rafmacalaba/datause-encoder-data huggingface dataset:
b. The general condition of roads you use About 70 percent of respondents from developed countries found road conditions sufficient. The second highest percentage satisfaction with road conditions was surprisingly given by businessmen in MNA region (about 40 percent), this despite ranking supply of infrastructure as the number one obstacle for doing business. The lowest satisfaction with road conditions was expressed by respondents from the CIS (less than 15WodLD percent).

<!-- Start of picture text --> 80<br>70 fg<br>60 -<br>40<br>30<br>20<br>10<br>C SA MN CE LC<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 22.b**

# **c.** The efficiency of mail delivery

Businessmen in MNA are more satisfied with mail delivery than their colleagues anywhere in the world.

There are at least 20 percentage points between the "better" respondents of MNA, DC, SSE and SEE and the other three regions. In other words, 59 percent of respondents from CEE countries thought that mail delivery is efficient and only 39 percent businessmen from SSA thought so for their countries. The lowest score for mail delivery resides in the CIS, where less than 30 percent of surveyed companies evaluated mail delivery system as efficient.

<!-- Start of picture text --> 80<br>X0<br>40<br>30<br>DCC SSA A CEE LAC SSA as<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 22.c**

40
where Y is output, KI the stock of public capital in infrastructure, L labor, KP the stock of private capital, and α,β ∈ (0,1). Constant returns to scale therefore prevail in all factors. The marginal product of private capital is given by (1-α-β)(KI/KP)<sup>α</sup> (L/KP)<sup>β</sup> , whereas the marginal product of labor is given by β(KI/KP)<sup>α</sup> (KP/L)<sup>1-β</sup> . Thus, a higher stock of public capital (relative to private capital) increases the marginal product of both inputs—although it does so at a decreasing rate, given that α < 1. In the endogenous growth model that we present in Section IV, we will show that the (steady-state) growth rate itself, in addition to the level of output, depends positively on the public-private capital ratio, KI/KP.

Of course, the positive effect of public capital on the marginal productivity of private inputs may hold not only for infrastructure but also for other components of public capital—such as in education and health, which may both affect the productivity of labor (see the discussion below). Moreover, other components of public spending, related for instance to the enforcement of property rights and maintenance of public order, could also increase productivity and exert a positive effect on private investment and growth, despite the fact that they may not be considered as being directly “productive.” But, as noted earlier, infrastructure capital may have a particularly large effect in countries where initial stocks are low and basic infrastructure services (such as electricity and clean water) are lacking, as is the case in many low-income countries<sup>4</sup> . Conversely, a study by the African Development Bank suggests that transport and energy costs, at 16 and 35 percent respectively, represent by far the largest share of firms’ indirect costs in Sub-Saharan Africa. A large fraction of these costs is the result of the poor quality of basic infrastructure. For instance, because of inadequate transport facilities and unreliable supply of electricity, firms often incur additional expenses in the form of more expensive transportation means and onerous energy back-up systems<sup>5</sup> .

> 4For instance, data from China (1978-97), India (1970-93) and Uganda (1992-99), countries that have managed to stimulate growth and reduce poverty on a large scale, show that the marginal returns to public incremental expenditures on rural roads were always among the highest. 5Firms that do not undertake these additional investments may still incur costs in the form of lost production resulting from equipment breakdowns.

