rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Sample Descriptive Statistics

|Variable|N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|_Electricity variables_||||||
|Electricity Access|69,521|0.654|0.442|0|1|
|Electricity Access Instrument|12,408|0.765|0.410|0|1|
|Generation Capacity, GW<sup>_∗_</sup>|69,521|0.144|0.072|0.013|2.35|
|_Economic variables_||||||
|GVA per capita, (BRL 1000)|54,830|3.840|7.156|0.085|830.7|
|GVA Share, Agriculture|69,521|0.306|0.210|0.000|0.971|
|GVA Share, Industry|69,521|0.168|0.157|0.000|1.000|
|GVA Share, Services|69,521|0.525|0.180|0.000|0.997|
|_Demographic/Geographic_||||||
|Population Density, (people/km<sup>2</sup>)|69,521|115.2|963.2|0.02|199,463|

for the majority of electricity generation in Brazil, accounting for 65.8% of electricity generation in the country in 2016 (ANEEL, 2017). The power potential of a hydropower plant depends on local topological and hydrological characteristics, such as e.g., slope, elevation, and the amount of water available. These characteristics are plausibly exogenous to the local social and economic conditions that may be also correlated with the structural transformation process. As described in Lipscomb et al. (2013), there are three key steps to the construction of their instrument. First, each time period they calculate the budget for plants based on the actual construction of major dams across the entire country. Second, they calculate a cost factor that sorts potential locations by their geographic suitability. Finally, they apply the suitability predictions to the areas where hydropower plants were actually built. Using the predictions of estimated construction site for each dam, they generate a hypothetical transmission network that depends solely on topological and hydrological characteristics that is used as an instrumental variable.

– Table 2 reports the estimation results. Columns 1 6 show respectively the OLS and IV estimates when the dependent variables are the natural logarithms of agriculture, industry 29
# **Annex C. Robustness test: Has the relationship between development policy operations and the quality of economic policy changed over time?**

We examine changes over time by splitting the full sample (1998 to 2015) into sub‐ periods. We define breakpoints from 2000 to 2011, and estimate (8) for both the earlier sub‐ period and the later. As before, we estimate γ by a grid search. We find that in almost all subsamples the optimal “between” R‐squared occurs where γ=0. For brevity’s sake we report only the coefficient estimates for α, their significance, the corresponding estimate for γ, and the size of the sub‐sample. We start with the estimates using Clusters A & B of the CPIA as the dependent variable.

**Table 15.  Estimates of α and γ in model (8) (with CPIA as dependent variable), for different sub‐periods**

||**Coefficie**|**nts**<br>|||**Coefficie**|**nts**|||
|---|---|---|---|---|---|---|---|---|
|**Sub‐**<br>**SMPL**|**α**|**Γ**|**N**|**Sub‐**<br>**SMPL**|**α**|**γ**|**N**|**Change**|
|98‐99|‐||266|00‐15|0.0645***|0.0|2014||
|98‐00|‐||399|01‐15|0.0616***|0.0|1881||
|98‐01|0.00150*|1.40|534|02‐15|0.0552***|0.0|1746||
|98‐02|0.00485*|1.00|669|03‐15|0.0526***|0.0|1611||
|98‐03|0.0891***|0.0|804|04‐15|0.0494***|0.0|1476|‐45%|
|98‐04|0.109***|0.0|938|05‐15|0.0467***|0.0|1342|‐57%|
|98‐05|0.113***|0.0|1061|06‐15|0.0429**|0.0|1219|‐62%|
|98‐06|0.111***|0.0|1188|07‐15|0.0394**|0.0|1092|‐65%|
|98‐07|0.105***|0.0|1315|08‐15|0.0277*|0.0|965||
|98‐08|0.104***|0.0|1443|09‐15|0.0177|0.05|837||
|98‐09|0.100***|0.0|1571|10‐15|0.00978|0.40|709||
|98‐10|0.0947***|0.0|1699|11‐15|0.00905|0.55|581||

Note: * = significance level of 10% or less; ** = 5% or less; *** = 1% or less. Standard errors are adjusted for 145 clusters. OLS used. Complete sets of country dummies and year dummies are included in all regressions. Before regressing, it was found by a grid search that γ=0. Cells are left empty in the few cases when there was no convergence; in these cases the estimated coefficient α was statistically insignificant.

