rafmacalaba/datause-encoder-data huggingface dataset:
database relatively new and unique in itself, but the massive plant-level matching undertaken in this study has not previously been possible.

# 4.3. Results

As an indication of results obtained using the methodology described above, Figure 4.1 charts the linearly-weighted acute human and terrestrial ecotoxicity index across the seventy-four 4-digit ISIC codes for which TRI data are available. The units of the pollution index are linearly risk-weighted pounds of TRI releases and transfers per $1,000 of shipment value. Table 4.1 presents the same information, together with the ISIC sector names.

Figure 4.1 clearly illustrates the extreme sectoral variation in pollutant intensity, ranging from Fertilizers and Pesticides (ISIC 3512) with 105.3 risk-weighted pounds of TRI releases and transfers per $1,000 of product shipped, to Soft Drinks and Carbonated Water (ISIC 3134), with only 0.22 pounds per $1,000. Despite a few surprises, such as the fifteenth ranking of the Musical Instruments sector, Table 4.1 generally confirms the intuition that the most intensive sectors in terms of toxic waste per dollar of output are industrial chemicals, plastics, paper and metals. The middle-ranked sectors are associated with consumer products such as electrical appliances, textiles, and cleaning preparations, followed by the high shipment value (and consequently relatively low intensity) machine-tool industry, with the food and drink sectors filling 22
**Table S3.S continued**

||(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|reput_4|reput_5|pcf_rep|lvademp|dlemp|growth1|growth3|pcf_perf|
|supbuy|0.281|0.369|0.342|0|-0.536**|-0.427|1.637|0|
||(0.614)|(0.637)|(0.784)|(0)|(0.222)|(0.674)|(2.479)|(0)|
|samesup|-0.104|-0.103|-0.00129|-1.579***|-0.245**|-0.871**|-0.745*|-0.486**|
||(0.142)|(0.152)|(0.158)|(0.486)|(0.120)|(0.351)|(0.401)|(0.212)|
|samecus|-0.105|0.116**|0.0534|-0.967|0.102|-0.572***|0.295|-0.0104|
||(0.113)|(0.0515)|(0.0881)|(0.682)|(0.146)|(0.201)|(0.445)|(0.228)|
|same_sec|-0.00128|0.0275*|0.0162|-0.127|-0.0553|-0.0454|-0.0611|-0.0760|
||(0.0186)|(0.0164)|(0.0189)|(0.157)|(0.0393)|(0.0681)|(0.132)|(0.0863)|
|ldist|-0.00318|-0.00529|0.00539|-0.0536|-0.0171|-0.0493**|-0.0491|-0.0106|
||(0.00282)|(0.00473)|(0.00705)|(0.0374)|(0.0109)|(0.0232)|(0.0334)|(0.0180)|
|ad_fmage|-0.000696|0.00154|-0.00117|-0.00484|0.00112|0.00862|0.00459|0.00602|
||(0.000812)|(0.00156)|(0.000984)|(0.00780)|(0.00216)|(0.00651)|(0.00746)|(0.00458)|
|ad_meduc|-0.00140|0.00733|0.00155|0.0423|-0.00472|-0.0337|-0.0207|0.0115|
||(0.00705)|(0.00940)|(0.00863)|(0.0469)|(0.0117)|(0.0228)|(0.0268)|(0.0252)|
|ad_mexper|-0.000771|-0.00118|0.000492|-0.00734|0.000762|0.00303|-0.00384|-0.00186|
||(0.000949)|(0.00144)|(0.00169)|(0.00577)|(0.00132)|(0.00696)|(0.00580)|(0.00313)|
|ad_mfemale|0.0157|0.0262|0.0370|0.369|-0.00735|-0.161|0.0564|-0.137|
||(0.0387)|(0.0462)|(0.0481)|(0.581)|(0.0615)|(0.134)|(0.275)|(0.0962)|
|ad_lemp|-0.0257***|-0.0286**|-0.00741|-0.0289|0.0637**|0.0797|0.0906|0.0626|
||(0.00946)|(0.0142)|(0.0141)|(0.110)|(0.0262)|(0.0739)|(0.119)|(0.0624)|
|sizdist|0.00162|0.00423|0.00595|-0.0566**|-0.00242|-0.0165|-0.0188|0.00228|
||(0.00398)|(0.00378)|(0.00518)|(0.0287)|(0.00573)|(0.0157)|(0.0236)|(0.0148)|
|sizlink|-0.00144|-0.0345|0.0535|-0.260|-0.118|-0.138|0.0733|0.0163|
||(0.155)|(0.151)|(0.139)|(0.525)|(0.140)|(0.272)|(0.539)|(0.239)|
|Constant|1.002***|0.890***|1.119***|3.797***|0.717***|1.531***|2.070***|0.971***|
||(0.0257)|(0.0492)|(0.0500)|(0.386)|(0.0992)|(0.295)|(0.337)|(0.220)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 35
GSS (Ghana Statistical Service). Various years. Population and Housing Censuses 1960, 1970, 1984, 2000, and 2010. Accra.

