rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7.  Saving and Real Exchange Rate Gaps, Median Inflation and Median Rating, Growth, and Investment Gaps by Quadrant 1990-2012**

<!-- Start of picture text --> II I<br>0.2 0.8 1.0 5.7 1.0 2.8<br>‐0.6 ‐0.04<br>Undervalued Overvalued<br>0.2 6.8<br>0.4 3.7<br>‐0.5<br>‐0.7 ‐0.7<br>‐1.3<br>III IV<br>Real Exchange Rate Gap<br>● Country Rating ● Growth ● Investment ● Inflation<br>Over‐saving<br>National Saving Gap<br>Under‐saving<br><!-- End of picture text --> Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure is based on Figure 3. It shows the median country rating gap, GDP per capita growth gap, investment gap and inflation rate for all the observations in each quadrant for the 1990-2012 period for the whole sample.

35
expenditure on pensions or assistance to the poor. Richer individuals are more likely to support hiking education spending but are less likely to support increasing expenditure on assistance to the poor. Individuals affiliated with a political party are more likely to support increasing expenditure on health care and lowering expenditure on pensions. Former members of the Communist Party are 20 percent more likely than others to support additional spending on health care. Membership in a religious organization is not correlated with policy preferences.

These findings are fairly homogenous across the countries covered in LITS II. Figure 3 shows the estimated coefficients associated with the age dummy variable “Older than 54 years”. In every country included in the sample (except for France), older individuals are less (more) likely to support increases in government expenditure on education (pensions). Accordingly, older individuals are more (less) likely to support increases in government spending on health care (assisting the poor) in almost every country, but the coefficients are mostly not statistically different from zero.

Table A2 shows the estimation results using a different definition of the dependent variable. In particular, we consider only the first (instead of the first and second) priority for government expenditure. The results shows that even though the point estimates are different in some cases, the signs and magnitudes of the age coefficients are very similar to those reported in Table 2. In other words, the results are not sensitive to this alternative definition of the dependent variable.

Based on the link between ageing and preferences over policy, the next step is to assess the potential for translating these preferences into actual policy through participation of different age groups in the political process. Because participation in elections tends to increase with age, as seems to be the case on the basis of the raw data, the preferences of older individuals are more likely to be reflected in policy making than those of their younger peers. To shed more light on this possibility, we interacted the age dummy with an indicator of participation in elections. The indicator is defined as a 0‐1 variable to identify the individuals surveyed in LITS II who have voted in the latest election.

The results, reported in Table 3, confirm the previous findings but also suggest that individual preferences over policy are more similar across age groups for individuals who vote than for those who do not. As before, the estimated coefficients show that older people are less likely to support increasing government spending on education. However, older people who do not vote are even less supportive of raising education expenditure than older people who do vote. The same pattern holds for pensions: the positive relationship between ageing and support for increasing spending on pensions is less 10
conditions (e.g., Berger, Dai, Ongena, and Smith 2003, Buch 2003).

Most of the efficiency studies of foreign-owned banks in developed countries find that the disadvantages outweigh the advantages. Foreign-owned banks are found to be less efficient than domestically-owned institutions with the possible exception of U.S. banks operating abroad (e.g., DeYoung and Nolle 1996, Berger, DeYoung, Genay, and Udell 2000), although a few studies find that foreign institutions are about equally efficient on average as domestic institutions (e.g., Vander Vennet 1996). In contrast, some research suggests that the advantages of foreign ownership may outweigh the disadvantages in developing nations (e.g., Claessens, Demirgüç-Kunt, and Huizinga 2001, Bonin, Hasan, and Wachtel 2004). This may be due to the superior access to capital markets or technologies or to problems of the domestically-owned institutions in these nations. Prior research on Argentina also supports the finding of better performance for foreign-owned banks in this developing nation (e.g., Clarke, Cull, D’Amato, and Molinari 2000, Dages, Goldberg, and Kinney 2000).

Some caveats are in order regarding analyses of foreign-owned banks. There may be crosssubsidies from the parent banking organization in another nation, including the ability to operate with very little financial capital, using the parent organization to absorb risks. As well, to the extent that foreign-owned banks serve multinational client bases in many nations, the booking location of the costs and revenues associated with these clients may be discretionary. Tax considerations may also shift revenues or costs.

