rafmacalaba/datause-encoder-data huggingface dataset:
in plant's district-industry per square kilometer, log share of local manufacturing employment in the unorganized sector, and log share of district-industry manufacturing employment in the unorganized sector. The first two of these controls are often associated with the urbanization and agglomeration premiums of dense locations, and we use a per square kilometer normalization as Indian districts vary in spatial size.<sup>12</sup> Column 6 likewise finds very similar results when further modeling the manufacturing share of local employment. Controlling for these extra covariates does not impact the reduced significance for specialization or diversity when they are modeled independently of each other.

Columns 7-9 consider heterogeneity in the sample. Columns 7 and 8 again split the Indian sample by traditional versus modern sectors, finding that the specialization effect is present in both sectors, but the diversity impact is concentrated in variation over traditional sectors. Column 9 determines for each district its specialized industry and then enters a dummy variable for that industry. We further interact this dummy variable with the two specialization and diversity indices. Interestingly, the indicator variable itself suggests an upwards productivity shift for the plants in the specialized industry, although this effect is not statistically significant. On the other hand, the interactions suggest that the marginal productivity gains to further specialization and diversity are associated with plants outside of the specialized industry itself. A similar unreported exercise introduces an indicator variable for plants in the organized sector and their interactions with district specialization and diversity. Organized plants demonstrate substantially higher productivity than unorganized plants, but most of the marginal productivity gains to further specialization and diversity are associated with plants in the unorganized sector.

We stress that these estimations only document partial correlations, and we have not identified an exogenous shifter in local specialization or diversity. The rapid changes noted in the earlier section may allow for such a metric to be identified in the future. Nevertheless, even in their current form these estimations show a potentially important link of the local industrial organization to the productivity of Indian plants not understood before.

> 12  For example, Ciccone and Hall (1996), Duranton and Puga (2004), and Rosenthal and Strange (2004, 2011). Indian studies include Lall et al. (2004), Lall and Mengistae (2005), Deichmann et al. (2008), and Fernandes and Sharma (2011).

19
(2013), _Jamkesda_ is the district level complement to the central government financed health coverage program called _Jamkesmas_ , whose target group includes all poor and near-poor households in the country. Since the target group of _Jamkesda_ is identified by a combination of means testing and local government eligibility criteria, there is a significant discretionary component in the implementation of the local health insurance scheme. A district government can, for example, use _Jamkesda_ to cover the poor and near poor in the district who were not covered by the budget allocation by the central program.<sup>37</sup>

## **[Table 6 here]**

Finally, the estimates in table 6 reveal that direct elections had no significantly positive impact on own revenue or budget surplus. If anything, the level and the share of revenue from own sources decreased significantly during the years of direct elections.

# **6. Concluding Remarks**

Given the sequencing of reforms in Indonesia, our findings shed light on the effects of political decentralization in a setting where the legal and institutional framework of fiscal decentralization is already in place. By isolating the impact of political decentralization in the form of electoral reforms from other aspects of decentralization, our analysis complements the literature on the impact of decentralization on the investments and responsiveness of local governments.

Our results suggest that electoral reforms that promote grassroots democracy did make some difference in the way local governments function in Indonesia. The reforms, which introduced direct elections to elect district government heads in a phased manner, did nothing to raise the district governments’ performance in terms of human development outcomes. Perhaps the analysis of human development outcomes is premature, due to the relatively short time horizon and the

> the same program in the undivided districts were higher in the districts that had direct elections in 2006, than the districts that had direct elections in 2008 or later.   These latter results shed some light on the factors behind the significant effects of _Pilkada_ on health expenditures in Table 4.

> 37 In 2005, Indonesia’s national statistics agency, Badan Pusat Statistik (BPS), conducted a National Poverty Census Survey that was then used to construct a proxy means test with 14 asset indicators for the purpose of identifying eligible households for _Jamkesmas_ , and producing a list of quotas for the poor and near-poor for each district in the country. Districts validated and verified data from BPS using various methods depending on their preference. For example, some districts conducted their own surveys, while other districts used the list from BPS that only identifies the head of household. If the number of poor and near-poor was greater than the BPS district allocation number, these individuals were encouraged to enroll in the local health insurance scheme ( _Jamkesda_ ), if available.

