rafmacalaba/datause-encoder-data huggingface dataset:
We disentangle the contribution of the enumeration and the ranking processes to targeting errors, and in particular undercoverage. Through an assessment of the counterfactual performance that would be observed if all households had been enumerated (as in a census), we find evidence of enumeration gaps in the UDB that lead to undercoverage of poor households. Under this counterfactual scenario, undercoverage of the poorest 10 percent of households falls by about one-third relative to a targeting system based only on those households actually included in the UDB. We also consider another popular policy of geographic targeting as an alternative to universal enumeration. In particular, we investigate the effectiveness of fully enumerating households in the poorest half of regions while retaining the existing enumeration approach in the remainder. This also leads to targeting improvements, albeit smaller in magnitude than with a full census scenario. In comparison to these improvements to the enumeration process, attempting to improve the ranking process through the use of additional information on ownership of valuable household assets that are difficult to observe (and not included in the UDB) yields relatively smaller improvements in targeting performance. This alternative ranking process reduces undercoverage by two to ten percent and leakage by nearly five percent.

This paper provides the first attempt to assess the relative contribution of the household registration and ranking processes to the overall accuracy of a centralized targeting registry. Depending on the social planner’s welfare function (i.e., the relative weights on the poorest households in the population), our findings suggest large gains from reallocating scarce public resources towards increasing survey coverage to minimize undercoverage of poor households in the UDB. Under certain assumptions about the generalizability of our survey sample, we show that increased enumeration costs to cover the full population in our study districts would amount to about 11 percent of the value of additional benefits that would be received annually by the poorest 30 percent of households. Our results point to the potential cost effectiveness of ensuring an adequate number of households are surveyed for inclusion in single targeting registries.

Our paper contributes to the literature in public and development economics on optimal targeting of social programs. Most studies use a single survey to identify intended and actual recipients, i.e., who 5
Survey of 1999 (ENECE99). Both surveys have the advantage of a large number of eligible nonparticipant respondents, which ensures good matching. The participant survey used is the 1998 CONALEP graduate tracer study. Although the participant and non-participant data come from different surveys, the surveys are comparable since some of the questions are identical, all are from similar survey periods, and all are nationally representative.

Next, the two samples were pooled and a logit model of CONALEP participation as a function of the variables that are likely to determine participation was estimated. The variables included were age, sex, education, region of residence, and the location where training was under taken. The predicted values of the probability of participation were created from the logit regression -the propensity scores. There was a propensity score for every sampled participant and non-participant.' The goodness of fit and the models estimations are shown in Tables AA.4, A1.5 and A1.6. These models consistently classified correctly 99 percent of the non participant group cases and 72 percent of the participant group cases. The overall percentage of correctly predicted cases is 98 percent. Then we calculated propensity scores of the three and five nearest neighbors. This means that for each individual in the CONALEP group, the three and five observations in the non participant sample that have the closest propensity score were found, as measured by the absolute differences in scores. Alternatively, another transformation was used, the lag-odds ratio log (p/(1-p)), where p is the propensity score for matching. Heckman and others (1998) have proposed an alternative method for the nearest neighbor. Instead of relying on the nearest neighbor, they use all the non-participants as potential matches but weigh each according to its proximity.

7Those individuals staying at home, in an education program, or with zero hours of work were excluded from the sample.

25
price cuts on imports are in Korea (6.8%), Thailand (6.3%), and South Asia (9.4%). The average price cut on imports to Latin America was only 1.6 percent, despite the huge reductions in applied rates of protection which have occurred in that region<sup>4</sup> . The reduction in import prices required in Sub-Saharan Africa is even smaller, at 0.1 percent, implying that commitments made under the Uruguay Round will impose very little discipline on manufactured goods protection in this region.

The figures reported for food import barriers in table 8 are aggregated from the estimates prepared by Ingco (1995) using average historical protection rates derived from OECD and USDA to represent the trade distortions which would have prevailed in the absence of the Round, and country schedule data to represent post-Uruguay Round rates of protection. In this data set, the rate of protection applied is taken to have been reduced only when the post-Uruguay Round rate of protection is below the historical average rate of protection. Once again, this means that the estimates neglect the liberalizing effects of tariff bindings introduced without any reduction in the applied tariff rates.

