rafmacalaba/datause-encoder-data huggingface dataset:
using a random coefficients estimator in which the impact estimate contains a stochastic component. Applying this type of estimator to the evaluation data for _PROGRESA_ (a conditional cash transfer program in Mexico), Djebbari and Smith (2005) found that they can convincingly reject the assumption of common (homogeneous) effects made by past evaluations of that program.

When there is such heterogeneity, one will often want to distinguish marginal impacts from average impacts. Following Björklund and Moffitt (1987), the marginal treatment effect can be defined as the mean gain to units that are indifferent between participating or not. This requires that we model explicitly the choice problem facing participants (Björklund and Moffitt, 1987; Heckman and Navarro-Lozano, 2004). We may also want to estimate the joint distribution of outcomes under treatment and outcomes under the counterfactual, and a method for doing so is outlined in Heckman et al. (1997).

**_External validity._** Arguably the most important things to learn from any evaluation relate to its lessons for future policies (including possible reforms to actual interventions being evaluated).  Although external validity is highly desirable for evaluative research, it can be hard to achieve.  We naturally want research findings to have a degree of generalizability, so they can provide useful knowledge to guide practice in other settings.  Thus empirical researchers need to focus on questions that tell us about why a policy or program has impact; I return to this question below. However, too often impact evaluations are a “black box”; under certain assumptions, they reveal average impacts among those who receive a program, but say little or nothing about the economic and social processes leading to that impact.  And only by understanding those processes can we draw valid lessons for scaling up, including expanding to other settings. Research that tests the theories that underlie the rationales for policy or program intervention can thus be useful in practice.

When the policy issue is whether to expand a given program at the margin, the classic estimator of mean-impact on the treated is actually of rather limited interest. For example, we may want to know the marginal impact of a greater duration of exposure to the program. An example can be found in the study by Ravallion et al. (2005) of the impacts on workfare participants of leaving the program, relative to staying (recognizing that this entails a nonrandom selection process). Another example can be found in the study by Behrman et al. (2004) of the impacts on children’s cognitive skills and health status of longer exposure to a preschool 16
inferred from the observed _PsiYsi_ by assuming an elasticity of demand of _−_ 1 _/σ_ . Thus, we raise _PsiYsi_ to the power _σ/_ ( _σ −_ 1) to arrive at _Ysi_ . This shortcut is one of the reasons that has made this approach so popular. Henceforth, even without data on firm output prices, it is possible to compute both _revenue_ and _physical_ productivity for each firm.

The final step is to calculate “efficient” output using equation (15) so we can compare it with actual output levels. For each industry, we calculate the ratio of actual TFP (equation 14) to this efficient level of TFP. Finally we aggregate this ratio across sectors using the Cobb-Douglas aggregator from equation (1): Before computing the degree of distortions and the TFP gain from resource reallocation we follow HK and remove the top and bottom percentiles of TFPR and TFPQ across industries. At this stage we calculate the industry shares ( _θs_ = _PsiYsi/Y_ ) that will be used for the aggregation.

# **5.1 Results for Resource Misallocation Levels**

Table 1 provides a measure of dispersion (standard deviation) for TFPR and compare it with other developed and developing countries. In the HK framework dispersion of TFPR represents the level of market distortions. First, results show that dispersion of TFPR in Morocco decreased between 2007 and 2013. Then, this decrease represents an improvement in resource allocation, although as we already noted, this result should be taken with caution since results using WBES2013 could not be fully representative of the Moroccan manufacturing sector in that year. In a subsequent sub-section we will show however that when using an alternative methodology to compute distortions, this result hold. Second, Table 1 shows that dispersion of TFPR in Morocco (whether we use WBES2007 or WBES2007) is not only higher compared to developed countries (US, France, South Korea) but it is also more important than the two largest developing countries (India and China). An additional set of country comparators could have been regional peers. Unfortunately no analogous studies exist in the literature for other countries in the region.<sup>11</sup> Table 2 shows the dispersion of TFPR by industry. Results are obtained using the WBES2007 since results from this survey are more robust compared to WBES2013. We observe a large heterogeneity across sectors ranging from 0.60 to 1.13. “Textiles” and “Machinery” are the industries with the lowest TFPR dispersion and “Food” and “Chemicals” are the ones with the highest dispersion levels.<sup>12</sup> This difference across industries can be explained first by sector-specific market distortions. Different degree of competition and concentration as well as

> 11In the robustness sub-section we show that market distortions in Morocco are also higher than the average of a large group of developing economies.

> 12Remark that in WBES2007 for Morocco “Confectionery” is treated as a separate industry and not included as usual in the “Food” industry.