21
# **1 Introduction**<sup>**1**</sup>

In this paper we investigate the extent to which the structure of Ethiopia’s urban labor market might have changed since the early 1990s by looking at changes in the rate of workers’ mobility across sectors, in sectoral earnings gaps, and in the sensitivity of mobility rates to earnings differentials. The aim is to see if these have changed in ways consistent with the market getting more or less segmented in the sense of the existence of entry barriers to relatively high paying sectors of employment. Our analysis is based on data from the 1994 to 2004 waves of the Ethiopia Urban Household Socio Economic Survey of the Addis Ababa University and the University of Gothenburg, Sweden.<sup>2</sup> There are good enough theoretical reasons and anecdotes that could lead us to suspect that urban labor markets in low-income economies like Ethiopia’s are segmented along the lines indicated in this paper. At the same time, there is consensus that the hypothesis of segmentation is difficult, if not impossible, to test formally in datasets like the one available to us.<sup>3</sup> We nonetheless think that the hypothesis provides a useful framework with the help of which one can analyze changes in the structure of labor markets. Although we cannot directly test its fundamental prediction that there are barriers to entry to certain sectors of the labor market, we can still interpret time series of mobility rates and wage gaps or the relationships thereof as indications of whether labor markets are getting more or less flexible. In other words, absent the possibility of segmentation, these same data would not be as interesting in terms of their implications for the structure or functioning of the market.

Segmentation is indicated whenever there are persistent sector wage premiums that are unexplained in the sense that we cannot attribute them to selectivity or to differences in comparative advantage, unmeasured ability, job attributes or taste.<sup>4</sup> One hypothesized

> 1 This paper is based on an earlier version prepared as part of a World Bank study of Ethiopia’s urban labor markets (World Bank, 2007). We thank Jeni Klugman and Caterina Laderchi , for very useful comments on that and a subsequent version as well as for the support and guidance they provided to our work on the paper.

> 2 As discussed in greater detail in section 2 these data are not statistically representative of labor market indicators for urban areas in Ethiopia as a whole. Those can be found, together with a thorough discussion of different labor market data sources for Ethiopia in World Bank (2007).

> 3 Although the balance of studies has so far found evidence against the segmentation hypothesis, almost everyone rejecting it are quick to point out serious limitations of data or methods used in the analysis.

> 4 Dickens and Lang (1985) define segmentation in terms of the interrelated phenomena of sustained and unexplained wage premiums arising from endogenous wage rigidities and the non-price rationing of “primary-sector” or high-wage jobs arising from it. Magnac (1991) defines a segmented labor

2
- <mark>Dixit, Kshitija, and Rupayan Pal. (2010) "The impact of group incentives on performance of small firms: Hausman–Taylor estimates."</mark> _<mark>Managerial and Decision Economics</mark>_ <mark>31, no. 6: 403-414.</mark>

- <mark>Dollar, David, and Aart Kraay. (2004) "Trade, growth, and poverty."</mark> _<mark>The economic journal</mark>_ <mark>114, no. 493: F22-F49.</mark>

- <mark>Duda-Nyczak, Marta, and Christian Viegelahn. (2018) "Exporting, importing and wages in Africa: Evidence from matched employer-employee data."</mark> _<mark>ILO Working Paper, International Labour Organization, Geneva</mark>_ <mark>.</mark>

- <mark>Dutz, Mark A., Ioannis Kessides, Stephen O’Connell, and Robert D. Willig. (2011) "Competition and innovation-driven inclusive growth."</mark> _<mark>Promoting Inclusive Growth</mark>_ <mark>: 221.</mark>

- Engel, Jakob, Deeksha Kokas, Gladys Lopez-Acevedo, and Maryla Maliszewska. Trade and Development. _Distributional Impacts of Trade: Empirical Innovations, Analytical Tools, and Policy Responses_ . Washington, DC: The World Bank, 2021. ISBN 978-1-4648-1704-5.

