rafmacalaba/data-use-mentions-extended huggingface dataset:
for some businesses. Survey data shows that most registered businesses were unable to calculate basic percentages and a non-trivial share did not have extensive transaction records. Instead, it appears many small businesses rely on heuristics to determine their tax liability, as a non-trivial share consistently pays the same amount in TOT each month despite numerous policy changes altering their tax liability up and down (consistent with similar findings in other settings such as Tourek (2022) in Rwanda).

To test an alternative design of an STR that aims to better balance the trade-offs inherent in these types of regimes, we implemented a randomized experiment across over 10,000 registered TOT payers in Kenya. Treated businesses were encouraged to pay a set fee in TOT that was customized based on their business type and location.<sup>3</sup> This alternative the for small businesses the design simplifies process by proposing amount of tax required to be paid. Ex-ante, there is a concern that a set fee may be less equitable than requiring a proportion of turnover to be paid, but customizing the TOT amount is more equitable than a single set fee being required across the country as there is much less variation in turnover of small businesses within the same location and business type. On average, taxpayers allocated to the alternative design of the TOT regime paid a much larger amount of tax (and slightly increased the share of taxpayers who filed). The intent to treat effect was in the order of a 11.7 percent increase in the amount of tax paid, and the treatment on treated effect was a 19.8 percent increase. These sizable effects were almost exclusively driven by taxpayers who were below the median in the distribution of the previous months’ tax payment amounts. In other words, encouraging taxpayers to pay a set fee led to relatively smaller businesses<sup>4</sup> paying more tax and had no meaningful effect on relatively larger businesses. Consequently, introducing greater simplicity into the TOT regime came at the cost of reducing vertical equity
_Social Capital, Household Welfare and Poverty in Indonesia_ 15

# **3. The Dimensions of Social Capital**

The effectiveness with which social capital, in the form of local associations, can fulfill its role in disseminating information, reducing opportunistic behavior, and facilitating collective decision making depends on many aspects of the association, reflecting its structure, its membership and its functioning. For this study we focus on six aspects of local associations.

(1) **Density of membership.** This is measured by the number of memberships of each household in existing associations. The provision of a map of local associations was one of the prime objectives of the LLI study and a complete inventory of all existing associations was made at the village level. Flach household was then given that inventory and asked which associations they were a member of. The total number of active memberships in the villages included in the sample added up to 6,210, which indicates that on average each household is a member of about five associations. However, there is significant variation by province and according to the characteristics of the households.

With an average of 3.7 associational memberships per households, density is lowest in Jambi. In Jawa Tengah, each household belongs on average to 6 groups and in NTT to 6.5 groups (Table 2). This is in part related to the religious composition of the population since Catholic households (who live only in NTT) are on average members of 8.3 groups, almost twice as much as households of other religions. Female-headed households belong on average to one group less than male-headed households. Memberships rise quite sharply with the level of education but, at an aggregate level, they are only slightly related to income level.
# **<mark>Figure 5. Distribution of children by month of birth</mark>**

<!-- Start of picture text --> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br>Month of birth<br>Proportion 95% confidence interval<br>12<br>10<br>8<br>6<br>4<br>Percentage of children<br>2<br>0<br><!-- End of picture text --> <mark>Note: The figure presents the proportion and the 95% confidence interval of the proportion of children age 1-6 by month of birth.</mark> <mark>Sources: Authors’ estimation from VHLSS 2010, 2012, 2014 and 2016.</mark> 31
More than half of the incidents recorded by ACLED during our sample period (August 2019-May 2021) represent battles. We construct household-level measure of exposure to battles by counting the number of battles within a 10, 20, 30, 40 and 50 km radius around each survey household location. We compute cumulative number of battles in the previous months until the end of the survey month. Table A1 shows that the number of battles has tripled after November 2020.<sup>13</sup> The HFPS sample continually declined in follow-up rounds due to non-response to calls and attrition. Further, the Tigray sample was especially affected by the war itself and disruptions in telecommunication services in the region, further reducing the sample to 1,982 households in

> 12 World Bank. Ethiopia-COVID-19 High Frequency Phone Survey of Households 2020. Dataset downloaded from <u>www.microdata.worldbank.org.</u> 13 Before November 2020, such events consisted of limited-scale armed conflicts between state police or armed forces and ethnic militias, typically reflecting localized political grievances.

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_Prices of internet services remained high in Uganda as of 2010._ Reduction in prices would boost Uganda’s already relatively high Internet

### _Source:_ AICD.

_Note:_ EAC = East African Community.

