rafmacalaba/datause-encoder-data huggingface dataset:
tegrity barely changes, but standard errors become larger and the coefficient is no longer statistically significant (p-value = 0.11). As in the model chosen by LASSO, we find that a higher number or radio headquartered in a district is a strong predictor of better health services, as is scores at the UCE national examination.

In column (4) we add the average integrity of bureaucrats as explanatory variable in order to assess if it has independent explanatory power. The coefficient on politicians’ integrity remain unchanged, and that of bureaucrats’ integrity is an order of magnitude smaller and not statistically different from zero. Given the large standard-errors of estimates, however, we cannot reject that these two coefficients are equal (p-value = 0.41). In a very demanding test, in column (5) we include all of bureaucrats’ and politicians’ traits simultaneously as explanatory variables, together with districts’ characteristics. In this model, the coefficient on politicians’ integrity is smaller (0.16) and not statistically different from zero. We should caution, however, that in this specification we are estimating 40 parameters with 75 observations. That is, the F test of a “horse race” between bureaucrat and politician integrity, is a weak test in our setting. We provide other tests below that reveal politicians’ integrity, not that of bureaucrats’, as a robust correlate of service delivery. The available economic theory does not provide any predictions about whether bureaucrat or politician characteristics should matter more. We do not conclude from the results that bureaucrat traits don’t matter, and indeed find that bureaucrats’ altruism does show-up as a significant correlate in several specifications. Other bureaucrat traits might matter as well, but perhaps we are unable to discern it for a variety of reasons (we are underpowered; we have not measured the relevant traits well; local bureaucrats may be strategically appointed by national bureaucrats and politicians, making bureaucrat traits more endogenous to district characteristics which directly predict service delivery.) The one result that is revealed systematically by the data is that political quality (and specifically, the characteristic of integrity) matters significantly for the productivity of bureaucratic organizations.

All results presented so far document how politicians’ integrity correlates with overall health scores. The details of the health service delivery index construction are presented in Annex 9.3, but it’s reasonable to assert that the weights attributed to each of the com23
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Micco, Alejandro and Carmen Pages (2007), “The Economic Effects of Employment Protection: Evidence from International Industrial Data,” Inter-American Development Bank, Working Paper No.592.

Olley, Steven and Ariel Pakes (1996), “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 64(6), pp. 1263-1297.

Pierre, Gaelle and Stefano Scarpetta (2004), “Employment Regulations through the Eyes of Employers: Do They Matter and How Do Firms Respond to Them?,” IZA Discussion Papers No. 1424.

Scarpetta, Stefano, Thierry Tressel (2004), “Boosting Productivity via Innovation and Adoption of New Technologies: Any Role for Labor Market Institutions,” World Bank Discussion Papers WPS3273.

# **Appendix**

**Table 1 Firm Coverage in Sectors**

|||2|002|2|005|
|---|---|---|---|---|---|
|ISIC|Sector|% of Firms|% of Exporters|% of Firms|% of Exporters|
|45|Construction|0.12|0.10|0.10|0.08|
|15-37|Manufacturing|0.25|0.44|0.40|0.32|
|60-64|Transport, Storage, Communication|0.08|0.36|0.07|0.34|
|50-52|Wholesale, Retail trade|0.31|0.13|0.25|0.12|
|70-74|Real Estate, Rent, Business Service|0.10|0.15|0.09|0.12|
|55|Hotels &Restaurants|0.07|0.13|0.05|0.14|
|92-93|OtherServices|0.06|0.10|0.05|0.10|
||Total|6,343|0.22|9,265|0.21|

