rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Results**

Before reporting the KCM results, Table 1 provides estimates of unconditional substitution (complementarity) elasticities. That is, the results come from a model conditioned only on time effects and country-pair fixed effects.<sup>11</sup> We present results based on both the aggregate and sector FCS data. The point estimates of China’s and India’s effect on LAC are reported at the bottom of the table. The first panel reports the unconditional effect of China and India on aggregate FCS. Column 1 shows the results of the OLS estimation. The impact of China is positive and significant for the rest of the world, and LAC does not appear to be statistically different. The coefficient on the Indian FCS variable also appears with a positive sign and is statistically different from zero. The effect on LAC is not statistically different either, but the corresponding point estimate is positive.

The second column shows the Poisson regression results. We use the Poisson conditional-mean estimator to control for country-pair fixed effects proposed by Hausman et al. (1984). The results are qualitatively similar to those of the OLS estimation, although the magnitudes of the impact of China and India are considerably smaller. We continue to find that China and India have a positive effect on the rest of the world, and the effect on LAC is not different. The overall effects of China and India on LAC are 0.22 and 0.19 respectively, which are about half of the corresponding OLS estimate. The third column shows the estimates for the Negative Binomial conditionalmean estimator also proposed by Hausman et al. (1984). Again, we find that China and India have had a positive effect on FCS in the world and LAC. In brief, the unconditional

> 11 For the sake of brevity, we do not report unconditional partial correlations without time or country-pair fixed effects.

11
# **Table 4A – Impact of FD Law on Bank Lending to Firms Given State Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms in states of varying ex ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volu<br>(1)|mef,b,m<br>(2)|Investm<br>(3)|entf,b,m<br>(4)|Working<br>(5)|Capitalf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.013**|0.015**|0.046***|0.051***|0.016**|0.017**|
||(0.006)|(0.006)|(0.013)|(0.013)|(0.007)|(0.007)|
|Observations|1,252,105|1,252,105|206,666|206,655|1,131,483|1,131,483|
|R-squared|0.803|0.803|0.870|0.870|0.799|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

50
# **Figure A8: Fiscal Position-Dependent Multipliers with Private Investment**

<!-- Start of picture text --> A. On Impact B. 1 Year<br>0.3 0.8<br>0.6<br>0.2<br>0.4<br>0.1<br>0.2<br>0.0<br>0.0<br>-0.1<br>-0.2<br>-0.2 -0.4<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br>C. 2 Years D. Long Run<br>1.0 2.0<br>1.0<br>0.5<br>0.0<br>0.0 -1.0<br>-0.5 -2.0<br>-3.0<br>-1.0<br>-4.0<br>-1.5 -5.0<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br><!-- End of picture text --> _Note:_ The graphs show the conditional fiscal multipliers for different levels of fiscal position at select horizons. These are based on estimates from the IPVAR model that includes private investment. The model coefficients are conditioned only on fiscal position. Government debt as a percentage of GDP is the measure of fiscal position and the values shown on the x-axis correspond to the 5<sup>th</sup> to 95<sup>th</sup> percentiles from the sample. Fiscal position is strong (weak) when government debt is low (high). Solid lines represent the median, and dotted bands are the 16-84 percent confidence bands.

36
4.5.1: <u>Restricting the Sample to Relatively Poor Households. The limited impacts of aid on food</u> consumption outcomes may be due to a number of reasons. The first is that aid really does have limited impacts on food consumption outcomes. The second is that the sample does not provide sufficient power to estimate modest impacts of aid on food consumption outcomes. For value of food consumed and calories per capita in particular, we would expect that the impacts of aid would be relatively higher for those with low levels of food consumption outcomes, since aid would then form a larger percentage of the food sourced. Targeting errors can then exacerbate the difficulty in estimating the impacts of aid on food consumption, since we would expect more muted impacts if aid is received by relatively wealthy households. But we cannot introduce yet additional instrumented variables. Instead, we limit the sample to those households with wealth index levels below the median (.195). The results for the four food consumption outcomes are reported in the top panel of Table 7 for the two-shock specification.<sup>23</sup> As can be seen, we are more likely to observe impacts of aid on food consumption outcomes versus results from the full sample, particularly for those households that have experienced a sequential shock. For the both the IHPS and FIAS samples, there are significant negative impacts of a sequential shock on the value of food consumed per capita, but also significant positive impacts of receiving aid under a sequential shock. For FIAS, calories per capita are also negatively affected by a sequential shock but aid under a sequential shock has a positive impact. For IHPS, households who experienced a currentperiod only shock but also received aid reduced food purchases. We do lose significance on the food consumption score for the IHPS sample, however.

