in the South and in the North. Because of the missing schools at baseline, we take characteristics measured in the rst follow-up among students in the control schools. A few interesting patterns emerge. Households in the South are poorer but have fewer children and better educated heads (and more prominently so for the families of female students).

Finally, Panel D considers the characteristics of projects being undertaken by schools with the school grant funds. This information comes from a survey conducted in treatment schools which asked principals about the project for which they got funding. We conducted two of these surveys, one at rst follow-up, and one at second follow-up. We report estimates from the second follow-up survey when, presumably, data about the project is more mature and complete.

In the South, students were much more frequently named as participants in the drafting of the proposal. Although it is not clear what input students may have had, this could indicate that principals were more sensitive to the needs of the students in the South. It is also signicant that projects in the South started later. By the end of year 2 of the study projects in the North had been running 7.6 months longer than in the South. If results faded out quickly this could explain why we observe eects of the more recent projects than in the earlier projects but this is unlikely to be the case, given our previous results about the sustainability of program impacts (although those are not very precise). If, on the other hand, a project needed time before it started to inuence children's learning (as in the case of activities that take time, such as training a teacher, or building a library), we would expect larger impacts for more mature projects, which goes against what we nd in terms of the South - North comparison.

Some of the most remarkable dierences relate to the components of the project. The schools in the North were more
support for strengthening women’s rights. Only 27 percent overestimate the public reform support. The remaining 13 percent guessed correctly.

# **4.1.3 Main Outcomes**

The proposal to reform gender discriminatory inheritance law is unpopular in Tunisia. Aggregating both genders, I find that 37 percent of Tunisians are favorable to the introduction of a gender equal inheritance law regime.<sup>13</sup> The other measured outcomes provide a consistent picture of respondents’ attitudes towards gender equality in inheritance as illustrated by figure 7. Among the respondents in the control group, 35 percent of respondents prefer a gender equal inheritance split in the hypothetical bequest scenario. By contrast, reported willingness to gift one’s daughter or another female relative is high: 66 percent of all respondents in the control group declared that they would be willing to use gifting for achieving a more gender equal inheritance split among their children.

Demand for gender equal inheritance arrangements differs by gender: women are significantly more likely to choose the ideal bequest (p-value _<_ 0.05) and significantly more likely to support legal reform (p-value _<_ 0.01). There is no statistically significant gender difference in the willingness to use gifting. The share is the same among respondents with a low score on the social desirability index, which suggests that this finding is not driven by experimenter demand effects. Table 2 shows the average marginal effects from a logit model regressing the main outcomes on respondents’ socio-economic characteristics, as well as second-order beliefs at baseline in the control group. Women are more likely to demand gender equality than men: they are almost 16 percentage points more likely to choose a gender equal ideal bequest and 19 percentage points more likely to favor legal reform, a finding which is significant at the 1 percent level.

Other respondent characteristics matter: differences across age cohorts are not statistically significant but on average, older respondents tend to endorse less gender progressive attitudes than younger respondents. Support for gender equality in inheritance
after factor analysis.<sup>10</sup> The specification attempts to ascertain whether social norms and personal beliefs contribute to a woman’s decision to work, controlling for “rational” determinants such as earning power (proxied by years of education), the laborleisure trade-off (proxied by responses to the reservation wage question in the survey), and substitution effects (proxied by household income without women’s earnings). The main finding is that social norms (in particular, social empirical expectations) add explanatory power to a simple “rational” model of FLFP for women currently working. As seen before, the variable “want to work” is better predicted by personal

> 10 Results are similar when using the social empirical and normative index with all original items.

22
rules of the tax statute, it was not possible to capture a good percentage of the exempt income and deductions.

