rafmacalaba/data-use-mentions-extended huggingface dataset:
denominator and a positive numerator. In this case, the true rate of protection is given by the absolute value of the ERPs, which we indicate by placing the estimates associated with this sector in parentheses.

But more intuitively interesting, is the case of agriculture. Agriculture’s, ERP declines when considering NTBs relative to excluding NTBs, because the NTBs applied to intermediate inputs used by agriculture. NTBs act to “disprotect” agriculture.

By contrast, textiles are protected by NTBs. By excluding them from the estimates the ERP due to tariffs on textiles is negative – tariffs act as a net tax on the sector (-7.5%) – but energy subsidies offset the tariffs for a total ERP of 20.3% . But including NTBs in the estimates raises the ERP to 50%, meaning that NTBs provide a net protection. There is a NTB tariff-equivalent of 6.39% protecting textiles, without much NTB protection on the intermediate inputs used by the activity.

# **8. Concluding Discussion: The Unexpectedly High ERP Dispersion and Strong Effect of Energy Subsidies**

This study has addressed four themes. First, it has compared for several selected disaggregated industries current ERP estimates derived from tariff schedules with those of a decade ago when at the initial stages of trade reforms. Second, it has presented recent effective rates of protection measure across the Egyptian economy using 23 aggregated sectors from available input-output matrix information. Third, it has incorporated and estimated the importance of energy subsidies in these ERP estimates, taking advantage of the fact that the input-output matrix has five disaggregated energyrelated sectors for which we have subsidy estimates. Fourth, it has incorporated estimates of the tariff-equivalents of non-tariff barriers into ERP estimates. Key results are presented in Tables 4 and 5, and show that, broadly speaking, trade liberalization since the late-1990s has had a considerable impact in reducing protection of some industries. Nevertheless, some sectors, such as the food and tobacco sector, remain relatively hightly protected, due to tariff escalation and
|0.485|0.458|0.234|0.478|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). Afternoon is a dummy that takes value 1 if the interview was conducted after 1 pm and 0 otherwise. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

41
/sup><sup>_Li×Ei_)</sup> for exports is then . Commodities are classified as _untaxed_ if the taxes <u>�</u> _i_<sup>_Ei_</sup> paid on the commodity constitute less than 5% of the value of domestic output of the commodity, and _domestic_ if more than 5% is taxed. Let _Uj_ be the valueadded of untaxed goods. The labor-output ratio for _untaxed_ is then calculated <u>�</u> _<u>j</u>_<sup>(</sup><sup>_Lj×Uj_)</sup> as . The same procedure is used to calculate the labor-output ratio for <u>�</u> _j_<sup>_Uj_</sup> _domestic_ .

The administrative costs of tax collection presented in Table 5 are derived from various sources. For the United States: IRS Data Book, FY2002, available at: www.irs.gov/pub/irs-soi/02db30cs.xls; For other OECD countries – authors’ calculations based on the following tax agency annual reports: Australia – ATO Annual Report 2002, available at www.ato.gov.au; United Kingdom – Inland Revenue, Annual Report for the year ending 31st March 2002, available at www.inlandrevenue.gov.uk/pdfs/report2002.pdf; New Zealand – Inland Revenue 31
wealth quintile (poorest)|0.09|-0.17|-0.08|
|2nd wealth quintile|0.01|0.01|0.01|
|3rd wealth quintile|-0.01|-0.01|-0.02|
|4th wealth quintile|0.00|0.00|0.00|
|5th wealthquintile(richest)|0.04|-0.08|-0.04|
|Number of observations overall|4668|4668|4668|
|Number of observations in 2003|2295|2295|2295|
|: Authors ̳ calculations using Demographic and<br> <br>Number of observations in 2008|Health Surveys m<br> <br>2373|icrodata.<br> <br>2373|<br>2373|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Based on regressions in column 6 of Tables 4 and 6. Shown is only decomposition of the explained part of the difference. Contributions of explanatory variables sum up to the explained part of the difference. Significance
# **Table 4: Productivity and the Business Environment in Sri Lanka: Evidence from Cross-sectional Industrial Census Data**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|**Dependent var(in logs):**|**Output per**<br>**worker**|**Value Added**<br>**per worker**|**Output per**<br>**worker**|**Value Added**<br>**per worker**|<br>**Output per**<br>**worker**|<br>**Value Added**<br>**per worker**|
|Competition with foreign firms|0.810+++|0.754++|1.284+++|1.339+++|0.688+|0.842+++|
||(0.231)|(0.217)|(0.053)|(0.051)|(0.308)|(0.232)|
|Government Ownership|-0.581++|-0.596+|-1.132++|-1.203+++|-0.947+|-0.912+|
||(0.234)|(0.285)|(0.324)|(0.307)|(0.491)|(0.428)|
|Losses from crime,
<!-- Start of picture text --> Kakwani Index<br>0.200“Ta n. I<br>000 ; | ; = 7 ‘ | a a 7 |<br>-0.200<br>-0.300<br>-0.400<br>MDA UKR BLR GEO RUS ARM TJIK KYG<br>=PIT mSIC @ Indirect Taxes m All Taxes and Contributions<br><!-- End of picture text -->
Where is the full subsidy, or difference between full and effective tariff. FFCCl From equation (17), we can replace and rearrange the terms to obtain: UU In other words, the full subsidy can be expressed as the sum of the pure subsidy plus the two inefficiency terms (inefficient losses for water and overstaffing for both water and sanitation).

