rafmacalaba/datause-encoder-data huggingface dataset:
higher. In a similar manner the ASC interaction terms for “seen an improved stove” and “only have wood burning stoves” is significant and positive, indicating that respondents that have previously seen an improved stove and respondents that currently only have a wood burning stove are more likely to choose an improved stove and therefore are willing to pay more for the improved stoves.

Table 4– Parameter Estimates from Interaction Effects Models

||**M**|**MNL**<br>|
|---|---|---|
||**Mean**|**Standard Deviation**|
|ASC|15.94***|7.137***|
||(3.416)|(1.446)|
|Durability|0.0653***|0.0550**|
||(0.0108)|(0.0215)|
|Time Reduction|0.0186***|0.0103|
||(0.00287)|(0.00778)|
|Fuel Reduction|0.0283***|0.0375***|
||(0.00939)|(0.00620)|
|Smoke Reduction|0.0153***|0.00806|
||(0.00474)|(0.0222)|
|Cook Part In Survey X Smoke Reduction|0.00370|0.0182|
||(0.00977)|(0.0183)|
|Cook Part In Survey X Time Reduction|0.00392|0.0119|
||(0.00611)|(0.0122)|
|Cook Part In Survey X Fuel Reduction|0.00500|‐0.0231|
||(0.00759)|(0.0150)|
|Children > 3 X Fuel Reduction|‐0.00909|‐0.0122|
||(0.00577)|(0.0198)|
|HH % Female X Fuel Reduction|0.00393|‐0.0223|
||(0.0171)|(0.0231)|
|Cook Part In Survey X ASC|8.234*|16.72***|
||(4.294)|(3.593)|
|Only Wood Burning X ASC|10.97***|6.632***|
||(2.495)|(2.127)|
|Seen Improved Stove X ASC|13.14***|2.304|
||(3.843)|(1.694)|
|Cost|‐0.0374***|0.0816***|
||(0.00855)|(0.00983)|
|Observations|8262||
|Log lik.|‐1675.0||
|Chi‐squared|203.0||

Standard errors in parentheses* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01 16
of communication, which allowed observations, both critical and complementary, to constantly inform innovations in design and implementation.

Unfortunately, KDP is among a very small group of World Bank funded participatory projects that have made an effort to build effective monitoring systems. We reviewed the monitoring and evaluation systems in place in all participatory projects in the World Bank from 1999 to 2007. In our review, we examined a range of project documents, including the Project Appraisal Document (PAD), which is among the main documents assessed by the World Bank’s Executive Board before approving a loan. The PAD should ideally include a detailed account of the monitoring system and the manner in which the project will be evaluated. We also looked at Implementation Status Reports (ISRs) and Implementation Completion Reports (ICRs) for a random sample of 20 percent of these projects to assess the effectiveness of proposed monitoring and evaluation (M&E) systems in the PAD. The former are typically prepared by the project manager after every supervision mission. The latter are self-evaluations of projects screened by the Independent Evaluation Group. Finally, we assessed information from project supervision documents that synthesized the results of regular project visits by Bank operational task teams. The analysis also encompassed a survey of project managers (task team leaders, in World Bank parlance), to gather information that was not available in project documents.

The PADs were striking in their similarity to each other. Often their language was cut and paste from one project to the next, suggesting a singular lack of attention to context. The documents and the survey also revealed a pervasive inattention to monitoring systems. Only 40 percent of PADs included a monitoring system as an essential part of the project design, and a third did not even bother to mention basic monitoring requirements such as a management information system (MIS). When monitoring was mentioned, it usually involved collecting extremely imprecise indicators, and even this was done irregularly. The most important reasons given for the paucity of effective monitoring were poor human and technical capacity and the lack of sufficient funding. On the crucial question of whether monitoring and evaluation data are a guide to learning by doing, only 14 percent of the projects explicitly outlined procedures that would be implemented if the monitoring and evaluation data indicated that a project had gone off track.

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franchise partners, helping the businesses set up bank accounts, or assisting with financial management and record keeping.

