rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Appendixes**

# **6.1 South Africa**

# **<u>Computation of TCC</u>**

# **1- Aggregate TCC in Dollars**

We computed TCC per worker by exactly following Coolidge, Ilic and Kisunko (2009). Then, we create three groups of firms according to the total number of tax staff (q17a): firms with only one tax staff, with one or two and finally, firms with any number of tax staff. Then, we multiply TCC per worker with the total number of tax staff for these three groups and use these three different TCC proxies separately as dependent variables in our regressions.

# **2- Aggregate TCC in Dollars (Under 4**<sup>**th**</sup> **task)**

Question q17c (in the survey) lists share of effort devoted to the four different tasks (listed above). We use this information to compute the component of TCC (computed in the previous section) spent for the relevant tasks.<sup>34</sup> As before, three different groups of firms – depending on their total number of tax staff – are created and used separately as dependent variables in our regressions.

# **3- Time Spent for VAT Compliance**

The survey data provides total time spent in hours for major taxes including VAT (q19_3nu) for each cycle. This number is computed from Q19 by considering 8 hours work a day, 5 working days a week and 20 working days a month. Utilizing this information and Q18_3, we calculate (1) the total time (in hours) spent on VAT related tax compliance over a fiscal year. Secondly, using Q19_3, (2) one can also recover time (hour) spent for each submission (or cycle).

> 34 17c_4 is specifically the percentage of time (of q17b) spent on filling out and submitting tax forms.

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Blake et al. (2002) conduct a static CGE analysis to assess the welfare effects of agricultural trade liberalization in Uganda and find a very moderate overall welfare effect which is slightly biased in favor of the urban and rural non-farm self-employed. However, the liberalization of coffee markets, which arguably has been Uganda’s most far-reaching agricultural trade reform, is captured by these authors’ model only very indirectly by assuming export price increases of up to 10 percent.

# Coffee price changes and their transmission to local markets

During the 1990s, Ugandan farmers were confronted with simultaneous major domestic market liberalization and pronounced fluctuations in coffee prices. World prices went up dramatically in the first half of the 1990s, more than doubling between 1992/93 and 1994/95. The coffee price boom began its reversal in 1996/97. Coffee prices reached a trough in 2001, when they fell below the levels of the early 1990s. At the same time, due to the liberalization, intermediation margins were reduced and domestic and international coffee prices begun to move more synchronously.

Krivonos (2004) confirms this by showing that coffee market liberalization induced a closer relationship between producer prices and world market prices, not only in Uganda, but also in other major coffee-producing countries. For the Ugandan case, 96 percent of the adjustment is realized after 6 months as compared to 30 percent in the pre-reform period. Fafchamps et al. (2003) – by examining the transmission of international coffee prices through the domestic value chain, with coffee growers, traders and exporters as the main market participants – find that fluctuations in international prices are not fully reflected in the prices received by coffee farmers. In a companion paper (Fafchamps and Hill 2005), this is mainly attributed to the fact that producers are more likely to sell at the farm gate rather than at the nearest market when prices go up, thereby lowering the price they actually receive. More frequent selling at the farm gate is consistent with a higher presence of itinerant traders purchasing coffee from farmers when prices increase. In contrast to producer prices, prices paid by large coffee traders and exporters track the international price relatively closely, which suggests a fairly smooth and competitive operation of the liberalized coffee value chain from large traders to exporters.

The notion of a competitive coffee sector is supported by Ponte (2001), who finds that coffee market liberalization stimulated entry of buyers, processors and exporters. When international 7
this variable to be associated with less severe financial constraints (i.e., positively linked to the ̳finance no obstacle‘ variable) and a greater likelihood of survival. The full set of performance variables enters the survival regressions.

For the survival analysis, all firm characteristics are measured at the time of the third round of the BEEPS survey—mostly in late 2008. There are two reasons for this. The first is practical—it allows us to include firms that were no longer operating at the time of the FCS survey in mid-2009 in the survival regressions. The second is that it reduces the likelihood of reverse causality. This is a particular concern for the measures of firm performance— performance is likely to have been affected by the crisis and those firms affected most seriously are also most likely to be forced to close during the crisis. It is important to note that many variables from the 2008-09 survey are actually measured for fiscal year 2007 or earlier (i.e., firms were reporting retrospective data for the previous completed fiscal year). For example, firm growth is for 2005 through 2007, return on sales is for fiscal year 2007 and the dummy variable for investment is for fiscal year 2007.