Other research on the static effects of foreign ownership in developing nations finds that foreign ownership and entry and fewer restrictions on these banks are associated with more competitive national banking systems (e.g., Claessens and Laeven 2004, Martinez Peria and Mody 2004). Some also find positive effects of foreign ownership on business credit availability, either overall or for SMEs in particular (e.g., Clarke, Cull, and Martinez Peria 2002, Berger, Hasan, and Klapper 2004).

Studies of business lending in Argentina in particular find very different portfolio allocations for foreign- versus domestically-owned banks, with foreign-owned banks tending to specialize in lending to the Buenos Aires province and to large-scale projects in manufacturing and utilities (Clarke, Cull, D’Amato, and Molinari 2000). As well, studies on Argentina find foreign ownership is associated with greater overall credit availability (e.g., Dages, Goldberg, and Kinney 2000), but has ambiguous or negative effects on SME credit availability (e.g., Berger, Klapper, and Udell 2001, Clarke, Cull, Martinez Peria, and Sanchez, forthcoming).

5
Chart 3 Gross Financing<sup>Requirements of Sub-Saharan Africa, 1989-2000</sup> (Billions<sup>of US dollars)</sup> _34_ <!-- Start of picture text --> Go ~ s<br>14<br>820<br>26<br>12<br>20<br>a t<br>24<br>19089 t990 1991 1992 1993 1994 196 1996 1997 199a 1999 20°0<br>a Gross financing requirements before debt relief<br>+ Gross financing requirements after debt relief<br><!-- End of picture text -->

- Debt service obligations<sup>before debt relief</sup>

- a Debt service obligations after<sup>debt relief</sup> _X_ Trade deficit including<sup>private transfers</sup> V Change in reserves
# **5.1 DID Results**

The first set of results focus on three aggregate food insecurity measures constructed from FIES questions – prevalence of moderate or severe food insecurity, probability of being food insecure and raw food insecurity score (Table 2). The coefficients in the first row of Table 2 show that there was no significant change in the overall food security of households outside of Tigray after the outbreak of the war. This is not surprising, given the war was confined to the Tigray region in the study period. Our key coefficient of interest is the interaction term of wartime dummy and the dummy variable for Tigray region. The results in column 1 show, in contrast to the trend in the other regions, the prevalence of moderate and severe food insecurity increased dramatically in Tigray in the post-war-onset period. The conflict led to 38 percentage points increase in moderate or severe food insecurity in Tigray. This amounts to 106 percent increase, relative to the prevalence rate in the pre-war period. Column 2 presents similar results for the probability of being food insecure. Compared to the pre-war period, the incidence of food insecurity in Tigray is 26 percentage points higher, which is a 47 percent increase from the pre-war level. In column 3, we show regression results for the raw food insecurity index calculated from FIES questions. The results are consistent with those obtained for the other two aggregate food insecurity measures. In Tigray, the war has led to 1.6 units increase in the number of dimensions of food insecurity reported by households. These large effects are plausible given the massive humanitarian crises the war has caused in the region (see WFP, 2022).

**Table 2: The Impact of violent conflict on aggregate measures of food insecurity**

||(1)<br>Moderate or severe food<br>insecurity|(2)<br>Food insecure|(3)<br>Raw score|
|---|---|---|---|
|Wartime dummy|-0.014|-0.025|-0.073|
||(0.017)|(0.015)|(0.106)|
|Tigray × Wartime dummy|0.384<sup>***</sup>|0.261<sup>***</sup>|1.607<sup>***</sup>|
||(0.052)|(0.033)|(0.236)|
|Household fixed effects|Yes|Yes|Yes|
|R-squared|0.008|0.004|0.007|
|Mean dep. var (pre-war)|0.361|0.557|2.054|
|No. observations|14523|14523|14523|

Notes: The outcome variable in the first column stands for a binary indicator assuming a value of 1 for those households classified as moderate or severe food insecure based on the severity of food insecurity generated from the eight FIES questions. The dependent variable in the second column stands for binary indicator assuming a value 1 for 25
- Hoekman, B., M. Fiorini and A. Yildirim. 2020. COVID-19: Export controls and international cooperation, in R. Baldwin and S. Evenett (eds.), _COVID-19 and Trade Policy: Why Turning Inward Won’t Work_ . London: CEPR Press.