7
Figure 2.7: Net Income Decomposition for Retail Business Line (BRL million) <!-- Start of picture text --> Interest<br>94.7<br>Margin<br>Fees 44.5<br>Gross<br>139.2<br>Income<br>Operating 89.2<br>Expenses<br>Credit<br>22.7<br>Allowance<br>Net Income 27.3<br><!-- End of picture text --> _Interest Margin and Fees represent net interest and non-interest income respectively. Operating expenses include payroll, overhead, tax and other operating expenses. Credit allowance refers to loan loss provisions. Source: Study calculations based on Bacen data._ Figure 2.8: Net Income Decomposition for Corporate Business Line (BRL million) <!-- Start of picture text --> Interest<br>28.2<br>Margin<br>Fees 8.9<br>Gross<br>37.1<br>Income<br>Operating 17.8<br>Expenses<br>Credit<br>9.6<br>Allowance<br>Net Income 9.7<br><!-- End of picture text --> _Interest Margin and Fees represent net interest and non-interest income respectively. Operating expenses include payroll, overhead, tax and other operating expenses. Credit allowance refers to loan loss provisions. Source: Study calculations based on Bacen data._ 14
There is a close association between per capita income and attraction of FDI across the income spectrum. Richer regions attract the most FDI per capita, and many of the poorest regions attract none (Figure 24). All seven regions that attracted no FDI at all are among the 10 lowest-income regions in Russia. Five are in the North Caucasus (Ingushetia, Karbardino-Balkar, Karachevo-Charles, North Ossetia, and Chechnya); one in the Southern (Kalmyk, bordering North Caucasus); and one in Siberia (Altai Republic). Most have difficult investment climates, as reflected in ratings from the advisory service Expert RA.

Firms in the Central and Northwest regions are more likely to have foreign majority ownership than those in other regions (Figure 25). The Far East region has absorbed the most FDI relative to regional GDP and on this measure has been the leading destination for FDI since 2002 (Figure 26 and Figure 28). Nationally, about 2.3 percent of all active firms in the Ruslana database had majority foreign ownership in the year most recently observed (usually 2012). This suggests that in the Far East foreign-invested firms may be significantly larger on average than in the Central and Northwest, probably due to a different sectoral composition of FDI.

Russian mining and quarrying firms are most likely to have foreign investors, followed by financial and insurance businesses (Figure 27). The attraction of mining and quarrying (including oil and natural gas) is consistent with Russia’s natural resource endowments and its pattern of exports. The relatively high frequency of majority foreign ownership in financial services has occurred even though restrictions on FDI in financial services are high (Figure 27), which suggests that there is room for policy moves to make these sectors even more attractive. From the fact that about half of all FDI in financial services is obvious round-tripping it can be inferred that many of the investments are driven by insufficient law enforcement and insecure property rights. The Central region, including Moscow, is relatively attractive for wholesaling, retailing, and most of the service sectors.

The types of FDI attracted by Russia’s regions tend to be consistent with the comparative advantage of those regions in goods markets and may drive future transformation of that comparative advantage (Table 6). The largest share of majority-FDI firms in mining and quarrying is in the Far East (11.2 percent), followed by Siberia (8.6 percent) and Ural (7.9 percent). At present, mineral fuels dominate Far East and Ural exports; but not Siberia’s, it may be that ultimately FDI will drive Siberia to specialize increasingly in mineral fuels. Agriculture, forestry, and fishing are also attracting FDI, in the relatively affluent Far East, in the South, and in the relatively poor North Caucasus. These three regions have the highest concentration of food and agriculture exports. Manufacturing FDI is a specialty of the Volga region, which has an export base of chemicals, machinery, and transport equipment.

13
Ashenfelter, Orley, and David Card (1985) `“` Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs. `”` _Review of Economics and Statistics_ , 67(4): 648 `–` 60.

Barbosa, Alex Francisco de Oliveira, Fabrício Machado Righetti, Henrique Peixoto Petrocchi da Costa and Maíra Vaz de Melo Patry (2011) _Minas Fácil Virtual_ . Junta Comercial do Estado de Minas Gerais.

Bertrand, Marianne, Esther Duflo and Sendhil Mullainathan (2004) “How much should we trust difference-in-differences estimates?”, _Quarterly Journal of Economics_ 119(1): 249-75.

Branstetter, Lee, Francisco Lima, Lowell Taylor and Ana Venâncio (2010) “Do entry regulations deter entrepreneurship and job creation? Evidence from recent reforms in Portugal”, NBER Working Paper no. 16473.