**Table 2 Industries in Manufacturing Sector**

|||2|002||2005|
|---|---|---|---|---|---|
|ISIC|Industries|% of Firms|% of Exporters|% of Firms|% of Exporters|
|17|Textiles|0.06|0.64|0.04|0.50|
|18|Garments|0.07|0.58|0.14|0.36|
|15|Food|0.23|0.33|0.29|0.22|
|27,28,29|Metals and machinery|0.18|0.63|0.27|0.43|
|31|Electronics|0.04|0.75|0.01|0.64|
|24|Chemicals and pharm.|0.05|0.63|0.03|0.64|
|20,36|Wood and furniture|0.12|0.48|0.07|0.39|
|25,26|Non-metallic and plastic|0.09|0.45|0.06|0.45|
|21|Paper|0.01|0.62|0.01|0.59|
|22|Printing & Publishing|0.06|0.27|0.04|0.25|
||Other manufacturing|0.08|0.64|0.05|0.57|
||Total|1,617|0.51|3,678|0.37|

21
**Table 5.5 Time allocation and water collection**

(Number of minutes spent yesterday in each activity among those who spent time on them)

|||Ur<br>|ban<br>|||Ru<br>|ral<br>||
|---|---|---|---|---|---|---|---|---|
||Fem<br>|ale<br>|M<br>|ale<br>|Fem<br>|ale<br>|M<br>|ale<br>|
||7-15|>15|7-15|>15|7-15|>15|7-15|>15|
|**Paid work**|||||||||
|Did not collect|34|168|36|355|20|94|55|330|
|Collected<br>Difference|13|104<br>***|16|368|14|42<br>***|16<br>***|254<br>***|
|**Unpaid work**|||||||||
|Did not collect|38|57|53|85|76|79|179|172|
|Collected<br>Difference|59|44|39|79|71|61<br>**|112<br>***|165|
|**Study**|||||||||
|Did not collect|315|317|296|300|305|308|309|288|
|Collected<br>Difference|278|236|320|202<br>**|286|168<br>***|306|300|
|**Housework**|||||||||
|Did not collect|127|368|50|60|174|417|54|49|
|Collected<br>Difference|252<br>***|468<br>***|124<br>**|95<br>***|262<br>***|526<br>***|97<br>***|110<br>***|
|**Errands/shopping**|||||||||
|Did not collect|11|23|9|12|8|17|7|10|
|Collected<br>Difference|5<br>**|26|6|12|7|15|6|12|
|**Leisure and other**|||||||||
|Did not collect|909|751|953|781|929|769|938|796|
|Collected<br>Difference<br>|903<br>|678<br>***<br>|892<br>*<br>|686<br>***<br>|843<br>***<br>|720<br>***<br>|920<br>|769<br>*<br>|

_Notes_ : Means of those who collected and did not collect water are significantly different at:<sup><u>***</u></sup> 99%,<sup>**</sup> 95% and<sup>*</sup> 90%. Minutes spent collecting water among those who did it.

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala

# **_Micro-enterprise productivity_**

Modern utilities, in particular electricity and telecommunications, have the potential to improve the productivity of household based micro-enterprises. The electrification of household enterprises lengthens the potential working day, and permits the substitution of manual labor with more efficient power-assisted tools. Telecommunications improve links between enterprises and downstream buyers, as well as upstream suppliers, thereby helping entrepreneurs to expand sales and reduce supply costs.

While these arguments are convincing at an anecdotal level, there is limited rigorous empirical evidence to back them up. Using the data provided by the ENCOVI, this section explores firstly, whether households in areas where modern utilities are available are more likely to have household enterprises, and secondly, whether household enterprises that have access to modern utilities are significantly more profitable than those enterprises that do not. An important caveat is that all of the analysis in this section refers _exclusively_ to households that own a micro-enterprise that operates in the dwelling.