- <mark>Frankel, Jeffrey A. and David Romer (1999) “Does Trade Cause Growth?” American Economic Review, 89(3): pp. 379-399.</mark>

- <mark>Fox, Louise, and Ana Maria Oviedo. (2008) "Institutions and labor market outcomes in subSaharan Africa."</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>4721.</mark>

- <mark>Halliday, Timothy; Lederman, Daniel; Robertson, Raymond (2018) “Tracking Wage Inequality Trends with Prices and Different Trade Models: Evidence from Mexico”</mark> _<mark>Review of World Economics</mark>_ <mark>(</mark> _<mark>Weltwirtschaftliches Archiv</mark>_ <mark>) 154(1): 47-73.</mark>

- International Trade Center (n.d.) “Egypt: Improving the international competitiveness of the textile and clothing sector (GTEX)” https://www.intracen.org/projects/gtex/EgyptImproving-the-international-competitiveness-of-the-textile-and-clothing-sector-GTEX/

- Korayem, Karima (1997) “Egypt’s Economic Reform and Structural Adjustment (ERSAP)” The Egyptian Center for Economic Studies” Working Paper No. 19.

- Krauss, Alexander (2015) “Creating and destroying jobs across East Asia Pacific: a country-level analysis on wages, exports, finance, regulation, and infrastructure,” IZA Journal of Labor & Development 4:10 DOI 10.1186/s40175-015-0032-8

- <mark>Lichter, Andreas, Andreas Peichl, and Sebastian Siegloch. (2017) "Exporting and labour demand: Micro‐level evidence from Germany."</mark> _<mark>Canadian Journal of Economics/Revue canadienne d'économique</mark>_ <mark>50, no. 4: 1161-1189.</mark>

- <mark>Lopez-Acevedo, Gladys; Robertson, Raymond. (2012)</mark> _<mark>Sewing Success? Employment, Wages, and Poverty following the End of the Multi-Fibre Arrangement.</mark>_ <mark>Direction in Development-</mark>

25
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|---|---|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Microfinance Development Project|Brett E. Coleman|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|Rural and Small Towns Water Supplyand Sanitation Project II(PR|AGMaria Angelica Sotomayor Araujo|
|JSDF TF054211 Access to Legal Services for Vulnerable Groups inc.|WLuz Meza-Bartrina|
|JSDF TF054211 Access to Legal Services for Vulnerable Groups inc.|WLuz Meza-Bartrina|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PE-(APL2)Health Reform Program|Andre C. Medici|
|PY Modernnization of Paraguay's Congress IDF|Roberto O. Panzardi|
|PY Modernnization of Paraguay's Congress IDF|Roberto O. Panzardi|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
|AR APL2 National HighwayAsset Mgt|Gylfi Palsson|
complete set of data for a so-called inter-regional input-output (IRIO) model with imports endogenous (Miller and Blair, 1985, Isard, et al. 1998).

# **3. EMPIRICAL TEST OF THE MODEL AND EVALUATION MEASURES**

# **_The Testing Data Set_**

How does the model specified above perform when applied to data from the real world? In order to evaluate the models’ performance, a benchmark data set from the real world is needed. Because good interregional trade data are quite rare and very difficult to obtain in any country, a natural place to find such data sets is existing global production and trade databases such as the GTAP (Global Trade Analysis Project) database. For instance, version 4 GTAP database contains detailed bilateral trade, transportation, and individual country’s input-output data covering 45 countries and 50 sectors (McDougall, Elbehri, and Trong, 1998). For our particular purpose, version 4 GTAP database was first aggregated into a 4-region, 10-sector data set. Then three of the four regions (the United States, European Union and Japan) were further aggregated into a single open economy which engages in both interregional trade among its 3 internal regions and international trade with the rest of the world. We will use this partitioned data set as the benchmark for a hypothetical national economy, and attempt to use our model to replicate the underlying inter-continental trade flows among Japan, EU and the United Sates as well as the individual country’s input-output accounts.

