rafmacalaba/datause-encoder-data huggingface dataset:
8

# **3. Modelling fiscal policy**

## **a) Fiscal consolidation in the advanced economies**

There is no specific number that indicates that a country’s debt is too high. Indeed, the debt is not the issue. Rather, the issue is the quality of expenditure increase or tax reductions that the debt has enabled. Nonetheless, a figure of 60 percent debt to GDP is generally taken to be a reasonable measure based on the following reasoning. A decade ago, gross debt/GDP ratios were a bit above 60 percent (see earlier figure 2). This is the generally accepted number for ‘reasonable stability’. It was, for example, one of the Maastricht criteria for EU members to enter the Euro. The important thing is to bring the primary fiscal balance (the total government deficit less interest payments on debt) into surplus to service the debt<sup>12</sup> . So, for advanced economies, average debt/GDP ratios have to fall from around 100 percent to 60 percent. For emerging economies, debt levels are, on average, less than 40 percent, with only India as one of the large standout economies with a ratio of 75 percent. The IMF takes 40 percent debt/GDP as a reasonable target for emerging economies.

The IMF ( _Fiscal Monitor_ , April 2011)<sup>13</sup> has calculated the reduction in the cyclically adjusted primary balance to bring gross debt/GDP down to 60 percent for advanced economies and 40 percent for emerging economies over ten years<sup>14</sup> . These are the starting consolidation numbers used here, except for the following. For those economies with debt/GDP ratios already less than 60 percent, the assumption the IMF makes is to stabilise debt at expected end-2012 levels, but this implies a significant contraction by Australia, which has the lowest debt/GDP ratio of the advanced economies. For others (for example, Korea), the implication is for negative consolidation — that is, stimulus. So the change in the fiscal position of Korea has been assumed at zero and for Australia and New Zealand it is assumed to be 1 percent of GDP. Of note too is the assumption by the IMF for Japan to consolidate to 80 percent debt/GDP, partly reflecting their special status where virtually all their borrowings are made from domestic residents.

The assumed ten year consolidations are set out in table 2 (for consolidation over four years the annual consolidation is proportionally adjusted).

> 12 For stable debt dynamics, the present value of the primary budget surplus (not necessarily a surplus in each and every period) must equal the initial stock of debt to be serviced. That is, a government with a large initial debt burden will have to run larger primary surpluses in future than one with smaller initial debt. These debt dynamic conditions are fully built into the G-Cubed model used here.

> 13 http://www.imf.org/external/pubs/ft/fm/2011/01/pdf/fm1101.pdf

> 14 A similar analysis is undertaken in the OECD _Economic Outlook_ , November 2010 (chapter 4) although the extent of consolidation varies due to different assumptions about rates of economic growth.
Figure 10: Capital Income Share: ICT/Residential/Non-Residential <!-- Start of picture text --> ICT Share<br>Non-ICT Share: Non-Residential<br>Non-ICT Share: Residential<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>30<br>25<br>20<br>15<br>% Income Share<br>10<br>5<br>0<br><!-- End of picture text --> _Notes:_ The figures displays a decomposition of the capital share with three types of assets: ICT, residential, and non-residential non-ICT. The computations are based on the methodology described in Section 3. The dashed vertical line indicates the year 2001.

# 3.3. The Role of Industrial Composition

In Section 4, we will calibrate a one-sector aggregate production structure that matches the trends 1. depicted in Figure To justify such an approach, we illustrate that these trends are not merely a result of changes in the industrial composition but rather a within-industry phenomenon.

While a number of contributions in the broad literature surveyed by Acemoglu and Autor (2011) have convincingly shown that differential trends in earnings and employment of routine and non-routine occupations are to a large extent driven by within industry variation, there is no evidence that the same is true for ICT and non-ICT shares. To address this question, we construct industry specific ICT and non-ICT shares as in Eden and Gaggl (2015) based on the BEA’s detailed fixed asset accounts. We then estimate the average annual growth rates in these income shares both with and without industry fixed effects.