F is a matrix of variables that summarize firms‘ use of financial services, which are relevant for the firm survival regressions. All else equal, we would expect firms that had readier access to finance to be more likely to survive. Although having access to finance would not affect firms that were basically insolvent, it might allow firms that have temporary liquidity problems due to the drop in demand to survive the crisis.

To reduce concerns about reverse causation, it would be best to have measures of use of financial services from before the start of the crisis (i.e., like for the performance measures). Unfortunately, the two best measures in the survey—whether the firm has a loan and whether the firm has a line of credit or overdraft—were asked ̳at the current time‘ (i.e., at the time of the third round of the BEEPS survey). Although this was before the FCS survey (mid-2009), the BEEPS surveys were mostly conducted between September and December of 2008. To the

> firms that did not invest in the previous year –and that therefore have to be dropped  include (i) those that did not need to invest and (ii) those that are unable to invest because they did not have enough internal funds and could not get bank/supplier financing.  Using this second variable would therefore be problematic.

> Note also that the ―no need for funds‖ question was available in a consistent format for only the 2005 and 2008-9 BEEPS. In constructing the ―no need‖ variable, we assign a firm a value of 1 for all years if reported no need in 2005 and 2008-9. This is true for sixteen percent of the firms in our sample.

16
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35
possibility that the global recession may have had negative demand-side implications placing downward pressure on Brazilian imports of this product as well.<sup>24</sup> Finally, is this particular trade barrier emblematic of one likely to affect multiple foreign countries (violating the national treatment principle) and thus an example where affected exporters might have difficulty coordinating due to a free-rider problem? The answer to this question as revealed by the data is also yes, given that there is no dominant foreign supplier of this product to this market. Even the largest foreign suppliers (the USA and Germany) have exports that are quite small by their country’s (relative) complainant standard threshold for pursuing WTO disputes; thus it is unlikely that these industrialised countries would put a priority on using litigation over this particular Brazilian measure.

The implication of this exercise is, had the assessment of question (1) resulted in a WTO violation, using the information provided by the GTA matched to publicly available trade data to assess question (2) suggests that this measure might be one that a development-focused outside analyst would want to generate more information on so as to assist the adversely affected exporters in India, Indonesia, Malaysia, China, and South Africa. Furthermore, it is important to point out that because of the work already provided by the GTA, with the free data from UN Comtrade available on the Internet, it took us less than 30 minutes to provide a quick snapshot answer to this question.

24 This is also likely given that the GTA reports the Brazilian tariff increase only took place at the end of August, meaning that eight months worth of 2009 imports of this product came in under the 2% tariff. Thus additional work and information would be needed to answer questions (3) and (4), if the information revealed by answering question (2) indicated that this particular measure was worth looking at in more detail.

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transfer – this is consistent with findings from Balboni et al. (2020), who estimate a poverty threshold of 504 USD PPP in Bangladesh, half the size of the average transfer in our sample.

To further motivate the geographic focus of this metaänalytic effort and interpret these magnitudes, we compare our estimates to comparable ones from richer countries (Auclert et al., 2018; Fagereng et al., 2019). UCT impacts are more persistent in developing countries – one year impacts of UCT on household consumption in developing countries are smaller, while impacts at three or more years are larger. In our model, this result is consistent with a larger marginal return on investment in developing countries, a well documented stylized fact (De Mel et al., 2008; McKenzie & Woodruff, 2008; Hussam et al., 2020). To provide additional context for our estimates, we note that cumulative impacts on consumption over the first three years are larger than the size of transfers. Following the approach of Banerjee et al. (2015), this provides strong evidence that UCT pass a cost-benefit analysis.

We next consider the impacts of adding complementary interventions in multifaceted graduation programs. Our estimates imply that TUP complementary interventions increase impacts on consumption. These complementary interventions are relatively expensive in our sample, and as a result the average TUP is 5%-43% less cost effective at increasing consumption than the average UCT. However, we also find evidence of greater heterogeneity in the impacts of TUP relative to UCT interventions, suggesting TUP may be more cost effective than UCT in a range of contexts. Lastly, the relative cost effectiveness of TUP increases over time, and our estimates imply TUP increases cumulative consumption per unit of program cost by more than UCT after 3.4-7.7 years; we note that only 4 of the 20 TUP estimates in our metaänalysis sample are more than 3 years since last transfer, highlighting the need for additional long run estimates.<sup>3</sup> This study makes three central contributions. First, we contribute to a large literature on

> 3Viewed in isolation, this increasing cost effectiveness is potentially consistent with perfect markets for TUP complementary interventions through the lens of a standard buffer stock model, as the implied internal rates of return from these estimates are similar to estimates of marginal returns to investment in developing countries. However, contrasted with our estimates of the impacts of increasing transfer size, it instead suggests a role of failures in the markets for these complementary interventions, and potentially also for frictional poverty traps.

