rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 1: Experimentation with Des**|**ign Choices**|f Adi|
|---|---|---|---|
|Hypothesis|Prediction|Survey Data Source|Reerence mn<br>Rate|
||Errors of representation|||
|H1: Household<br>sample selection|P1: Households included in the phone survey will display<br>higher rates of vaccine uptake compared to the general<br>population in a nationally representative sample.|ESS 5 in-person<br>vaccination survey in<br>Ethiopia (April – June<br>2022)|GP: June 5, 2022|
||P2.1: Respondent selection at random will significantly<br>reduce estimated vaccine uptake compared to purposive<br>selection.|Phone survey rounds in<br>Burkina Faso (Sep ’22),<br>Malawi (Sep ’22) and<br>Uganda (Aug ’22)|AP: Oct 2, 2022<br>(BFA), Sep 4, 2022<br>(MWI, UGA)|
|H2: Respondent<br>selection|P2.2: Eliciting the vaccination status of all household<br>members (via proxy-reporting) will significantly reduce<br>estimated vaccine uptake compared to purposive selection.<br>P2.3: Randomly selecting a household member to be<br>interviewed or collecting (proxy-reported) data on all<br>household members will lead to statistically<br>indistinguishable estimates ofvaccine uptake.|as above<br>as above|GP: same dates<br>n/a|
||Errors of measurement|||
||||GP (all members of|
|H3: Survey mode|P3: In-person surveying will display reported vaccine<br>uptake similar to administrative records.|ESS 5 in-person<br>vaccination survey in<br>Ethiopia (Apr – Jun ‘22)|<br>phone survey HHs)<br>and AP (main<br>respondents): June 5,<br>2022|
|H4: Panel<br>conditioning|P4: Respondents interviewed for the first time, as opposed<br>to repeat respondents, will display lower rates of vaccine<br>uptake.|Phone survey round in<br>Nigeria (Jan ’22)|AP: Jan 16, 2022|
|H5: Proxy<br>reporting|P5: Self-reported vaccine uptake will be significantly lower<br>than proxy-reported vaccine uptake.|Phone survey rounds in<br>Burkina Faso (Oct ’22),<br>Malawi (Sep ’22) and<br>Uganda(Aug’22)|AP: Oct 2, 2022<br>(BFA), Sep 4, 2022<br>(MWI, UGA)|
|H6:<br>Experimenter<br>demand|P6.1: Deliberately inducing positive experimenter demand<br>will lead to significantly higher reported vaccine uptake.<br>P6.2: Deliberately inducing negative experimenter demand<br>will lead to significantlylower reported vaccine uptake.|Phone survey rounds in<br>Burkina Faso (Jan ’23)<br>and Ethiopia (Jan ’23)|AP: Feb 19, 2023<br>(BFA), Jan 22, 2023<br>(ETH)|

Note: The table summarizes the empirical hypotheses we test in the vaccine survey data, the associated prediction(s) and survey data source. It also indicates the reference administrative coverage rate to which estimates can be compared. The date of the administrative rate refers to the admin data point closest to the time of survey completion. GP = General population, i.e. the entire population irrespective of age; AP = Adult population, i.e. the coverage rate calculated for the population aged 15 and above by assuming no vaccinations among younger persons.

8
The paper is organized as follows. First, is a general mapping of Croatia’s product market policies guided by the structure of the OECD PMR indicator. The objective is to benchmark Croatia to OECD countries and outline the areas where divergence from average OECD levels or from selected comparator groups is the largest. Second, is a more detailed exploration of product market policies following the structure of the PMR indicator. Third, is benchmarking of Croatia’s product market policies to OECD countries using the OECD ETCR indicators of regulation in network industries from the early 1990s to the present, with results of growth regressions including the ETCR indicator.