# **3.9 Extrapolating subsidies within countries**

IBNET contains data for utilities in 91 countries,<sup>22</sup> but not all utilities providing either water or sanitation services in each country are included in the database. We can measure the degree of coverage for water and sanitation services respectively in each country by comparing the population served by utilities listed in the IBNET database with the total population served in the country overall. The total population served for water and sanitation services was estimated by multiplying coverage rates from the World Health Organization/United Nations Children’s Fund for piped water facilities and sewered sanitation facilities by population data from the World Bank.<sup>23</sup> By dividing the total population served in a country by the population served in IBNET, we compute an extrapolation factor for each service. Assuming that the average tariff charged by utilities not included in IBNET is equal to that of the utilities in IBNET, and that the average efficiency of both groups is also similar, we estimate total subsidies for each service at the country level by multiplying the total subsidies of the service for utilities in IBNET by the extrapolation factor for that service. The total estimated subsidy level for the WSS sector in each country is then the sum of all utilities’ subsidies in that country.

Nine countries represented in the IBNET database are estimated to have a negative subsidy. Most of these are developed countries, where efficiency may be pushed beyond the level assumed in the Chilean model.<sup>24</sup> Where this is the case, subsidies are assumed to be zero.

# **3.10 Estimating
_Economics_ : 302–308.

- Xi, Tianyang, Yang Yao, and Muyang Zhang. 2018. “Capability and Opportunism: Evidence from City Officials in China.” _Journal of Comparative Economics_ 46 (4): 1046–1061.

- Xu, Chenggang. 2011. “The Fundamental Institutions of China’s Reforms and Development.” _Journal of Economic Literature_ 49 (4): 1076–1151.

- Xu, Guo. 2018. “The Costs of Patronage: Evidence from the British Empire.” _American Economic Review_ 108 (11): 3170-98.

- Yao, Yang, Muyang Zhang. 2015. “Subnational Leaders and Economic Growth: Evidence from Chinese Cities.” _Journal of Economic Growth_ 20 (4), 405-436.

- Yu, Jihai, Robert De Jong, and Lung-fei Lee. 2008. “Quasi-Maximum Likelihood Estimators for Spatial Dynamic Panel Data with Fixed Effects when Both _n_ And _T_ Are Large.” _Journal of Econometrics_ 146 (1): 118–134.

- Yu, Jihai, Li-An Zhou, and Guozhong Zhu. 2016. “Strategic Interaction in Political Competition: Evidence from Spatial Effects across Chinese Cities.” _Regional Science and Urban Economics_ 57: 23–37.