As a preliminary step, the proportion of households with micro-enterprises is tabulated against the various indices of access to modern utilities developed above (Table 5.6). The results show that the probability of having a micro-enterprise is significantly higher among households with _coverage_ of 40
**Table 7: Average annual number of workdays spent on certain accounting tasks in 2009**<sup>**23**</sup>

|**Activity**|**Tbilisi**|**Other Regions**|**Total**|
|---|---|---|---|
|Finding & analysis of||||
|accounting<br>&<br>tax<br>legislation|12.1|19.6|16.0|
|Finding and analysis of<br>legislation specifically on<br>filingandpayingtaxes|8.8|15.6|12.4|
|Preparation of additional<br>information upon request<br>of tax authorities|3.3|4.0|3.6|
|_Sample size_|_225_|_356_|_581_|

Source: IFC Tax Compliance Cost in Georgia, 2009 Moreover, the Georgian Revenue Service launched a compliance management strategy targeting micro and small enterprises following the introduction of the new simplified presumptive tax regimes. The administration introduced the function of advisory District Officers (DO) to change the adversarial nature of interactions between small businesses and the revenue service and to facilitate the identification of non-registered taxpayer. DOs are tasked to walk through assigned areas and offer assistance to businesses. They are not allowed to issue penalties. However, when they come across unregistered businesses they note their address, visit the owner and explain the risk of facing an inspection and penalties from regular SRS staff. The officer typically explains the registration process and support taxpayers in understanding the resulting tax liabilities and reporting obligations. The new administrative concept was launched in May 2011 in Tbilisi and subsequently rolled-out country wide. Given the constraints for public administration in reaching remote areas of the country, it seems likely that the DO campaign disproportionally increased the tax administration’s (perceived) presence in the regions.

Figure 7 plots the distribution of registration for the new regimes over time, i.e. it shows the number of firms that signed up for a new regime in each quarter since the first quarter of 2011. The majority of firms signed up for a new regime after the first quarter of 2011, which implies that they were taxed under this regime for the first time in tax year 2011 (tax returns need to be submitted by April of the year following a tax year). The spike in registrations at the end of 2011 is likely linked to the increased administrative efforts by the Revenue Service starting in May 2011. We thus expect the impact of the reform to be relatively small in tax year 2010 and to be larger in tax years 2011 and 2012. However, it is notable that the percentage of firms that had signed up for the tax regime by the end of 2012 is not high overall – covering only 10 percent of eligible micro firms and 9 percent of small firms with revenue of GEL 30,000 – 100,000 (see also Table 4).

> 23 Specifically, business owners were asked: “How many person-days were spent in 2009 on each of the following?: 1. On finding and analysis of bookkeeping/accounting and tax-related legislation

> 2. Specifically on finding and analysis of legislation on how to calculate, file and pay taxes

> 3. On preparation of additional information upon request of tax authorities, if any”

14
should be interpreted as a push factor or as a pull factor. On the other hand, the bilateral migration data included in Poprawe (2015) lack the time dimension, with information on 230 countries only for the year 2000.<sup>4</sup> Although this paper improves on the existing literature by having access to a panel dataset, it may potentially still suffer from reverse causality. Because of this, the analysis remains about correlation, and all the conclusions of this paper should not be interpreted as causal statements.

Lastly, by controlling for many drivers besides corruption levels, this paper speaks to the large body of research interested in quantifying the role of the different determinants of migration flows. A key driver of migration found in the literature is the presence of networks of previous migrants of the same nationality in the destination country, with cultural distance affecting migration decisions (Clark et al. 2007, Bauernschuster et al. 2014, Falck et al. 2012, Falck et al. 2018, Krieger et al. 2018). Especially relevant for low-skill migration, these diasporas explain the majority of variation in migration flows (Beine et al. 2011). By sharing a common language and culture, the economic and psychological costs associated with migration are greatly reduced. Migrant networks provide an essential service to newer waves of migrants during their process of job search, improving labor market outcomes in the destination country (Edin et al. 2003, Munshi 2003). From the perspective of the destination country, good education and health systems tend to attract migrants (Geis et al. 2013). This is in contrast with the significantly less clear role of social safety nets, which only seem to be salient for migrants from the least developed countries (Pedersen et al. 2008). From the perspective of sending regions, local amenities (e.g., security and public services) play an important role in shaping the intentions to emigrate (Dustmann and Okatenko 2014).