4.5.2: Restricting the Sample to Households Interviewed during September-March. Another reason we might not be able to detect changes in food consumption outcomes from receipt of food aid under shocks may be due to the time between receipt of aid and the household interview date. While WFP distributed

> 23 Full results for the poorer household sub-sample are in Online Appendix 8.

38
# **Table A8 - Impact of FD Law on Bank Lending to Firms (with Long vs Short Credit History) Given State Public Debt and Fiscal Spending Composition**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms of different credit history length in states of varying ex ante public indebtedness and spending composition. A firm is defined to have a long (short) credit history if the relationship duration with its bank is above (below) the median at the time of the FD Law. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _NonInfrastructures,2015_ is the ratio of public spending on all items except for infrastructure over the total public spending of state _s_ in 2015. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

|variable definitions are provide|d in Table|A1 in the A<br>**Firms**|ppendix.<br>**with Lon**|<br>**g Credit H**|**istory**<br>Wo|kin||**Firms**|**with Shor**|**t Credit**|**History**<br>Wo|rkin|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Volu<br>|mef,b,m<br>|Invest<br>|mentf,b,m<br>|<br>Capi<br>|g<br>talf,b,m<br>|Volu<br>|mef,b,m<br>|Investm<br>|entf,b,m<br>|<br>Capi<br>|g<br>talf,b,m<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Postm*DebtStates,16Q1|0.01*|0.02***|0.03**|0.05***|0.02***|0.02***|0.02*|0.01|0.09***|0.09***|0.02*|0.02|
||(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|
|Postm*NonInfrastructures,2015||-0.84***||-0.80**||-0.65***||0.09||-0.40||0.08|
|||(0.17)||(0.40)||(0.18)||(0.30)||(0.77)||(0.32)|
|Postm*DebtStates,16Q1||-0.12||0.19||0.08||1.94***||1.52**||1.65***|
|*NonInfrastructures,2015||(0.20)||(0.51)||(0.21)||(0.30)||(0.73)||(0.33)|
|Observations|721,946|721,946|112,551|112,551|661,194|661,194|530,145|530,145|94,024|94,024|470,261|<br>470,261|
|R-squared|0.80|0.80|0.86|0.86|0.79|0.79|0.81|0.81|0.87|0.87|0.81|0.81|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

73
enable in the first place) an inpatient stay.<sup>16</sup> Rural facilities in Kenya and Sierra Leone are particularly lacking in this domain compared to their urban counterparts. For example, in Kenya, on average there are seven functioning inpatient toilets in urban facilities compared to four in rural facilities (Appendix Table 4). While this difference could be signaling an underlying difference in the patient volume served by urban and rural facilities, what remains concerning is that only slightly over half of facilities in rural areas have gender-segregated inpatient toilets.

Acceptable waste disposal practices<sup>17</sup> are relatively common across all countries, although Kenyan facilities, especially in rural areas, could benefit from interventions for improvement. Tanzania and Sierra Leone, respectively, with over 90% and 100% of facilities with appropriate waste disposal practices, indicate the possibility of ensuring effective practices despite limited resources.

