rafmacalaba/datause-encoder-data huggingface dataset:
fluctuates more than that of small companies. These patterns are true for any given country (see the Appendix, Figures 1A to 1E).

In contrast to the sideways fluctuation of gross capital formation, gross savings show a steady increase over time that results in a positive trend in excess savings. Interestingly, when looking at the median trends, gross savings has a slight negative trend, and the positive trend in the ES is explained by the sharp negative trend in gross capital formation. This suggests that the propensity to generate increasing excess savings is not determined exclusively by a secular drop in investment or by an unusual growth of gross savings, but by a combination of these two factors with varying impact across the firms sampled.

Overall, our sample of firms exhibit the very same trends as those found in studies that use national accounting data (IMF, 2006, OECD, 2007), suggesting that our sample is representative of the aggregate behavior.

Figure 1 also illustrates the changes in the balance sheet (stock variables) generated by the trends in the ES (flow variable). First, the companies in the sample experienced a sustained fall in the indebtedness, which declined from 70.5 to 58 percent of total assets. This can be explained by the higher availability of internal funds from the ES. In addition, the recurrence of financial crises during the sample period might have shifted the corporate sector preferences towards internal funds and away from external sources of financing. Our firmlevel data thus confirms the findings of previous studies based on national accounting data that identify the increase of excess savings with a reduction of indebtedness as one of the main uses of the ES (IMF, 2006).

However, the changes in the balance sheets of the sampled firms were not just limited to the capital structure alone; they also changed the composition of the assets held. From Figure 1, it can be observed that the proportion of long term non-operating assets, which includes holdings in affiliated companies and other similar investments, increased consistently from 14 to 22 percent. Liquidity holdings displayed little change, and even came down slightly.

Some comments are needed regarding the differences in size-weighted averages and medians. The time series of the size-weighted average and the median are greatly similar for the ES, non-operating assets, and debt. For these series, the size-weighted average is always higher than the median, which seems to indicate that larger firms had consistently higher values than smaller firms.

5
|KOF Political globalization||||(1.301)|0.028**<br>(2.356)||||||
|---|---|---|---|---|---|---|---|---|---|---|
|**_Regulation and Supervision_**|||||||||||
|Security market liberalization||||||0.241***|||||
|||||||(3.998)|||||
|International capital liberalization|||||||0.643***<br>(7.537)||||
|Banking supervision||||||||-0.129**|||
|||||||||(-2.553)|||
|Bank concentration|||||||||0.445*||
||||||||||(1.867)||
|Deposit Insurance Coverage||||||||||0.211***|
|||||||||||(3.178)|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|AdjR2|0.287|0.253|0.256|0.276|0.277|0.285|0.303|0.284|0.265|0.305|

# **Table A1.  Country-Level Variables Used in the Empirical Analyses**

This table describes the country-level variables used in the analyses in this paper. The data sources are provided under the „Source‟ column.

33
22 Estimation results of the coefficients of Ebel and Yilmaz variables also appear in Panel A of Table 7. Simple regression results presented on the right side suggest a negative and significant coefficient for tax autonomy and a positive and significant one for non-tax autonomy. These results are the same as in the Ebel and Yilmaz study. Simple regression results in Panel A also reveal a significant and positive coefficient for tax sharing and a negative and significant one for fiscal dependency. These results are a little hard to explain. However, when controlling for the effects of other variables, only coefficients of tax autonomy and non-tax autonomy remain significant, with respect to results of the Ebel and Yilmaz study. Table 8 also illustrates this.

_Table 8 :_ **_Sign and significance of estimated coefficients, decentralization variables of the Oates Model_**

|_Variables_|**Public se**|**ctor size**|
|---|---|---|
||**Multiple**<br>**regression**|**Simple**<br>**regression**|
|Subnational revenue<br>ratio|(+)***|(+)|
|Subnational spending<br>ratio|(+)**|(+)|
|Subnational tax<br>autonomy|(-)*|(-)***|
|Subnational tax<br>sharing|(+)|(+)**|
|Subnational fiscal<br>dependency|(-)|(-)***|
|Subnational non-tax<br>autonomy|(+)**|(+)*|
|Subnational revenue<br>autonomy|(-)|(-)*|
|Subnational own<br>revenueratio|(+)|(-)|
|Subnational dependent<br>revenue ratio|(+)|(+)**|

* Significant at the 1 % level. **Significant at the 5 % level. ***Significant at the 10 % level.