In Table 15 we contrast the earlier with the later periods, e.g. in the second line, we compare 1998‐2000 with 2001‐2015. We are on the surest grounds when we compare periods _both_ of whose coefficients are significant at 5% or better: this is the shaded (yellow) part of the table. (In the remainder of the table there are too few observations to get significant estimates, making it hazardous to draw conclusions.) The estimate of α is larger in each earlier period than in the equivalent later period: for instance, in the earlier period 1998‐2003 the coefficient is 0.0891, and in the later period 2004‐2015 the coefficient is 0.0494. The decline between the earlier and the later periods varies between 45% and 65%. The differences are bordering on 35
the uncertainty of the import process. We take PSI as an example of “protectionist” NTMs.

# _2.3. NTM Data Construction_

The original information on NTMs comes from the _TRAINS_ database collected by the _Economic Research Institute for ASEAN and East Asia_ (ERIA) and UNCTAD. This data is based on official regulations that were in effect by January 2015 with an in-force date by January 2016. The building block of our explanatory variables are dummies taking value 1 if in a given year, the Indonesian government applied an NTM on an HS10 digit product. Since we consider the universe of HS-10 digit products, if a particular NTM is not in our data, then it means that the Indonesian government never applied it.<sup>8</sup> The resulting yearly panel at HS-10 digits code starts in January 2008 and ends in December 2015. We adjust the data in a number of ways. First, when the same NTM appeared multiple times for an individual product, we verified whether the corresponding regulations effectively introduced a new measure. Forty percent of repeated NTM codes corresponded to regulation updating an older one, in which case we do not consider it as a separate measure (as the original ERIA dataset does). Second, we adjust the date of entry into force of certain measures when their associated regulation (as recorded in January 2015) was in fact an update of earlier regulations.<sup>9</sup> In such cases, we also record whether there are changes in the list of products the NTM regulation applies to. Finally, as the dataset is based on the coding of data collected in January 2015, there may be concerns about measures that were in force during part of the period of analysis but eventually eliminated before January 2015. To address these concerns, we check for such instances through the _Global Trade Alert_ data, which records both trade restrictive and trade liberalizing measures for all countries since 2009.<sup>10</sup> To link the NTM dataset to our panel of manufacturing plants, we follow the literature on trade reforms.<sup>11</sup> We build two types of variables. The first is an NTM output measure,

> 8Given that our focus is on the effect on imports of NTMs imposed by the Indonesian government, we exclude from our analysis bilateral contingent measures and measures imposed by the exporting country.

> 9In those cases we attribute the date of entry into force of the measure as that of the first regulation. 10 The dataset can be accessed at its own web page.

> 11Our methodology closely follows Amiti and Konings (2007) and De Loecker et al. (2016).

9
**Urbanization rate** . See Milanovic (2006, p. 461), based on Bairoch’s (1985) cut-off point of 5,000 inhabitants.

**Mean income in $PPP** .  Average income (6.22 _nomisma_ ) divided by the estimated subsustence minimum (3.5 _nomisma_ ), and the latter priced at $PPP 400 at 1990 international prices. This gives (6.22/3.5*400) mean income of $710 in $PPPs.  From Milanovic (2006, pp. 456-7).

# **REFERENCES**

Andreades (1924), “De la monnaie et de la puissance d’achat des metaux precieux dans l’Empire byzantin”, _Ext. B.N_ ., pp. 75-115.

- Bairoch, Paul (1985), _De Jėricho à Mexico: villes et economies dans l’histoire_ , Paris: Arcades, Gallimard.

- Harl, Kenneth H. (1996), _Coinage in the Roman Economy, 300 B.C. to A.D. 700_ , Baltimore and London: Johns Hopkins University Press.

- Lefort, Jacques (2002), “The rural economy, seventh –twelfth Century” in Angeliki Laiou (ed.), _The Economic History of Byzantium: from the Seventh through the Fifteenth Century_ , Washington DC: Dumbarton Oaks, pp. 231-310.

- Lefort, Jacques (2002), “The rural economy, seventh –twelfth Century” in Angeliki Laiou (ed.), _The Economic History of Byzantium: from the Seventh through the Fifteenth Century_ , Washington DC: Dumbarton Oaks, pp. 231-310.

- Milanovic, Branko (2006), “An estimate of average income and inequality in Byzantium around year 1000,” _Review of Income and Wealth_ 52 (3).