Bruhn, Miriam. 2011. “License to sell: the effect of business registration reform on entrepreneurial activity in Mexico.” _Review of Economics and Statistics_ , 93(1): 382-86.

Bruhn, Miriam. 2012. “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal Business Owners in Mexico” Policy Research Working Paper 5971, World Bank, Washington, DC.

De Mel, Suresh, David McKenzie and Christopher Woodruff, 2012. “The demand for, and consequences of, formalization among informal firms in Sri Lanka”, _American Economic Journal: Applied Economics_ , forthcoming.

Dehejia, Rajeev and Sadek Wahba (2002) “Propensity score matching methods for nonexperimental causal studies”, _Review of Economics and Statistics_ , 84 (1):151–161 Descomplicar (2012) _Programa Estruturador Descomplicar – Minas Inova Planejamento 2012_ . Descomplicar: State Government of Minas Gerais.

De Soto, Hernando. 1989. _The Other Path_ , New York: Harper and Row Publishers.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Schleifer 2002. “The Regulation of Entry,” _Quarterly Journal of Economics_ , 117(1), pp. 1-37.

Gxp (2011) “Minas Fácil Virtual comença a funcionar em dezembro”, <u>http://www.gxp.com.br/2011/11/15/minas-facil-virtual-comeca-a-funcionar-em-dezembro/</u> [accessed December 2, 2012].

International Finance Corporation (IFC) 2009. _Doing Business 2010: Reforming through difficult times_ . IFC: Washington, D.C.

Jaramillo, Miguel, 2009. “Is there demand for formality among informal firms? Evidence from microfirms in downtown Lima”, German Development Institute Discussion Paper 12/2009.

Jornal S’Passo (2011) “Programa Minas Fácil Virtual chega a Itáuna”, <u>http://www.jornalspasso.com.br/noticia.php?id_noticia=8485</u> [accessed December 2, 2012].

15
16 some support to the proposition that skills shortages become more acute constraints in relatively sophisticated, high-productivity, economies.

# 3.2 <u>Country Profiles and Other Country-Level Indicators.</u>

Having developed a picture of how constraints are perceived to bind at different levels of income, we consider whether the incidence of complaints aligns with cross-country indicators of the business environment. Table 2 contains a summary of some comparisons, first with a number of macroeconomic and survey-based indicators and then with several _Doing Business_ (DB) indicators.

From Table 2, the intensity of perceived constraints is reasonably well-correlated with a number of country-level indicators. Considering first the six macroeconomic and surveybased indicators, there is a close relationship between firms’ perceptions of the severity of the electricity constraint and an indicator of estimated losses due to power outages (which is also taken from the Enterprise Surveys). Firm perceptions of corruption are significantly correlated with the WorldWide Governance Indicator (formerly KaufmannKraay Indicator) measure of corruption at the country level. Concern over access to finance is very strongly correlated with macro-level indicators, being less frequent in countries with financial depth as measured by high ratios of private credit/GDP and with higher _Institutional Investor_ ratings. Concern over the cost of finance is strongly correlated with country-level interest-rate measures. In areas where the linkage could be considered as less direct –for example, between concern over macroeconomic instability and measures of government effectiveness, the relationship is not significant but is of expected sign.
countries to return to fiscal rule compliance. We address this question in Section 3 using the IMF Fiscal Rule Dataset (IMF 2022), which provides us with a sample including every instance in which a budget balance rule was either suspended, revised upward, and/or an escape clause was used over the period 2000-2019.