By comparison with the manufacturing cuts, the reductions in protection of food and agriculture listed in table 8 are much more varied. Average price cuts are negligible in North America, Europe and much of the developing world. Yet they are deeper than manufacturing price cuts in much of East Asia, where initial levels of protection are quite high. South Asia is shown to subsidize food imports on average, and the extent of this subsidy is increased slightly under the Round because of reductions in protection on some commodities with positive protection.

The tariff reduction experiments, S1 and P1, implement the cuts summarized in table 8, as well as 36% cuts in agricultural export subsidies. We do not shock output subsidies in agriculture. While modest reductions in these producer subsidies have been specified under the Round, they are given with respect to a base period where support was extremely high. Consequently, it is unlikely that these cuts will have a substantial impact on world prices (Hathaway and Ingco, 1995).

4. Dean, Desai and Riedel (1994) report a reduction in the average applied rate in this region from 44 to 15 percent, implying a reduction in import prices of 20 percent.

12
in that sector over the period 1995-2003.<sup>5</sup> In this Section, we briefly discuss our methodology, compare it with alternative approaches, and present some basic descriptive results from the World Bank EU Standards Database (EUSDB).

As discussed above, there are no New Approach Directives covering the textiles and clothing sector. It is therefore not possible to use dummy variables for the application of Directives, as was done in previous work such as Brenton et al. (2001), Henry de Frahan and Vancauteren (2006), Chen and Mattoo (2004), and Baller (2007).

One alternative would be to use TBT notification data from the WTO, as in Fontagné et al. (2005) and Disdier et al. (2007).<sup>6</sup> However, we do not favor that approach for two reasons. First, WTO rules only require Members to notify technical regulations, not product standards (Article 2.9 of the _Agreement on TBTs_ ). Given the importance of voluntary product standards in Europe, it seems unduly restrictive to focus only on mandatory measures. Second, it is far from clear that individual Members interpret Article 2.9 in the same way, thereby raising concerns of data consistency. For instance, Belgium has lodged 207 TBT notifications since 1995, whereas Ireland has apparently not submitted any.<sup>7</sup> It seems unlikely that such a large discrepancy can be fully explained by substantive differences in standardization practices between the two countries. As a result, we are not convinced that WTO notifications data always provide an accurate picture of the standards environment in all Members.

> 5 The World Bank EUSDB will be made freely available through the website <u>http://econ.worldbank.org/projects/trade_costs.</u>

> 6 The non-tariff measures section of UNCTAD’s TRAINS database only has EU data for the year 1999.

> 7 See the online search feature available through http://www.wto.org/english/tratop_e/tbt_e/tbt_e.htm, accessed on 31 January 2007.

- 8 -
from surveys. Such inconsistency, along with the apparent lack of variation in the response to the satisfaction question for two different types of services (education and health) seems to limit the usefulness of the GDS-2 data on satisfaction. High reported satisfaction has been attributed more to cultural norms or social pressure rather than the superior quality of service delivery in Indonesia. The problem with the apparent lack of variation in Indonesia satisfaction data is not unique to Indonesia, but nor is it universal. High variation in satisfaction with education and health services among respondents is seen, for example, in a number of countries where Core Welfare Indicators Questionnaire (CWIQ) surveys have been administered.<sup>6</sup> Researchers and policymakers alike have long harbored doubts regarding the accuracy of perception-based measures of quality, due to the subjective nature of these instruments (e.g. Bertrand and Mullainathan, 2000). The absence of a common baseline against which respondents’ ratings can be benchmarked makes interpreting these data difficult and comparing data-points across regions and countries even trickier. These problems may explain, for example, why respondents from citizen report card surveys in the Indian state of Bihar have reported higher levels of satisfaction with schooling than those in Kerala, even though Kerala vastly outperforms Bihar in most measures of access to and quality of public education and education outcomes.

