rafmacalaba/datause-encoder-data huggingface dataset:
Table D6: Ethnicity randomization checks 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|-0.000333<br>(0.00218)|||
|Def. plur. Masai|0.000336<br>(0.000782)|||
|Pla. plur. Meru||0.00203<br>(0.00313)||
|Def. plur. Meru||-0.000889<br>(0.00268)||
|Pla. plur. Mijikenda|||-0.000673<br>(0.00299)|
|Def. maj. Mijikenda|||0.00369<br>(0.00408)|
|DV mean|0.00589|0.0260|0.0110|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14944|14944|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

42
## **c. Generosity (benefits as % of MYPP)**

## **d. Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 63.1<br>Marginal Contributions to<br>0.01<br>Inequality<br>Change in Poverty Gap 1.94<br>Marginal Contributions to<br>0.99<br>Poverty<br>4.0<br>0.3 0.0 0.1<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

# **Finally, the fiscal cost analysis shows that, with a full take-up, the fiscal cost can increase substantially;**

**this represents an upper bound.** Under Policy Scenario 2, the simulations show a substantial cost increase, with the MSA program cost rising to almost 60% more than in the first policy scenario (146.5 million BGN more) and a similar cost for the heating allowance. The simulations show a substantial change in fiscal spending, with an increase of 350.3 million BGN for social assistance, 38.6 million BGN for the heating allowance, and a cumulative rise of 387.0 million BGN in the total fiscal cost (table 5). These changes underscore the financial implications of the policy simulation, highlighting a substantial expansion in government expenditure in these support programs.

**Finally, the fiscal cost analysis reveals that the fiscal cost can increase significantly with full uptake, representing an upper limit** . Under Policy Scenario 2, simulations indicate a substantial rise in costs, with the MSA program cost nearly doubling compared to the first scenario (230.2 million BGN versus 382.6 million BGN, respectively) and a similar increase in the heating allowance fiscal cost (the change in takeup changed only from 99% to 100%). The simulations demonstrate a notable shift in fiscal spending, showing an increase of 350.3 million BGN for social assistance and 38.6 million BGN for the heating allowance, resulting in a cumulative rise of 387.0 million BGN in the total fiscal cost (Table 5).

**Table 5. Estimated Fiscal Costs, Baseline vs Policy Scenario 2, Million BGN.**

||**Social**<br>**assistance**|**Heating**<br>**allowance**|**Total**|
|---|---|---|---|
|Fiscal Cost, Baseline (Million BGN)|32.3|140.2|172.5|
|Simulated Fiscal Cost, Policy Scenario 2<br>(Million BGN)|382.6|176.8|559.5|
|Change in Fiscal Cost, Million BGN|350.3|36.6|387.0|

_Source: Own estimates using administrative accounts for the baseline and CEQ and Bulgaria SILC 2021 for the simulated fiscal costs._ 33
explicit tax3s collected from banks. Quantifying these taxes in a credible way is not easy. Apart from the problems of gathering reliable data there is the conceptual difficulty of measuring the market clearing rate of interast on short-term risk-free paper (r*) which is the key variable. We cannot hope to estimate it precisely, especially in very distorted market conditions. Several approaches were explored: _Measure 1_ begins with the idea that the expected rate of inflation as a guide to likely market clearing nominal interest rates on the assumption that in equilibrium the market clearing rate of interest is normally positive, though it might ba small6. This assumption is implemented by taking actual inflation as a proxy for expected inflation and taking it that the market clearing rate of interest is at least one per cent higher. Specifically we took r* to be one per cent above the actual future inflation rate<sup>7</sup> , except where this was lower th'n the actual interest rate on treasury billss, in which case r* _was_ set equal to the actual rate.

_Measure 2_ adopts a different and more subjective approach. For it we attempted to identify periods eduring which the comparative absence of interest rate controls could justify the assumption that actual rates during those periods were close to market clearing rates. The jump in interest rates which occurred at the beginning of such periods of liberalization was taken as a measure of the gap between actual and market clearing interest rates for other periods. Thus measure 2 assumes that the degree of repression was constant outside of the periods identified as liberal.

_Measure 3_ takes the actual foreign interest rate adjusted for actual exchange rate change9 as a measure of r*, while _Measure_ 4 differs only by using the parallel market exchange rate instead of the official one. These two measures are marred by the fact that sharp movements in the 6 Note, for example, the often quoted figures for US Treasury bills, the annual average real yield on which during the the second and third quarters of the century was as lictle as 0.2 per cent.

7 Actually we used the continuously compounded rate equivalent to fourquarter inflation. Using four quarters instead of, say, one has the advantage of making the measure robust to noir in the data; but it also nmeans that the measure has difficulty in pinpoi..ing the timing of rapid changes in taxation as occurred in Zambia 1984-87.