# **_Experiment Design_**

In the first experiment, we do this without use of the region-specific input-output coefficients as the situation encountered in the real world, where only the national IO table is available to economists (it is the three region’s weighted average in our experiment and are defined as _zij_ • _r_ = ( _zij_ /( _x j_ − _v j_ ))× ( _x rj_ − _v rj_ ) to make full use of the known information). Initial estimates of interregional commodity flows are from the ‘true’ interregional trade data in the 16
a discrete number of inspections in each bin. We do not interpret _N_ as the literal number of shipments, but rather a representation of relative sizes of the number of shipments within each bin.<sup>52</sup> The values of _sl_ and _pl_ should therefore be interpreted as continuous rates. Equations (7), (12) and (13) define the computational model used in our exercises.<sup>53</sup>

# **Data summary**

We report the data that we seek to replicate in Table 5. Using the lagged shipment frequency bins we defined in section 3, we calculate key summary statistics from the 2016 data, statistics that we seek to match in our numerical model. The summary statistics we calculate are: (1) the total share of 2016 shipments in each bin, (2) the number of inspections conducted per 100 shipments in each bin, (3) the average number of lagged shipments (across product-country-firm combinations) in each bin, and (4) the number of inspections per 100 shipments in aggregate.<sup>54</sup> In aggregate, the inspection rate in 2016 was 7 _._ 3 inspections per 100 shipments. The key lesson from Table 5 is that the inspection rate is much higher for shipments that were not observed in the previous year (14 percent of shipments) than for shipments in the other six bins. Inspection rates fall sharply with lagged shipment frequency in the bins with fewer lagged shipments, and then rise somewhat for bins with larger shipment frequencies.<sup>55</sup> Also important for calibration are the bins’ shares of overall shipment activity. Trade flows are exogenous in our model, and we match all of the shipment bin weights exactly in our model calibration. We also calculate the mean number of lagged shipments per bin, a numerical value we use in calibrating ambiguity. The first three lagged shipment bins are

> 52We normalize the total number of shipments _N_ to 100, and allocate shipments across bins according to their proportions in the data.

> 53We solve this constrained optimization program using the GAMS software.

> 54As in the econometric estimates we report, inspections are defined broadly, notably including documentary inspections.

> 55The non-monotonicity in inspection rates across bins likely reflects variation across bins in the riskiness of the shipment types in each bin. The monotonic and decreasing progression of regression coefficients in most columns of Table 2 suggests a consistent inverse relationship between lagged shipment frequency and conditional inspection rates. In an earlier draft, we defined shipments at the HS6 level and observed a monotonic pattern in the unconditional inspection rates as well. We focus here on the sharp drop-off of inspection rates at low frequencies, as these are central to the problem.

37
employed or working compared to other women living in communities with similar average time spent on housework and who were not exposed to the LOA module . Moreover, although among women assigned to the LOA the estimated difference in the reporting of work by being exposed to an average of one hour spent on housework by other women in the community is not statistically significant at the conventional level, it is worth noting that the magnitude of the difference is large (8.4 pp). This, therefore, indicates that time spent on housework is a potentially relevant mechanism that requires further exploration with a larger sample size.<sup>20</sup> Lastly, women’s average time spent on housework in the community does not affect the effectiveness of the ESR in any of the samples under analysis. These findings are consistent with the hypothesis that norms around domestic obligations affect mostly women’s own assessment of what constitutes work or employment.

# **7 Robustness Checks and Additional Results**

## **7.1 Sample Characteristics**

To address how our study sample compares to the average household in the regions surveyed we look at the average characteristics of our respondents and the average Salvadoran respondents of the 2022 Household and Multipurpose Survey (EHPM) living in San Salvador and Usulután. We present this comparison in Table A9. Overall, we find that the two samples are similar in terms of age, sex composition, human capital, and marital status, all of which confirm that the average household members who participated in our experiment are similar to the average adult respondent of the EHPM sample.

## **7.2 Assessing Sensitivity from Selection of Control Variables**

As we discussed in Section 4, we use a double LASSO approach to determine the control variables for our estimations. For each main outcome, we test for the stability of our estimated coefficients after excluding the control variables that LASSO selected. As we show in Tables A10 to A12, the estimated coefficients and their statistical significance are stable for most of the results after excluding all control variables selected by LASSO and included in Specifications (1) and (2).

> 20 To account for a potential reduction of statistical power due to the separation of the sample by sex, we interact Equation (2) with an indicator for female. These results are presented in Figure 3 and Table A8.

19