35
these comparisons, we turn to a direct comparison between non-EU15 migrants and refugees. Figure 8 shows the differences in employment rates between the two groups for different specifications. The baseline does not take differences in education levels or origin countries into account. We then introduce origin countries fixed effects and education dummies, first separately, then together.<sup>31</sup> It turns out that accounting for education differences or for countries of origin yields very similar results; the combination of both does not alter this conclusion. According

> 29. This does not seem to be due to changes in education levels between cohorts. When education levels are included in regressions using the restricted sample, these effecs do not disappear. See Figure A.1 in the appendix. 30. As we observe education levels in the Structural Surveys between the years 2010 and 2017 (and not at the moment of arrival), this seems to imply that only a very small number of refugees who arrived in Switzerland between the ages of 18 and 25 years were able to obtain a tertiary education degree in Switzerland.

> 31. As above, the specifications without education levels rely on the main sample, whereas those with education dummies use the restricted sample. The detailed results of these estimations are given in Table A.4 in the appendix.

17
New estimates of flood exposure in developing countries using high-resolution population data.</mark> _<mark>Nat. Commun.</mark>_ **<mark>10</mark>** <mark>1–7 (2019).</mark>

> 36 Kocornik-Mina, A., McDermott, T. K. J., Michaels, G., and Rauch, F. Flooded Cities. _Am. Econ. J. Appl. Econ._ **12** , 2, 35–66 (2020).

> 37 <mark>Jongman, B., Ward, P. & Aerts, J. Global exposure to river and coastal flooding: long term trends and changes.</mark> _<mark>Glob. Environ. Chang.</mark>_ **<mark>22</mark>** <mark>, 823–835 (2012).</mark>

38 Tiggeloven, T. et al. Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. _Nat. Hazards Earth Syst. Sci._ **20** , 1025–1044 (2020).

39 Rentschler, J., Salhab, M., Jafino, B. A., Flood exposure and poverty in 188 countries. Preprint at https://www.researchsquare.com/article/rs-965657/v1

> 40 Sampson, C. C. et al. A high-resolution global flood hazard model. _Water Resour. Res._ **51** , 7358–7381 (2015).

> 41 Smith, A., Sampson, C. & Bates, P. Regional flood frequency analysis at the global scale. _Water Resour. Res._ **51** , 539–553 (2015).

> 42 Yamazaki D. et al. A high-accuracy map of global terrain elevations. _Geophys. Res. Lett_ .
-20-

**Table** 7: **Income Shares by Deciles**

|Decile|Share in total declared|Share in capital|
|---|---|---|
||income|income|
|1|1.2|0.2|
|2|2.9|0.1|
|3|4.0|0.4|
|4|5.2|0.8|
|_5_|6.5|1.4|
|6|7.8|1.9|
|7|9.4|2.8|
|8|11.6|3.8|
|9|15.4|8.1|
|10|35.9|80.6|
|Total|100.0|100.0|
|Gini-coefficient|<br>0.49|0.84|

Source: TARKI, 1995.

Does this build a strong case for taxing capital income, on distributional grounds, since capital income is extremely concentrated on the richest households? No, because the tax base is too small so that using capital income taxation as a tool to make income distribution more egalitarian would not be effective. Consider the highest income decile which earns 35.6% of total income (HUF 610.4 billion). Capital income makes up HUF 31.6 billion, i.e., 5% of total declared income of this decile. Taxing this amount would have a negligible impact on income distribution, regardless of whether taxation is are flat-rated or progressive. The tax base is too small. In addition, progressivity will increase the incentive to avoid or evade taxes, further undermining the already small potential for distributive impact.

As an illustration, we present in table 8 a simulation using the 1994 data. The table shows how average tax rates on total income (that is, labor income plus capital income) of representative households
labor to be linked positively to educational attainment, as a proxy for cognitive skills, so that recall bias is lower among more educated households.<sup>11</sup> Second, we consider gender differences – particularly the question whether recall bias differs between male and female workers. Time use data from Africa show that women often carry out different roles and activities simultaneously, rather than sequentially. This holds particularly for childcare, which is typically embedded within other economic activities (Blackden and Wodon 2006). This may make it more difficult to recall farm labor for female than for male household workers.