Gwartney, J., R. Lawson, and J. Hall. 2012. _Economic Freedom of the World: 2012 Report_ . With contributions from S. L. Baier, C. Bjørnskov, M. Clance, A. M. Crisp, A. Dreher, G. P. Dwyer, N. J. Foss, and K. Gehring. Vancouver, BC: Fraser Institute.

Haggblade, S., P. B. R. Hazell, and T. Reardon. 2007. _Transforming the Rural Nonfarm Economy_ . Baltimore: Johns Hopkins University Press.

Haltiwanger, J. 1997. “Measuring and Analyzing Aggregate Fluctuations: The Importance of Building from Microeconomic Evidence.” _Federal Reserve Bank of St. Louis Review_ 79 (3): 55–78.

Herrendorf, B., R. Rogerson, and A. Valentinyi. 2013. _Growth and Structural Transformation_ . Working Paper 18996. Cambridge, MA: National Bureau of Economic Research.

Jerven, M., and D. Johnston. 2015 _. Statistical Tragedy in Africa? Evaluating the Data Base for_ _African Economic Development_ . London: Routledge.

Johnston, B. F., and P. Kilby. 1975. _Agriculture and Structural Transformation: Economic Strategies in Late-Developing Countries_ . New York: Oxford University Press.

Johnston, B., and J. Mellor. 1961. “The Role of Agriculture in Economic Development.” _American Economic Review_ 51 (4): 566–593.

Lewis, W. A. 1954. “Economic Development with Unlimited Supplies of Labor.” _Manchester School of Economic and Social Studies_ 22 (2): 139–191.

Matsuyama, K. 1992. “Agricultural Productivity, Comparative Advantage, and Economic Growth.” _Journal of Economic Theory_ 58 (2): 317–334.

McMillan, M. 2013. “The Changing Structure of African Economies.” Unpublished paper.

McMillan, M., and K. Harttgen. 2014. _What Is Driving the Africa Growth Miracle?_ Working Paper 20077. Cambridge, MA: National Bureau of Economic Research.

McMillan, M., and D. Rodrik. 2011. “Globalization, Structural Change, and Productivity Growth.” In _Making Globalization Socially Sustainable_ . Geneva: International Labour Organization and World Trade Organization.

Mokyr, J. 1976. _Industrialization in the Low Countries, 1795–1850._ New Haven: Yale University Press.

Mundlak, Y., R. Butzer, and D. F. Larson. 2012. “Heterogeneous Technology and Panel Data: The Case of the Agricultural Production Function.” _Journal of Development Economics_ 99 (1): 139–149.

Murphy, K., A. Shleifer, and R. Vishny. 1989. “Industrialization and the Big Push.’’ _Journal of Political Economy_ 97 (5): 1003–1026.

38
3.3. Robustness Tests (Controls, Samples and Estimation Methods). To address potential omitted variable bias in the main results, I conduct a suite of robustness tests to dierent controls, dynamics, specications and estimation methods, which are presented in the remaining panels of Table 1.

3.3.1. Additional Controls. Table 1 Panel B controls for additional economic variables (one-by-one) that might be correlated with both state-level growth and transfer size.