- Kaufmann, D., A. Kraay and M. Mastruzzi. 2011. The worldwide governance indicators: methodology and analytical issues. _Hague J Rule Law_ 3, 220–246.

- Kutlina-Dimitrova, Z. and C. Lakatos, 2016, Determinants of direct cross-border public procurement in EU Member States, _Review of World Economics_ 152: 501-28.

- <mark>Leibovici, F. and A. Santacreu. 2020. International trade of essential goods during a pandemic. Covid Economics 21. At</mark> <u><mark>https://cepr.org/sites/default/files/news/CovidEconomics21.pdf</mark></u>

- <mark>OECD. 2020a. Public procurement and infrastructure governance: Initial policy responses to the coronavirus (COVID-19) crisis. July.</mark> <u><mark>http://www.oecd.org/coronavirus/policyresponses/public-procurement-and-infrastructure-governance-initial-policy-responses-to-thecoronavirus-COVID-19-crisis-c0ab0a96/</mark></u>

- <mark>OECD. 2020b. The Face Mask Global Value Chain in the COVID-19 Outbreak: Evidence and Policy Lessons, OECD, Paris, https://bit.ly/3gtfWqc</mark>

- Sanchez-Graells, A. 2020. Procurement in the time of COVID-19. _Northern Ireland Legal Quarterly_ 71(1): 81-87.

- Santos Silva, J. and S. Tenreyro. 2006. The Log of Gravity. _The Review of Economics and Statistics_ , 88(4): 641–658.

- Shingal, A. 2015. “Econometric Analyses of Home Bias in Government Procurement,” _Review of International Economics_ , 23: 188–219.

- Shingal, A. and V. Ereshchenko. 2020. Public procurement, in A. Mattoo, N. Rocha and M. Ruta (eds.), _Handbook of Deep Trade Agreements_ . Washington, DC: World Bank.

- UNCITRAL. 2014. _Model Law on Public Procurement_ . Vienna: UNCITRAL Secretariat.

- Vecchi, V., N. Cusumano and E. Boyer. 2020. Medical Supply Acquisition in Italy and the United States in the Era of COVID-19: The Case for Strategic Procurement and Public–Private Partnerships, _American Review of Public Administration._ At: <u><mark>https://doi.org/10.1177/0275074020942061</mark></u>

- World Bank. 2017. _Benchmarking Public Procurement: Assessing Public Procurement Regulatory Systems in 180 Economies_ . Washington, DC: World Bank.

- World Bank. 2020. Doing Business Database, Contracting with the Government Data. Washington, DC: World Bank. <u>https://www.doingbusiness.org/en/data/exploretopics/contracting-with-the-government</u>

18
**<u>Table 3</u>** indicate that NSP improves attitudes to government figures at almost all levels, including district and provincial governors, central government officials, the President of Afghanistan, Members of Parliament, and government judges. Magnitudes of these effects vary from between 8 percentage points for Members of Parliament to 4 percentage points for the national police. There is also a positive effect of NSP on the attitudes of villagers toward NGOs and ISAF soldiers. The results for the summary measure indicate that NSP improves villagers’ attitudes by 13 percent of a standard deviation. Results for the two eastern districts, however, are completely different. There is no positive effect of NSP on attitudes toward any government bodies, ISAF soldiers, or NGOs, and the effect on attitudes towards the president and the police is significantly negative.

Overall, NSP improves attitudes to government and allied entities. This provides strong support for Hypothesis 2. However, the positive effects on attitudes are not observed in areas with high levels of initial violence, with villages receiving NSP in the eastern districts reporting significantly lower approval of the President and of police.

# **VI.3. Hypothesis 3**

Hypothesis 3 is tested using data from the follow-up survey on male and female villagers’ security perceptions and data on security incidents from villager surveys and from ISAF data. A summary index is also used here to estimate the aggregate effect of NSP on perceptions of security.