In **Table 8** , we compare mean hours per person per plot between weekly visit and recall households for less educated households, more educated households, female workers, and male workers. Panel A shows that in less educated households, the average hours of farm labor are about 25 percent higher than in more educated households, and importantly there is no evidence of recall bias in the latter group. Recall households report, on average, 44.2 hours per person per plot more than weekly visit households among less educated households, whereas the difference is only 2.3 hours (and not statistically significant) among more educated households. Panel B summarizes hours per person per plot for male and female workers. Male farmers work almost 50 percent more hours per plot than female farmers, but contrary to our expectation the recall bias appears to be greater for males than for females.

**Table 8: Heterogeneity in recall bias, descriptive estimate**

|**A: By educatio**n|Weekly visit|Recall|Difference|Weekly visit|Recall|Difference|
|---|---|---|---|---|---|---|
||Hh educa|tion primary o|r below|Hh educ|ation above p|rimary|
|Season-wide hours per<br>person per plot|
# Estimation Results

Our baseline results are summarized in Tables 4-6. Each table presents results for a different measure of institutional quality – Bureaucratic Quality, Control of Corruption and Protection of Property Rights. As mentioned above, we estimate separate equations for each indicator due to their high correlation. Since our tariff data is available for only 43% of the sample for which trade data are available, we estimate all specifications first without tariffs and then add them as a regressor. We address the potential endogeneity of institutional quality to trade by instrumenting for the sum of institutional quality with the sum of absolute latitude for each country-pair.

Our estimates imply that different legal origins do have a detrimental effect on trade, between 10% and 25%, depending on the specification of our regressions<sup>26</sup> . This is a large effect. Using a similar variable, Helpman Melitz and Rubinstein (2005) find a slightly larger impact in their regressions; this could be because they do not control for institutional quality. Our estimate for the detrimental effect of tariffs yields an elasticity of 1 to 2, thus every percentage point increase in tariffs decreases trade by 1%-2% on average.

However, we also find large positive effects of institutional quality on trade – in some cases very large. In order to assess this effect, the last line in Tables 4-6 report the effect of a change of one standard deviation in institutional quality on trade<sup>27</sup> . For instance, take Bureaucratic Quality in Table 4; our estimates imply that one standard deviation increases trade by 50%-120%. This is a much larger effect than that of differences in legal origins. A similar

> 26 The largest point estimate is in table 6, column 4. The effect is exp{-0.29}-1 = -0.25. 27

> The percent increase in trade due to 1 s.d. of institutional quality is calculated as follows: exp{coef*
ASQ scores at baseline benefited more. The difference in results here may be due, in part, to dynamic complementarities in skills formation, where skills produced at earlier ages increase the productivity of investment at later ages (Heckman et al., 2010; Conti et al., 2016; Garcıa and Heckman, 2014). As highlighted in our discussion of mechanisms, parental practices matter: predicted impacts are higher across all three outcomes for children whose parents scored higher on the parental stimulation index at baseline. Across the three outcomes, we only observe statistically significant gender heterogeneity for the skills index, where girls were 3.5 percentage points more likely to be represented among students with a lower CATE (p-value of 0.03).

28
# **6. Other supporting evidence**

In this section, we analyze the characteristics of firms that receive earmarked credit and the features of banks that channel these loans. We also study the conditions in which banks issue simultaneously earmarked loans and working capital loans (i.e., loan bundles).

# **6.1. Recipients of Earmarked Loans**

We examine which firms obtain earmarked credit, and which banks are more likely to issue these loans. To study the firm and bank characteristics associated with earmarked lending, we use the universe of firms in the credit registry. There are 871,392 firms that obtain working capital loans in the sample period (a total of 1.9 million loan-level observations) from 137 banks. Among these, around 67,000 receive at least one earmarked loan from a group of 35 banks.