13
# ACKNOWLEDGEMENTS

A number of persons provided us with important information for this work or assisted us in preparing it in other ways. The Estimates and Projections Section of the U.N. Population Division has been continually svpportive in providing us with data, even before their publication as U.N. assessments. Partly as a result, nonessential differences between these projections and the biannual U.N. assessments have been minimized. We are gratefiul to the former acting section chief, Shunichi Inouc, and to his continually accommodating staff, as well as to the head of the division, Jean-Claude Chasteland, and hope that the collaboration will continue.

World Bank country economists, population specialists, and the few demographers in the operatir. divisions have also provided us with recent population estimates, bringing to our attention government reports we might not otherwise see.

This exercise was carried out using a personal computer program, ProjPC-II, written by Kenneth Hill of Johns Hopkins University.

Both the Population, Health, and Nutrition Division and the Population and Human Resources Depanment have been supportive of this work, despite the time required. We are grateful to the division chief, Dr. Anthony R. Measham, and the department director, Ann 0. Hamilton.

We also wish to acknowledge the interest of the Bank's International Economics Socioeconomic Data Division (IECSE), which not only utilizes our figur--for such important Bank purposes as estimating per capita GNP--and incorporates them into the databases it maintains and publications it distributes but also helps coordinate communications with country economists. Sulekha Patel of that division has been particularly helpful in the past year.

In producing the manuscript, Inga Schweiger was responsible for some typing and coordinating the paper flow.

Rodolfo A. Bulatao Eduard Bos Patience W. Stephens My T. Vu
by the government sector. However, all these sectors have a significantly higher number of male employees compared to the number of female employees (CBS, 2009).<sup>24</sup>

**Table 9:  Women Labor Force Participation by Economic Activity in Nepal (2008)**

|**S.No.**|**Economic activity**|**No. of**<br>**Men**|**No. of**<br>**Women**|**Share of**<br>**women in**<br>**sectoral**<br>**employment**<br>**(%)**|**Share of the**<br>**sector in total**<br>**women**<br>**employment**<br>**(%)**|
|---|---|---|---|---|---|
|1|Agriculture, hunting and forestry|3407|5256|60.7|83.2|
|2|Fishing|2||||
|3|Manufacturing|18|8|30.8|0.1|
|4|Electricity, gas and water supply|460|302|39.6|4.8|
|5|Construction|39|69|63.9|1.1|
|6|Wholesale and retail trade; repair of motor vehicles, etc.|323|40|11.0|0.6|
|7|Hotels and restaurants|444|244|35.5|3.9|
|8|Transport, storage and communications|91|102|52.6|1.6|
|9|Financial intermediation|186|6|3.1|0.1|
|10|Real estate, renting and business activities|22|9|29.0|0.1|
|11|Public administration and defense; social security|57|13|18.6|0.2|
|12|Education|281|106|27.4|1.7|
|13|Health and social work|45|30|39.5|0.5|
|14|Other community, social and personal service activities|76|22|22.4|0.3|
|15|Activities of private households|13|19|59.4|0.3|
|16|Extraterritorial organizations and bodies|3|0|0.0|0.0|
|17|Not elsewhere classified|371|83|18.3|1.3|
|18|Total employees (in thousand)|5845|6316|51.9|100.0|

Source: ILOSTAT database, International Labor Organization In addition, when it comes to paid employment a very significant gender pay gap exists in Nepal as women are paid less than their male counterparts for the same job in mostly all the sectors, owing to such circumstances women are poorer than men in the country.<sup>25</sup> A very skewed gender pay gap exists in the agriculture, hunting and forestry with women earning 40 percent less as compared to men for the same work. Other major sectors where majority of women are employed show similar trends (Table 10).

24 CBS. 2009. Report on the Nepal Labor force survey 2008. Kathmandu, National Planning Commission.

25 Published 18 December 2015. The Kathmandu Post at <u>https://ceslam.org/index.php?pageName=newsDetail&nid=7054.</u> 23
|Armenia First Development PolicyOperation|Robert R. Taliercio|
|---|---|
|Armenia First Development PolicyOperation|Robert R. Taliercio|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|CO Protection Land and Patrimonyof IDP|Victoria Stanley|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Disaster and Climate Risk Management Project|Anatol Gobjila|
|Pakistan: Social SafetyNets Development PolicyCredit|Cem Mete|
|Pakistan: Social SafetyNets Development PolicyCredit|Cem Mete|
|Pakistan: Social SafetyNets Development PolicyCredit|Cem Mete|
|Pakistan: Social SafetyNets Development PolicyCredit|Cem Mete|
|Pakistan: Social SafetyNets Development PolicyCredit|Cem Mete|
|DRC EmergencyProject to Mitigate Impact of Financial Crisis|Franck Bousquet|
|DRC EmergencyProject to Mitigate Impact of Financial Crisis|Franck Bousquet|
|DRC EmergencyProject to Mitigate Impact of Financial Crisis|Franck Bousquet|
|DRC EmergencyProject to Mitigate Impact of Financial Crisis|Franck Bousquet|
|DRC EmergencyProject to Mitigate Impact of Financial Crisis|Franck Bousquet|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
|E-Societyand Innovation for Competitiveness(EIC)Project|Sandra Sargent|
# **Table A.14 Matching Estimates for the Share of Projects Rated MS+ in Countries with Low and High Shares of Programmatic Political Parties: PSM versus Non-PSM Projects**