> 16 UNICEF. “Gender-Responsive Water, Sanitation and Hygiene: Key elements for effective WASH programing”. March 2017. https://www.unicef.org/gender/files/Gender_Responsive_WASH.pdf 17Acceptable waste disposal methods include open burning in a pit or protected ground, placed in covered pit or pit latrine, placed in pit or protected ground, off-site removal (with protected storage) and one or two chamber incinerators. This definition is from WHO’s “Better Health Care Waste Management: An Integral Component of Health Investment” (2005).

> <u>https://apps.who.int/iris/bitstream/handle/10665/119762/dsa515.pdf?sequence=1&isAllowed=y</u>

14
14. Institut National de la Statistique, Ministère du Plan (Guinée); ICF International. _Enquête Demographique et de Santé et à Indicateurs Multiples (EDS-MICS 2012)_ . Conakry (Guinée) and Calverton (USA) 2013.

15. Dowell SF, Mukunu R, Ksiazek TG, Khan AS, Rollin PE, Peters C. Transmission of Ebola Hemorrhagic Fever: A Study of Risk Factors in Family Members, Kikwit, Democratic Republic of the Congo, 1995. _Journal of Infectious Diseases_ . 1999:S87-91.

16. The DHS Program. STATcompiler. Available at: <u>http://www.statcompiler.com/. Accessed October</u> 2014.

17. Scarpino SV, Iamarino A, Wells C, et al. Epidemiological and Viral Genomic Sequence Analysis of the 2014 Ebola Outbreak Reveals Clustered Transmission. _Clinical Infectious Diseases_ . December 2014.

18. Meltzer MI, Atkins CY, Scott S, et al. Estimating the Future Number of Cases in the Ebola Epidemic — Liberia and Sierra Leone, 2014–2015. _Morbidity and Mortality Weekly Report: Supplements_ . September 2014;63(3):1-14.

19. Bernstein L. With Ebola crippling the health system, Liberians die of routine medical problems. _The Washington Post_ . September 2014.

20. Hosegood V, Floyd S, Marston M, et al. The effects of high HIV prevalence on orphanhood and living arrangements of children in Malawi, Tanzania, and South Africa. _Population Studies_ . 2007;61(3):327-336.

21. Monasch R, Boerma JT. Orphanhood and childcare patterns in sub-Saharan Africa: an analysis of national surveys from 40 countries. _Aids_ . 2004;18(S55-S65).

22. Grant MJ, Yeatman S. The relationship between orphanhood and child fostering in sub-Saharan Africa, 1990s–2000s. _Population Studies_ . 2012;66(3):279-295.

23. Beck S, Vreyer PD, Lambert S, Marazyan K, Safir A. _Child Fostering in Senegal_ : UNICEF & Paris School of Economics.

24. UNICEF. _Thousands of children orphaned by Ebola_ : United Nations Children's Fund; 2014.

25. Gettleman J. An Ebola Orphan's Plea in Africa: 'Do You Want Me?'. _The New York Times_ . December 2014.

26. Bitchell RE. After Losing Parents To Ebola, Orphans Face Stigma. _Goats and soda, NPR_ . October - 03, 2014. Available at: http://www.npr.org/blogs/goatsandsoda/2014/10/03/353473707/after <u>losing parents-to-ebola-orphans-face-stigma. Accessed December 17, 2014.</u> 9
individual test point observations.<sup>60</sup> This procedure of averaging performance over a forty year period is meant to proxy the educational performance of the whole labor force, because the basic objective is not to measure the quality of students but to obtain an index of the quality of the workers in a country. If the quality of schools and skills of graduates are constant over time, this averaging is appropriate and uses the available information to obtain the most reliable estimate of quality. If on the other hand there is changing performance, this averaging will introduce measurement error of varying degrees over the sample of economic data (1960-2000). The analysis in Hanushek and Wößmann (in process) shows some variation over time, but there is no clear way to deal with this here.