The remainder of the paper is organized as follows. Section 2 describes the dataset assembled for this study. Section 3 presents the empirical results. Section 4 concludes.

# **2 Data**

We have built a novel dataset on corruption levels and bilateral migration flows for EU28 and EFTA countries between 2008 and 2018. Exploiting both a cross-section and a time dimension is the main contribution of this study. First, bilateral migration flow data (instead of net migration rates) allow us to disentangle the overall effect of corruption on migration into two distinct parts: push (encouraging emigration) and pull (discouraging immigration) factors. We also exploit the time dimension to include fixed effects absorbing the impact of unobserved and time-invariant factors.

> 4International bilateral migration stocks come from the World Bank Global Migration Database.

3
# **(c) New capacity or capacity added during 2000-2010 and 2010-2020 period (GW)**

<!-- Start of picture text --> 489 498<br>451<br>305<br>240<br>164<br>18 38 39 29 42 34<br><!-- End of picture text --> <!-- Start of picture text --> Oil<br><!-- End of picture text --> <!-- Start of picture text --> Nuclear<br><!-- End of picture text --> <!-- Start of picture text --> Hydro<br><!-- End of picture text --> <!-- Start of picture text --> Wind<br><!-- End of picture text --> Other RETs refer to other renewables, including biomass, geothermal, tidal, etc. Source: IEA online database. <u>https://www.iea.org/data-and-statistics/</u> The total investment in clean technologies, including renewables and energy efficiency (EE), has remained stagnant at around US$600 billion during the 2015-2019 period. It dropped to US$562 billion in 2020 due to the COVID pandemic (Figure 1). One key reason for the downward trend of investment despite continued expansion of clean technology adoption is falling unit costs for key renewable technologies (e.g., solar PV, wind, and electric vehicles).

The EE improvement means using technology that requires less energy to perform the same task. It has multiple benefits, including reducing energy bills<sup>3</sup> , mitigating climate change, reducing air pollution, improving energy security, and increasing energy access (IEA, 2020b). The IEA, (2020c) report estimates that the efficiency gains since 2000 in IEA member countries resulted in

> 3 In some cases, EE improvements might not reduce energy consumption due to rebound effect.

7
Table 13 Summary statistics for selected comparable variables used to impute consumption, by visit of the 2018/19 GHS

||2018/19 GHS post-<br>planting (%)|2018/19 GHS post-<br>harvesting (%)|Difference<br>(percentage<br>points)|
|---|---|---|---|
|North-Central|14.08|14.07|-0.01|
|North-East|8.29|8.27|-0.02|
|North-West|19.50|19.53|0.03|
|South-East|13.20|13.07|-0.13|
|South-South|17.91|17.88|-0.03|
|Gender|80.43|79.90|-0.53|
|Dependency Ratio|42.88|43.30|0.42|
|Employed: Non-farm|42.64|36.90|-5.74|
|Employed: Waged|16.24|18.17|1.93|
|Main cooking fuel: Wood|60.52|60.62|0.10|
|Main flooring: Cement|71.37|71.42|0.05|
|Fresh Fish|15.07|16.34|1.27|
|Imported Rice|46.92|47.34|0.42|
|Beef|42.37|45.44|3.07|
|No Toilet|29.09|28.97|-0.12|
|Recharge Cards|84.24|85.88|1.64|
|Air Conditioner|1.69|1.66|-0.03|
|Washing Machine|1.98|1.95|-0.03|
|Cars and other vehicles|10.00|9.98|-0.02|
|Generator|25.50|25.48|-0.02|
|Microwave|1.76|1.73|-0.03|
|TV Set|47.71|47.76|0.05|
|Computer|4.19|4.15|-0.04|

Note: the table shows the average value of non-monetary indicators used in the consumption model for survey-to-survey imputation purposes. Columns 1-2 show the average value for the post-planting and post-harvesting wave of the 2018/19 GHS, columns 3-4 show the summary stats for the two visits in the 2010/11 GHS. The purpose of the table is to check whether nonmonetary indicators are subject to seasonal variation over the year and could bias the imputed estimates.