We aggregate the loan-level data at the firm-bank-year level and run OLS regressions using as dependent variable a dummy variable equal to one if a firm-bank pair is observed with a new earmarked loan in a given year, and zero otherwise. We include as covariates firm size, firm age, the length of the bank-firm relationship, and the indicator variable _Riskfy_ , which equals one for firms with loan ratings below the median in year _y_ . To analyze if the probability of issuing earmarked loans to firms of varying credit risk depends on the market power of lenders, we include as covariate the indicator variable _MPfy_ and its interaction with the variable _Riskfy_ . Finally, to capture the persistence of earmarked loans within a firm-bank pair over time, we include the covariate _Earmarkfby_ .<sup>16</sup> This variable is equal to zero if the firm _f_ has not obtained and earmarked loan from bank _b_ prior to year _y_ and is set to one for firm-bank pairs after the first earmarked loan is issued. To control for other bank- and time-specific factors related to the issuance of earmarked loans, such as aggregate changes in the earmarked program or banks’ specialization in
about 52 percent from 17,300 rupiahs in 1984 to 26,300 rupiahs in 1996 (Friedman 2003).

Table 3.2. A Profile of Poverty in Indonesia in the 1990s

|PovertyMeasures|1993|1996|1999|2000|2002|
|---|---|---|---|---|---|
|Headcount|61.55|50.51|55.30|55.38|52.42|
|Poverty Gap|21.03|15.33|16.56|16.15|15.68|
|Squared PovertyGap|9.16|6.02|6.49|6.23|6.09|

Source: Computed<sup>12</sup>

# **_3.2. The 1997 Financial Crisis and Its Aftermath_**

Both the GICs and the data presented in table 3.2 show a significant increase in poverty between 1996 and 1999. This reversal of the track record of Indonesia in strong growth and poverty reduction is attributed mainly to the 1997 Asian financial crisis. The full impact of this crisis, which hit Southeast Asia in April 1997, began to be felt in Indonesia late in 1997 and early 1998. The crisis took the form of rapid currency depreciation. The exchange rate went from it pre-crisis level of 2,200 rupiahs per dollar in mid 1997 to 6,000 in December of the same year, and then to 17,000 in January 1998 (Pritchett et al. 2002). To avoid a complete collapse of the financial system, the central bank provided a massive liquidity support to banking system while the government offered a guarantee of all its liabilities. Cole and Slade (1998) explain that political factors made this financial crisis much worse and more difficult to deal with. The exchange rate responded more to political perceptions or expectations than to changes in the interest rates.

> 12 Our empirical analysis is based on
# **Table 4: IV**

## **Dependent Variable Cols 1, 3: Completion Binary [yes=1] Dependent Variable Col 2: CS-Monitoring/Incentives Management Index, 2015 Dependent Variable Col 4: Initiation Binary [yes=1] Dependent Variable Col 5: Completion Index Standard Errors: Robust**

||**OLS**|**First Stage**|**IV:**|**Second Stag**|**e**|
|---|---|---|---|---|---|
||**(1) Baseline [IV**<br>**Sample]**|**(2) Monitoring**|**(3) Project**<br>**Completion**|**(4) Binary**<br>**Initiation**|**(5) Completion**<br>**Status**|
|**CS-Autonomy**|0.04**||0.11**|0.14***|0.35***|
||(0.02)||(0.05)|(0.02)|(0.10)|
|**CS-Incentives/Monitoring**|-0.09***|0.44***|-0.27**|-0.38***|-0.95***|
||(0.02)|(0.04)|(0.11)|(0.06)|(0.21)|
|**CS-Other**|-0.06*||-0.03|0.01|-0.04|
||(0.03)||(
gold—managed carefully so as not to disturb gold markets. The Fund has 90.5 million ounces of gold,<sup>96</sup> currently worth about $164 billion, which is the third largest holding of gold in the world.<sup>97</sup> A third direct approach would be the issuance of IMF bonds; given that regional development banks already issue bonds for leveraging their membership’s paid-in and contingent capital, there is a precedent. Some commentators (for example, Richard Cooper)

> 94 See Calvo (1998) and Calvo et al (2004).

> 95  Article 1 of the IMF Articles of Agreement refers to six purposes for the IMF’s establishment.

> 96 See “Gold in the IMF” Factsheet on the IMF’s website www.imf.org. The value stated above is end-August 2011 gold prices.  According to the

> 97 The United States and Germany have larger holdings (according to World Gold Council data).

41