Panel B of Table 7 reports estimation results of the new decentralization variables. Multiple regression results suggest that no variable has a significant impact on the public sector size. This is coherent with the findings of Oates. However, simple regression results show a negative and significant relation between revenue autonomy and the public sector size. They also show a positive and significant relation between subnational
predetermined with respect to present-day aid, but they may have been located in areas that were more suitable for missionary work, or areas where missionaries could survive and be self-sustained. If these areas are more or less likely to be selected for aid projects for some reason other than mission station presence, _β_ will be biased. Detailed information is available on the determinants of the location of mission stations from historical sources, notably Johnson (1967) and Robinson (1915). Moreover, increasing amounts of detailed and spatially disaggregated historical data are available, so the regression analysis can plausibly control for most determinants: precolonial ethnic institutions, distance from coast and rivers, water accessibility, malaria prevalence, altitude, terrain characteristics, historical population and cities, and distance from historical routes. In addition to controlling for these factors, Oster (2019) bounds are also calculated to formally assess the extent of the bias due to unobserved controls.

A second source of bias is lack of common support in the distribution of the control variables. Even if the set of controls **X** _ik_ fully accounts for the selection problem, the simple OLS estimator will still be biased if the cells hosting a historical mission (the treated observations) are very different in their covariates from cells without a historical mission (the control observations) and if the control function is incorrectly specified (Imbens & Rubin, 2015). Several measures are taken to deal with this issue. First, the sample always excludes cells where more than 90 percent of the surface area was covered by desert or forest in the 18th century. Second, in some specifications the sample is further restricted to coastal cells and cells that intersect one of the main African rivers.<sup>11</sup> Third, in other specifications the analysis is conducted on subsamples obtained using propensity score matching.

Finally, a third source of bias derives from the spatial nature of the data. Since mission stations are spatially clustered, the regression analysis may be overestimating or underestimating the impact of a single mission station. To address this issue, alternative specifications also include spatial lags of mission stations as regressors.

> 11The Nile, the Niger, the Senegal, the Zambezi, and the Congo and its tributaries the Ubangi and Kasai.

7
# Table 5. % of Women borrowers w/ Interaction term (MFI-level regression)

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel A: Overall Score Nominal (0 100)</u>

|||Dep var: % of W|omen borrowers<br>||
|---|---|---|---|---|
||Version 1 (|<br>Ful samples)|Version 2 (Dro<br>sam|<br>pping Rural bank<br>ples)|
||OLS|Robust Reg|OLS|Robust Reg|
||(1)|(2)|(3)|(4)|
|Overall Score Nominal|-0.0066***|-0.0043***|-0.0066***|-0.0024**|
||[0.002]|[0.000]|[0.001]|[0.012]|
|NBFI dummy * Overall Score Nominal (0-100)|-0.0022|-0.0034**|-0.0013|-0.0021|
||[0.340]|[0.029]|[0.543]|[0.120]|
|Combined group dummy * Overall Score Nominal (0-100)|-0.002|-0.0021|0.0006|0.0001|
||[0.518]|[0.204]|[0.761]|[0.967]|
|NBFI dummy|0.0145|0.09|-0.0109|0.0516|
||[0.894]|[0.240]|[0.919]|[0.440]|
|Combined group dummy|-0.1018|-0.0947|-0.1508|-0.1092|
||[0.422]|[0.253]|[0.110]|[0.194]|
|Observations|611|611|557|557|
|Number of countries|39|39|39|39|
|R-squared|0.472|0.507|0.483|0.57|
|Adjusted R-squared|0.455|0.491|0.464|0.555|
|_Microscope index+ interaction term (P-value in brackets)_|||||
|NBFI dummy|-0.00881***|-0.00774***|-0.0079**|-0.00449***|
||[0.006]|[0.000]|[0.015]|[0.000]|
|Combined group dummy|-0.00861***|-0.00647***|-0.00594**|-0.00231|
||[0.007]|[0.000]|[0.035]|[0.154]|