38
The largest market failures in labor markets occur across space, with very different employment opportunities for the same skills depending on where individuals are located. We have seen some of the more successful screening and matching interventions were ones that provided assistance with learning about job opportunities in a different location (Jensen, 2012), or subsidizing job search in different parts of the city (Franklin, 2015; Abebe et al. 2016a). More striking evidence comes from Bryan et al. (2014) who show a small subsidy equal to the cost of a bus ticket spurred new seasonal migration in Bangladesh, increasing household consumption by 30-35 percent during the hungry season (they do not measure household income). Even larger gains can be had from facilitating international migration. Gibson and McKenzie (2014) show that sending seasonal workers to New Zealand increased per-capita incomes in Tonga and Vanuatu by more than 30 percent. Luthria and Malaulau (2013) discuss the process of facilitation used by governments and the World Bank to allow this movement to happen. However, such facilitation is not always successful, especially if it focuses only on barriers on the worker side. For example, Beam et al. (2016) conducted several interventions in the rural Philippines to facilitate more international migration, and were unsuccessful in generating additional international employment.

# **Concluding Lessons for Impact Evaluations**

The modal study surveyed in this review is from 2016, reflecting rapid recent growth in the body of evidence around active labor market interventions in developing countries. This body of work has generated substantial new knowledge, but also suffers from several limitations that future work can attempt to learn from:

- 1) _Given the likely effect size of active labor market interventions, sample sizes may need to be a lot larger._ Based on the current body of research, it seems many interventions may have only a modest impact on employment, such as a 2 percentage point increase. In some cases, such as expensive training programs, such an effect size is too small to be economically meaningful. But cheaper programs such as search and matching assistance could still deliver gains that exceed the costs with these modest impacts. Taking as an example the 13 percent employment rate in the control group of Abel et al. (2016), a study needs to have 6,424 individuals in the treatment group and 6,424 in the control group to detect a 2 percentage point employment impact with 80 percent statistical power. This is much larger than existing studies.

18
36 that vehicle price, fuel efficiency (operating cost), size, and age affect purchase and holdings decisions. Socioeconomic variables of importance include: number of persons per household, age, and income. As a class, these models generally ignore the effect of miles traveled on vehicle purchase or holding decisions, and more important, ignore the reverse causation, introducing simultaneous equation bias when the VMT variable is treated exogenously and misspecification error when this variable is ignored. Winston and Mannering (1984) estimate a model with VMTs endogenous, but do not permit persons to own no vehicleE, a serious limitation for an analysis of developing countries.21 Train's own model, which was applied to data collected in 1978 from a U.S. national sample of over 1,000 households, addresses all of the above objections and represents an econometric state-of-the-art approach to estimating choices of auto ownership, type, auto VMTs, and fuel demand. However, it ignores modal choice, hardly acknowledging the presence of a nonauto option, except in the use of a "number of transit trips" variable to help explain vehicle ownership decisions. Appended to this model is a component for disaggregating VMTs into work/non-work and intercity/intracity VMTs, although it is non-utility-based with no feedbacks to other choices.

The theoretical model is specified with the conditional indirect utilitv function for a household: 21. Some modelers focus on only a small piece of the problem.<sup>For</sup> instance, Berkovec (1985) is one of the few papers to focus on the vehicle scrappage decision. He regressed data on scrappage for 13 vehicle against variables for current market value (excluded vehicle characteristics were used as instrumental variables for price), model year and make dummies, and several other variables to estimate this equation. The theoretical model is based on an individual's decision, weighing the repair cost against the value of the vehicle<sup>in use or</sup> sold (less scrap value).