— = Not <u>available.</u> penetration and foster the development of ICT services and IT-enabled exports, helping the country reduce the gap with higher performing peer countries (figure 24a). Fixed broadband pricing has shown little traction and digital subscriber line (DSL) tariffs remain high given the limited number of wired telephone lines. High-speed wireless networks on the other hand offer more attractive tariffs and will drive Uganda’s Internet progress.<sup>3</sup> Prices are expected to fall following Uganda’s connection to the EASSy undersea cable

## **Figure 24. Uganda’s Internet market, 2008-2009**

### **a. Internet service trends, users, 2000–09**

### **b. Internet service trends, COMESA, 2008–09**

<!-- Start of picture text --> 25 1600<br>9.0 70<br>8.0 1400<br>60 20<br>7.0 1200<br>50<br>6.0 15 1000<br>5.0 40 800<br>4.0 30 10 600<br>3.0 400<br>20 5<br>2.0 200<br>10<br>1.0<br>0 0<br>0.0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person) International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>2000 2001 2002 2003
-|---|---|
||rate|rate|rate|rate|rate|rate|rate|
|Conflict debt|11.27<sup>***</sup>|4.672<sup>**</sup>|2.279|||7.005<sup>**</sup>|2.581|
||(3.252)|(1.880)|(1.484)|||(3.557)|(2.121)|
|Country conflict debt||||16.01<sup>***</sup>|7.785<sup>***</sup>|12.03<sup>***</sup>|6.374<sup>**</sup>|
|||||(3.225)|(2.240)|(3.469)|(2.519)|
|Country fixed effects|No|No|Yes|No|No|No|No|
|Continent fixed effects|No|Yes|No|No|Yes|No|Yes|
|Adjusted R-squared|0.0279|0.527|0.737|0.0352|0.530|0.0435|0.531|
|Observations|1885|1885|1885|1885|1885|1885|1885|

Notes: Robust standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is poverty rate (in percent). Conflict debt is the discounted history of conflict years where a conflict year is defined as by more than 0.08 fatalities per 1,000 population. Country conflict debt is computed in the same manner at the country level. The regressions weighted by population
projects at different levels of abatement are calculated.<sup>19</sup> In terms of average cost of mitigation, the relative attractiveness of different types of projects appears to be similar to that resulting from the estimation of net abatement cost as reported in table 5. The structures of average cost curves for different project types are similar to those as depicted in figures 4 and 5.

We also estimate the project costs in Brazil, China, and India separately. Relative attractiveness of different types of project within each of these countries, as well as the comparative advantage of these countries for different types of projects appear to be similar to that of the full model (i.e., model IV in table 7) with little exceptions.

# **5 Conclusions and Policy Implications**

In this paper, we look at the cost of producing emission reduction credits under the Clean Development Mechanism using project data. We control for the duration of the projects, the type of technology used in the project and the year in which the project began. In our preferred (full) model, we employ a complete set of fixed effects associated for each host country and for each technology type, but we also estimate versions of the model that use broader classifications of projects and regional dummies rather than hostcountry dummies. We repeat the analysis for China, India and Brazil, countries that host a large number of CDM projects, and for specific types of projects. Many of the projects generate CERs and simultaneously create additional power-generating capacity, so we consider a separable cost function based on an explicit disentanglement of costs and revenue. We also consider an alternative joint cost functions that is consistent with inseparable costs.

In general, we find no evidence of increasing returns to scale. At mean levels, all calculated elasticities were less than one, although constant returns to scale could not be ruled out in some cases. We found significant variation in scale effects by type of projects; for example, H/PFCs & N2O reduction
changes in the macroeconomic environment. Examples of these changes are decisions on training, child conception, etc. (Bourguignon and Spadaro, 2006). The microsimulation model studied in this document belongs to those static with behavioral 'agents' responses.

Among the behavioral models, Olivieri et al. (2014) present a microsimulation model that evaluates the distributional impacts of a macroeconomic shock with low data and computational requirements. It allows accounting for labor and non-labor income mechanisms and captures impacts at the micro level for the entire income distribution. The model focuses on labor market adjustments in employment and earnings and changes in non-labor income and prices (with a view to the variation in food and non-food prices). However, it does not allow for capturing labor informality, an important feature for Latin American countries. Moreover, Olivieri (2020) adapts the previous version of the microsimulation model to assess the effects of the triple crisis in Ecuador. This version incorporates labor informality only in estimating the labor market structure in the three main sectors of economic activity. Yet, its predictive capacity is limited at the labor income level.