33
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Policy Research Working Paper 9238

# **Abstract**

In many regions of the world, the persistent, and growing, proportion of young people who are currently not in employment, education, or training is of global concern. This is no less true of Morocco: about 30 percent of the Moroccan population between ages 15 and 24 are currently not in employment, education, or training. Drawing from various rounds of Moroccan labor force surveys, this paper contributes to understanding the complex dynamics of labor markets in developing countries. First, it identifies the socioeconomic determinants of Morocco’s young population not in employment, education, or training. Second, employing a synthetic panel methodology in the context of labor market analysis, the paper describes how the conditions of individuals in this group has changed over time. One striking, and worrisome, pattern that emerges from the 2010 synthetic panel data is that, even after 10 years, a majority of the young population not in employment, education, or training remained outside the labor market or education, with very little chance of moving out of their situation. Their chronic stagnancy confirms the powerful effect that initial conditions have on determining young people’s future outcomes.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vmolini@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_page 23_ Table 3. Panel Regression: In y,1 = oc + 01 In TOT,, + 132 In TOT,,., + .. + ell. coeficients unrestricted across the subperiods 1965-1980 and 1981-1989.

|Dependent variable|Terms of trade<br>sum of coef.<br>1965-1980|Terms of trade<br>sum of coef.<br>1981-1989|P-value for<br>coef.<br>stability|R<sup>2</sup>||N|
|---|---|---|---|---|---|---|
|Ln Real exchange rate|-0.6746|-0.5484|0.0235|0.6807|0.2471|431|
|Ln Consumption|0.3791|0.3110|0.25|0.2815|0.3245|431|
|La Private Consumption|<br>0.3042|0.2276|0.403|0.2140|0.3115|431|
|Ln Govt Consumption|0.5395|0.4211|0.0931|0.2974|0.4428|431|
|Ln Savings|-0.1439|0.1884|0.0894|0.0747|0.6637|429|
|Ln Investment|0.3124|0.3510|0|0.4505|0.4253|413|
|Ln Private Investment|0.4647|0.1246|0|0.3689|0.5819|413|
|Ln Govt Investment|0.2590|0.6564|0|0.4226|0.4627|413|
|Trade Balance|-16.9939|-10.9972|0.419|0.2181|15.9906|395|
|Ln GDP|-0.0488|0.0699|0.0893|0.0274|0.2990|431|
|Ln Oil value added|-0.4854|-0.0789|0.0258|0.2807|0.3491|178|
|Ln Non-oil value added|0.2449|0.2710|0.703|0.4341|0.1448|178|
|Ln Agriculture|0.1125|0.0087|0.259|0.0784|0.2153|293|
|Ln Manufacturng|0.2021|0.2877|0.281|0.0908|0.4018|240|
|Ln Construction|0.3898|0.4083|0.76|0.3601|0.2721|193|
|Ln Public Utilities|0.3213|0.3142|0.713|0.3258|0.2802|158|
|Ln Services|0.0842|0.1241|0.399|0.0341|0.2439|293|
|Ln Transportation|0.1601|0.4157|0.0798|0.2020|0.2445|175|
|Ln Trade|0.2396|0.2250|0.936|0.1585|0.2400|158|
|Ln Other services|0.2597|0.2674|0.53|0.3883|0.1536|175|

For the real exchange rate, the trade balance, non-oil value added, agriculture, construction, utilities, and trade, the regressor matrix also includes DEBTk; in all the investment equations, it also includes DEBT,, and In INTRATE. Sample period: 1965-1980, and 1981-1989. The P-value denotes the minimum significance level at which we can reject the null hypothesis of identical terms-of-trade coefficients across subperiods, using the F-test described in Hsiao (1986), ch. 2.2, equation 2.2.20.
4). The gender difference went up to 55 percent in 2006, and even though it decreased in 2008 to 44 percent, it was still higher than the pre-MFA phase-out level.