## **3.1.2 The Grant Treatment**

Applicants assigned to the cash grant treatment were offered an unrestricted transfer of 20,000 Kenyan shillings (or 239 US dollars at the prevailing exchange rate of 83.8 shillings to the dollar).<sup>9</sup> Individuals assigned to the grant arm were contacted by phone and invited to meet privately with a member of the disbursement team to discuss the grant. During the meeting, individuals were told that there were no restrictions on how the grant could be used and that the grant did not need to be paid back. Disbursements to the grant recipients were timed to coincide with the launch of the microfranchise businesses.

# **3.2 Data Collection**

Our analysis draws on three main sources of data. First, we administered a brief baseline survey to all eligible applicants prior to randomization. We also conducted a midline survey 7 to 10 months after the end of the intervention.<sup>10</sup> The midline surveys were conducted via phone. The midline included detailed questions about income-generating activities, but did not ask about a broader range of outcomes (this was not feasible in a short phone survey). We conducted a more comprehensive endline survey 14–22 months after the end of the intervention.

Attrition rates are extremely low in both the midline and the endline surveys: we successfully surveyed 94.0 percent of the baseline sample at midline and 92.5 percent of the baseline sample at endline. Regressions testing for differential attrition across treatment arms are reported in the Online Appendix. Attrition is not associated with either treatment.

# **3.3 Sample Characteristics**

Table 1 describes the baseline characteristics of the young women in our sample. As expected, there is little variation in age: 94.6 percent of the young women in the sample were 18, 19, or 20 years of age at baseline. 11.6 percent of women in our sample did not have a living parent at the time of the baseline survey. 16.5 percent were married or cohabitating,

> 9Though the US dollar value of the shilling has since declined, the exchange range was fairly constant during the grant disbursement period (from November 1, 2013 to January 13, 2014). The value of the grant was selected to make it roughly comparable to the value of the microfranchising package of training and capital; the 20,000 shilling amount is also identical to the grant size in another study of cash grants for Kenyan youth (Hicks, Kremer, Mbiti, and Miguel 2016).

> 10We also conducted an extremely brief phone survey 2 to 5 months after the intervention, but we did not ask about income-generating activities at that time. The goal of that survey was to collect better contact information than had been gathered at baseline.

13
to people. Arguments that have been advanced to support the positive impact of decentralization in reducing corruption include: enhanced accountability and reduced corruption in view of competition among local governments (Weingast (1995), Arikan (2004)); exit and voice mechanisms at the local level; higher level of information (Seabright (1996), Boadway and Shah (2009)); lower expected gains from corruption but higher probability of detection and punishment at the local level (Carbonara (1999), Wildasin (1995)); enhanced transparency (Ahlin (2000)), and lower transaction costs for citizens and improved countervailing institutions (Boadway and Shah (2009)).

Thus the conceptual literature is inconclusive regarding the impact of decentralization on corruption. This literature suggests that such an impact is better assessed through empirical work on this question.

# 2.2 Empirical perspectives

The empirical literature on this subject also presents diverse perspectives - both negative and positive.

## 2.2.1 Negative impacts

Treisman (2000) from analysis of cross-country data concluded that decentralized countries have higher perceived corruption and poorer service delivery performance in public health services. A recent study by Fan et al. (2009) using a cross section data of eighty countries finds that in countries with a large number of government or administrative tiers and (given local revenues) a larger number of public employees, reported bribery was more frequent.

