rafmacalaba/datause-encoder-data huggingface dataset:
# 10. Main **Conclusions**

The main conclusions of this review mainly relate to design and process issues of tax and customs administration projects rather than to their impact. This can be remedied in future work only if better information becomes available which, however, depends on project design and implementation planning incorporating measures to allow for improved impact assessment.

The maj or limnitation of Bank operations in the area of tax and customs administration pertains to the inadequate institutional framework For knowledge accumulation from loan operations. Unlike several other areas of operation, theoretical underpinnings for efficient and effective tax and customs administration are still rudimentary. The quality of administration can only improve in a sustained manner if there is accumulation of knowledge as to what the determinants of successful administration are. While recent theories stress the importance of institutions which lead to contestability and transparency and harness voice, evidence on the efficacy of such reforms in the specific context of tax administration is still lacking. Furthermore, these features only pertain to a part of the array of potential determinants of tax administration perfonnance identified in existing work.

Other major conclusions are:

1. Pre-project diagnosis and project design could be improved in most projects particularly in examining environmental, accountability, cost of administration, managerial autonomy, performance incentives for staff and taxpayer equity and services, and pre-project work could draw more systematically on lessons from previous experience.

2. In particular, institutional components in project design were biased towards organization, IT-related procedures and manpower upgrading, with insufficient attention to accountability and anti-corruption institution building and cost-effectiveness of administration.

3. There are a short list of projects which, despite sharing some of these problems, can serve as guides to good practice.

4. While no overall conclusion can be drawn about project sequencing nor about coordination across projects, instances of poor sequencing and coordination, as reported in ICRs, exist.

_5._ Projects made inadequate use of performance indicators of different kinds. Comparing indicators in projects where these were present, reveals a lack of uniformity in their choice.

6. Project appraisal methods were seldom used and when used were inadequate.

7. Methods used for evaluation of project outcomes are possibly capable of improvement and greater uniformity.

This review has the benefit of hindsight. Most projects in the sample predate the recent recognition of the importance of institutions and performance assessment in the Bank and so almost inevitably fail to meet standards set in the review. Indeed, the Latvia best practice project, which passes most tests in this review, dates from 1998.

# 11. Suggestions For **Future Bank Operations**

Background work is essential to improve .Bank assistance for revenue administration. A strategy needs to be articulated for the Bank, incorporating current perceptions of a best practice 28
# **Appendix Table A3: Effect of the information intervention on accessing SEBRAE material**

||A|ccessed SEBRA|E material with|in|
|---|---|---|---|---|
||3 months|6 months|12 months|18 months|
||(1)|(2)|(3)|(4)|
|_Panel A: Pooled treatment effect_|||||
|Any information sheet|0.069**|0.068**|0.022|0.012|
||(0.028)|(0.031)|(0.038)|(0.041)|
|R2|0.285|0.242|0.205|0.187|
|Control mean|0.131|0.167|0.304|0.381|
|_Panel B: Effects for each treatment group_|||||
|(1) No motiv, no pre-enroll|0.035|0.066|0.023|-0.013|
||(0.035)|(0.041)|(0.049)|(0.051)|
|(2) No motiv, pre-enroll|0.037|0.038|-0.002|-0.019|
||(0.036)|(0.040)|(0.049)|(0.051)|
|(3) Motiv, no pre-enroll|0.057|0.042|-0.001|0.007|
||(0.036)|(0.039)|(0.048)|(0.052)|
|(4) Motiv, pre-enroll|0.147***†††|0.126***†|0.068|0.073|
||(0.040)|(0.043)|(0.049)|(0.052)|
|R2|0.296|0.247|0.208|0.191|
|F-test p-value (2=1)|0.962|0.493|0.605|0.909|
|F-test p-value (3=1)|0.552|0.559|0.615|0.703|
|F-test p-value (4=1)|0.006|0.184|0.361|0.092|
|F-test p-value (1=2=3=4)|0.027|0.182|0.437|0.251|
|_Panel C: Heterogenous treatment effects by_|_baseline busine_|_ss practices (BP_|_s)_||
|Any information sheet*Low BPs|0.080**|0.056|0.055|0.054|
||(0.038)|(0.045)|(0.054)|(0.057)|
|Any information sheet*High BPs|0.057|0.078|-0.027|-0.053|
||(0.046)|(0.048)|(0.059)|(0.063)|
|R2|0.288|0.245|0.209|0.193|
|F-test p-value (Low BPs=High BPs)|0.707|0.751|0.318|0.220|
|Control mean low BPs|0.093|0.151|0.244|0.314|
|Control mean high BPs|0.177|0.190|0.380|0.468|
|N|866|866|866|866|