# **2. Benchmarking Product Market Policies in Croatia: The OECD PMR**

**Enhancing competition in product markets has been found to help increase GDP per capita by providing incentives to firms to reallocate resources to more productive activities, increase innovation and technological diffusion.** In addition, less restrictive regulations may positively affect employment by reducing the rents that some firms extract from overregulation and force firms to expand their activities.<sup>2</sup>

**Benchmarking product market regulation has proved to be a useful tool for monitoring the performance of policies and institutions in OECD countries and for identifying specific policy gaps** , thus offering the opportunity to benefit from the experience of other member states. Two surveys have been conducted so far collecting data for 1998 and 2003.  Results from these surveys point to a convergence in product market policies across OECD countries, with substantial improvements achieved by countries that originally exhibited relatively restrictive product market regulations, such as Poland, Turkey, Czech Republic, Greece, Italy, France, Mexico, Korea, Hungary, and Spain (Figure 1). Substantial improvements in easing product market policies have also been achieved among EU15 countries where the average PMR score fell from 2.1 in 1998 to 1.4 in 2003.  While this reflects the increasing harmonization of EU common market rules, the PMR benchmarking may have been instrumental in fostering this improvement.

**Figure 1: Product Market Regulation among OECD countries, a comparison between 1998 and 2003**

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1998 2003<br>United KingdomAustraliaIcelandDenmarkU.S.New ZealandIrelandCanadaSwedenLuxembourgJapanFinlandAustriaNetherlandsBelgiumSlovak Rep.GermanyNorwayKoreaPortugalSpainCzech Rep.FranceSwitzerlandGreeceHungaryItalyMexicoTurkeyPolandEU15<br><!-- End of picture text --> _Source: Conway, Janod, Nicoletti 2005. Note: A lower figure indicates a better performance._

> 2 See Conway, Janod and Nicoletti (2005) and Conway, De Rosa, Nicoletti and Steiner (2006).

4
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35
the highest level of income in the sample of countries (i.e., the level to which the other countries are converging) acts as a proxy for country-specific effects in cross sectional regressions, and thus the resulting estimates are inconsistent. Closer to the present paper, Manzano and Rigob6n (2001) found in a 1980-1990 cross section that Sachs-Warner's negative correlation of natural resources with growth disappears when they control for the initial ratio of foreign debt to GDP.

Cross sectional regressions clearly suffer from endogeneity problems as well. In the growth context, Knight, Loayza and Villanueva (1993) point out that, by construction, the initial level of income is correlated with the growth variable. But the problem is much larger, as Caselli, Esquivel and Lefort (1996) note, extending as is often the case in macroeconomic studies to the interdependence of virtually all of the relevant growth related variables. Other papers on economic growth attempting to deal with both unobserved country-specific effects and endogenous explanatory variables include Easterly et al. (1997), Levine et al. (2000), and Bond et al. (2001).

Panel data offer a potential solution to the endogeneity problem through the use of lagged values as instruments for endogenous variables. The issue of unobserved country specific effects can also be addressed although the, standard fixed or variable effects estimators are not consistent in the present context where we implicitly include a lagged dependent variable -- the initial level of GDP per capita. The assumption of a lack of correlation between **.t;** and the explanatory variables required for variable effects estimators is not defensible in this context since both y,t dot and yt-I are a function of p. On the other hand, OLS is clearly inconsistent and FGLS is also should the errors show either heteroskedasticity or serial correlation (Sevestre and Trognon 1996). Further, the usual elimination of gi; by subtracting off the time mean induces a negative correlation between the transformed error and the lagged dependent variables of order 1/T, which, in short panels such as those used here remains substantial.

7
42 of agroexporters failed to pursue such a course - the share of government expenditures directed to agriculture continued to fall after the election of Cordero.