**Table 3 Wage Premium Regressions forCambodia**

||**1996**|**1999**|**2004**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|
|**Female**|-0.266***|-0.141***|-0.120***|-0.128***|-0.133***|-0.115***|
|**Hours**|0.010***|0.011***|0.016***|0.014***|0.017***|0.017***|
|**Age**|0.059***|0.044***|0.038***|0.052***|0.066***|0.056***|
|**Education**|0.053***|0.042***|0.009***|0.079***|0.066***|0.050***|
|**Textiles and apparel**|-0.301***|0.781***|0.287***|0.354***|0.374***|0.130**|
|**Hazard**|-0.000|0.094|-0.085|0.362**|0.132|0.040|
|**Constant**|10.640***|9.973***|10.747***|9.942***|9.930***|10.394***|
|**Number of observations**|4,706|3,000|7,068|2,294|1,287|3,800|

_Source:_ Authors’ calculations based on Cambodian Socio-Economic Surveys.

_Note:_ The grand mean effects of the industries are calculated; additional controls include age squared, hours of work, industry, and occupation dummies. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

**Table 4 Wage Premium Regressions forSri Lanka**

||**2002**||**2006**||**2008**||
|---|---|---|---|---|---|---|
|**Female**|-0.401|***|-0.553|***|-0.443|***|
|**Age**|0.111|***|0.154|***|0.118|***|
|**Education**|0.040|***|0.039|***|0.041|***|
|**Textiles and apparel**|0.196|***|0.050|***|0.082|**|
|**Hazard**|0.525|***|0.790|***|0.528|***|
|**Constant**|6.070|***|5.742|***|6.181|***|
|**Number of observations**|12,213||17,828||14,998||

_Source:_ Authors’ calculations based on the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys.

_Note:_ The grand mean effects of the industries are calculated; additional controls include age squared, industry, and occupation dummies. Hours worked were not available in the Sri Lankan surveys.

# _4.4. Working Conditions_

Working conditions directly impact a worker’s well-being through the number of hours that are worked overtime, a hazardous work environment, social benefits, or workplace 14
# **Pollution in Ugandan Cities: Do Managers Avoid It or Adapt in Place?**<sup>_∗_</sup>

**Vittorio Bassi**<sup>_†_</sup> **Matthew E. Kahn**<sup>_‡_</sup> **Nancy Lozano Gracia**<sup>_§_</sup> **Tommaso Porzio**<sup>_¶_</sup> **Jeanne Sorin**<sup>_∥_</sup>

July 29, 2021

> _∗_ We gratefully acknowledge financial support from CEGA, IGC, PEDL, and USC. We also acknowledge support from the Pollution Management and Environmental Health Program (PMEH) supported under the World Bank’s TF0A3415. The PMEH program focuses on providing technical assistance to countries, facilitating pollution management, promoting public awareness, and helping them reduce the negative effects of pollution on health. We thank Bjoern Brey for an insightful discussion. We also thank Simon Franklin, Jonas Hjort, Namrata Kala, Paulina Oliva, Munir Squires, as well as seminar participants at USC, John Hopkins, the European Meeting of the Urban Economics Association, and PacDev for useful feedback. Kasey Chatterji-Len, Jiong Gao, Jung Hyuk Lee and Leire Sarasola provided excellent research assistance. We also thank everyone at BRAC Uganda who made this project possible, in particular Raffaela Muoio and Esau Tugume. This study received ethical approval from the Mildmay Research Ethics Committee of Uganda (MUREC), Reference No. 1106-2018. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own.