## 2.2.2 Positive impacts

A number of studies provide support for the positive influence of decentralization in controlling corruption. Crook and Manor (2000) examined the process of political decentralization in India (Karnatka state), Bangladesh, Cote d’Ivoire and Ghana and found that decentralization led to enhanced transparency and reduced incidence of corruption. They conclude that decentralization reduces grand theft but increases petty corruption in the short run but in the long run, both may go down. Fiszbein (1997) based upon a review of political decentralization in Colombia concluded that competition for political office opened the door for responsible and innovative leadership that in turn became the driving force behind capacity building, improved service delivery and reduced corruption at the local level. Kuncoro (2000) found that in Indonesia, administrative decentralization led to lower corruption as firms relocated to areas with lower bribes. Wade (1997) found that over-centralized top down management accompanied by weak communications and monitoring system contributed to corruption and poor delivery performance for canal irrigation in India. Huther and Shah (1998) using international cross-section and time series data found that fiscal decentralization was associated with enhanced quality of governance as measured by citizen participation, political and bureaucratic accountability, social justice, improved economic management and reduced corruption. Arikan (2004) reconfirms 4
efforts in the financial sector, we need to know more about post-intervention portfolio quality (su a as the percent of portfolio assets that are non-performing and measures of diversification and capitalization), banking supervision (the number of supervisors, their salaries and skill levels), and governance (board composition), to name but a few major areas. In addition, firm level balance sheet, income, and production data would greatly assist researchers in evaluating the impact of financial reforms on the real sector. These more detailed measures should enable us to better assess whether change is actually taking place as a result of Bank interventions. Finally, the loan instruments themselves may be the source of some of the difficulties in achieving financial sector adjustment. That is, financial sector development is a process that may require lending instruments that disburse slowly as small steps are achieved, and that are less intimately tied to a country's balance of payments situation. Evaluating that conjecture is, however, beyond the scope of this paper as all of the interventions examined were relatively quick-disbursing and tied to balance of payments considerations. Indeed, the only way to generate the data necessary to evaluate slow-disbursing instruments may be to experiment with them.

59
24 countries. Nonetheless, we find stronger correlations for NRXGDP than for MFGSERV.

We also know the main structural material used to construct the tall buildings in the data set. Steel is more durable, resistant and safe than concrete but also more expensive. Cols. (2)-(3) show the results with the dependent variable being log urban height density for buildings whose structural material is steel vs. concrete, respectively. Interestingly, we find strong correlations between steel use and MFGSERV (but steel could be cheaper in MFGSERV countries) and between concrete use and NRXGDP.

Third, we examine if the correlations are stronger for larger cities. In col. (4), the dependent variable is log height density considering only buildings in the capital city. We find a stronger correlation with NRXGDP than MFGSERV, although the differences with respect to MFGSERV is not significant. If we consider both the capital city and the largest city, we find a weaker correlation ((5)). For other cities, we also find a possible gap, indicating higher height density in NRXGDP than MFGSERV countries ((6)).

The excess of tall buildings in NRXGDP countries may be accompanied by lower occupancy rates. Since we do not have information on occupancy rates, we instead examine how the overall construction sector is potentially affected. For 80 countries, we know the annual cement production from 1970 to date.<sup>32</sup> Cement – the main ingredient of concrete – is typically not traded (World Cement, 2013). Using UN COMTRADE data we find that the world trade of cement (tons) only accounted for 3.5% of world cement production (tons) during the period of study, and for just 2.5% today. For this reason, cement production should be a very good proxy for cement use.

We on the same model as for tall but use as our rely long-difference buildings dependent variable the log sum of cement production over the period 1970-2020 while controlling for log urban population in both 1970 and 2020, thus capturing _cement consumption per urban capita_ . The results in Cols. (1)-(2) of Table 7 show that cement production is positively correlated with MFGSERV and NRXGDP (significant for MFGSERV), consistent with the tall construction results. However, once we control for urban economic development, no differences are observed.

If we use the COMTRADE data to reconstruct cement consumption, we find that the log sums of cement consumption and production over the period 1970-2020 are highly correlated (corr. = 0.98). Likewise, using the log sum of cement consumption produces the

> 32We obtained the data from the _Minerals Yearbooks_ of the U.S. Geological Survey (USGS).
in Dustmann et al. (2005), Basso and Peri (2015) and Edo et al. (2019), a way to minimize the correlation between past immigration and current outcomes is to use a sufficiently long time lag to predict the actual number of immigrants. To mitigate this concern, we derive initial migration shares using data from 2002 – the first year for which data are available in the EU Labour Force Survey, and benchmark time that is nine years before the beginning of our period of analysis. In addition, we control for region fixed effects that take care of unobservable regional characteristics in the IV specification, to ensure that the exclusion restriction is met.