As reported in **<u>Table 4</u>** , NSP improves villagers’ perceptions of security. The proportion of male respondents in NSP villages who report an improvement in the security situation in the past two years is 6 percentage points higher, whereas the proportion of respondents who think that security has deteriorated is 3 percentage points lower.<sup>19</sup> Among females, the proportion of female respondents who think that women and girls feel safer compared to two years ago is higher in NSP villages by 5 and 4 percentage points respectively, while the number of respondents who think that women and girls feel less safe is lower in NSP villages by 4 percentage points in both cases. The summary measure indicates that NSP improves villagers’ perception of the security situation by 10 percent of a standard

> 19 Note that the three pairs of questions on improvement / deterioration of the security situation are not independent, since each pair is based on one question on the changes in the situation with three possible answers – the situation has improved, the situation have not changed, and the situation has deteriorated. We construct two dummy variables for improvement / deterioration of the security situation based on these questions to provide a meaningful comparison of the averages between the treatment and control villages. Since the measures are not independent we do not combine them using summary indices.

17
to Early Primary and Level 6 to College). Appendix C.1 presents the progress and placement logic of the test.<sup>9</sup> However, contrary to our expectations, ceiling effects in the LEAPS test were small, with 11.2% of children achieving the maximum in English, 2.7% in Mathematics, and 13.1% in Urdu. There was considerable variation in test scores, with a mean of 51.5%, 54.2%, and 55.2% correct in English, Mathematics, and Urdu, and a standard deviation of 33%, 33.6%, and 34.4%, respectively. In contrast, the adaptive test elicited little meaningful variation: 43%, 64%, and 77% of respondents were classified as Level 1 for Urdu, Mathematics, and English, respectively, and the rest were largely classified as Level 2.

We aggregated all the items from both types of assessments into Urdu, Mathematics, and English scores, using Item Response Theory (IRT) with a two-parameter logistic (2PL) model.<sup>10</sup> Formally, each item characteristic curve is given by the two-parameter logistic: 1 where ) = 2 is the difficulty parameter, which is the ability level at which the child will jj jj <u>j</u> answer a given question bb ≡θθ<sup>∗</sup> |PP (θθ<sup>∗</sup> _j_ correctly half the time and ∝ at , is the discrimination ∂∂PP (θθ) jj jj parameter, which specifies the steepness of the item characteristic curve at the point that the ability of aa ∂∂θθ θθ= bb the child is equal to the difficulty of the question ( ). The joint estimation of and these parameters follows the standard maximum-likelihood procedure in IRT.jj<sup>11</sup> We then assess model fit by comparing bb θθ actual with predicted item responses based on estimated item parameters and the model assumptions of the 2PL model for each subject (Appendix C.2, Figures A3 to A5 for the LEAPS test, and A6 to A8 for the adaptive test). For most items, the actual and predicted responses match closely, although

> 9 The organization that designed the adaptive test developed 324 items ranging from early primary level to college level. The test classified respondents into six groups corresponding to different grades. The mapping between level and grades is as follows: Level 1: Nursery, Grades 1 to 3 (early primary); Level 2: Grades 4 and 5 (late primary); Level 3: Grades 6 to 8 (middle school); Level 4: Grades 9 and 10 (high school); Level 5: Grades 11 and 12 (intermediate); Level 6: College. Like with the LEAPS test, ceiling effects were minimal. In English, Mathematics, and Urdu, 0.3%, 0.2%, and 7% of respondents placed at Level 6. In English and Mathematics, fewer than 3% of respondents were placed in Levels 3 to 6. In Urdu, 12% are placed at Level 3, and 5-7% at each subsequent level.

> 10 We exclude items that less than 50 respondents answered as well as items that less than 5% or more than 95% of respondents got the correct answer.

> 11 We use Stata’s OpenIRT command (see Das and Zajonc, 2010).

11
the work done by their client and often go back and correct errors (due to misunderstandings about SARS regulations, calculation errors, etc.). Thus work is carried out twice – once by the SMME using in-house resources and then re-done (or at least rechecked, which also takes time) by the TP. In this situation, most SMMEs would have been better off outsourcing all the work from the outset to a professional tax practitioner.