- Morrisson, Cecile and Jean-Claude Cheynet (2002), “Prices and wages in the Byzantine world” in Angeliki Laiou (ed.), _The Economic History of Byzantium: from the Seventh through the Fifteenth Century_ , Washington DC: Dumbarton Oaks, pp. 807-870.

- Ostrogorsky, Georgije (1969), _Istorija Vizantije_ , Beograd: Prosveta. English translation _History of the Byzantine State_ , New Brunswick, N.J.:Rutgers Byzantine Series, 1999 [1969].

Treadgold, Warren T. (2001), _A Concise History of Byzantium_ , Palgrave.

46
Madagascar in 2001 and Mauritius in 2003. The results from these surveys are reviewed by Jacques Charmes (2005). For the measurement of time and consumption poverty using the approach proposed in this paper, household surveys are needed that include both time use and consumption data. Currently, time use modules have been included in a range of household surveys similar to the Living Standard Measurement Surveys (LSMS) promoted by the World Bank. In Sub-Saharan Africa, examples of LSMS-type surveys with time use modules include Ghana in 1991-92, 1998-99 and 2005-2006 (Harold Coulombe and Wodon, 2009), Guinea in 2002-03 (author’s work, 2006), Malawi in 2004 (Beegle and Wodon, 2006), Mauritania in 2000, and Sierra Leone in 2003. This is by no means an exhaustive list, but it does indicate that more data are becoming available to conduct work on these issues. In turn, the availability of better data for time use analysis in developing countries makes it important to provide tools for analyzing such data, so that the data end up informing public policies, for example through the countries’ Poverty Reduction Strategies.

# **4. Data and results**

# **_4.1 Time use statistics_**

To illustrate time poverty measurement and comparisons, we use data from Guinea for the year 2002-2003. The data are from the EIBEP ( _Enquête Intégrale de Base pour l’Evaluation de la Pauvreté_ ) survey implemented between October 2002 and October 2003 by the _Direction nationale de la statistique_ (DNS) of the Ministry of Planning.<sup>6</sup> The Republic of Guinea, in West Africa, is a country of about 9 million people, 75 per cent of whom live in rural areas. It ranks among the poorest countries in the world—in 2005 54 per cent of the population was living below the consumption poverty line, and 19 per cent was in extreme poverty. Disparities between rural and urban areas are large, especially in access to education and health and basic infrastructure such as drinking water and sanitation. Women, especially rural women, are mainly employed in subsistence agriculture and informal work and bear the greatest burden in meeting daily basic needs of their families. Moreover, they are discriminated against in the labor market—both in employment and wages.

> 6 This is a nationally representative and somewhat standard priority survey with detailed modules on household composition, income, consumption, education, and health, among others.  The survey also includes a time use module. The survey includes data on 7611 households, but 516 households were deleted from the analysis for various reasons leading to poor data for these households.

13
40

# **Incidence of Trade Taxes and Exchange Controls**

**60. The Fund's Annual Report on Exchange Arrangements and Exchange Restrictions** reports that, in general, most developing countries had adopted, or were edging towards more liberal trade policies at the end of the **1980,** than they had before the external balance difficulties of the late 1970s, albeit from a considerably restrictive base. On the other hand the industrial economies, especially the **EEC** and the **US,** have intensified their recourse to quantitative controls. Given their varying forms and purposes and lack of transparency, it has been difficult to assess the incidence and quantitative impact of nontariff barriers on trade. They take many forms: import licensing, quotas, quantitative restrictions in the form of outright prohibition, voluntary export restraint arrangements, government procurement and state trading practices that may have some hidden discrimination against specific foreign suppliers, etc. But the end result of tariffs and quantitative controls is the same: they raise the price of imported goods in a particular market relative to prices without restrictions and relative to world prices.