Another possible concern with the shift-share instrument is that using an earlier distribution pattern of immigrants as an instrument can violate the exclusion restriction in the presence of general equilibrium adjustments and strong serial correlation of immigrant flows (Jaeger et al., 2018). If it takes time for markets to adjust to shocks, the error term can also include other influences that reflect ongoing general equilibrium adjustment effects of past migration shocks. This is a greater concern in contexts where the mix and share of immigrants does not vary much over time, like in the US, and where, as a result, the first stage of the IV estimation is stronger due to more stable migration inflows. As noted by Jaeger et al. (2018), the serial correlation in the number of immigrants is lower in Europe compared to the United States as immigration pattern to Europe have changed substantially over time.<sup>17</sup> In particular, the period between our initial measure of immigrant share in 2002 and our period of study for labor market outcomes, which starts in 2011, has been marked by the EU enlargement which triggered important changes in migration flows to the EU (Elsner, 2013).

A final concern is that the total inflows of immigrants in the EU (the shift) could be correlated with local demand-pull factors, given that the inflows include the immigration growth rates in the NUTS2 region of reference. To mitigate this issue, prior studies have adjusted the measure of the exogenous supply-push factor by using migration flows to destination areas other than the one of reference (Ajzenman, Dominguez, and Undurraga, 2020). As a robustness check, we adjust the PS and MOP instruments including just the growth rate of immigration to all EU NUTS2 regions besides the one of analysis in the “shift” component. Our main results are robust to this alternative

> 17 In section 5, we evidence that the first stage of the IV estimation is weaker than what has been evidenced in the US context, once region fixed effects are accounted for.

24
given by (7), the _SOE_ dummy loses its significance for all TFP estimates with the exception of those obtained using the FE method, whose characteristics, as seen in the previous section, appear to be different from those from all other methodologies.

This result, together with the estimates of the covariates included in the specification, implies that the differences in TFP growth between state-owned and private firms can be explained by (a) their different dynamics in terms of adjustment to the equilibrium (mirrored in some significant parameters for the interaction of _SOE_ and log _Ait_  1 ) and (b) differences in the accumulation of production factors. One of the specifications indicate that the lower TFP growth emanates from the fact that firms have increased employment in a framework where production was either decreasing or increasing at a lower rate than labor input. The specification based on the Levinsohn and Petrin (2003) method indicates that the source of productivity growth differences is based on unproductive investment on physical capital. The conclusion that can be inferred from the OLS results in Table 8 is thus that growth of inputs factors might at least partially explain lower TFP growth rates in state-owned enterprises.

Table 8: Convergence equations: TFP

|||OLS esti|mation|||Fixed effects|estimation||
|---|---|---|---|---|---|---|---|---|
||OLS|FE|OP|LP|OLS|FE|OP|LP|
|Lagged TFP|-0.090**|-0.057**|-0.008**|-0.026**|-0.217***|-0.212***|-0.018***|-0.058***|
|Lagged TFP|(0.044)<br>-0.043|(0.023)<br>0.051**|(0.004)<br>0.001|(0.013)<br>-0.003|(0.044)<br>-0.503***|(0.054)<br>0.009|(0.004)<br>-0.028***|-0.058***<br>-0.099***|
||||||||||
|State-owned|||||||||
||(0.050)|(0.023)|(0.004)|(0.014)|(0.100)|(0.052)|(0.006)|(0.025)|
|Employment|-0.058*|0.019|-0.012|-0.001|-0.053|0.033|0.002|0.009|
|growth|||||||||
||(0.033)|(0.038)|(0.041)|(0.036)|(0.039)|(0.046)|(0.046)|(0.038)|
|Capital|-0.050|0.013|-0.050|-0.086**|-0.028|0.007|-0.029|-0.054|
|growth|||||||||
||(0.039)|(0.049)|(0.046)|(0.043)|(0.042)|(0.049)|(0.047)|(0.048)|
|State-owned|-0.106|-0.201**|-0.172|-0.138|.|.|.|.|
||(0.104)|(0.094)|(0.124)|(0.120)|.|.|.|.|
|Intercept|0.379*|0.340|0.563**|0.497**|.|.|.|.|
||(0.221)|(0.223)|(0.262)|(0.246)|.|.|.|.|
|R-squared|0.016|0.011|0.023|0.021|0.049|0.035|0.054|0.049|
|Obs.|727|727|727|727|727|727|727|727|