As for contributions to social security for pension, when the workers report that they pay a portion and their employer the remainder, the employee contribution is equal to 4 percent of his wage, and the remainder 12 percent is paid by the employer. When the worker reports paying the full contribution, the worker’s contribution is 16 percent of the wage. In like manner, the contribution of the workers for health care corresponds to 4 percent, while the employer's contribution equals 8.5 percent of the salary, provided that the employee earns more than ten monthly minimum wages.<sup>33</sup> If the employee reports paying the full contribution or if the contribution is deducted from the pension, the rate is equivalent to 12.5 percent. In the case of work accidents and professional disease insurance, a rate of 0.52 percent was applied for those who report been enrolled. The rates for this subsystem depend on the economic activity and are higher for the highest risk activities. In this case, the lowest rate was used from a lack of sufficient evidence on the type of rate to be used.<sup>34</sup> Regarding social spending, all the social programs whose beneficiaries were directly identifiable in the household survey were included. The per capita cost of the program was calculated by dividing the total budget by the number of beneficiaries, taking information from administrative records. This per capita value was assigned to those who reported receiving the transfer.<sup>35</sup> When regional information was available, the additional transfer from the department or city to which the beneficiary belongs was attributed, as was the case with transfers for early childhood care and school meals. For Más Familias en Acción and Colombia Mayor, the incentive value provided by the government to its beneficiaries was assigned, according to the municipality or department in which they reside
24, though within the period it fell (to 1.21 in 1998) then rose (back to 1.33 in 2002).

> 28 We tested two alternative definitions, one that excludes auction land and one that additionally excludes land contracted from state farms.  The second gives a slightly higher landlessness rate (14.5% of households) in 2004 but as it can only be calculated for 1998 and 2004 we stick with the first definition.

> 29 The surveys have different land modules, although comparisons are still feasible. A limitation of the 2002 survey is that it only gives the amount of land that is cultivated by households, whether they have long term use rights to the land or are renting it in.  While we know if the household rents-out land, we cannot identify how much and, hence, the total amount over which the household has use rights.

> 30 For 1993 one can also calculate an unemployment rate for whether work was wanted but could not be found in the last 7 days; this gives an even lower rate for rural areas of 0.5% (Gallup, 2004).

> 31 We found that the 2004 Lorenz curve was nowhere above that for 1993 for either annual crop land or perennial land.

19
PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 13 Table 2. Calibration

|**A. Universal Parameters**||||**Target/Source**|
|---|---|---|---|---|
|ρ= 10.5 (Within Sector Elasticity)<br>η = 1.24 (Between Sector Elasticity)||||EMX(2015) based on Taiwanese Manf. Data.|
|θ = 0.385 (Firm prod. log-normal SD)||||AB(2008)|
|Dj ∈{1,1.5,2}w.p <sup>1</sup>/3 (Sectoral Iceberg tra|de cost)|||Distrib. import shares (Feenstra & NBER Manf Data)|
|**B. Parameter/Equilibrium Object**|**US+10C Model**|**3C Model**|**Data/Target**|**Source**|
|KUS (US frms/Sector)|20|15|-|AveraesizeoftradeartnersGDPrelativetoUS|
|K (Foreign Firms per Country per sector)|3|15|-|g p|
|Sales-weighted ave markup|31%|32%|30%|AB (2008), value used in literature|
|Import share|28%|34%|28%|F&NBERMfD|
|Import-weighted ave import share|55%|58%|48%|eenstra an ata|
|Median Herfndahl|0.165|0.17|0.1-0.18; 0.26|DoJ “Moderately Concentrated” (AB); EMX#|
|Sales-weighted demand elasticity|5.3|5.2|≈5|Gopinath & Itskhoki (2010)/Broda & Weinstein (2006)|
|Mean share of largest dom. frm in sector|0.27|0.27
# **1 Introduction**

Low- and middle-income countries spend an average of 1.5% of GDP on social assistance [World Bank, 2022]. These public programmes aim to protect the poor and vulnerable from shocks, help them cope with crises, support investment in human capital and enjoy a decent standard of living throughout the lifecycle [ILO, 2021]. Policymakers design a social protection programme with a specific target population in mind. A crucial aspect of policy implementation, commonly referred to as the targeting problem, is how to identify households or individuals who are members of the target population and therefore eligible for the programme. Due to limited fiscal resources, social assistance tends to be limited to the most economically disadvantaged segment of society. Consequently, socio-economic criteria are a prevalent eligibility category [Grosh et al., 2022], whether applied in isolation – such as in a food subsidy programme for the poor – or in combination with another eligibility category, such as conditional cash transfer programmes that support poor families with school-age children.

In settings where nearly all households’ income is reported to the tax authority, means testing via income tax records can determine socio-economic eligibility. However, countries’ average informal employment share is above 50% in Asia Pacific, South Asia and Sub-Saharan Africa, while the poverty rate of the informally employed is around six times higher than that of those in formal employment [Ohnsorge and Yu, 2021]. This paper is concerned with such settings, where means testing is unviable due to a lack of tax records among poorer households. There, proxy means testing (PMT) has become a widespread targeting approach since its description by Grosh and Baker [1995].