8 For Cote d'Ivoire, where there are no treasury bills, the official money market rates were used.

9 This is close to the approach adopted by Giovannini and de Melo (1989) 8
wage bill and capital share across firms in each cell.

Figure A7 in the Online Appendix plots the labor-wedge distribution across locations for each sector in the baseline year. The wedges between the formal and informal sectors are very similar to the ones found by Busso et al. (2012). Formal firms face larger distortions. On average, the wedge in the formal sector is approximately 1.67 times the wedge in the informal sector. Furthermore, panel B of figure A10 in the Online Appendix shows the spatial distribution of labor wedges after I construct ventiles across locations. In places in the center of the city, where there is more economic activity and formal firms locate, wedges are larger.

Moreover, for the counterfactual analysis, I also use a constant wedge for formal firms based on the work from Levy (2018) (Table 7.9). For the labor wedge, I use a conservative value of 0.95, and for the commercial floor space, a value of 0.75. These wedges include several distortions such as implicit taxes on salaried workers, regulations on dismissals and reinstatements, non-contributory social insurance, standard labor taxation like state payroll taxes, and firm taxation including REPECO and value-added taxes.

# **5.5 Other Parameters**

I calibrate other parameters of the model using simple moments of the data, or take them directly from the previous literature. I calibrate the expenditure share on housing using the ENOE and find, on average, a value of _α_ = 0 _._ 75. Similarly, for the labor share, I use data from the Economic Census in 1999 and find a value of _β_ I = 0 _._ 70, and _βF_ = 0 _._ 6. To calculate the total amount of housing _H_<sup> ̃</sup> and commercial floor space _Z_<sup> ̃</sup> in each location, I use the area in square kilometers of buildings in each census tract from the Global Human Settlement Layer (GHSL) in 2000 weighted by the total number of employees and residents. To calibrate the fixed costs, I use the log-linear relationship between the total number of firms and the workforce in each sector from the model,and find _FI_ = 0 _._ 15, and _FF_ = 1 _._ 2. Section C.2 in the appendix specifies the details for this estimation. This result is consistent with the fact that for a firm, it is more difficult to produce in the formal sector. In addition, I use the estimate of the elasticity of substitution across sectors _ξ_ = 2 from Edmond et al. (2015), which is similar to the estimates of other papers (Asturias et al., 2016). Also, I compute the counterfactuals using a value of _η_ = 1 _._ 50 which is the lowest value of the migration elasticity that Tsivanidis (2019) finds for Bogotá, a similar context to Mexico City. This value is consistent with the assumption that _η ≤ κ_ from the theoretical framework.<sup>52</sup>

# **5.6 Model Inversion**

In this section, I recover the fundamental parameters _Bn, Bns_ , which capture differences in amenities that attract residents to each location and sector; and the parameters _Ais_ , which represent differences

> 52In section 6, I show that the results are robust to different values of the migration elasticity and the elasticity of substitution across sectors.

31
better employment situation and did not experience the frustration. The cancellation of anticipated policy interventions can also lead to frustration. This is apparent in South Sudan when youth who expected to receive business grants that were not delivered reduced their consumption and reported lower levels of trust (Budjan, Pape, and Ralston 2022).

3. Peer-induced frustration: Witnessing peers' achievements and improvements in their well-being may initially raise observers' aspirations but eventually lead to frustration. For example, a housing intervention in slums in El Salvador, Mexico, and Uruguay initially inspired nonbeneficiary neighbors to aspire to similar housing improvements. However, the higher aspirations eventually disappeared and were replaced by frustration as the housing improvements came to be viewed as unattainable (Galiani, Gertler, and Undurraga 2021).

4. Large shocks: Major shocks, such as economic crises, epidemics, conflict, and natural disasters, may generate frustration. For instance, during the COVID-19 pandemic in Bangladesh, the motivation for learning and the aspiration for higher education declined, particularly among girls (Seager et al. 2022). Conflict and natural disasters have also crushed hope and aspirations (Kosec and Mo 2017; Moya and Carter 2019).

5. Macroeconomic factors: Frustrated aspirations may be driven by and generate macro-level outcomes. For example, economic growth or an increase in educational attainment may foster higher aspirations, which may evolve into frustration if country-level outcomes do not translate into higher individual welfare (Genicot and Ray 2017, 2020; Ray 2006). Frustration fueled by improvements in education outcomes without corresponding economic opportunities may then devolve into country-level unrest. This pattern is considered to have been a driver of protests in Chile and Tunisia in the early 2010s (Campante and Chor 2012; Flechtner 2017; Urzúa 2012).