In the absence of CGE models, an alternative way to feed microsimulation models under the top-down approach is using macroeconomic projections of output that are almost always available. However, since employment and labor income estimations are usually unavailable, the output growth estimates must be translated into employment and labor income changes at the sector level. These estimates are typically made using sectoral output-employment and productivity-labor income elasticities based on aggregate output and labor market past data, which are then applied to the output growth projections to generate changes in employment and labor income by sector (Braga, C. et al., 2023). The predicted sectoral employment and income, along with the macroeconomic output projections, are the final inputs for the microsimulation model. In this paper, these essential inputs come from actual data to isolate biases from the model and the macroeconomic inputs.

This paper considers the model proposed by Olivieri (2020) and assesses the gains in
Placzek, 2010; Farber, 2017; Goldschmidt and Schmieder, 2017; Schmieder, von Wachter and Heining, 2022). This literature typically compares workers who lost their jobs to workers who did not lose their jobs and uses mass layoff events for identification. Our work uses another source of exogenous variation in job loss, namely firm takeovers. Firm takeovers substantially increase the probability of job loss and job loss has longer-term negative consequences for workers.

Our findings have important implications for assessing welfare effects of firm consolidations. First, they suggest that takeovers can have negative, long-lasting impacts on target workers’ labor market outcomes. Second, we show evidence that labor restructuring is an important determinant of job loss: workers who are over-placed or workers in acquisitions where the acquirer is more likely to have workers already serving a given role experience higher rates of separation. These findings suggest that even consolidations that are typically viewed favorably from a consumer welfare perspective, those that increase efficiency without likely increasing product market concentration, can still have large, long-term welfare impacts for target workers through labor restructuring.

The remainder of this paper proceeds as follows. In Section 2, we describe the data and our empirical approach. In Section 3, we show that takeovers have long-lasting, negative impacts on incumbent worker outcomes. In Section 4, we demonstrate that labor restructuring is the primary mechanism driving these results through a series of heterogeneity analyses. Finally, in Section 5, we conclude.

# **2 Data and Methods**

## **2.1 Data Sources**

We study the impact of takeovers on labor demand and the labor market outcomes of workers of acquirer and acquired firms using several administrative datasets from Statistics Netherlands (CBS). We begin with an administrative dataset which identifies various “firm events”, such as when firms are established, closed down, or taken over. A takeover is defined as an event where one business takes over one or more other businesses in the sense that the production factors of the acquirer continue
22</sup> The quality of infrastructure and transport costs are important factors explaining the elevated performance of urban manufacturing enterprises in Ethiopia vis-à-vis their rural counterparts (Rijkers et. al., 2010).<sup>23</sup> Exploiting geographical variations in greenfield foreign direct investment (FDI) in Ethiopian woredas (districts), Abebe et. al. (2018) find that the number of domestic plant openings increased significantly in the three-year window following the entry of a greenfield FDI plant in a district. Their study suggests that the benefits from knowledge spillovers outweigh the costs (competition effects) mainly due to labor flows from foreign to domestic plants. In addition, there could possibly be backward and forward linkages in the supply chain, and horizontal linkages that occur because of more casual interactions.

# **III. Data Description**

The data used for this paper comes from the Large and Medium Manufacturing Industries Survey (LMMIS) conducted by the Ethiopian Central Statistical Agency (CSA) on a yearly basis since 1976. LMMIS is a

> 19 Bigsten and Gebreeyesus (2009) find evidence on both self-selection and learning-by-exporting, whereby more productive firms enter exports and over time exporters have higher productivity vis-à-vis non-exporters.

> 20 Firms without initial bank ties exhibit significantly lower average investment rates and longer durations of investment inaction relative to firms with initial bank ties. For small enterprises in Ethiopia, access to credit is a binding constraint for their growth as they are ‘too big’ for microfinance institutions, but they are ‘too small’ for formal banks in terms of the size of loan, reflecting the ‘missing middle financial intermediaries’ that serve small enterprises.

> 21 This is not contradictory to the findings of Bigsten et al. (2012) because agglomeration puts a competitive pressure on firm products’ prices and hence could dissuade them from co-locating.