A potential contributing factor to the increase in escape clause usage and temporary rule suspensions during the pandemic was the development of a “second-generation” of fiscal rules in the decade preceding the COVID-19 pandemic. As noted by Eyraud et al. (2018), in the years following GFC 2008-09, a “second-generation” of fiscal rules emerged which sought to construct rules in such a way that allows for an appropriate degree of short-run flexibility when necessary while still promoting long-term fiscal responsibility. In this context, many reforms made post-GFC introduced new escape clauses covering a larger set of contingencies during crisis periods in which unexpectedly large fiscal expansion is necessary, while providing guidance on the path back to compliance. The trend shown in Figure 2 highlights the fact that these enhancements allowed for a high degree of flexibility in fiscal rule enforcement through COVID-19 pandemic. Our empirical results reported in Section 3 provide evidence that with this added flexibility, fiscal rules continued to serve as a signal of fiscal responsibility during the pandemic, even for countries that enacted escape clauses and used discretion to suspend rules. Interestingly, we show that the existence of fiscal rules compressed sovereign spreads through the pandemic regardless of whether enforcement continued during the COVID-19 period.

**Figure 2. Suspensions or Activations of Escape Clause by Year**

<!-- Start of picture text --> Number of countries<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1986 1991 1996 2001 2006 2011 2016 2021<br>Suspension Escape clause<br><!-- End of picture text --> Source: Davoodi et al. (2022a) Note: Figure includes all middle- and low-Income countries with fiscal rules

# **2.2       Sovereign Spreads**

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# **Table 3 Means of Alternative Occupational Placement Indices, by level of education and**

**year**

||**Pr**|**estige Ind**|**ex**|**Edu**|**cation Ind**|**ex 1**|**Edu**|**cation Ind**|**ex 2**|
|---|---|---|---|---|---|---|---|---|---|
||**1980**|**1990**|**2000**|**1980**|**1990**|**2000**|**1980**|**1990**|**2000**|
|**None or preschool**|31|32|33|18|28|31|11.1|11.7|11.9|
|**Grade 1, 2, 3, or 4**|31|31|32|16|26|29|11.0|11.5|11.7|
|**Grade 5, 6, 7, or 8**|32|32|33|17|27|31|11.1|11.7|11.9|
|**Grade 9**|33|32|34|19|28|33|11.3|11.8|12.1|
|**Grade 10**|33|33|35|21|30|35|11.4|12.0|12.3|
|**Grade 11**|34|33|35|22|31|35|11.6|12.0|12.3|
|**Grade 12**|37|35|36|26|36|39|11.8|12.4|12.6|
|**1 to 3 years of college**|41|40|42|36|47|53|12.5|13.1|13.4|
|**4+ years of college**|55|54|56|62|72|78|14.2|14.8|15.1|

**Table 4. Estimates Based on Equation (1)**

|**EDUCATION INDEX 1**||**DECADE: 1970-19**|**79**|**DECADE**|**: 1980-1989**|
|---|---|---|---|---|---|
|**VARIABLES**|**CENSUS 1980**|**CENSUS 1990**|**CENSUS 2000**|**CENSUS 1990**|**CENSUS 2000**|
|**Age**|0.00316<br>[0.00410]|0.00684<br>[0.00482]|-0.0069<br>[0.00624]|0.00884***<br>[0.00163]|0.00568***<br>[0.00207]|
|**Age squared**|0.0000214<br>[0.0000700]|-0.000106*<br>[0.0000610]|0.0000401<br>[6.32e-05]|-0.000113***<br>[2.38e-05]|-9.29e-05***<br>[2.37e-05]|
|**Years since arrival (dummy with value 1**|-0.0596***|-0.0300***|-0.0352***|-0.0413***|-0.0317***|
|**for second half of the decade)**|[0.00573]|[0.00490]|[0.00502]|[0.00344]|[0.00332]|
|**EDUCATION DUMMIES**||||||
|**five to nine years of education**<br>**ten to twelve years of education**<br>**high-school level**<br>**some college**<br>**Bachelor's Degree or higher**|0.00444<br>[0.00705]<br>0.0566***<br>[0.00933]<br>0.168***<br>[0.00974]<br>0.451***<br>[0.0134]<br>1.011***<br>[0.0123]|0.0231***<br>[0.00646]<br>0.0646***<br>[0.00874]<br>0.173***<br>[0.00836]<br>0.419***<br>[0.0105]<br>0.825***<br>[0.0101]|0.0382***<br>[0.00697]<br>0.0866***<br>[0.00969]<br>0.191***<br>[0.00860]<br>0.430***<br>[0.0107]<br>0.826***<br>[0.0103]|-0.000464<br>[0.00491]<br>0.0195***<br>[0.00571]<br>0.109***<br>[0.00564]<br>0.317***<br>[0.00735]<br>0.700***<br>[0.00740]|0.0201***<br>[0.00531]<br>0.0606***<br>[0.00604]<br>0.146***<br>[0.00576]<br>0.380***<br>[0.00712]<br>0.746***<br>[0.00702]|
|**Constant**|5.610***<br>[0.189]|5.654***<br>[0.181]|6.056***<br>[0.221]|5.950***<br>[0.0785]|6.015***<br>[0.0828]|
|**Observations**|41234|39445|35880|75851|78745|
|**Country fixed effects**|yes|yes|yes|yes|yes|
|**R-squared**|0.51|0.51|0.51|0.49|0.48|

*** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in brackets

35
|||||_Average_|_Total_||
|---|---|---|---|---|---|---|
|||||_annual_|_percentage_||
|||_Increase in_|_Increase_|_Import_|_bias in_|_Gains from_|
|||_number of_|_in variety_|_(% of_|_price_|_Variety_|
||_Country_|_products_|_(%)_|_GDP)_|_indices_|_(% of GDP)_|
|26|Zambia|21%|131%|29%|37.3|7.35%|
|27|Zimbabwe|11%|43%|35%|31.3|7.94%|
||**Average (18-27)**|**36%**|**144%**|**29%**|**29.7**|**5.47%**|
|28|Fiji|23%|176%|43%|8.8|3.39%|
||**Average (1-28)**|**39%**|**148%**|**41%**|**20.0**|**5.49%**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Now let us move on to the welfare effect of the fall in the exact import prices. It should be noted that the welfare gain from this price fall is based on the functional forms assuming the Dixit-Stiglitz structure and cannot be general. Although our estimate of the impact of imported varieties on import prices is correct for any domestic production structure (Broda and Weinstein, 2006), it is not possible to translate this into a welfare gain without making explicit assumptions about the structure of domestic production. Following Broda and Weinstein (2006), our choice is to assume the same structure of the selected countries’ economies as in Krugman et al. (1980). There are two reasons for this. First, since Krugman’s model is the dominant model of varieties, to understand the potential welfare gains, it provides a useful benchmark. The second reason is the lack of the necessary data and model of the economies’ input-output linkages to estimate variants of the monopolistic competition model with more complex interactions between imported and domestic varieties.

Column (4) of Tables 4 to 6 presents the gains from variety for every year between 27
6