||Nearest Neighbor M|atching Estimates|Coarsened Exact Mat|ching Estimates|
|---|---|---|---|---|
||(1)<br>Share of Programmatic<br>Political Parties <=0.5|(2)<br>Share of Programmatic<br>Political Parties >0.5|(3)<br>Share of Programmatic Political<br>Parties <=0.5|(4)<br>Share of Programmatic<br>Political Parties >0.5|
|Average Treatment Effect on<br>the Treated|-0.0837***|0.0283|-0.070**|0.028|
||(0.0304)|(0.0266)|(0.029)|(0.024)|
|Constant|||0.683***|0.724***|
||||(0.018)|(0.014)|
|Observations|1,261|1,744|1,093|1,524|
|Percent<br>Exact<br>Country<br>Matches|92.40%|88.36%|100%|100%|
|Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||

**Table A.15 Matching Estimates for the Share of Projects Rated MS+ in Countries with Low and High Shares of Programmatic Political Parties: PSM Projects versus Non-PSM Projects**

||Nearest Neighbor M|atching Estimates|Coarsened Exact Mat|ching Estimates|
|---|---|---|---|---|
||(1)<br>Share of Programmatic<br>Political Parties <=0.5|(2)<br>Share of Programmatic<br>Political Parties >0.5|(3)<br>Share of Programmatic Political<br>Parties <=0.5|(4)<br>Share of Programmatic<br>Political Parties >0.5|
|Average Treatment Effect on<br>the Treated|-0.120*|0.0813|-0.135**|0.053|
||(0.0644)|(0.0506)|(0.065)|(0.051)|
|Constant|||0.688***|0.707***|
||||(0.027)|(0.020)|
|Observations|876|1,255|359|626|
|Percent<br>Exact<br>Country<br>Matches|89.00%|88.02%|100%|100%|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Table A.16 Matching Estimates for the Share of Projects Rated MS+ in Countries by Aid Dependency (ODA as a Share of GNI): PSM versus Non-PSM Projects**

||Near<br>|est Neighbor Matching Es<br>|timates<br>|Coa<br>|rsened Exact Matching Esti<br>|mates<br>|
|---|---|---|---|---|---|---|
||(1)<br>ODA as a Share<br>of GNI <5%<br>IEG Outcome<br>Rating [binary]|(2)<br>ODA as a Share of<br>GNI >=5% &< 20%<br>IEG Outcome Rating<br>[binary]|(3)<br>ODA as a Share of<br>GNI >=20% IEG<br>Outcome Rating<br>[binary]|(4)<br>ODA as a Share<br>of GNI <5%<br>IEG Outcome<br>Rating [binary]|(5)<br>ODA as a Share of<br>GNI >=5% and < 20%<br>IEG Outcome Rating<br>[binary]|(6)<br>ODA as a Share<br>of GNI >=20%<br>IEG Outcome<br>Rating [binary]|
|Average Treatment Effect<br>on the Treated|-0.00632|-0.0762**|-0.00353|0.000|-0.084**|-0.022|
||(0.0284)|(0.0349)|(0.0639)|(0.025)|(0.033)|(0.067)|
|Constant||||0.722***|0.694***|0.707***|
|||||(0.014)|(0.021)|(0.049)|
|Observations|1,614|923|251|1,470|831|193|
|Percent Exact Country<br>Matches|95.69%|92.03%|74.57%|100%|100%|100%|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 _Note:_ ODA = official development assistance; GNI = gross national income; IEG = International Evaluation Group.

**Table A.17 Matching Estimates for the Share of Projects Rated MS+ in Countries by Aid Dependency (ODA as a Share of GNI): PSM versus Non-PSM Projects**

53
there are issues of implied benefits and guarantees even where none formally exist. This seems to be an issue for mutual insurers as well, with mutual insurance tending to be an afterthought in regulations, as well as an issue with some other faith based organizations offering discretionary benefit or voluntary sharing in medical expenses which are not effectively regulated.