_Notes:_ Each column refers to a different TFP estimate, abbreviations as in preceding section. Robust standard errors in parenthesis. *(*)[***] stands for significance at the 10%(5%)[1%] level.

Focusing on the fixed effects results, each firm is assumed to converge to an enterprise-specific 23
_Figure IV.2: Female decision-making index in sampled districts in 2014_ _Figure IV.3: Female decision-making index in sampled districts in 2021_

# b. Conflict event data: ACLED

Data on conflict come from the Armed Conflict and Location Event Database (ACLED) which codes the location and date of a wide range of conflict-related events. We use this geolocated information to assign conflict data to subnational regions at the ADM 2 level (i.e., district). While there have been some violent events before the start of the current conflict, we select the year(s) 9
where ‘s’, ‘i', ‘e’ are respectively the gender, age, and level of education of the individual and ‘j’ is the economic sector where he or she operates. Hence the marginal effect that age (experience) has on wages depends on the characteristics of the individual and the economic sector.

Given equation (12), for any given individual we can compute net income flows across life. These are given by: where R=1 if the individual is retired or R=0 otherwise, P(.) is the function returning the pension of the individual as a function of policy parameters of the relevant pension system in the sector, and E(e,i) is a function that is equal to one if an individual that will achieve education level ‘e’ is out of school at age ‘i' or zero otherwise. Also for simplicity we set the initial age and the initial time at 1 so ‘t’ and ‘i' are substitutes.

Using (13) we can compute internal rates of return to marginal investments in education. The internal rate of return r<sup>*</sup> (s,i,e,j) of moving from education level ‘e’ to education level e+1 is characterized by: Finally, we can compute the expected net gain of taking a job in sector ‘j’ at time ‘t’, or waiting to take a job in sector j′ at time t+1 with probability p(j’). This is given by: _i ngwt_ ( _s_ , _i_ , _e_ , _j_ , _j_ ́ ) = ( _p_ ( _j_ ' ) _PVt_ = _Xi_ + 1 [ _nif_ ( _s_ , _t_ , _e_ , _j_ ' )] + ( 1 − _p_ ( _j_ ' )) _PVt_ = _Xi_ + 1 [ _nif_ ( _s_ , _t_ , _e_ , _j_ )]) − _PVt_ = _Xi_ [ _nif_ ( _s_ , _t_ , _e_ , _j_ )] − ∑ _nif_ ( _s_ , _k_ , _e_ , _j_ ) _b_ 1( + _r_ ) ( _i_ − _k_ ) _k_ = _a_ (15) where PV[] is the present value function. The last term in equation (15) is used to take into account that an individual who continually delays taking the job in sector ‘j’ might have to accumulate some level of debt to finance its consumption (the parameter ‘a’ gives the age when the individual is first offered the job in sector j′). Here the assumption is that the individual borrows a fraction ‘b’ of the net income flow that he or she would have achieved by taking the job in sector ‘j’.

We apply this framework to the case of Morocco. Equation (12) was estimated by simple OLS using the 1998 household survey for Morocco. We defined 10 levels of education for the purposes of the analysis. In addition we consider four economic sectors: private, formal non-agricultural sector, the public sector, and the agricultural/informal sectors. In the analysis we constructed 120 career paths. These paths cross 10 levels of education, with four economic sectors, and with three types of pension funds. As before, in all cases, the growth rate of real average earnings is set at 3% per year and the discount rate is 4%.

18