6
40 doing business and creating uncertainty about property rights. Detragiache, Gupta and Tressel (2005) show that for low-income countries political instability and corruption have a detrimental effect on financial development. Investigating the business environment for 80 countries using firm level survey data, Ayyagari, Demirguc-Kunt and Maksimovic (2005) find that political instability and crime are important obstacles to firm growth, particularly in African and Transition countries. Further, Beck, Demirguc-Kunt and Maksimovic (2004a) show that the negative impact of corruption on firm growth is most pronounced for smaller firms.

Given a stable political system, well functioning financial systems also require fiscal discipline and stable macroeconomic policies on the part of governments. Monetary and fiscal policies affect the taxation of financial intermediaries and provision of financial services (Bencivenga and Smith, 1992; Huybens and Smith, 1999; Roubini and Sala-i-Martin, 1992 and Roubini and Sala-i-Martin, 1995). Often large financing requirements of governments crowd out private investment by increasing the required returns on government securities and absorbing the bulk of the savings mobilized by the financial system. Bank profitability does not necessarily suffer given the high yields on these securities, but the ability of the financial system to allocate resources efficiently is severely curtailed. Empirical studies have also shown that countries with lower and more stable inflation rates experience higher levels of banking and stock market development (Boyd, Levine and Smith, 2001) and high inflation and real interest rates are associated with higher probability of systemic banking crises (Demirguc-Kunt and Detragiache, 1998 and 2005).

# **_3.2.2. Legal and information infrastructure_**

Financial systems also require developed legal and information infrastructures to function well. Firms’ ability to raise external finance in the formal financial system is quite limited if the
events. Educated (or richer) Hutu were also killed, both in the genocide as well as in the war with the Rwandese Patriotic Front (RPF). The literature offers dramatic examples of this. Mamdami (2001) reports, for example, that, in order to escape killing, Tutsi girls ‘ _had to dress and behave like peasants_ ‘implying that the victims were also targeted based on class (rich vs. poor or urban vs. rural). Since our dependent variable captures all deaths in 1994, not just in the three months of the genocide, it includes all deaths in the genocide and the war (Tutsi as well as Hutu) as well as deaths by disease (e.g. in the refugee camps in Democratic Republic of Congo).

Our econometric strategy is as follows: the 1992 DHS contains the ethnicity of the respondents. We are thus able to analyse differences between Hutu and Tutsi in 1992. Panel A of table 2 analyzes the correlation between being Tutsi, and education and urban residence in the female sample of the 1992 DHS. The results show that even when controlling for region and age, there is a strong positive correlation between education, urban status and being Tutsi. This confirms our fear that the relationship between mortality in 1994 and education and urban residence is at least partially driven by the fact that Tutsi were more likely to die during the genocide.