> 22 Lall et al. (2017) suggest that the depressed level of agglomeration externalities in African cities reflects the underlying distortions in the functioning of key factor and
# **References:**

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A log scale for the x-axis has been used to improve the display (the value of domestic bonds outstanding increases exponentially with every unit increase along the x-axis).**

Yet despite their strong growth, emerging local currency bond markets remain small relative to those of the advanced economies, and to other asset classes. As of the end of 2011, the total value of the local currency government securities outstanding of the BIS sample of 22 EMs employed above was around US$4.7 trillion. While up sharply from only US$863 billion at the end of 1999, this is still equal to only about 13% of the US$37 trillion in domestic government securities of the 30 advanced economies tracked by BIS statistics, and roughly comparable to the value of the local currency government securities of France, Germany, and the United Kingdom combined (about US$5 trillion as of end-2011). The total bond market value of the six largest Southeast Asian economies (the ASEAN-6), at US$883 billion, is equivalent to about 7% of Japan’s.<sup>3</sup> Such aggregate statistics in fact exaggerate the investible universe for foreign investors, who may face restrictive regulations or taxes, and often have minimum liquidity requirements that exclude smaller markets and bond issues. The total market capitalization of the most widely followed EM local currency bond benchmark, the JP Morgan Government Bond Index-Emerging Markets (GBI-EM) Global Diversified Index, was US$832 billion in mid-2012. This compares with nearly US$21 trillion for the equivalent JP Morgan developed market-only index (GBI-Global).<sup>4</sup> The combination of increased foreign investor interest and the still relatively small size of the EM local currency bond asset class has resulted in a sharp increase in the share of local currency EM bonds held by foreign investors. As is the case when examining broader trends in local currency bond market development, the degree and impact of this rise in foreign involvement differs widely across EMs.
the program presents a number of interesting aspects, a key methodological challenge to overcome for a rigorous socio-economic assessment of the pilots was the lack of a baseline data. To deal with this, we adopt a spatial discontinuity design and administered a short survey to some 3,500 households on both sides of the boundaries of the four pilot cells. This section describes the sample design and the econometric methodology, relying on the use of spatial fixed effects that is used to interpret the data.

# **4.1 Sampling strategy**

As there was no usable baseline survey to assess the impact the pilot program, we rely on cross-sectional data, sampled from both sides of the boundaries of the pilot cells to allow comparison of outcome variables between treated and non-treated households (inside and outside the borders of the pilot cells, respectively) close to the borders of the pilot cells. We thus exploit the discontinuity which administrative boundaries created in the introduction of the pilot program as an identification strategy. The key assumption is that before the start of the program, households close to a cell boundary were similar in unobservable and observable factors affecting relevant outcomes.

> 7 The fee of RWF 1000 (US$ 1.84 at the 2008 rate) per parcel or about RWF 4000 (US$ 7.36) per household taking the national average of four parcels per household compares to a cost of approximately US$ 9-11 in the pilot—since then reduced to about US$ 5—per parcel.

10
Debt restructurings: Delay and Replay,” Vox EU, March.

Haber, Stephen. (2005.) Mexico’s experiments with bank privatization and liberalization, 19912003 _, Journal of Banking and Finance_ 29: 2325–2353.

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International Monetary Fund (2020). _World Economic Outlook,_ October.

Kaminsky, Graciela, and Carmen M. Reinhart, (1999). “The Twin Crises: The Causes of Banking and Balance-of-Payments Problems,” _American Economic Review_ , Vol. 89 No. 3, June, 473-500.

<!-- Start of picture text --> .<br><!-- End of picture text --> Kaminsky, G. L., and Vega-García, P. (2016). Systemic and idiosyncratic sovereign debt crises. Journal of the European Economic Association, 14(1), 80-114.

Kindelberger, Charles (1978). _Manias, Panics, and Crashes: A History of Financial Crises_ (New York: Basic Books).

Laeven, Luc and Fabian Valencia (2013), “Systemic Banking Crises Database”, _IMF Economic Review_ , Vol. 61, June, 225-270.

35
do they compare with the current secured transactions frameworks in MENA. The main sources of data contained in the report are related to (i) internationally accepted practices on modern secured transactions systems (The World Bank Principles on Insolvency and Creditor’s Rights and the UNCITRAL Legislative Guide on Secured Transactions); (ii) Data survey from the World Bank Enterprise Surveys, Doing Business Report and SME Lending Surveys to Financial Institutions in MENA; (iii) country specific diagnostic reports from the World Bank Group and other multilateral organizations. Given the complexity of providing an overview of the secured transactions system in each country limiting the paper to a manageable size, the report focuses on general principles and how they are applied in the region.

9