Notes: This table shows OLS estimates for the effect of receiving the information sheet on accessing SEBRAE material (either via a service center or SEBRAE's website). The outcome variables are dummy variables equal to one if the firm accessed SEBRAE-PR material within the stated number of months after the intervention and zero otherwise. Panel A pools all treatment groups. Panel B shows separate treatment effects for each of the four different versions of the information sheet. Panel C shows heterogenous treatment effects for firms using below and above the median percentage of baseline business practices. BP stands for business practices. Low BP refers to below the median (50%), while high BP refers to above the median. All regressions control for the fraction of firms that accessed SEBRAE material within 12 months before the intervention and strata fixed effects. The regressions in Panel C additionally control for a dummy variable equal to one if the firm had low BPs at baseline. ***, **, and * indicate significance at the 1, 5, and 10 percent level. To deal with multiple hypothesis testing, †††, ††, and † indicate sharpened q-values statistically significant at the 1, 5, and 10 percent level.
nonlinearities. This is a comparison of non-nested models with an unknown, possibly realvalued, difference in degrees of freedom.<sup>8</sup> To decide on the right number of effects, we start by estimating a model that includes all fixed effects. We then remove the least significant dummy, and obtain new results. We repeatedly evaluate the out-of-sample prediction performance while shrinking the effects, and select the model with the optimal out-of-sample density across all fixed effect models. Intuitively, this approach starts with a model similar to a linear Fixed Effects model, as the penalty heavily discounts the thresholds introduced by the effects resulting in flattened marginal effects, and gradually allows fixed heterogeneity to be explained by nonlinearities across covariates instead. As a result, our final estimates are guaranteed to be preferred over the standard linear Fixed Effects model, as judged by the out-of-sample criterion.

# **3 Data**

We combined measures of tree loss, air pollution concentrations and carbon emissions on one side, and GDP indicators of economic structure on the other. Our data are from a variety of sources and includes 95 countries measured over 1999 _−_ 2014 containing approximately 85% of the world’s population, 83% of the world’s carbon output, and 72% of the world’s forest cover. The selection is mainly driven by availability of data as detailed in the following sub-sections. We have removed areas below 1500 square kilometers _−_ essentially all small islands _−_ from the analysis. A summary of the data as it enters our regressions is given below. A list of all countries included in our sample can be found in fig. 1.

Table 1 summarizes the data, all predictors are mapped into the [0 _,_ 1] interval to ensure the penalization effect is not driven by differences in variance of the different variables. We scale back the estimation results for easier interpretation.

## **3.1 Forest cover data**

We use data from (Hansen et al., 2013), which contains estimates of global tree cover extent (2000) and annual tree cover loss (2001-2014) at a spatial resolution of 30 meters.<sup>9</sup> Hansen et al. (2013) analyzed satellite images from Landsat 5, 7, and 8 to identify tree cover extent, defined as vegetation taller than 5 meters in height, and loss, defined as complete

> 8Degrees of freedom is a parametric concept whose translation to the non-parametric setting is complex. One can approximate the degrees of freedom empirically, which may result in numbers that are real-valued.

> 9The data can be found at https://earthenginepartners.appspot.com/science-2013-globalforest/download ~~v~~ 1.2.html.