PMT is a statistical approach that relies on two large-scale surveys. The first, conducted at regular intervals in most countries, is a socio-economic population sample survey that serves as training data for the PMT predictive model. The second is a targeting survey that collects current data to determine household eligibility. Due to the large population
Note: data from household surveys. Unless otherwise noted, regressions are of the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for catchment areas’ exposure to the collaborative quality improvement and citizen scorecards interventions, region, household wealth quintile, and age.

a Sample of children under-5. Regressions include a control for the gender of the child. b Sample of women with a live birth in the preceding two years. The indicators relate to care received during the most recent pregnancy. Regressions include controls for employment status, and education level.

c Sample of women age 15-49 who were pregnant in the preceding two years.

d Sample of adults 40 years and above. Regressions include controls for employment status, education level, gender, and marital status.

e First antenatal consultation within the first three months of the pregnancy.

f The PBF impact is estimated using a propensity score weighting specification as the baseline survey did not distinguish between postnatal care provided before and after women were discharged from the facilities where they gave birth. Standard errors are clustered at the district level.

g Modern methods include: sterilization, IUD, injectables, implants, pills, condoms, and diaphragms. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 28
PTA of 2007, the Colombia-Northern Triangle PTA of 2009, and the Peru-Chile PTA of 2009. With the exception of the EFTA-Peru PTA of 2011, none of the other PTAs includes harmonization or mutual recognition of TBT.<sup>17</sup>

# **2.2 Firm Customs Data**

Our analysis relies on transaction-level customs data for Chile, Colombia, and Peru over the 1997-2015, 1996-2015, and 2000-2015 periods, respectively. The data obtained from the countries’ customs agencies was collected by the Trade and Integration Unit of the World Bank Research Department, as part of their efforts to expand the Exporter Dynamics Database described in Fernandes et al. (2016). Each country’s raw database covers the universe of exports and provides information at the exporting country-firm-destination-HS 6-digit product-year level on export values (in current USD) and weights (in kilograms but only for Colombia and Peru). Each country’s raw database was subjected to uniform reformatting and to a series of cleaning procedures detailed in Fernandes et al. (2016).<sup>18</sup> Firms are identified by their actual names, their tax identification number, or an artificial unique code randomly created by the customs agency, which allows us to have a panel of firms for each country. Regarding product nomenclatures, we convert the HS 1996, 2002, 2007, and 2012 versions (used in the raw data) to the HS 1992 version (as this is convenient to merge with the tariff variable described in Section 2.3).

While we have information for each firm on its exports in all sectors, we exclude from the analysis all observations pertaining to HS Chapters 25-27 to avoid potential biases related

> 17In the content of preferential trade agreements database, if two countries sign more than one PTA with each other, the numbers of provisions in each policy chapter are cumulated across all PTAs. For example if country A and country B signed an agreement in 2000 with provisions 1 and 2 in the SPS chapter
literature would anticipate. These findings remain robust, irrespective of the use of different dependent variable specifications, distributional assumptions, omitted variables (small/large datasets), or outlier observations.

# **Do Voters Use the EDD/NEDD Ratio to Explain the Level of Political Rent Extraction?**

Do voters really use the EDD/NEDD ratio to anticipate the level of rent extraction? So far this paper has shown that perceptions of the behavior of EDDs by elite experts vary, as anticipated, given the EDD/NEDD ratio. However, an additional test of the robustness of the theory is to examine whether in fact voters perceive that the EDD/NEDD ratio affects the average level of rent extraction in ‘political life.’ If the model is correct, and rent minimization occurs in the first stage due to the anticipated ability of voters to retrospectively punish/reward only EDDs (second stage), this outcome should also be observed. This is because voters can use the EDD/NEDD ratio to infer the proportion of senior policy-makers insulated from the incentives of elections, and will thus use this to infer the magnitude of rent extraction in political life (positive test). Furthermore, if this assumption of the model is correct, it should also be the case that perceptions of the level of rent extraction undertaken by policy-makers far removed from the control of elected officials, should not be predicted variation in the EDD/NEDD ratio, as such activities are rarely directly handled or supervised by elected officials (e.g. petty street level corruption) (negative test).