**61.** Table **8** below shows the frequency distribution of developing countries **by** the size of the tariff rates, measured here as total, import, and export taxes as a percent of total, imports, and exports of goods and nonfactor services, respectively? It is clear that many developing countries still resort to tariffs and import surcharges to deal with acute balance of payments difficulties. Dudes on overall trade of **10** percent or greater are registered in about one-third of developing countries for which data are available. Other notable features

> **30** Since trade flows and taxes used here are at the aggregated level, the implicit assumption is made that the tariff barrier, which in fact applies only to certain product groups, affects all trade.
# It Takes a Village Election: Turnover and Performance in Local Bureaucracies<sup>*</sup>

Samuel Bazzi Masyhur Hilmy Benjamin Marx Mahvish Shaukat Andreas Stegmann<sup>†</sup> Originally published in the Policy Research Working Paper Series in _September 2024_ . This version is updated in _April 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

**JEL Classifications:** D73, H41, H70, O12

**Keywords** : Bureaucracy, Local Government, Elections, Public Goods

> *We thank the Indonesia Research Fund, the World Bank Knowledge for Change Program, the Ministry of Home Affairs in Indonesia, J-PAL Southeast Asia, SurveyMETER, and Dewi Susanti for their invaluable collaboration and support. We also thank Ben Olken and Dan Rogger for sharing survey instruments, and Diana Moreira and Philipp Barteska for detailed feedback. We are grateful to Astuti Kusumaningrum, M. Radhiyan Pasopati Pribadi, Soala Ekine, Colley Windya, Chaerudin Kodir, Naretta Veronica, Faiz Essa, and Ronin McCobb for excellent research management and assistance. Finally, we thank conference participants at the NBER Political Economy Program, the NBER Organizational Economics Working Group, the Berkeley-VSE Conference on Political Economy and Governance, the Australian Conference of Economists, the 7th Conference on the Political Economy of Democracy and Dictatorship at the University of M ̈unster, the 2024 SIOE Conference, the World Bank DIME-KDI Conference, and seminar participants at CEMFI, Princeton University, Tor Vergata, Tulane University, University of Bochum, University of Bologna, UCLA, University of Manchester, University of Maryland, University of Padova, University of Toronto, and World Bank DECRG. All errors are our own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Bazzi: UC San Diego, CEPR & NBER (sbazzi@ucsd.edu); Hilmy: University of New South Wales (m.hilmy@unsw.edu.au); Marx: Boston University, CEPR & NBER (bmarx@bu.edu); Shaukat: World Bank Development Research Group (mshaukat@worldbank.org); Stegmann: University of M ̈unster and CEPR (andreas.stegmann@wiwi.uni-muenster.de).
_Coordination and implementation planning:_ Table 8 presents information on discussion in project documents on the relation of projects with other Bank projects and with other agencies. Again the criterion used for the Table is existence of such discussion with no judgement being made as to its adequacy. If SAR/MOP discussion of coordination is taken at face value, then such coordination was adequate in the majority of projects where it is present. In fact, for one project, the ICR highlighted the inadequate coordination between the project and other donor financed and Bank projects as an important cause of its unsatisfactory outcome. An interesting feature documented in Table 8 is the relatively low incidence of coordination with the IMF in TLSR projects. Further examination of the causes and impact of this lack of coordination could prove useful. A final feature worth noting is the prevalence of dedicated client project teams and the relatively intensive use of consultants in TLSR project design.

**Table 8. Other Design Features**

||43 p|rojects with a major ad<br>(% of proj|ministration com<br>ects)|ponent||
|---|---|---|---|---|---|
|Type of Project|SAR/MOP<br>Discussion of<br>Relation with<br>other Bank<br>Projects|SAR/MOP<br>Discussion of<br>Coordination with<br>Other Agencies/<br>Projects (including<br>IhF)|Coordination<br>with IMF|Projects with<br>Dedicated Client<br>Country Project<br>Management/<br>Monitoring<br>Units|Projects with<br>One or More<br>Components<br>Implemented by<br>Consultants|
|TALs, LILs,<br>SILs, RILs|66.7|81.8|45.5|90.9|68.2|
|SALs and<br>SECALs|85.7|100|_95.2_|19.0|26.3*|
|All Projects|76.2|90.7|69.7|_55.8_|_48.8*_|

Notes: Details of 2 projects are not available.

# **6. How To Gauge Success: Performance Indicators**

_After discussion the rationale and design ofperformance indicators, their limited use and the lack of uniformity across projects is documented here._ _The nature and content ofperformance indicators:_ A distinction can be made between indicators to judge the performance of a tax system as a whole, a tax or customs administration, and a reformn project. A country should, in principle, be interested in the performance of its tax system as a whole and, within this, the tax and customs administrations. Thus, pre-project diagnostic work should use the same set of indicators as those needed to measure tax system performance. On the other hand, for project implementation such indicators are insufficient and, when there is a time lag between reforms and their impact, of little evaluative use during project execution. Besides outcome indicators, a system of PI for a project should therefore include input, process and output indicators."' In all cases, benchmarks or guidance at interpreting performance indicators are needed to make interpretation feasible:

- For outcome indicators - cross-country, previous year, or targeted performance benchmarks.