12
# Table 2.  Testing institutional variables

|||**Depend**|**ent variable: CORR(g, y)**||||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**<br>**(4)**|**(5)**|**(6)**|**(7)**|
|**Government Stability**|**-0.09 ****||||**-0.01**|**-0.07**|
|**Law and Order**||**-0.13 *****|||**-0.12 *****|**-0.06**|
|**Fiscal Rules**|||**-0.61 *****|**-2.11 *****|**-0.31 ****|**-1.26 ****|
|**Sovereign Wealth Funds**|||**-0.53 *****||**-0.37 *****|**-0.21**|
|**F-test**|||||***|***|
|**R-squared**|**0.05**|**0.24**|**0.17**<br>**0.13**|**n/r**|**0.47**|**0.15**|
|**Observations**<br>Notes: Cross-section regression f<br>means not reported by STATA. F<br>Regression (7), centered R-square<br>regressions (5) and (7), the first-s<br>in both cases. *, **, and *** deno|**137**<br>or full sample. R<br>or regressions (6<br>d. Overidentific<br>tage F-test of exc<br>te significance at|**137**<br>egressions (1)-(4<br>) and (7), the F-te<br>ation tests do not a<br>luded instruments<br>the 10, 5, and 1 p|**106**<br>**194**<br>) and (6): OLS. Regressions (5) and<br>st reported evaluates the joint signifi<br>pply to regressions (5) and (7) sinc<br>implies rejection of the null hypothe<br>ercent level, respectively.|**83**<br>(7): IV (two-stag<br>cance of the three<br>e equations are pe<br>sis of weak identi|**83**<br>e least squares).<br>institutional vari<br>rfectly identified<br>fication (p-value|**83**<br>n/r<br>ables.<br>. In<br>= 0.00)|
to the income pool of all agents participating in the risk-sharing agreement.

On the other hand, the expanded model solves a problem that plagues all conventional risk-sharing regressions that include average consumption among the explanatory variables. As noted by Deaton (1990), pooling the observations on consumption of all agents in a linear regression that includes the cross-sectional average of the dependent variable among the regressors leads, mechanically, to a regression coefficient of unity on that variable. Crucini (1999) dealt with this problem by estimating different coefficients of partial risk sharing for each agent (regions or countries in his analysis), even though the model is built on the assumption of a common coefficient for everyone. To obtain a single estimate of partial risk sharing, as implied by the model, Crucini then takes the average of the estimated risk-sharing parameters of all agents.<sup>4</sup> In contrast, our model does not suffer from this problem because our estimating equation is stated only in terms of the (quasi)innovations to the present value of income growth of all agents. Unlike conventional risk-sharing regressions, it does not include the cross-sectional average of the dependent variable among the regressors.<sup>5</sup> Another difference between our framework and that employed by earlier literature concerns the decision problem that agents solve after the cross-country transfers are made. While the traditional literature assumes that agents act as permanent-income consumers with quadratic preferences, we assume that agents maximize a utility function of the constant relative risk aversion (CRRA) form allowing for country-specific coefficients of risk aversion and interest rates, along the lines of Blundell et al. (2008). We follow Campbell and Mankiw (1989) and derive an estimating equation log-linearizing the Euler equation and intertemporal budget constraint of the representative household’s problem.

We estimate the model using a time-series cross-section dataset comprising 50 industrialized and developing countries over the period 1970-2010. We find an average coefficient of partial risk sharing of around 0.5. In terms of the model, this means that the average country contributes half its income to the income pool. The estimates, however, vary systematically between industrial and developing countries, with the former exhibiting, on average, higher degrees of risk sharing than the latter (centered around 0.59 and 0.43, respectively). Re-estimating the model over rolling time samples, we find evidence that the degree of risk sharing has been on the rise for the average country during most of the sample period. When we split the sample between industrial and developing countries,

> 4Crucini’s paper reports a Monte Carlo experiment to check if this procedure leads to major biases in the risk-sharing estimate.

> 5Moreover, even if we had written the estimating equation in terms of a weighted consumption average, the nonlinearity of the model would break the linear relation between right and left-hand side variables inherent to the linear regression.