It is relatively easy to identify the sort of activities likely to fall (or potentially fall) under the oversight of elected officials, and those activities that will, even in high EDD/NEDD ratio countries, fall under the control of NEDDs. Rent extraction involving the passage of legislation, the abuse of parliamentary allowances, illicit payments to elected officials by lobbyists etc. are clearly the preserve of elected officials. Conversely, the demand for
>* p_<_0.01, ** p|by region in par<br>_<_0.05, * p_<_0.1|entheses||||
|||Data sourc|e: World Bank|survey on trust|(2021)||||

The results presented in Table 4 suggest that institutional trust in Morocco is tied to positive perceptions of both policy outcomes and processes. All regression coefficients reported in Table 4 are positive and statistically significant at the 0.01 level (except for economic performance at the 0.05 level). These positive coefficients indicate a positive relation between institutional trust and the di↵erent independent variables tested. They show that as perceptions of government performance, integrity or equity improve, trust in institutions also increases.

The consistently larger coefficients strongly suggest the larger e↵ects of the process-based variables. Specifically, regression results show that:

- Integrity appears to play a particularly significant role in feeding trust in institutions.

15
ing changes in the economic outcome variables of employment and enterprise counts. For the percentage change in enterprise counts for the whole period from 2004-2012 (column 1), the estimated elasticity with respect to nighttime lights is -0.0356 which is statistically significant. This finding differs vastly from the cross-sectional regression where the elasticity was positive. The employment regression in column (2) yields a similarly small SOL coefficient. This suggests that the enterprise growth is negatively correlated with increases in nighttime light output, which is counterintuitive. The analysis focuses now on the simple Landsat indices and assesses the changes in the mean NDBI and NDVI as predictors for changes in the economic outcome variables. The estimated coefficients for both measures are positive and highly significant. Thus an increase in urban areas and vegetation is associated with expansion of enterprises and employment at the commune level. Overall, the model performs poorly at capturing the overall variation over time and yields very little predictive power with R-squared measures of close to zero.

To control for serial correlation, a long-difference regression is estimated for the same period in columns (3) and (4). This yields positive coefficients on nighttime light intensity, although this parameter is imprecisely measured in the enterprise regression. Again there are strong positive effects for NDBI and a negative effect of NDVI in the enterprise regression, but an insignificantly positive effect of NDVI in the employment model. The sign changes in these regressions suggest the presence of large measurement error. Again, there is very low predictive power with R-squared statistics of at most 0.017. In summary, the time series models perform poorly and the parameter instability suggests that the model can not nail down the relationship between changes in satellite measures and economic activity well.

The expenditure data is limited by the lower sample size and the shorter time period of consistent data. The results indicate negative coefficients for the SOL measure which are imprecisely estimated. While the coefficients on NDBI are largely similar in both the 2010-2012 and 2004-2008 regressions and estimated to be around 0.3
services that are consistent with the reports of humanitarian and news agencies on the ground.<sup>25</sup> Furthermore, the regions that the WFP survey identifies as receiving the most assistance roughly align with the population-level estimates of the prevalence of food assistance (e.g., Tandon and Vishwanath 2021).

And lastly, our sample is limited to only those displaced households reached both prior to and following displacement (1116 households). It does not include households reached only after displacement (6910 households); and if such households differ from our sample, it would limit the generalizability of our results. Thus, we examine differences in these samples by pooling all displaced households (8026 households) and regressing an indicator for whether the household was reached before and after displacement on averages of all the 14 food security variables collected in the WFP survey.<sup>26</sup><sup>_,_27</sup> Table 2 demonstrates that one cannot reject the hypothesis that there is no joint difference in all food security information between the primary sample and all other displaced households; a test of all coefficients jointly equaling zero has a p-value of 0.352.<sup>28</sup>

# **Section 4. Empirical Strategy**

We estimate how food access indicators were changing leading up to and following displacement. Specifically, we estimate variants of the following baseline specification:

> 25Appendix 3 compares key variables from the WFP survey to population estimates of mobile phone users from the 2014 HBS data. Every single comparable food security indicator dramatically declined since the 2014 HBS survey, consistent with the reports of wide-spread food insecurity (e.g., IPC 2017; FEWNET 2018; etc.). Home ownership declined, the prevalence of renting increased, and the size of households increased, which are all consistent with the widespread issue of internal displacement (e.g., TFPM 2019). And access to services declined dramatically, where essentially no households had access