- For input indicators - quantity, timeliness (e.g. agreed dates), cost, and, if possible, quality benchmarks.

- A detailed discussion is in World Bank, Operations Policy Department (1996).

17
and the remaining businesses operate from various fixed locations. In particular, the fact that a notably higher percentage of migrant businesses are road-side establishments may partially elucidate their lower returns. Approximately one half of the businesses are in whole sale and retail trade, 15 percent belong to the transportation sector, while manufacturing and construction sectors host roughly 20 percent of primary household enterprises. Across an overwhelming majority of locational or sectoral definitions, the average self-employment income is lower for migrant households.

Moreover, return migration is quite a new phenomenon, taking shape only recently in Albania. In Figure 1 (in Annex), we report the trends of return migration from the two main host countries, Greece and Italy. As expected, given the different nature of the two migration flows, and partly driven by proximity, returns from Greece are more common, and the trend overtime generally steeper. As evident in Figure 2, the spike in returns of the last few years is likely to be due to the fact that a large portion of these most recent returnees have yet to complete their “migration cycle”, being the most likely to migrate again in the near future. These most recent returnees are also on average worse off than earlier returnees (Figure 3), again suggesting that their “accumulation” abroad is not yet complete.

In Table 4, we provide selected descriptive statistics for variables of interest. Mean values are reported for the total sample, as well as by business ownership status. P-values for the t-tests are reported in the last column, and show that, in most cases, the differences are statistically significant at the 1 percent level. More specifically, on average, households with non-farm businesses report more months of past international migration experience, both in Greece and Italy and beyond, have younger household heads, are more educated, are more populous, and are generally wealthier. A higher percentage of households with non-farm businesses are also situated in urban areas, the coastal region, Tirana and in communities with functional infrastructures. Given that the coastal region and Tirana are central locations for manufacturing, construction, and tourism industries, these findings are not surprising.

# **4. Modeling the Impact of Return Migration**

# **_4.1. Econometric Considerations_**<sup>**_7_**</sup>

> 7 The presentation of this section is influenced by McKenzie, 2005.

- 10 -
Table 1: Female participation rates are much higher when broader measures of unemployment are considered

||LFPR|LFPR adjusted for<br>those recorded as not<br>in labor force but<br>registered with<br>placement agency|<br>LFPR adjusted for those<br>recorded as not in<br>labor force but who<br>worked/sought work<br>under MNREGA|
|---|---|---|---|
|Males||||
|Rural|81.3|82.9|81.7|
|Urban|76.4|78.8|76.4|
|Females||||
|Rural|35.8|38.0|38.4|
|Urban|20.5|25.3|20.5|

_Notes:_ Estimates for 2011-12 based on the Usual Status definition of labor force participation.

This is consistent with employment indicators from the population Censuses of 2000-01 and 2010-11, which show only a small decline in female participation rates in rural areas compared to the NSS figures for the same time period. The population Census differs from the NSS in what it considers to be employed and unemployed. The broadest measures of employment – “all workers” in the Census and Usual Status in the NSS – yield comparable figures. The main difference between the two sources is in the definition of unemployment. To be counted as unemployed, the Census does not require a person to have been searching for a job for at least six months during the year, as the NSS does. This less stringent definition results in a much higher unemployment rate in rural areas in the Census in 2010-11 (Figure 5). The possible underestimation of unemployment is a hint that jobs became relatively scarcer during this decade, and not all the decline in female labor force participation was voluntary.

This paper takes a fresh look at female labor force participation in India incorporating not only the standard supply-side considerations, but also the demand-side and the urbanization perspectives. This broader view is intuitively articulated in Section 2. Section 3 contains the analytical model underlying the paper. Section 4 presents the empirical strategy and the data used to implement the model. The econometric results are presented in Section 5. Their robustness to problems of spurious correlation, endogeneity and circularity is assessed in Section 6. Section 7 brings together the main findings by decomposing the decline in LFPR between individual effects, household composition effects, urbanization effects, measurement bias and employment effects, along the entire rural-urban gradation. The results suggest that the 8