3
> **Table 2 Labor force indicators in Croatia and in** OECD **countries by gender and** age **categories**

|||All wor|kers|||Men|||Women||
|---|---|---|---|---|---|---|---|---|---|---|
||15to64|15to24|25to49|50to64|15to24|25to49|50to64|15to24|25to49|50to64|
|Croatia(2001.11)|||||||||||
|Unemploymentrate|16.8|41.9|14.2|8.3|41.8|11.5|<br>8.5|42.0|17.2|<br>8.1|
|Laborforceparticipation rate|62.2|41.9|82.4|40.9|43.3|88.2|54.0|40.4|77.0|<br>29.2|
|Employmentrate|51.8|24.3|70.7|<br>37.5|25.2|77.9|49.5|23.8|63.8|<br>27.1|
||15to64|15to24|25to54|55to64|15to24|25to54|55to64|15to24|25to54|55to64|
|OECD (1999)|||||||||||
|Unemploymentrate|6.4|11.8|5.4|5.2|11.7|4.9|<br>5.6|11.9|6.1|4.6|
|Laborforceparticipationrate|70.4|53.0|80.3|<br>51.6|57.8|93.0|64.5|48.0|67.8|<br>39.4|
|Employmentrate|65.9|46.7|75.9|<br>48.9|51.1|88.5|<br>60.8|42.3|63.6|<br>37.6|

# Source:

> Croatia: Labour Force in the Republic of Croatia, Second half-year of 2001, Central Bureau of Statistics; Author's calculations.

> OECD: Employment Outlook, 2000.

-4-
Policy Research Working Paper 6695

# **Abstract**

This paper evaluates the impact of strengthening legal rights on the types of economic opportunities that are pursued. Ethiopia changed its family law, requiring both spouses’ consent in the administration of marital property, removing the ability of a spouse to deny permission for the other to work outside the home, and raising women’s minimum age of marriage. Thus both access to resources and the removal of restrictions on employment served to strengthen women’s bargaining position within the household and their ability to pursue economic opportunities. Although this reform now applies nationally, it was initially rolled out in the two chartered cities and three of Ethiopia’s nine regions.

Using nationally representative household surveys from just prior to the reform and five years later allows for a difference-in-difference estimation of the reform’s impact. The analysis finds that women were relatively more likely to work in occupations that require work outside the home, employ more educated workers, and in paid and full-time jobs where the reform had been enacted, controlling for time and location effects. As the relative increase in women’s participation in these activities was 15–24 percent higher in areas where the reform was carried out, the magnitude of the impact is significant too.

This paper is a product of the Finance and Private Sector Development 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 mhallward@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 8: Impact on Wage Earners**

||Dependent variable:<br>Low-risk registered business<br>owner dummy|
|---|---|
|Reform dummy*Wage earner has a written contract|0.0013<br>(0.0011)|
|Reform dummy*Wage earner has no written contract|0.0054*<br>(0.0029)|
|R-squared|0.022|
|No. of observations|520,804|

_Note:_ Standard errors in parentheses (clustered at municipality level). Sample includes past wage earners only. Written contract variables are pre-reform characteristics when the person was first observed. Regressions include quarter and municipality fixed effects, as well as dummies for firm characteristics interacted with quarter and municipality fixed effects, and individual background variables. They also include 1999 municipality log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita, all interacted with a linear time trend. Significance levels: * 10%, ** 5%, *** 1%

**Table 9: Impact on Non-Registered Business Owners**

||Dependent variable:<br>Low-risk registered business<br>owner dummy|
|---|---|
|Panle A: Impact byfirm size||
|Reform dummy*Owner is alone in firm|-0.0025<br>(0.0083)|
|Reform dummy*Owner is not alone (has partners and/or workers)|0.0050<br>(0.0439)|
|R-squared|0.044|
|No. of observations|85,691|
|Panel B: Impact bytype ofpremises||
|Reform dummy*Fixed premises|-0.0056<br>(0.0307)|
|Reform dummy*Mobile premises (street stand or door-to-door)|-0.0036<br>(0.0067)|
|R-squared|0.062|
|No. of observations|78,518|

_Note:_ Standard errors in parentheses (clustered at municipality level). Sample includes past non-registered business owners only. Firm size and type of premises variables are pre-reform characteristics when the person was first observed. Regressions include quarter and municipality fixed effects, as well as dummies for firm characteristics interacted with quarter and municipality fixed effects, and individual background variables. They also include 1999 municipality log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita, all interacted with a linear time trend. Significance levels: * 10%, ** 5%, *** 1% 42
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