The simulations for Ecuador, however, also point out how little room governments have to maneuver when they must undertake wide-ranging adjustment packages, a fact that is known intuitively by those who have to implement the measures but often overlooked in academic discussions of the social costs of adjustment. What is lacking is an economywide political-economy<sup>framework</sup> that clearly highlights the point that distributional shifts are an unavoidable component<sup>of adjustment</sup> packages and that a reduction in fiscal expenditures (when there is no access to foreign funds<sup>to</sup> cushion the impact) is likely to be unfeasible if economic policies are open<sup>to interest-group lobbying.</sup> The other country studies did not address the issue of sustainability as explicitly as the Ecuador study, although the authors usually alluded to the political consequences of alternative adjustment packages. For example, in comparing the outcomes of the various counterfactual scenarios with the adjustment package adopted by the Indonesian government (the base run), Thorbecke concluded that the adjustment package selected by the govermnent was consistent<sup>with</sup> most objectives Oaid out by the government). In particular, it sheltered the incomes of the civil servants in both the short and long run more than each alternative counterfactual simulation<sup>except</sup> one that called for an even greater level of government current expenditures than in the<sup>base run</sup> scenario. A scenario that emphasizes public investment more than in the base run yields<sup>the most</sup> favorable policy outcomes in the long run in terms of higher GDP growth, lower<sup>inflation, and higher</sup> incomes for most agricultural household groups, but these advantages have to be weighed<sup>against</sup> significantly lower standards of living for urban and rural higher-income groups. Given<sup>the political</sup> power of civil servants, Thorbecke concludes that this cost could not be borne by the government.
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44
growing sorghum and groundnuts. Table 1a shows that for the entire sample (N = 2629 households), there are, on average, 6.6 members in each household, of whom 1.6 are children under 84 months and 1.9 are children of school going age (7 to 15). Mean annual household per capita expenditures (including own consumption) were 99,951 FCFA (approximately $220 USD using the January 2010 exchange rate of $1 USD = 455 FCFA). Of the children ages 7 to 15, 65.7 percent are reported by their parents to be enrolled in school, but when enrollment is measured using school administrative rosters, the enrollment rate is only 49.2 percent, suggesting survey respondents may overstate school participation as Baird and Özler (2012) document.<sup>11</sup> School attendance conditional on enrollment as measured from school rosters is high at 98.1 percent, suggesting that, once enrolled, children are very likely to attend classes. The attendance rates are consistent with other research in Africa using administrative school data (Miguel and Kremer, 2004 in Kenya; Benhassine et al., 2012 in Morocco; Kazianga, de Walque, and Alderman, 2012 in another region of Burkina Faso). Furthermore, at least for Burkina Faso where enrollment is low, they are suggestive of an environment where parents strategically choose which children they register and then make sure the children attend regularly. We also report attendance unconditional on enrollment, a broader measure of school participation that incorporates enrollment and attendance effects. On an average school day, 46.2 percent of children ages 7 to 15 are in class. Mean education expenses per child are $9.66 per year.

> 11 To obtain the school roster information about enrollment and attendance, survey enumerators took the list of children reported to be enrolled by their parents and searched for them in the school’s administrative rosters. After matching the child’s name, they then recorded the child’s enrollment status and the number of days the child was absent or present for each month during the academic year. Unfortunately, we were not able to collect school administrative data for every year for all children. There were 225 school rosters to be collected (3 rounds of data collection for 75 villages), but we could not collect 5 of them due to the school being closed and the teachers and principal having left for summer vacation. In addition, for some children, it was difficult to identify a match between the names on the school and household rosters because many children in a given class often have the same first and last name. For these difficult cases, we used child age, gender, and the mother and father’s names to confirm a match. In Section 5.5, we discuss the robustness checks we perform to confirm there was no differential selection across treatment groups in which children we were not able to collect administrative data for.

9
<!-- Start of picture text --> Fixed Θ Random Θ<br>40 40<br>FLS<br>30 30<br>20 20<br>10<br>SDM 10<br>0<br>-9 -8 -7 -6 -5 -4 -9 -8 -7 -6 -5 -4<br>log�g� log�g�<br>m m<br><!-- End of picture text -->

**Fig. 4.** Equal Probability Contours for Different Ratios _Ri_<sup>2</sup><sup>_/R_</sup> _j_<sup>2</sup> Left panel shows also the choices of (log( _g_ ) _, m_ ) for FLS and SDM ( _n_ = 88 _, k_ = 67 _, ki_ = 8 _, kj_ = 7, and _Rj_<sup>2= 0</sup><sup>_._75)</sup>

the penalty. In this particular case with _k_ and _n_ corresponding to the SDM data, we notice that the combination of _g_ = 1 _/n_ with _m_ = 7, as implicitly assumed in SDM, is on a contour rather close to the one corresponding to the settings in FLS: _g_ = 1 _/k_<sup>2</sup> with _m_ = 33 _._ 5. These combinations are represented in the left panel. This explains the similarity of the results with the SDM and FLS prior settings, mentioned in the Introduction. If we adopt the hierarchical prior with random _θ_ , the trade-off between _g_ and _m_ almost disappears, as the choice of _m_ now only has a very small role to play.

# **4. Some Illustrative Examples: Complete Sample Results**

In this section we will present posterior results for three data sets that have been analyzed in the literature. All results in this section are based on MCMC runs of 2 million retained drawings after a burn-in of 1 million.