|||**Table**|**S. 2 – continue**|**d from previous page**||||
|---|---|---|---|---|---|---|---|
|TUS code|TUS desc.|% Rur. time|% Urb. time|FAO desc.|Factor|Compendium desc.|Factor|
|892|TRAVEL RELATING TO SEARCH OF|0.0001|0.0004|depends on type|.|depends on type|.|
|911|SLEEP AND RELATED ACTIVITIES|0.3766|0.3738|sleeping|1|sleeping|0.9|
|921|EATING AND DRINKING|0.0563|0.0586|eating and drinking|1.5|eating sitting|1.5|
|922|SMOKING, DRINKING ALCOHOL AND|0.0011|0.0005|eating and drinking|1.5|eating sitting|1.5|
|931|PERSONAL HYGIENE AND HEALTH|0.0444|0.0425|Average of washing hands/face|2|grooming|2|
|932|WALKING, EXERCISE MINING, JOG|0.0028|0.0044|Between running and batting/bo|5.5||5|
|941|RECEIVING MEDICAL AND PERSONA|0.0002|0.0003|sitting quietly|1.2|sitting reading|1.3|
|942|RECEIVING MEDICAL AND PERSONA|0.0001|0.0001|sitting quietly|1.2|sitting reading|1.3|
|951|TALKING, GOSSIPING AND QUARRE|0.0486|0.0402|standing average for men and w|1.45|sitting talking on the phone|1.5|
|961|DOING NOTHING, REST AND RELAX|0.0651|0.0540|sitting quietly|1.2|lying quietly doing nothing|1|
|962|FORCED LEISURE|0.0012|0.0009|sitting quietly|1.2|lying quietly doing nothing|1|
|971|INDIVIDUAL RELIGIOUS PRACTICE|0.0037|0.0069|standing|1.2|inactivity light meditation|1|
|981|OTHER ACTIVITIES|0.0038|0.0043|standing|0||0|
|982|RESTING/CONVALESCING DUE TO P|0.0016|0.0015|lying|1.2|lying quietly doing nothing|1|
|991|TRAVEL RELATED TO PERSONAL CA|0.0037|0.0032|depends on type|.|depends on type|.|
|_Source_: Aut|hors’ calculation usingIndia Time-Use Survey19|98-1999, Annex 5|of(FAO/WHO/|UNU Expert Consultation,2001), and|(Ainswort|h et al.,2000)||
schedules for home owners and for other pensioners reflecting the benefit that a person gains from owning rather than renting (Table 2).

**Table 2: Asset Test Details (current as at March 2007)**

|**Family situation**|**For full payment***|**For part payment**|**From September 2007**|
|---|---|---|---|
|Homeowners||||
|Average annual wage|$54,808|$54,808||
|Single rate|$161,500 or 2.95 AAW|$338,500 or 6.18 AAW|$520,750 or 9.50 AAW|
|Combined rate (couples)|$229,000 or 4.18 AAW|$523,500 or 9.55 AAW|$825,500 or 15.06 AAW|
|Non-Homeowners||||
|Single rate|$278,500 or 5.08 AAW|$455,250 or 8.31 AAW|$641,750 or 11.71 AAW|
|Combinedrate (couples)|$346,000 or6.31 AAW|640,500 or 11.69AAW|$946,500 or 17.27AAW|

* Prior to 20 September assets over these amounts reduced the rate of pension payable by $3.00 per fortnight for each $1000. From 20 September 2007 assets exceeding the thresholds reduce the pension by $1.50 per fortnight.

# **Source: FaCSIA**

The halving of the clawback provision in the asset test has made it less onerous and will reduce the impact of the accumulated balances under the superannuation guarantee scheme since the upper threshold for the combined rate for home-owners has been raised from 10 times average annual earnings to 15 times. A person on average earnings who contributes for 40 years and experiences a 2 percent annual wage growth would require an average investment return of 8.5 percent to reach the new higher level compared to 6.5 percent before the change in the rules. The new less onerous asset test is also likely to reinforce the aversion to the purchase of life annuities, since the impact of the income test has not been weakened.

Assets are defined for assessable purposes in a way that also influences pensioner behavior. The Social Security Act also exempts the value of certain ‘life interests’, burial plots paid for in advance, and certain “assets test exempt income streams”.

The pensioner’s principal home is an exempt asset no matter what its value. This is largely as a result of the strong attachment to home ownership and the associated financial security it involves in the Australian culture. As a result, however, it is possible to accumulate substantial wealth in a principal residence and avoid any impact on social security entitlements arising from this wealth.

Australia exhibits a high level of home ownership and, as a result, the influence of the treatment of the principal private residence is particularly important in retirement plans and is politically material in considering policy options. In 2001, overall home ownership was 70 percent (Table 3), a relatively high rate by global standards.

7