However, it is also possible that partial outsourcing may appear more expensive than inhouse tax compliance work “after the fact” if such firms tend to resort to TPs only after they have started to encounter problems. Therefore, the next questions addressed in this analysis were differences between SMMEs who outsourced various “post-filing” activities and those who did them in-house. As noted above, post-filing tasks are more likely to be outsourced than more simple and routine tax returns. However, these tasks are themselves relatively rare, with only about 6% of SMMEs reporting any experience with them in the survey. In general, the incidence of post-filing issues is positively associated with outsourcing (except that firms that fully outsource everything reported no post-filing issues at all).

With regard to late submissions and related penalties/interest, TPs interviewed by FIAS noted that many clients wait until the last minute to bring their accounts and documentation to their TP, who are then overwhelmed and unable to complete all the work by the statutory deadlines.

With regard to other “post-filing issues”, one possibility is that SMMEs are more likely to hire an outside TP when their business activities are relatively more complicated and/or when they have started to encounter problems. In this view, the causality runs from “complications” to “hire a TP”. If so, the best approach would be to encourage businesses to bring in the necessary expertise sooner rather than later – better to avoid and prepare for possible problems than try to fix them after they have occurred.

Another possible interpretation of the positive relationship between outsourcing and postfiling issues, however, is that there may be a number of poorly-qualified TPs serving SMMEs, including TPs who may have been certified in the past but fail to keep up with changing tax laws, regulations and interpretations.

# **Policy Implications**

The data on SMEs in South Africa display an inverted “U” shape for outsourcing of tax compliance tasks: the smallest firms – (those under R 300,000 turnover or well under US$50,000) tend not to outsource, due to a combination of relatively higher cost-burden and less complexity (e.g., less likelihood to be registered for VAT, smaller/simpler payroll), while relatively larger firms (e.g., those with over R14 million turnover or about US$ 2 million) report they have sufficient in-house capacity and therefore no need to outsource. Those in the middle are most likely to outsource at least some of their tax compliance work. Further, those who engage in “partial outsourcing” appear to be duplicating efforts: it appears work is first attempted in-house and then external (and 27
explain the subsequent productivity growth in reform-municipalities, we should expect a positive correlation between the probability that a municipality (is forced to) eliminate a regulatory barrier and that municipality’s initial productivity (capturing the effect of other municipal productivity determinants). Table 9 shows that this is not the case. The probability that a municipality eliminated any regulatory barrier or any different type of barrier is not correlated with municipalities’ initial average establishment productivity for the preferred translog specification. The results corroborate our identification assumption that the ‘roll-out’ of the elimination of regulatory barriers to entry across municipalities is exogenous; i.e., independent of other contemporaneous policy changes at the municipal level.

Finally, we provide some additional analysis to better understand the markup results. Recall that the elimination of barriers to market access is not only expected to reduce establishments’ rents but also to increase the demand for their products and their ability to differentiate their products across markets, both of which raise firms’ markups. Our finding that the reforms did not change establishments’ markups in most specifications (in the others they declined) is thus not surprising. To further analyze which of the two opposing effects on establishments’ markups dominates, we estimate the impact of the reforms on establishments’ market shares (based on their share of sales in their corresponding 4-digit ISIC Rev. 4 sector). That is, we expect establishments’ market shares to increase if the product demand and differentiations effects dominate and to decline if the reduced rent-seeking effect dominates. The results are provided in Table 10 and show that the elimination of regulatory entry barriers tend, if anything, to have reduced establishments’ market shares. Especially, we find that market shares declined among establishments located in municipalities that eliminated technical requirements to operate in their jurisdiction. Moreover, a larger number of any type of reforms or licenses reforms reduced market shares of establishments operating in these municipalities. The results support our conjecture that the elimination of regulatory barriers reduced firms’ rent-seeking opportunities.

# **7. Conclusion**

We use a unique data set for Peru that captures the elimination of subnational regulatory barriers to competition across about 1,800 municipalities and departments in 2013 and 2014 to estimate the impact of pro-competition reforms on establishment productivity growth. We argue that the specific legislative changes expanding Peru’s competition authority’s mandate in 2013/14 24