As mentioned above, the 2001 and 2008 one-o transfers were explicitly countercyclical measures. While the aggregate business cycle is subsumed into quarter FEs, dierent states might have dierent sensitivities to the national cycle (e.g. if they specialized in cyclically sensitive industries like manufacturing). If more of these more-sensitive states happened to receive relatively smaller transfers, that could drive a positive multiplier. I address this concern in three ways. First, for the regression in Table 1 row B1, I include US GDP growth _×_ state FEs (also included in the benchmark) which increases the estimated temporary transfer multipliers marginally but does not aect signicance. Second, I interact time FEs with the average manufacturing share of the state's GDP, which allows manufacturing-intensive states to grow more slowly in recessions  which also has little eect (row B4). Finally, I construct a peak-to-trough measure of the depth of the 2001 and 2008 recessions in each state and interact it with the mean size of the transfer variable each quarter again multipliers are unchanged (row B6).<sup>19</sup> Although my sample of Social Security benet increases are not explicitly countercyclical (by Romer and Romer's narrative), I also control for industrial structure and dierential state sensitivities to the aggregate business cycle for robustness. As mentioned above, adding 50 control variables for US GDP growth _×_ state FEs (Table 1, row B2 rst column), reduces the estimated multiplier to 1.14 (signicant at the 5% level). Controlling for state average manufacturing share of GDP _×_ quarter xed eects (92 variables, Table 1, row B5 rst column) reduces the permanent transfers falls to 0.92 (signicant at the

> 19The peak (trough) is the maximum (minimum) per-capita income in the two years before (after) the NBER recession, 19992000 and 20062007 (20022-03 and 20091910). These controls turn out to be insignicant at the 5% level.

19
## **Box 2. Validating the synthetic panel methodology with Moldova panel data**

The methodology of synthetic panel is tested and validated using panel data for Moldova for the periods 19992002 and 2001-2004. The data is divided into two different randomly-generated subsamples: subsample A is used as a reference point whereas results from the synthetic panel model build only on subsample B. Estimated bounds generated in the synthetic panel exercise from subpanel B are compared to the true transitions observed from the descriptive statistics of subsample A.

The basic consumption model uses the welfare aggregate as dependent variable. The poverty line is fixed to 2.5 PPP 2005 USD. The set of independent variables only includes time-invariant characteristics regarding the household and the household head, specifically: dummy for female headed households, birth cohort dummies from cohort 1945-1950 to cohort 1970-1975, education categories and dummy for rural areas, region dummies.

Results in Figure 14 show that all estimated bounds are able to “sandwich” the true panel value across all specified models. These results, along with other validation exercises for other countries, provide strong support in that the analysis carried out in this paper captures, to some extent, the true mobility patterns observed in the country. Further details and tests are provided in Annex A1 and Annex A2.

**Figure 14. Transitions in and out of poverty, true value vs. synthetic panel results**

**Panel 1: 1999-2002 Panel 2: 2001-2004**

<!-- Start of picture text --> 60<br>60<br> 50<br>50<br> 40<br>40<br>30 30<br>20 20<br>10 10<br>0 -<br>PP PN NP NN PP PN NP NN<br>TRUE Lower Bound Upper bound TRUE Lower Bound Upper bound<br>Source: World Bank staff calculations using HBS data.<br><!-- End of picture text -->

# **5. Economic Mobility in Moldova: Impressive Record of Upward Mobility**

**Moldova experienced high upward economic mobility in the first half of the 2000s** . The transition matrix presented in Table 1 shows the percentage of households that moved across consumption categories – for example, out of poverty - in the period from 1999 to 2004. The analysis shows that a large share of households (29 percent) moved from poverty to living in moderate poverty (between $2.5 and $5/day) in this period. Similarly, around 10 percent of households crossed the $5/day threshold. Overall, results point to significant upward mobility, jumping from one category to the next (no skipping

18
Policy Research Working Paper 7049

# **Abstract**

There is substantial evidence that with the progressive global decline in tariffs over several decades, trade costs are a more significant barrier to trade than tariffs, especially in SubSaharan Africa. This paper decomposes trade costs into three categories: costs that can be lowered by trade facilitation, nontariff barriers, and the costs of business services. The paper develops a 10-region, 18-sector, global trade model that includes Kenya, Tanzania, Uganda, and Rwanda of the East African Customs Union. The analysis finds that deep integration in the East African Customs Union that lowers these trade costs results in significant gains for the four countries, especially from improved trade facilitation. Extending the lowering of nontariff barriers and services liberalization multilaterally would increase the gains between two and seven times, depending on the country. that the analysis also finds that reducing nondiscriminatory services barriers in Kenya and Tanzania would increase welfare even more than multilateral reduction of discriminatory services barriers. The paper is innovative both conceptually and empirically. It contains foreign direct investment in services and is the first paper to numerically assess liberalization of barriers against domestic and multinational service providers in a multi-sector, multi-region, applied general equilibrium model. The paper uses new databases of the ad valorem equivalents of barriers in services and the time in trade costs. Both databases are shown to be important to the results.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at dtarr@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
# **Telecommunications**