Risk based capital requirements for Takaful insurers are likely to be higher than for conventional insurers as the investment requirements of Takaful insurers push them towards equities, property and property based securities and funds (Tolefat). Ideally an active Sukuk market that provides some greater degree of security will develop however recent setbacks appear to be holding back the rate of issuance.

Reinsurance is another key emerging issue for Takaful, particularly for those that wish to compete in commercial and industrial and liability markets. Strictly speaking any reinsurer used by a Takaful insurer should also satisfy Shariah requirements (Re-takaful) as should any retrocessionaires. Taking this chain to its ultimate reaches creates an almost impossible requirement, and some Takaful insurers have opted for co-insurance, raising the scope for unmeasured accumulations within individual jurisdictions. As at the date of writing there were 8 fully established Re-takaful operators in the Middle East and a number of the major European reinsurers had opened Takaful windows so direct reinsurance capacity is not likely to be an issue.

The remaining major issue in the development of the Takaful sector is the relative powerlessness of the policyholders relative to the TO under all current hybrid structures. This has been considered by an IFSB/ IAIS joint working group, which has pointed to the need for absolute transparency as to the contractual terms and the possible need for enhanced explanation at the time of sale to achieve this. Another recommendation being considered is that a separate board be established consisting of independent directors and a representative of the Shariah Council to oversight consumers‟ interests.

The most recent survey of Takaful insurers carried out by the Middle East Insurance Review show that Malaysia and the GCC region continue to be the main areas of activity. Summary MENA data is shown in Appendix Table 1.

# **Appendix Table 1**

|**Country**|**No. Takaful Insurers**|
|---|---|
|Algeria|1|
|Bahrain|9|
|Egypt|6|
|KSA|26|
|Kuwait|13|
|Jordan|3|
|Lebanon|1|
|Oman|1|
|Qatar|6|
|Syria|2|
|Tunisia|2|
|Yemen|2|

Source: Middle East Insurance Review 41
The heterogeneous effect of CB reform is much more consistently observed when considering the experience of top managers. Firms with inexperienced managers gained more access to finance and greater fraction of working capital and fixed asset financing from the CB introduction.

# **_Robustness checks: estimates using a panel of firms and instrumenting for reforms_**

Firms interviewed in each round of survey by WBES are a nationally representative sample of the private sector in that year. Since the same firm is not necessarily interviewed again in subsequent surveys of the country, our baseline estimation sample essentially consists of repeated cross sections over time. However, for a smaller subset of firms in some countries of our baseline sample, the WBES interviews and identifies when the same firms are re-interviewed over time. This firm-level panel consists of about 3,600 firms in 38 countries out of the CB reform baseline sample. While the sample size is much smaller (only about 14% of the baseline sample), the panel data allows us to control for time invariant firm-level heterogeneity. As a check of robustness of our baseline results, we estimate the impact of CB reform on firm financing for this sample with firm fixed effects. Our estimates are thus able to capture within firm effects of CB reform on access to finance.

As shown in Table 11, we find that estimates using firm fixed effects on panel data are largely consistent with our baseline results. The within firm estimates show that a firm without access to a loan, line of credit or overdraft is 5 percentage point more likely to have access following CB introduction. The panel regressions for interest rate and maturity, by definition, cover firms who have a loan both before and after CB introduction (for the treatment sample). We see that among this sample, firms had loans with a 12 percentage point lower interest rate 20
26

# _Personal Income Taxes_

Our results in Table 3 generally produce a progressive incidence profile for the Ivorian income tax, but not for the top income group in the formal sector where use of tax loopholes and avoidance changes the incidence profile. Other developing country incidence studies using partial equilibrium generally show personal income taxes to be progressive, since these taxes are assumed to be borne by those individuals who pay them. These studies ignore complications stemming from effects on labor migration (urban-rural, for instance) or tax evasion,<sup>22</sup> and interhousehold transfers which (as will be shown) can alter the tax incidence profile.

# _Production (sales, value-added and excise) Taxes_

Column 2 in Table 3 presents incidence results for production taxes, finding their incidence profile to be neither progressive nor regressive, with the major effects from removing these taxes operating against the non-export agricultural sector (food croppers in the Savannah, and other food croppers), and with exporters and urban dwellers gaining from the removal of the tax (and its replacement by a yield-neutral alternative). A series of factors account for these results. Food croppers are largely tax free in tne base-case situation, and are adversely affected by the broadly based equal-yield tax which they, along with all households, pay. Urban dwellers are heavy payers of the manufacturing level VAT in CMte d'Ivoire.

22 For anecdotal evidence on tax evasion see for example Klitgaard (1988).