Empirical studies offer cases in which aspirations do not materialize —or are likely not to— and are likely to culminate in frustration. For instance, longitudinal studies in Ethiopia, India, Peru, and Viet Nam show that only one child in three who aspires to complete university or postgraduate studies at age 12 had either completed university or remained enrolled by age 22 (Favara, Chang, and Sánchez 2018). In The Gambia, households with high aspirations for their children invest more in education (in terms of both financial resources and their children’s time), leading to only slightly higher performance on literacy and numeracy tests three years later compared with other children (Eble and Escueta 2023). This modest improvement in performance may not be sufficient to achieve meaningful levels of learning, potentially leading to limited choices and frustrated aspirations later in life.

9
We obtained from the Tunisian authorities a list of 662 firms that were owned by the Ben Ali clan and confiscated in the aftermath of the revolution (before December 2014), and were able to identify 206 of these firms as importers by merging the business register, the Repertoire National des Enterprises (RNE), with annual firm-HS6 product-origin data on import transactions for the period 2000-2009 from Tunisian customs. The RNE contains information on the age, sector, and employment of all registered non-agricultural firms operating in Tunisia, including for firms not employing any salaried workers, i.e. the self-employed (see Rijkers et al., 2014). Moreover, it has information on firms’ tax status, and whether or not firms are publicly or privately owned. These data are complemented with information on output declared to the Tunisian tax authorities from the Tunisian Ministry of Finance.

We thus do not identify all Ben Ali owned importers, yet do identify the vast majority and, moreover, our sample of connected firms is most likely skewed towards the largest and economically most relevant firms since these are easier to identify (and hence also easier to confiscate).

In order to calculate evasion gaps and assess how they relate to tariffs, the Tunisian HS6product-origin import data are merged with bilateral tariff data by product from WITS, which is available for 2002-2009 though missing for 2007 and 2009 for which we use instead tariff data from 2006 and 2008 respectively; data on non-tariff measures by product available for the period from 2000 to 2010, and information on imports and exports to Tunisia by HS6 product and year from COMTRADE for the period from 2002 to 2013. To ensure our results reflect systematic mismatches, rather than erratically reported incidental transactions, the sample is confined to (i) products which account for more than 0.01 percentage points of cumulative total exports to Tunisia reported by partner or more than 0.01 percentage points of cumulative imports reported in Tunisia over this 15
an industry and compare it to the actual firm-size distribution. They find that in China, India, and the U.S. the efficient firm-size distributions were more dispersed than the actual distributions. Panel B of Figure 1 illustrates their general finding. In contrast to the work of Tybout (2014), there are not enough very large or very small firms. They also find that the distortion is larger for the developing countries: the observed U.S. firm-size distribution is closer to the hypothetical efficient firm-size distribution than either the observed Indian or Chinese firm-size distribution. Hsieh and Olken (2014), using data from India, Indonesia and Mexico, find that the fraction of “missing” firms is increasing in firm size. There are more missing large firms than missing medium-sized firms, and this helps explain why less developed countries have lower income. Bento and Restuccia (2014) use comparable firm-census data for 124 countries with size measured by employment and show that the average firm size is increasing in level of development. They develop a model to explain this fact, using distortions correlated with productivity to produce too little investment, especially among firms that should grow large and such distortions can explain productivity differentials between higher- and lower-income countries. This body of research points to a “truncated top” of the firm-size distribution.

A wealth of research shows that small firms do not export because of high fixed entry costs.<sup>12</sup> In Figure 1, it is therefore only the right half of the firm-size distribution that is relevant for exporters. A “missing middle” in less efficient (less developed) economies would suggest that exporters are on average relatively large and their size distribution is more concentrated at the top. A “truncated top”, with firms missing at the top of the size distribution in less efficient economies,

> 12 See Bernard, Jensen, Redding and Schott (2007) for a summary of the literature.

11
# **Stuck in a Conflict Trap: The Case of the Central African Republic Civil War**

Pierre Mandon<sup>a</sup> , Vincent Nossek<sup>a,b</sup> , Diderot Sandjong Tomi<sup>a,c</sup> a: World Bank Group b: CERDI, University Clermont Auvergne (UCA) c: University of Ottawa _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._ Keywords: C31; D74; O11 JEL codes: Central African Republic; Civil war; Development trap; Synthetic control method
price for shares, is at least as great as the value of the firm to the government if it were not privatized. Taxes therefore result in a redistribution of revenues from the private investors to the government. Given that Vp is constant, the greater the amount of taxes paid by the firm, the lower the transaction price Vg as seen from equation (2).

Therefore, taxes play an important role in determining how revenues are split between the government and investors. Any over- (under-) estimation of taxes would result in the transaction price (Vg) being under- (over-) estimated. Thus, it is important for both tax authorities and the investors to understand how taxes will be applied after the firm is privatized.