Kenya’s telecommunications services are expensive compared with other sub-Saharan African countries and even more when compared with those of East and South Asia. Relative to countries with comparable income, Kenya has fewer fixed lines per capita, less than half the level of international calls per subscriber and higher Internet charges. Perhaps more important, is the low efficiency of service provision (see World Bank, 2007, pp.45-47). Kenya requires that telephone companies must be at least 30 percent owned by Kenyan nationals. Problems related to the licensing off the third mobile telephone provider<sup>10</sup> and the “Second National Operator” are primarily due to this restraint. In fact, the Government has acknowledged that the 30 percent ownership requirement is delaying licensing of additional telecom operators.<sup>11</sup> Data transmissions are especially expensive by international standards. In early 2007, these are being done by satellite. The government is attempting to connect to a fiber optic cable on the seabed. When successful, it is estimated that the costs per month for data transmissions will drop several fold. On international surveys of doing business, long distance telecommunication charges are considered burdensome on average less than ten percent of the time; but in Kenya the figure is 44 percent. The World Bank (2007, p. 46) assesses that delays in implementing the reform agenda are the reason for the high cost and low quality of telecommunication services in Kenya.

While there are obviously serious economic problems in the telecom sector, the government has implemented significant reforms in the sector in the last ten years. The government’s strategy for the sector is outlined in the Postal and Telecommunications Policy Statement of 1997.<sup>12</sup> The strategy outlines 10 Regarding the third mobile telephone operator, a consortium of a local investor (Kenya National Federation of Cooperatives, KNFC) and foreign investors (Econet Wireless) won the tender in February 2004. But the consortium was put together to meet the 30 percent local ownership requirement, not because of business reasons. Citing deals made by a previous CEO of KNFC, KNFC at one point wrote to the Communications Commission of Kenya (CCK) withdrawing from the consortium. KNFC later withdrew its letter of objection, but lost its controlling share of the consortium. CCK, nonetheless, awarded the license to Econet Wireless and court battles ensued. The Government eventually suspended the entire CCK board and its Director General, and suspended the license of Econet. In April 2007, Econet has agreed to withdraw its court case and settle the matter out of court.

.

> 11 “SNO to get a year to meet local ownership rule,” _The Saturday Standard_ , Business section, April 14, 2007.

> 12 This statement is consistent with the government’s Economic Recovery Strategy for Wealth Creation (ERS)

7
On the gender differentiation issue, results confirm the findings of the first set of estimations (Table 2). On average, female workers earn lower salaries. Moreover, the gender penalty appears to be stronger in the private sector.

Finally, being married has a positive effect on compensation in both the public and the formal private sector. Coefficients estimated to control for urban/rural location are not significant.

The third and last estimation approach focuses on what drives pay differentials between the public and the formal private sector. Pay differentials are the result of different human capital endowments and other unobservable factors. The well-known Oaxaca-Blinder decomposition described above (Equation 4) can be used to break down the difference between public and formal private sector compensation into an “explained difference”, which is the result of differences in the observable characteristics affecting compensation (education, experience, gender, marital status, and location), and an “unexplained difference” which summarizes the difference in returns to the aforementioned observable characteristics and also captures all potential effects of differences in unobserved variables. Results of this decomposition are shown in Table 5.

The first column of the table focuses on the whole sample. The overall difference is not statistically significant (-0.022). However, it is important to note that this result stems from two factors at play that off-set each other. On one hand, the endowment coefficient is negative and statistically significant (-0.231), meaning that the observed compensation in the public sector is lower than expected given the employees’ endowments, such as higher education and greater experience. On the other hand, the coefficient on returns is positive and statistically significant (0.209), meaning that returns to education and the remaining workers’ characteristics are higher in the formal private sector.