We focus, in particular, on the effect of the prior choices on posterior model size distributions, the spread of the posterior mass over model space, posterior model probabilities and the inclusion of individual regressors.

Results are presented for eight combinations of prior settings, taking _g_ = 1 _/n_ and _g_ = 1 _/k_<sup>2</sup> , _m_ = 7 and _m_ = _k/_ 2 and using either a fixed or a random _θ_ . The choice of _m_ = 7 was motivated by the restriction to models with up to 7 regressors in Levine and Renelt (1992) and exactly 7 regressors in Sala-i-Martin (1997). Choosing _m_ = _k/_ 2 corresponds to a “vague” model size prior, which is, as discussed in Subsection 3.1, uniform for random _θ_ , and symmetric (but far from uniform) for fixed _θ_ .

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**Table 3** . Sample sizes

||obs.<br>Full sa|share<br>mple|obs.<br>Any l|share<br>ottery|obs.<br>High|share<br>lottery|obs.<br>Low l|share<br>ottery|obs.<br>Con|share<br>trol|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Baseline|3029||1821||967||854||1208||
|16 months|2829|0.93|1717|0.94|916|0.95|801|0.94|1112|0.92|
|24 months|2888|0.95|1746|0.96|930|0.96|816|0.96|1142|0.95|
|36 months|2865|0.95|1735|0.95|921|0.96|814|0.95|1130|0.94|

Note: Sample sizes by survey round (baseline, 16 months, 24 months and 36 months). Sample of individuals aged 18-32 at baseline with complete individual characteristics and biomarker data. Any lottery is high and low lottery combined. Share is number of observations at follow-up rounds out of total number of observations at baseline.
acquisitions by industry. We also find that foreign takeovers of Korean firms are in the same industries where the world's largest multinational corporations operate, implying that there is a higher equilibrium level of internationalization, as characterized by sales and ownership, for these industries. Together, these results suggest that internationalization is a form of consolidation, which is not very different from domestic consolidation, but which occurs on a larger scaLe.

Using data on nearly all firms listed on the Korean Stock Exchange, we find that foreign investors disproportionately target larger and more profitable firms, exporting firms, and firms with low debt ratios. We interpret this as evidence that the correlation between foreign ownership and growth found in earlier work may be the result of foreigners investing in better and bigger firms, and not a result of technology transfer from foreign investment.

17
estimated coefficients of this region is lower (see column (2)). In column (1) only SSA countries are included. The estimated coefficients of both TAXTIME and TAXPAY are highly significant. The magnitude of the coefficients of the tax complexity indicators is high as well. LAC countries present a similar result. As can be seen in column (3) the estimated coefficients of both TAXTIME and TAXPAY are significant and their size is large. Column (4) combines the MENA and ECA countries in our dataset. The size of estimated coefficients is low, but statistically significant.

**Table 9 - Regions: Impact of Tax Simplification on Tax Corruption (in percentage terms)**

||10% drop in<br>taxpayments|10% drop in tax<br>time|10% drop in tax<br>payments and<br>tax time|
|---|---|---|---|
|**ECA+MENA**|-2.817|-1.746|-4.563|
|**SSA**|-5.362|-4.457|-9.819|
|**LAC**|-6.224|-4.756|-10.980|
|**EAP+SASIA**|-1.480|-1.092|-2.572|

Source: Authors' calculation. Note: The outcomes are calculated using the estimated coefficients reported in Table 8.

The economic significance of the estimated coefficients can be better understood with the help of experiments run with hypothetically changing values of the tax complexity variables. Experiment outcomes are presented in Table 9. The predicted changes are generated using the regression specifications given in Table 8’s corresponding columns, based on which region is analyzed in the experiments. For each region three experiments are run. First the impact of a 10 percent drop in TAXPAY on tax corruption is investigated. Then the effect of a 10 percent drop in TAXTIME is studied. The combined effects of 10 percent cuts in TAXPAY and TAXTIME are reported in the last column of Table 9. A 10 percent decrease in TAXPAY or TAXTIME has the highest impact on tax corruption in the LAC region, where the cut in tax corruption is predicted to be 6.2 percent for TAXPAY and 4.7 percent for TAXTIME. When two effects are combined, 10 percent declines in TAXTIME and TAXPAY lead to almost 11 percent decline in tax 33