Our next step is then to find a proxy for being Tutsi, a variable that would be correlated with Tutsi but that would _not_ be correlated, by itself, i.e. independently from being Tutsi, with the probability of dying in 1994. We believe that the fact that Tutsi married at a later age satisfies these conditions. Panel B of table 2 indicates that even after controlling for education, urban residence, region and age, Tutsi women married later. Thus, age at marriage is positively correlated with being Tutsi. This fact had 13
|Table 4: Multinomial Logit and Mixed|Logit Model<br><br>Multino<br>Cost/|s<br> <br>mial Logit<br>Wage|Multinom<br>ln(Hicksia|<br>ial Logit<br>n bundle)|<br>ln(|Mixed Logit<br>Hicksian bun|<br> <br>dle)|
|---|---|---|---|---|---|---|---|
||Coef|T value|Coef|T value|Implied Coef|Parameter|T value|
|Const:Rail(Mean)|-1.829|-27.0|-1.831|-27.1||-2.906|-42.9|
|Const:Rail(Band/Variance)||||||0.263|3.4|
|Const:Bus(Mean)|-1.803|-23.0|-1.874|-24.1||-5.050|-22.6|
|Const:Bus(Band/Variance)||||||15.058|8.1|
|Const:Rail+Bus(Mean)|-2.866|-32.0|-2.925|-32.9||-4.517|-98.6|
|Const:Rail+Bus(Band/Variance)||||||0.195|3.5|
|Const:Two wheeler(Mean)|-0.475|-4.8|-0.532|-5.4||-1.262|-7.5|
|Const:Two wheeler(Band/Variance)||||||7.100|2.0|
|Const:Car(Mean)|0.023|0.2|-0.070|-0.5||-0.848|-7.3|
|Const:Car(Band/Variance)||||||0.695|2.8|
|Cost/Hicsian bundle(Mean)|-0.033|-15.7|13.340|14.3|25.071|3.222|54.2|
|Cost/Hicsian bundle(Band/Variance)||||||0.255|7.2|
|Walk(Mean)|-0.040|-32.4|-0.039|-32.3|-0.076|-2.580|-77.0|
|Walk(Band/Variance)||||||0.106|5.3|
|In vehicle(Mean)|-0.033|-13.4|-0.034|-13.6|-0.043|-3.152|-73.8|
|In vehicle(Band/Variance)||||||0.260|2.3|
|Log Likelihood|-3413||-3429||-3312|||
|Sample Size|4958||4958||4958|||
|Value of time (Rs./hour at mean)||||||||
|Walking|34.9||39.7||40.7|||
|In vehicle|29.2||33.9||23.0|||

26
equivalency adjustments were considered conservative and would bias the results towards showing lower poverty rates for households with many children.

Relative poverty lines were calculated by determining the median per capita household income (weighted by individuals, not households) in the population and multiplying this figure by 0.5. The number of children or old persons living in households with incomes per capita below this figure were classified as relatively poor (i.e., they were below 50% of the median per capita income line) and as absolutely poor (below 35% of the median per capita income line). The percentages of each demographic group falling into this category are reported in Figure 3.1. Some of the analysis extended to urban/rural differences, male/female differences, poverty among working age persons and the very old and income distribution by demographic group. Appendix IV presents some of these results.

Figure 3.2 was based on Smeeding (1992). In this case, the relative poverty line chosen was 40 percent of adjusted family income after tax and transfers. The adjustments include family size adjustments based on the U.S. poverty line equivalence scale which takes into account both economies of scale and age of members. Smeeding's data come from the Luxembourg Income Study (LIS) which is an ongoing attempt to produce an intemationally comparable data set on incomes across industrialized countries.

# E. Wage **growth**

Appendix Table A.3 and Issue Brief Table 2.2, 2.3 and 2.4 present data on real earnings growth for a large range of countries. Real wage growth data refer to wage and salary earnings and therefore exclude large segments of the labor force in lower income countries. In many cases the data refer to nonagricultural wage labor or even to manufacturing or other sub-sectors of the labor force receiving wage income. The types of workers to which the data refer vary across countries.

The main source of wage growth data used in Table A.3 and Issue Brief Tables 2.2 and 2.4 was the UNIDO database which is kept in the World Bank computer data files. The annual wage bill and number of workers covered are provided by UNIDO. Dividing the wage bill by the number of workers 55
# Figure la

Partial scatterplot of growth GDPPW arnd Educational Capital (1-1K) BL data, 1960-85 <!-- Start of picture text --> 0<br>00 IRO~~~~~ I p<br>0 O NPL IRMLI<br>Q o TGO<br>CL 0- CAF<br>-0<br>o IRN<br>-<br>._- 0 .TUN<br>0 o GHA SYR<br>C14 ~ZAR SLE LBR<br>s o 6] MO MnBD IDN ZMB DZA PAI( KEN BWA<br>-FITi L PER $~RTWN ECU CYP<br>NIC ~ niUGADOUfMI 14R<br>~~ (NJ ~~~~Ll(AzL CRI C~M~AT F Ei ISR HC<br>IE O GUY R FIN ITA MLT<br>,_ - E ~~GUY PG I- A<KI§P BRA<br>o I PNG 01rG<br>o NER<br>I-0.03 -0.02 -0.01 0.00 0.01 0.02 0.03<br>Growth of GDPPW I Growth capital per worker<br><!-- End of picture text -->