**Table 5. Decomposition of Mean Log Compensation Differentials**

|**Oaxaca**<br>**Decomposition**|**Whole**<br>**Sample**|**Ed. Lvl. 1 **|**Ed. Lvl. 2 **|**Ed. Lvl. 3 **|**Ed. Lvl. 4 **|**Ed. Lvl. 5 **|**Ed. Lvl. 6**|
|---|---|---|---|---|---|---|---|
|Log Wage Difference|-0.022|1.292*|0.182|0.187|0.002|0.002|0.577***|
|Explained Difference<br>(Endowments)|-0.231***|0.037|-0.040**|-0.084***|-0.082***|-0.115***|-0.090**|
|Unexplained<br>Difference (Returns)|0.209***|1.255*|0.222|0.271*|0.084|0.117*|0.668***|
|Observations<br>Note: Mean log wage<br>private sector wages.|7702<br>differentials|509<br>are compu|2374<br>ted by subt|1055<br>racting pub|1679<br>lic sector w|1685<br>ages from|400<br>formal|

14
Moayed, T., S. GuggenHeim, and P. Von Chamier. 2021. “Trillions Sitting on the Sidewalk? How to Reform Fuel Subsidies without Depriving the Poor.” Medium, December 3, 2021. https://medium.com/sdg16plus/trillions-sitting-on-the-sidewalk-how-to-reform-fuelsubsidies-without-depriving-the-poor-28a1814151bd.

Nyhan, B., and J. Reifler. 2010. “When Corrections Fail: The Persistence of Political Misperceptions.” Political Behavior 32 (2): 303–30.

Paz, V., W. Jimenez and E. Yañez. 2020. “CEQ Master Workbook: Bolivia (2015),” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University). December, 2020.

Pradiptyo, R., A. Susamto, A. Wirotomo, A. Adisasmita, and C. Beaton. 2016. “Financing Development with Fossil Fuel Subsidies: The Reallocation of Indonesia’s Gasoline and Diesel Subsidies in 2015.” International Institute for Sustainable Development, Winnipeg, Canada.

Rentschler, J., and M. Bazilian. 2017. “Policy Monitor—Principles for Designing Effective Fossil Fuel Subsidy Reforms.” Review of Environmental Economics and Policy 11 (1): 138–55.

Ross, M., C. Hazlett, and P. Mahdavi. 2017. “Global Progress and Backsliding on Gasoline Taxes and Subsidies.” Nature Energy 2: 16201.

Stantcheva, S. 2021. “Understanding Tax Policy: How Do People Reason?” Quarterly Journal of Economics 136 (4): 2309–69.

54
# **_Annex 2 ‐ Attrition_**

Attrition is largest in the pure control group (25.5%) when compared to the control and treatment groups (20.2%). Table 4 suggests a random pattern of attrition for the 3 treatment arms when compared to the placebo control group, but a large and significant differential attrition in the pure control group. This differential attrition is reinforced by the balance results from Table 1, and may be resulting from the fact that pure control business owners were only contacted at baseline and follow‐up, whereas all other groups had another intermediate contact to receive the screening invitation making them (i) more aware of the activities and (ii) easier to track. Given the significantly lower response rate in the pure control group, we subsequently analyze treatment effects by comparing the placebo screening group with the different treatment arms.

When data are analyzed by simply excluding respondents with missing values for any relevant outcome measures (item non‐response, or INR), this could again cause biased results if missingness is systematically related to a respondent’s potential outcomes. Table 5 presents INR rates for main outcomes measures across different treatment and control groups. For instance, for the question of basic understanding of inflation, it can be seen that 100 percent of the surveyed micro‐entrepreneurs are asked this question (column 1) and that 2.37 percent of those who are asked do not give a response (column 2). Overall, the data in Table 5 indicate that INR for main outcome measures is not a critical issue (most of the times INR rates are <5%) and non‐differential across treatment and control groups. Interestingly, INR is the lowest for measures of intentions, savings and borrowing behavior, whereas the highest INR rates (between 10 and 20 %) can be observed for questions related to perceptions about MFBs, possibly reflecting cases where business owners have not interacted with MFBs and therefore have not been able to form an opinion. Table 5 also reveals a striking increase in INR for the questions of perceptions about MFBs at the end‐line survey relative to the data that were collected shortly after the screening. This increase does not interact with a particular treatment status and may owe to different modes of interviews and the design of the questionnaires: While the short survey right after the screenings was self‐administered by attendees, the end‐line survey was conducted face‐to‐face. To avoid unit nonresponse and potential measurement errors, the self‐administered questionnaire was designed to be as simple as possible and only asked dichotomous ‐ Yes or No ‐ type of questions with no explicit “Don’t know” or “Refusal” choices. This means that direct comparison over time (e.g., through a difference‐in‐difference approach) would present challenges; however, similar response patterns across treatment groups support the idea that responses are at least internally consistent.

44