Table 2 illustrates that the ICT share was growing at about 3 % annually while the non-ICT share showed no significant trend growth over the period 1968 - 2012. Importantly, columns 21
when holding the poverty line and the distributions the same.<sup>26</sup> Compared to our survey estimates, on average, country-level poverty estimates at the international poverty line (IPL) of $1.90 are on average 34 percent lower using HFCE, and 61 percent lower using GDP.<sup>27</sup> Figure 5, Panel A, compares survey estimates of poverty at the $1.90 line to corresponding measures using national accounts means combined with distributions from surveys. Only in 11 percent of observations is poverty higher using HFCE rather than using survey estimates, and only 2.5 percent in the case of GDP. This illustrates the much lower level of poverty resulting from using national accounts means in combination with survey distributions and the $1.90 line. Naturally, when aggregated to global estimates, poverty measures using national accounts means is much lower compared to that using survey means, as seen in the right pane of Panel A of Figure 5. Our estimates using national accounts means and survey distributions suggest that the World Bank’s 3 percent global poverty target for 2030 was reached by year 2011 in the case of GDP, and is estimated at 5.7 percent when using HFCE the same year.<sup>28</sup> It is worth noting that with NAS measures being much higher than survey measures at any point in time, any starting point for poverty trajectories based on national accounts are also much lower than for survey estimates. For example, estimates for 1990, the baseline year of the UN Millennium Development Goals, show global poverty measured using GDP at 9 percent, 25 percent with HFCE, and above 35 percent using survey means.

The $1.90 poverty line may not be the appropriate threshold for international poverty measurement when the distribution is adjusted to the level of national accounts means. Since the $1.90 line is itself estimated from national poverty lines originating from household surveys (see Ferreira et al., 2016; Jolliffe and Prydz, 2016), the poverty line is likely also underestimated, and therefore not necessarily suitable for use with living standards measured using national accounts. A simple adjustment of the international poverty line for use with national accounts, would be to scale the poverty line up by the same proportion as the survey mean (to equate with national

> 26 This is essentially true by assumption. In our exercise, we simply scale the survey mean to the national accounts mean and apply the international poverty line of $1.90 per day. 27 We analyze the differences for surveys where the household survey has more than 3 percent poverty. At low poverty estimates small changes can lead to very large changes in percentage terms.

> 28 These measures are broadly consistent with Sala-i-Martin and Pinkovskiy (2016) who calculated measures up to 2010 using 2005 PPPs and the $1.25-line. To our knowledge, the measures presented here are the first poverty estimates using national accounts data and the updated $1.90 poverty line.

22
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(x) appear that a detailad study would be needed in any case. For economy wide polLeLes an econometrcL study of the country ln que-tlon could be undertaken if there were sufflcLent data avallable. **ThLe** should be ponsLble for both the power and LndustrLal sectors Ln the **cae-** where the attractlve fuel **was** already in use. **Where** lt Lad not prevLously been used it mlght be posesble to carry out econometrLc LnvestlgatLons for sLmilar countrLes where the fuel was already ln **use.** For small economLes it mlght be posslble to carry out a parallel 4 -vestlgation of fuel choice ln tho power sector using both iconometrLc and engineering fuel prlce swltchlng analyaLs on an ax post basLs. Only wlth such an lnvestlgatLon would it be poseLble to know whether the varlous simplificatLons Lntroduced ln econoeotrLc modelling dlitort the quantlficatlon of the potential for fuel switchLng.
of couples where the man refused but the woman accepted is only slightly higher than the opposite case (6.5 vs. 5.2 percent). It seems that it is only in Ghana, where there are 5.7 percent of couples where the man refused but the woman accepted the test in comparison with only 1.4 percent of couples in the opposite configuration, that bias due to nonrandom refusal of the test could contribute significantly to the proportion of discordant female couple.

Comparing HIV prevalence for men and women in a cohabiting union in the individual level data set and in the couple level data set is another way to investigate whether there is a bias due to the coverage of HIV testing, potentially aggravated by the fact that studying couples rather than individuals might reinforce that bias. In the couple recode, if one of the partners refuses to be tested, the entire couple is lost as an observation, even if the other partner accepted to be tested.