When looking at effects of the distribution of educational quality, we go beyond the mean performance of a country’s students and calculate the share of students above a certain test-score threshold, as well as the performance of students at different percentiles of a country, both based on our transformed metric of scores. This requires going into the details of the student-level micro data for each international test, which we can do for each test in mathematics and science with the exception of FIMS, where the micro data seem no longer accessible. See Hanushek and Wößmann (in process) for details.

By now, there are a total of 77 countries that have ever participated in any of the nine international student achievement tests in mathematics and science. 50 of these are included in the analyses of economic growth. 25 countries are not included in the growth database due to lack of data on economic output or because they drop out of the sample for a standard exclusion criterion in growth analyses (15 former communist countries, 3 countries for which oil production is the dominant industry, 2 small countries, 3 newly created countries, 2 further countries lacking early output data). Two countries (Nigeria and Botswana) turn out to be strong outliers in the growth regressions and are therefore dropped from the sample.<sup>61</sup> 60 Note that the descriptive representation in Figure 4.1 also reports performance on the reading tests, which are not used for the growth analyses here because they may be affected by testing in different languages and may not be easily combined into a common one-dimensional scale with mathematics and science tests.

61 There are four countries with test-score data which have a few years missing at the beginning or end of the 19602000 period on the income data in the Penn World Tables. In particular, data for Tunisia starts in 1961 rather than 1960, and data for Cyprus (1996), Singapore (1996), and Taiwan (1998) end slightly earlier than in 2000. These countries were included in the growth regressions by estimating average annual growth over the available 36-39 year period rather than the whole 40 year period.

50
# **Annex 1:  Calculation of total tax compliance cost**

<u>Calculation of the total tax compliance cost</u>

1. Q16a was recoded<sup>28</sup> . For all respondents who didn’t provide exact answer and did provide a category a middle category point was taken as a value.

2. One outlier (15 million rand) was taken out of the calculation.

3. The highest category was replaced with the minimal recorded value of those respondents providing exact figure - 200K rand (the new variable in the database is a16a).

4. Q17a and b were combined and variable was created with sum of all employees time shares spent on tax accounting (the new variable in the database is a17ab).

5. Gross monthly salaries of inside accountants were estimated based on firm turnover band. The assumption is that accountants in the larger firms have higher salaries. The following estimates were used:

- (a) Up to 300 K rand the salary is estimated at 6 000 rand

- (b) 300K – 1 million – 10 000 rand

- (c) 1 – 6 million – 12 000 rand

- (d) More than 6 million – 15 000 rand

6. For respondents not providing information about their turnover, number of employees was taken as a proxy based on the analysis of cross-tabulation between number of employees and turnover (the new variable in the database is “salary”):

- (a) 3 or less employees – 6 000 rand

- (b) 4 – 6 employees – 11 000 rand (approximately same share of respondents belonged to groups 2 and 3 in above turnover categories)

- (c) 7 – 15 employees – 12 000 rand

- (d) 16 – 30 employees – 12 000 rand

- (e) 31 employees or more – 15 000 rand.

7. Annual cost of staff was calculated by multiplying monthly gross salary by 12 (months) and adding 20% for overhead (the new variable in the database is “salary2”).

8. The full salary variable (“salary2”) was multiplied by the value of “sum of employees’ time shares spent on tax accounting (variable a17ab in the database)

> 28 For details, please refer to:

> http://www.ifc.org/ifcext/fias.nsf/AttachmentsByTitle/SouthAfricaFormalSMMETaxComplianceReport200 8/$FILE/Formal+SMME+Tax+Compliance+Report1.pdf

29
Most migrants (40 percent), regardless of their origins and destinations, migrate to accompany someone. Marriage is the second most frequent reason for migration in any direction; with the exception of rural to urban migration where 26.6 percent migrants move to seek work.