Support for skepticism about perception-based measures of quality can be found in several studies demonstrating little or no correlation between objective indicators of quality and satisfaction levels (Brown and Coulter 1983; Stipak 1979). Other studies, such as Deichman and

> 6 In Pakistan, a survey (2006-07) based on CWIQ with a district-representative sample of 73,000 households showed satisfaction rates of 35 and 61 percent for government basic health facilities and schools, respectively. A CWIQ survey in Sierra Leone (2007) yielded satisfaction rates of 38 and 42 percent among those attending primary and secondary schools respectively.

6
security or pension beneficiaries. If the CPI does not reflect the true COL, some population groups are at risk of falling into poverty <u>(Colavecchio et al. 2011). Fritzer and Glatzer (2009)</u> demonstrated that the interaction of household composition and income level defines the impact of inflation. In Austria, families with two or more adults or a single person with low to medium income and single parents with high income experienced an inflation 0.2 percentage points above the headline inflation rate. Brachinger (2008) finds that in the period of high inflation in 2007 in Germany, the inflation rates faced by families with more than three children were significantly higher than the official CPI inflation. Michael (1979), using US data from 1967 and 1974, finds that the relationship between household characteristics and true COL is not stable over time. In some periods, households with specific characteristics may face higher inflation than the average, but in other periods, these same households may face lower inflation.

<u>Amble and Stuart (1994) argue that relatively higher inflation for the elderly is due to price</u> increases for medical care that represent a larger proportion of older households’ budgets. <u>Oldfield et al. (2018)</u> document that pensioners in the UK spend a larger share of their budget on food and fuel and less on housing than non-pensioners and thus are more affected by food and fuel price inflation. <u>Kalwij et al. (2018) report higher inflation faced by households of pensioners than</u> average households in the Netherlands. <u>Hobijn and Lagakos (2005)</u> find similar results for the US during 1987-2001. They show that inflation exceeds the official CPI inflation for the elderly, lowincome households, and households with children over 18 years old. In Ireland, inflation impacts more rural than urban households, followed by lower-income families and pensioners, because of differences in spending on heating and transportation (Lydon 2022). At the same time, the aggregate cost of living in cities larger than 100,000 inhabitants is higher than in rural areas of Spain (Lasarte-Navamuel et al. 2017).

<u>Hagemann (1982)</u> reports geographical differences in the rates of inflation in the US. He also shows that households with the head 45 to 64 years of age and those headed by retiaries face different inflation rates compared to CPI inflation. <u>Antón et al. (2016)</u> analyze data from 31 European countries, and Cullinan et al. (2011) data from Ireland to study the implications of high inflation on the well-being of people with disabilities. Both studies conclude that people with disabilities are more vulnerable to high inflation than the general population.

5
# <u>Table 3. Panel cointegration tests</u>

The table reports the statistic and associated p-value of the different variants of Pedroni's (1999) panel cointegration test. The null hypothesis in each case is no cointegration.

||VAR includin|g GDP and TT|VAR includin<br>and|g GDP, TT,<br>AID|
|---|---|---|---|---|
|Alternative hypothesis:|common AR co|efs.|||
||Statistic|Prob.|Statistic|Prob.|
|Panel v-Statistic|6.12|0.00|3.31|0.00|
|Panel rho-Statistic|5.38|1.00|10.38|1.00|
|Panel PP-Statistic|0.61|0.73|1.73|0.96|
|Panel ADF-Statistic|-1.48|0.07|1.46|0.93|

Alternative hypothesis: individual AR coefs.

||Statistic|Prob.|Statistic|Prob.|
|---|---|---|---|---|
|Group rho-Statistic|6.83|1.00|12.64|1.00|
|Group PP-Statistic|0.71|0.76|0.91|0.82|
|Group ADF-Statistic|-2.7|0.00|-1.19|0.12|

34
pay cash. It is attractive for the wholesalers to buy oil from the DOTs for two reasons: ( _i_ ) a significant proportion of sales by DOTs to the wholesalers are on credit, and ( _ii_ ) DOTs buy in bulk and get price discounts from the refiners, part of which they can share with the wholesalers. Estimates from a survey conducted by the authors in 2013 shows that about 32 percent of quantity transacted between the DOTs and wholesalers are on credit without collateral; relational credit is thus an important aspect of the marketing network.