## JEL Codes: O18, R00, Q53, D22, N57

Keywords: Air Pollution, Urban Development, Traffic, Small Firms, Managerial Decisions, Uganda

- University of Southern California, CEPR and IGC; email: vbassi@usc.edu.

- University of Southern California and NBER; email: kahnme@usc.edu.

- _§_ World Bank; email: nlozano@worldbank.org.

- Columbia University, CEPR and NBER; email: tommaso.porzio@columbia.edu.

- _∥_ The University of Chicago; email: jsorin@uchicago.edu.
encompasses a large coverage (i.e. general government); (ii) whether the FC make forecasts; (iii) whether the FC makes forecasts on the preparation of the fiscal policy; (iv) whether the FC makes forecasts assessments on the fiscal policy; (v) whether the FC makes recommendations to the government; (vi) whether the FC makes long-term sustainability analyses; (vii) whether the FC establish consistency with the objectives; (viii) whether the FC evaluates the costs of fiscal measures; (ix) whether the FC makes ex-post analysis; (x) whether the FC publish public reports; and (xi) whether the FC has a high media impact. We focus on the period 2009-2016 to evaluate the efficiency of postcrisis policies. The comparison of the procyclical bias across groups of countries with and without these features (Table C.2 and Figure C.2) shows that countries with fiscal councils have lower procyclicality bias; but the largest impact comes from attributes of the council, especially those that examine sustainability and evaluate the costs of different measures. The features are associated with countercyclical spending. Transparency through public reports and the media exposure also matters, in line with predictions by Alesina et al. (2008).

# **[insert Table C.2; Figure C.2]**

# **_Bottom-up solution: Fiscal decentralization_**

Though fiscal decentralization may be an effective mechanism to enhance the provision of public service delivery in developing countries, such as the access to primary education, the access of drinking water, the access of refuse and sewage disposal facilities. The discussion still exists around the optimal level of fiscal decentralization for both spending and expenditures. So, the decentralization might be also effective in the struggle against fiscal procyclicality of the general government because of the application of subsidiarity principle. Indicators of fiscal decentralization generally refer to the fiscal composition ratio established by the IMF’s Government Finance Statistics Yearbooks (see Blume and Voigt (2008) and Blume et al. (2009) for more details). Instead of establishing arbitrary thresholds for the fiscal ratio, we focus on indicators of political decentralization (PD) established by the Database of Political Institutions provided by the World Bank Group. Namely, we focus on (i) whether there are contiguous autonomous regions; (ii) whether local governments and legislature are elected; (iii) whether state/provinces governments and legislature are elected; (iv) whether states/provinces have authority over taxing, spending, or legislating; and (v) whether the constituencies of the upper house members are the states/provinces. We focus on the period 2009-2015 (2016 not available yet in data) to evaluate the efficiency of post-crisis policies. The correlations (Table C.3 and Figure C.3) show that the closest proxy of fiscal decentralization (i.e. the authority of states/provinces over spending, taxing or legislating) is negatively correlated with fiscal procyclicality, suggesting that decentralization may be a tool to mitigate the procyclicality bias.

20
<!-- Start of picture text --> Baseline Macro projections Predictions<br>(Pre-crisis} (Contemporaneous)<br>Micro data a g<br>(Household/LF a<br>survey) ~~ = £<br>N<br>~<br><!-- End of picture text --> <!-- Start of picture text --> Baseline (Calibration) simulation Euture (Projection)<br>Input Macro projections<br>ry<br>4= 3 JT)2.<br>Fi = &<br>A ‘<br>Occ. status model Ind. Ain occ. status<br>. . Ind. real earnings Aggregate to hhd level<br>Output Earnings equation : .<br>Mieratiigration/remittances. >) Hhd. remittances Consumption<br>Poverty incidence<br>Population<br>: wn<br>g<br>=<br>Population growth i<br>Income/consumption<br><!-- End of picture text --> <!-- Start of picture text --> Rule: Best fit to micro Rule: Replicate. racro Headcountdistributions ratio<br>data proporticanal changes at GINI, Thiel T<br>micro level<br><!-- End of picture text -->