Table 10 reports the results of this comparison. In most cases, the HIV prevalence calculated from the couple recode (which corresponds, in table 1, to adding the concordant positive and the discordant male rows for males and adding the concordant positive and the discordant female rows for females) is very similar to the HIV prevalence obtained from the individual recode<sup>10</sup> . Only for males in Burkina Faso (2.8 vs. 2.1 percent) and for females in Cameroon (6.2 vs. 5 percent) and for both genders in Tanzania (7.8 vs. 6.9 percent for males and 6.9 vs. 6.1 percent for females) is the HIV prevalence larger when calculated for people in union in the individual recode than from the couple recode. This suggests that looking at couples might slightly underestimate HIV prevalence among married individuals, but there does not seem to be a systemic pattern of bias by which only the HIV prevalence among males would be underestimated and which would therefore inflate the proportion of discordant female couple.

> 10  The sample size is larger in the individual recode than in the couple recode, especially for women, because the Demographic and Health Survey does not systematically interview the male partners of the interviewed women, but only a sub-sample of them. It is however surprising that in Burkina Faso, for males, the sample size is smaller in the individual recode than in the couple recode.

15
37

# **Box** 7

# **The Nature** of Egypt's Uruguay Round Induced Export Changes

Table 14 suggested that the Uruguay Round tariff cuts should produce only minor gains for Egypt -- probably about $20 million in increased exports to the EU, Japan and United States combined. The possibility exists that this aggregate<sup>figure</sup> may conceal important differences in the effects on various product sectors. The statistics provided below examine<sup>this</sup> possibility by showing the projected gains and losses for major product groups exported to the EU and United States.

||1992 Imports|Projected|Trade Change|
|---|---|---|---|
|Product Group (SITC No.)|($000)|||
|||Value($000)|Percent Change(%)|
|EUROPEAN UNION||||
|All Items (0 to 9)|2,893,427.0|8,680.3|0.30|
|All Foods and Feeds (0+ I +22+4)|111,294.4|8,224.7|7.39|
|Agricultural Materials (2-22-27-28)|26,549.4|2.7|0.01|
|Ores & Nonferrous Metals (27+28+68)|126,555.1|-189.8|-0.15|
|Mineral Fuels (3)|1,969.5|-1.6|-0.08|
|Manufactures (5 to 8-68)|649,156.4|644.3|0.10<br>|
|Textiles (65)|168,934.9|-270.3|-0.16|
|Clothing (84)|83,064.3|-91.4|-0.11|
|UNITED STATES||||
|All Items (0 to 9)|465,598.3|11,220.9|2.41<br>|
|All Foods and Feeds (0+1+22+4)|6,230.3|16.2|0.26|
|Agricultural Materials (2-22-27-28)|6,210.0|-7.5|-0.12|
|Ores & Nonferrous Metals (27+28+68)|312.7|-1.2|-0.37|
|Mineral Fuels (3)|242.4|0.8|0.34|
|Manufactures (5 to 8-68)|191,698.0|11,212.6|5.84|
|Textiles (65)|42,493.1|871.1|2.05|
|Clothing (84)|122,678.5|4,858.1|3.96|

Source: World Bank-UNCTAD SMART Database In the European Union the Uruguay Round tariff cuts on food products should increase Egyptian exports by about $8 million which represents an increase of about 7 percent. Small net gains are also projected for the manufactures sector as a whole in spite of losses associated with the erosion of preferences on textiles and clothing.

In the United States Egypt's projected trade gains are concentrated in the manufactures sector with over 50 percent of the increase accounted for by increased textile and clothing exports. Outside manufactures very small positive or negative changes are projected. The decline for agricultural materials is almost entirely associated with the erosion of GSP preferences for several combed or carded wool products. Erosion of preferences for refined copper products accounts for the slight decline within the ores and nonferrous metals group.
countries, many of them located in Africa or Latin America, interest rate caps are used as an active policy instrument with the explicit aim to change the overall price of credit in specific segments of the market or the entire economy.