The amount of taxes to be paid by the firm after the privatization will depend on three sets of issues, as discussed in the introduction. The first is related to transition issues - the amount of tax paid after privatization will depend on the valuation of assets and liabilities at the time of the privatization. The second is related to efficiency issues that can affect economic performance of the industry since the privatized firm competes with other producers. The third is related to the transfer of revenue from one government to another as a result of privatization.

# 1. **Transition Issues**

At the time of privatization, the assets and liabilities of the company are assessed not only in terms of their fair market value but also in termns of tax values that are used to assess tax liabilities after the privatization. There are a host of issues that need to be considered by tax authorities and investors in determining the amount of taxes to be paid overtime. Below, we provide a list of transition issues, which is not exhaustive.

paper that was found was Sinclair [1988] that reviewed income tax aspects of privatization in Canada. 7
detail, sensitivity to changes in classification and the number of countries reporting imports.

Looking at data availability in the COMTRADE database (via WITS) showed that (for a recent 10 year growth period) the largest sample of importers could be obtained for data reported according to the third revision of the Standard International Trade Classification (SITC) (102 countries reported in both 1995 and 2004). A slightly smaller number of countries (95) reported import data according to the Harmonized System (HS) in both of these years.<sup>2</sup> The data reported under the HS is available at a slightly more detailed level of commodity disaggregation (5016 products at the 6 digit level) than that under the SITC (3118 products at the 5 digit level). However, the HS has been subject to two revisions over the sample period in 1996 and 2002, while there have been no revisions to the SITC.<sup>3</sup> While data are concorded to a consistent version of the HS, different countries have been reporting under different revisions of the HS. For these reasons we prefer to use the SITC data. We also wanted to undertake some sensitivity analysis using data for earlier years, which also pushes us toward the use of the SITC data.

We collect data for 99 developing country exporters<sup>4</sup> and for each year we have a matrix with 3118 product rows and 102 importer columns. We exclude trade in crude petroleum and gas and also 34 specific steel products for which there is clearly a problem of consistency over time, leaving 3078 products. This can be compared with Besedes and Prusa (2006a) who investigate the exports of 27 developing countries for the longer period of 1975 and 2003 for 380 manufacturing categories (4 digit level of SITC Rev 1). Evenett and Venables (2002) decompose the export growth of 23 developing countries (to 92 importers) over 1970 to 1997 for what appears to be around 200 product categories (the “3 digit level of trade” is mentioned). Thus, in this study we utilize a dataset with a much fuller set of exporters and import markets and a more detailed commodity breakdown than

> 2 While the number of countries reporting under both classifications is higher in 2002 and 2003 than in 2004, the number of reporters declines substantially in the years before 1995. The number of reporters in both 1994 and 2003 declines to 90 under the SITC and 88 under the HS.

> 3 On the other hand, most countries are now collecting data at the customs level according to the HS. Thus, the data reported according to the SITC may be influenced by the revisions to the HS, although these are likely to be minimal due to the slightly higher level of aggregation of the SITC data.

> 4 We exclude small island economies and middle east oil exporters. A number of other countries, typically African countries in conflict, were excluded due to obvious data problems.

6
- 15 -

**Figure 11. Estimated time-specific fixed-effects**

**Figure 12. Estimated commodity specific fixed-effects**

<!-- Start of picture text --> Clothing, apparel,<br>texitile<br>Crops, food products<br>Consumer and<br>other goods<br>Minerals, metals,<br>other materials<br>Equipment<br>-.2 -.1 0 .1 .2 .3<br>Coefficient and Confidence Interval<br>.6<br>.4<br>.2<br>0<br>-.2<br>Coefficient and Confidence Interval<br>-.4<br>2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2016m1 2017m1 2018m1 2019m1 2020m1 2020m12<br><!-- End of picture text -->

# **V. Heterogeneity and Robustness**

32. As discussed above, there seems to be a systematic difference in freight costs across commodities. To investigate potential heterogeneity in freight cost determinants, the sample data is divided into five groups: (i) clothing and apparel, (ii) equipment and vehicles, (iii) agricultural commodities and food, (iv) minerals and metals, and (v) consumer goods. The logarithmic model is separately estimated by applying OLS ( **Table 4** ).<sup>6</sup> The results are broadly consistent with the above, but some coefficients are different among groups of commodities. For instance, driving speed or road conditions matter most for equipment and minerals ( **Figure 13** ). For other types of shipments, i.e., food products and consumer goods, the elasticities are small. It can be interpreted to mean that production-oriented capital goods are more sensitive to delivery efficiency than consumer goods.

33. For shipping distance, it is found that the freight cost increases with distance for all kinds of merchandises but equipment. The elasticities are small for clothing and food products. Thus, the freight cost does decrease with distance, but very marginally. This may make sense because some agricultural commodities are perishable, and thus, it is costly to transport for a long

> 6 The estimation results are largely similar for the linear and quadratic models. Thus, the only results from the logarithmic estimation are shown in the table.