Table 7 – Direction of Migration and Reasons

|||Direction o|f Migration||
|---|---|---|---|---|
|Reason of Migration|Urban-Urban|Urban-Rural|Rural - Urban|Rural - Rural|
|For work only|18.8|16.3|26.7|17.5|
|Education|1.3|0.3|0.7||
|Marriage|33.3|33.9|23.4|28.1|
|Accompany|40.9|40.2|42.1|45.2|
|Others|5.7|9.5|7.2|9.2|
|Total|100|100|100|100|

Source: Authors’ calculations using Labor Force Survey Most men migrate either to work or accompany another migrant (more than one-third each). Employment is the reason behind migration from rural to urban localities (53 percent) as well as from one rural area to another. Women mostly migrate because of marriage or to accompany a migrant. The percentage that migrates for work is negligible (see Tables A3 and A4 in the appendix).

# **Internal Migration – Labor Mobility**

Those who migrate to work are mostly males (97.8% of migrants to work are males while 2.2% are females), and the distribution of these migrants by level of education is multimodal (Table 8): the biggest fraction is composed by illiterate workers (26%) and starts decreasing gradually with the level of attainment until it reaches the technical secondary level (25%), and then the university level at 17%.

Table 8 – Educational Attainment of Migrants (for work only)

|Education level|Percent|
|---|---|
|Illiterate|26.1|
|Read & write|13.0|
|Less than Intermediate level|10.2|
|General Secondary|2.7|
|Technical Secondary|25.3|
|Above Intermediate level|4.4|
|University|17.1|
|Above university|1.2|

Source: Authors’ calculations using Labor Force Survey 8
**Table 3. Droughts in SSA: death toll and economic damage**

||**Year**<br>**Death toll**<br>**Damage in thousand USD**|
|---|---|
|**Angola**|2001<br>58|
|**Benin**|1983<br>651|
|**Burundi**|2005<br>120|
|**Ethiopia**|1983<br>300000|
||1987<br>367|
||1998<br>15600|
|**Guinea**|1998<br>12|
|**Kenya**|1999<br>85|
||2004<br>80|
||2005<br>27|
|**Madagascar**|1988<br>200|
|<br>**Malawi**|2002<br>500|
|**Mauritius**|1999<br>175000|
|**Mozambique**|1981<br>100000|
||1991<br>50000|
|**Namibia**|1991<br>50000|
||1998<br>1000|
|**Nigeria**|1983<br>71103|
|<br>**Rwanda**|1989<br>237|
|**Somalia**|1987<br>600|
||2000<br>21|
|**South Africa**|1991<br>1000000|
|**Swaziland**|1983<br>500|
||1990<br>1739|
|**Uganda**|1998<br>1600|
||1999<br>115|
||2002<br>79|
|**Zimbabwe**|1982<br>1000|
||1991<br>50000|

Source: "EM-DAT: The OFDA/CRED International Disaster Database www.emdat.be - Université Catholique de Louvain - Brussels - Belgium" The economic impact of a _disaster_ usually consists of direct (e.g. damage to infrastructure, crops, housing) and indirect (e.g. loss of revenues, unemployment, market destabilisation) consequences on the local economy. In EM-DAT estimated damage are) given in US$ (‘000). For each disaster, the registered figure corresponds to the damage value at the moment of the event, i.e. the figures are shown true to the year of the event.

Droughts often lead to a substantial reduction of food production and the potential for famines. Even if famine could be avoided, rural output is destroyed and rural income reduced, exacerbating rural poverty. Loayza et al. (2009) study the impact of natural disasters on economic growth. Findings from a panel of 94 developed and developing countries over 1962-2005 indicate that droughts have a negative impact on economic growth with the largest impact on agricultural growth, but also a statistically significant negative impact on industrial growth and no statistically significant impact on services. They find that in developing countries a typical drought produces a reduction of agricultural and industrial annual growth rate of the order of 1 percentage point, resulting in a decline of GDP of 0.6 percentage points per year or 3 percentage points over a period of 5 years. Clearly the impact of droughts on economic growth is substantial, but given that agricultural and labor intensive sectoral growth is especially important to the poor, the droughts are going to affect poverty disproportionately. Christiaensen, Demery and Jesper (2011) estimate that a 1 percentage point of agricultural growth is three 13