# Recent trends

The headline number of restrictions is similar to past studies on the use of interest rate caps (see for example Maimbo and Gallegos, 2014) and more than 85% of the countries identified by Maimbo and Gallegos (2014) still use some form of interest rate caps. However, this does not mean that the state of regulations regarding interest rate caps is static over time. Since 2011, we find at least 30 instances when either new interest rate caps have been introduced or existing restrictions were tightened (i.e. the level of the cap was lowered or the scope of restrictions was broadened). For example, in Kenya and the Kyrgyz Republic authorities introduced new caps on interest rates in 2016 and 2013, respectively, and the United Kingdom set caps on payday loans in 2015. Most recently, in 2017 Nigeria set ceilings on mortgage rates and Cambodia implemented caps on the maximum rates charged by microfinance intuitions. In other places existing restrictions were tightened. In the WAEMU the cap was reduced from 18% to 15% for banks and from 27% to 24% for microfinance institutions in 2014. Similarly, South Africa reduced the level of caps and removed some exemptions in early 2016. Turkey and Indonesia reduced caps on outstanding credit card balances in 2013 and 2016, respectively.

**Figure 4: Recent trends by income group**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>2011 2012 2013 2014 2015 2016 2017<br>High Income Upper middle income Lower middle income Low income<br>Easing Tightening Easing Tightening Easing Tightening Easing Tightening Easing Tightening Easing Tightening Easing Tightening<br><!-- End of picture text --> _Notes:_ 2012 tightening includes the six CEMAC countries; 2014 tightening includes the eight WAEMU countries _Source:_ Authors’ calculation 10
For China, the overall elasticity of the headcount index to GDP growth was –2.6. However, when one decomposes growth by sector, it is clear that its composition mattered greatly to the rate of poverty reduction. The impact of growth in the primary sector was far higher (by a factor of about four) than for growth in either the secondary or tertiary sectors. The impacts of the latter two sectors are similar.

For India, too, the sectoral composition of growth was important, although tertiary sector growth was relatively more important than in the case of China.<sup>11</sup> This probably reflects the difference between the two countries in the distribution of agricultural land. In rural China, starting conditions at the outset of the reform process entailed relatively low levels of inequality in access to land. The de-collectivization process that started in the late 1970s achieved a relatively equal allocation of access to agricultural land, at least within communes. (Between communes, the only way to equalize land allocation would have been to allow mobility of people, which was not considered a desirable option.) This meant that agricultural growth was a powerful instrument against poverty and inequality in China (Ravallion and Chen, 2006). The distribution of agricultural land was and is clearly more unequal in India, and that naturally attenuates the impact of agricultural growth on poverty relative to that found in China. Note also that India’s overall growth elasticity of poverty reduction is appreciably lower than China’s (table 1).

**_Table 2: Poverty reduction and the urban–rural composition of growth_**

||China|India|
|---|---|---|
|Growth rate of mean rural income (share-weighted)|–2.56|–1.46|
||(–8.43)|(12.64)|
|Growth rate of mean urban income (share-weighted)|0.09|–0.55|
||(0.20)|(–1.37)|
|Population shift effect|0.74|–4.46|
||(0.16)|(–1.31)|
|R<sup>2</sup>|0.82|0.90|

**_Note:_** t-ratios in parentheses.

**_Source:_** Ravallion and Datt (1996) (for India) and Ravallion and Chen (2006) (for China).

Increases in rural incomes, whether from agricultural growth or (particularly in the case of China) from increased rural non-farm employment, also turn out to have been critical for overall poverty reduction. Table 2 gives regressions of the rate of change in poverty over time (difference in the log headcount index) on the share-weighted growth rates of rural and urban mean incomes and a term capturing the effect of any shifts in population from rural to urban areas. It can be seen that in both countries, growth in rural incomes is the only statistically significant correlate of poverty reduction. Ravallion and Chen (2006) also report an alternative decomposition for China, which exploits the analytic (additivity) properties of the headcount index, whereby the national index is the population-weighted mean of the urban and rural 11 Note that the coefficients on secondary and tertiary-sector growth for India are of approximately equal size but opposite sign (table 1). This suggests that the (share-weighted) difference in growth rates is picking up a distributional effect on poverty reduction.