The DOTs buy the DOs for oil deliverable by the refiner after a stipulated period of time (usually 2 weeks). It is important to appreciate that the DOTs do not own any storage, and do not take delivery of the oil. The DO layer of the market interacts vertically with the refiners upstream and the wholesalers downstream, and also horizontally with other DOTs (relatively smaller DOTs buy from the large DOTs). The horizontal transactions among the DOTs have evolved into something like an embryonic commodity exchange in Moulovibazar in Dhaka and Khatunganj in Chittagong where speculators operate with the help of brokers, primarily during the upswing in the market.<sup>8</sup> However, note that the DO layer is not a futures market, because there is no “settlement” at the end of the day (it is not marked to market). Also, unlike a futures contract, the payment is made at the time of the contract, not at the delivery date. The DO is also not a standard forward contract, because the stipulated delivery date is almost never enforced. Thus the DO contract resembles something like an American call option. In some cases, a DO may not be executed for many months. This implies that the DOTs bear less price risk compared to a standard forward contract.

# **(3.2) The Policy Reform**

The policy reform focused on the DO layer of the market. The law banning DO (Delivery Order) transactions and instituting SO (Sales Order) dealers in its place (i.e., Essential Commodities Marketing and Distributor Appointment Order 2011) was passed in March 23 2011. 90 days were allowed to implement the policy change, implying that the directive implementing the law came into effect on June 21, 2011.

> 8Our post-reform data period coincides with a downswing in the international market, and thus speculative activities in the secondary (horizontal) DOT market were almost nonexistent.

8
**Table 5 (cont.). Lagged drivers of sending a permanent work migrant, household level**

||(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES||_Credit &_|_savings _||||_Networks & I_|_nformation_|||_Distance_|
|HH has a formal loan of any type|0.03***<br>(0.01)|0.01<br>(0.02)||||||||||
|Any HH member has a savings<br>account with a formal institution|||0.06***|-0.01||||||||
||||(0.01)|(0.02)||||||||
|Share of migrants within head's||||||||||||
|ethnicity|||||0.07<br>(0.05)|0.16<br>(0.27)||||||
|Reliance on networks for insuring||||||||||||
|shocks|||||||-0.03***|-0.01||||
||||||||(0.01)|(0.01)||||
|HH owns a mobile phone|||||||||0.03***|0.00||
||||||||||(0.01)|(0.01)||
|Log of HH's distance from||||||||||||
|Kampala (km)|||||||||||-0.00|
||||||||||||(0.01)|
|Constant|-0.17***|0.01|-0.17***|0.00|-0.19***|-0.03|-0.16***|0.01|-0.18***|0.00|-0.18***|
||(0.02)|(0.05)|(0.03)|(0.05)|(0.03)|(0.06)|(0.03)|(0.05)|(0.02)|(0.05)|(0.03)|
|Household fixed effects?|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|
|Observations|8,295|8,295|8,281|8,281|7,795|7,795|8,266|8,266|8,306|8,306|7,780|
|R-squared|0.09|0.59|0.09|0.59|0.09|0.58|0.09|0.59|0.09|0.59|0.09|

_Source: Authors’ calculations with UNPS. Regressions include all lagged covariates reported in Table 3 and year fixed effects. Region fixed effects are included in the specification without household fixed effects. Robust standard errors, clustered at the enumeration area level, are in parentheses. Significance levels are reported as follows: * p<0.10, ** p<0.05, *** p<0.01._ 14
VA net SSC. All the other components are calculated by applying Households’ income shares ( _σhh_ from DF estimation, see Table 1) to the capital income of households from the national macro SAM ( _KYhh_<sup>_macroSAM_</sup> ). The macro SAM is a SAM that includes only macro aggregates. The shares _σhh_ represent the income source for households across the regions (e.g., 7% of income from capital goes to families in _Piemonte_ ). As an example, the household’s capital income _KY_ is the following:

- Enterprises income is the sum of enterprises’ capital income (€342.2 billion) and interest paid to enterprises (€239.2 billion). The first is the enterprises’ capital income from macro SAM redistributed on Gross Operating Surplus (GOS). The GOS is equal to the sum of value added over the regions minus compensation of labor, social security contributions (SSC), taxes on production, business tax (IRAP), and corporate income tax (CIT or IRES). The second, interest to enterprises, is obtained as in Equation 8;

- Government income is the sum of depreciation, SSC by employer, personal income tax (PIT) , taxes on production, CIT, taxes on commodities, value added tax (VAT), excises, import tax, and interest payments to the Government. The government capital income is reconstructed from the national macro SAM.

- Local Governments’ income is the sum of property tax (IMU), regional PIT, municipal PIT, and IRAP.

# **3.5 National Taxes**

In our regional SAM, the Italian national taxes are disaggregated as follows:

- PIT at national level net tax for the tax year 2016. The aggregation is conducted on regions and the total national amount is equal to €238 billion;

- Production taxes redistributed on value added plus IRAP paid by public administrations. The production taxes are the sum of other taxes on production and negative taxes on production. The total collected production taxes are equal to €34 billion;

- IRES (CIT) is regionalized starting from national SAM 2016 data. The total national amount is equal to €30 billion;

11
# **Table 15.1 Impact on enrollment and child development outcomes by household wealth for 1 year old cohort**

|||Impact o<br>|n poor<br>||
|---|---|---|---|---|
||Village is<br>|Batch 1<br>|Village i<br>|s Batch 3<br>|
||Coefficient|Robust SE|Coefficient|Robust SE|
|**Enrollment in ECED Programs**|||||
|Whether enrolled in project playgroups|0.607***|(0.043)|0.534***|(0.043)|
|Whether enrolled in non-project playgroups|-0.259***|(0.051)|-0.223***|(0.052)|
|Whether enrolled in RA programs|-0.090**|(0.045)|-0.079*|(0.047)|
|Whether enrolled in TK programs|-0.011|(0.029)|0.046|(0.032)|
|Total Number of Months of project playgroup|9.278***|(0.816)|7.203***|(0.724)|
|Total Number of Months of non-project playgroup|-3.176***|(0.710)|-2.918***|(0.696)|
|Total Number of Months of RA programs|-0.655|(0.447)|-0.200|(0.505)|
|Total Number of Months of TK programs|-0.030|(0.350)|0.330|(0.349)|
|**Standardized Child Development Outcomes**|||||
|EDI: Physical Health & Well-Being|-0.086|(0.103)|0.043|(0.101)|
|EDI: Social Competence|0.107|(0.080)|0.035|(0.080)|
|EDI: Emotional Maturity|-0.043|(0.085)|-0.047|(0.084)|
|EDI: Language & Cognitive Development|0.109|(0.072)|0.069|(0.072)|
|EDI: Communication & General Knowledge|-0.079|(0.075)|-0.012|(0.075)|
|SDQ: Emotional Symptoms|-0.036|(0.085)|0.039|(0.084)|
|SDQ: Conduct Problems|-0.056|(0.088)|0.023|(0.088)|
|SDQ: Hyperactivity & Inattention|0.032|(0.091)|-0.021|(0.089)|
|SDQ: Peer Problems|-0.142|(0.092)|-0.059|(0.091)|
|SDQ: Pro-social Behavior|-0.085|(0.080)|-0.003|(0.085)|
|SDQ: Total Difficulties|-0.076|(0.086)|0.002|(0.088)|

Notes: *** p<0.01, ** p<0.05, * p<0.1; Batch 5 is used as the comparison group. All regressions reported here correspond to the adjusted specifications reported in Tables 10-12 50