10
# **Table 2 - The effect of treatment on women's labor market activities**

# <u>ITT (OLS)</u>

||Regular earner|Average Regular<br>Income, monthly<br>(HN), Winsor|
|---|---|---|
|RMG|0.774***<br>(0.023)|173,558.246***<br>(7392.307)|
|Control Group Mean|0.050|16,249.033|
|N (Obs.)|1,099|1,099|
|Adj R-sq|0.634|0.350|
|p-val|0.000|0.000|

# <u>ToT (2SLS)</u>

||Regular earner|Average Regular<br>Income, monthly<br>(HN), Winsor|
|---|---|---|
|RMG|0.807***<br>(0.022)|181,147.937***<br>(7384.186)|
|Control Group Mean|0.048|16,859.697|
|N (Obs.)|1,099|1,099|
|Adj R-sq|0.676|0.374|
|p-val|0.000|0.000|

Robust standard errors in parentheses. We control for the following baseline characteristics: age, a binary indicator for married, an indicator for ever attending school, indicators for ethnicity being LaoTai, Mon-Khmer, Sino-Tibetan, Hmong-Iumien or other, household size and monthly household income. Data on gender-based violence was not collected at baseline so we cannot control for exposure before the RMG program. Outcomes are (1) binary indicator of whether she is a regular earner, and (2) her average regular monthly income in Laotian Kip. USD 1 = LAK 9, 350 at time of writing. * p < 0.1, ** p < .05, *** p < 0.01.

19
to register once all documents are provided is 51 days (30 days), whereas in practice the average time taken is 7 to 9 days. Almost 30 percent have no idea of the cost of registering, and among those who estimate the cost, the mean estimate is R$1,304, and the 90<sup>th</sup> percentile of R$2,500 is more than 10 times the 10<sup>th</sup> percentile of estimated cost of R$200. As described above, the actual upfront cost of registering is only R$236, or R$366 if one includes the sanitary and municipal taxes due within 30 days of registering. The mean (median) estimated tax rate is 22 (20) percent, compared to the actual tax rate of 4 to 8 percent.

The baseline survey asked firm owners open-ended questions about what they see as the main benefits and costs of formalizing. The main benefits mentioned were being able to open a bank account in the business name (51%), a better reputation for the business (47%), reduced risk of being fined (44%), ability to get business loans (43%), and being able to sell to other firms which require registration (39%). Only 13 percent said they saw no advantages. The main disadvantages mentioned were the initial costs of registration (62%), having to pay taxes (58%), having to pay for an accountant (34%), and the process of registering taking a lot of time (32%).

The baseline survey also asked firms if they had received an inspection visit from various types of inspectors in the past year. Thirty-two percent of firms responding to the baseline survey had received a visit from the municipal inspector (typically just to check on whether they had paid for a sign outside), 5.5 percent from a state tax inspector, and 3.1 percent from a federal tax inspector. The other main form of inspection was sanitary inspections, which 20.1 percent had received. Only 2 percent report having paid a fine for being informal, with the mean (median) fine being R$2,340 (R$600), with the majority of these fines being made to the municipality. Note though that these data come from surviving firms, and so may understate the rate of inspection and of fine receipt among all informal firms if many of the firms inspected or fined close down as a result.

# **3. Interventions**

The context is thus one of pervasive informality, despite the introduction of the simplified taxation program SIMPLES and the efforts taken by the Minas Facil unit to streamline the registration process. Given this context, _Descomplicar_ , a unit within the state government of Minas Gerais which has the mandate to simplify relations between citizens, firms and the state, worked with the World Bank to test various mechanisms that could be used to induce more firms to formalize under the existing system in place. The focus was on trying to target firms that fell under the eligibility criteria for SIMPLES, which 7