rafmacalaba/datause-encoder-data huggingface dataset:
**Income distribution data.** The basis for calculations is provided by Goldsmith’s (1984) estimates. Goldsmith provides minimum wealth (census qualification) for the three top classes (senators, knights and municipal senators) and an estimate of their mean incomes. The problem was that –taking these estimates as given, and assuming that the bulk of the working population lived at slightly above the subsistence minimum ($PPP 400)—one finds an overall lower mean income than given by Goldsmith and used here (HS 380). This is why we introduced, following Goldsmith who spoke of that class but did not put any numbers on it, a fourth rich class of “other rich people” who were neither Roman knights nor municipal senators (both of which needed to fulfill the census requirements). There is no doubt that that “fourth” rich class existed but putting a number on its size and average income is obviously difficult. We decided to take as their mean income the average of the two other higher classes’ incomes (leaving out as decidedly the richest the class of Roman senators).

_Notes to the table above_ 1/ From Goldsmith (1985). Total amount for senators includes HS15 million of Augustus’ and Imperial households’ (100 people) private fortune. The censuses, according to Goldsmith, were 1 milion for senators and 250,000 for the knights. According to Finlay (p. 46), the census for the knights was 400,000 HS. The average annual income of senators’ class is calculated to be 15 percent of the census (note: census is the _threshold_ ) and for knights, 12 percent of the census amount.

2/ Calculated from Clark (p. 676): 225 denarii (1 denarius = 4 HS) _plus_ 50 modii of wheat valued at 110 HS (Milanovic, 2006, Table 3). This makes the average wheat price 2.2 HS per modius. Harl (p. 276) gives modius price range from 8 asses (2 HS) in Egypt to 32 (8 HS) in Rome. Temin (2006, p. 138) gives free market price in Rome at 4-6 HS. After the huge Rome’s fire in 64, Tacitus (Book XV, Chapter 39) mentions that the price of wheat in Rome, due to the sudden impoverishment of the population, dropped to 3HS per modius. We select a relatively low price to avoid inflating incomes by using Roman prices for the goods that were essentially consumed outside the capital.

Tacitus (Book I, Chapter 17) quotes soldiers (in year 14) complaining that a soldier is worth only 10 asses per day. That would be 2.5 HS per day or 912 HS per annum, some 10 percent below our estimate of HS 1010. Tacitus’ number may refer to the monetary pay only, _i.e_ ., it likely excludes payments in kind.

Size of the army (250,000) from Temin (2006, p. 147) quoting Goodman (1997). Similarly, Walbank (p.19) gives 250-300,000.

3/ Clark (p. 676). The size of the Praetorian guard was 9 cohorts each with 1,000 men.

4/ From Milanovic (2006, Table 4), based on Goldsmith (1984, p. 268) and the amount of _alimenta_ paid from the public treasury to boys under 15 years of age.

5/ From Temin (2006, p. 136). We assume that their income was twice the subsistence. They are assumed to account for 10 percent of the urban population.

65
Table 5 compares sales and profits measures across the two mediums. Overall, there are similar patterns of results for each medium with no statistically significant differences across the two. Specifically, we find that moving from a self-reported or calculated measure to the corresponding AAA measure leads to more plausible (i.e. larger) figures for both sales and profits. Furthermore, the AAA measures are more precise for both mediums. We find a marginal decrease in the CVs moving from a self-reported sales measure to the AAA sales measure and a substantial decrease in CVs moving from a calculated profits measure to the AAA profits measure. Finally, we do not find significant differences between the two mediums across all estimates; average monthly sales and profits estimates are comparable regardless of whether collected via the paper or electronic tool. These results affirm that it is the AAA methodology and not the electronic medium that leads to improvements in sales and profits measures.

# **3.6. AAA “Light”**

The preceding sections present multiple pieces of evidence on how the AAA measurement methodology improves both the precision and accuracy of business outcomes over traditional measures. The analysis shows AAA sales are larger in magnitude, have lower coefficient of variation, and are closer to administrative sales as compared to traditional self-reported sales. The same pattern is present for profits; however, the methodology produces two measures for profits, aided recall (Profits2) and the AAA estimate (Profits3). The comparison between these two estimates offers a meaningful choice in survey design and implementation. On the one hand, tables 2A, 2B, and 2C consistently show that the AAA estimate (Profits3) is significantly higher than aided recall (Profits2) across all settings, and this measure is the closest match to administrative records as shown in Tables 5A and 5B. As such, the AAA value is the most accurate estimate of “true” firm profits.

On the other hand, the analysis also shows that both aided recall and AAA estimates reduce the CV substantially from Profits1, have high autocorrelation coefficients, and are also highly correlated with each other (Pearson coefficient range: 0.58-0.81; Spearman coefficient range: 0.630.82). Furthermore, the aided recall measure saves at least 30 minutes of survey time. Hence, researchers with binding budget constraints or limited capacity to implement the full AAA survey methodology can still achieve many of the gains in measurement precision by measuring AAA 25
ulations based on Monte Carlo, however, predict a somewhat lower debt-to-GDP ratio at the end of the projection horizon. This might happen because the bootstrap distribution is right-skewed. The remaining plots in Figure 4 show inter-percentile ranges of projected debt-to-GDP ratios. For example, the lower left panel reports the inter-quartile range of debt forecasts. The inter-quartile range is wider under the Monte Carlo approach. The same is true for the 60th-40th inter-percentile range. The reason for this result is the following. Relative to the associated normal density, the histogram of tted residuals has more probability mass around zero, more probability mass in few extreme values, and less probability mass in medium-sized values. Thus, while it may occasionally draw extreme shocks, the bootstrap most often draws small shocks relative to those of the normal distribution. This makes condence bands tighter under the bootstrap approach. On the other hand, the 95th5th inter-percentile ranges are similar in both methods. This might be reecting that these percentiles are capturing the extreme realizations occasionally drawn by the bootstrap.

# 4 A case study: Senegal

This section considers the case of Senegal to highlight some issues in implementing the methodology. It is argued that following a mechanical approach could be misleading. To obtain reasonable results, the analyst needs to consider carefully the choice of the countryspecic intercept. Failure to do so could lead to overly optimistic or overly pessimistic debt scenarios. This section also compares the predictions obtained under the proposed methodology with those of the baseline IMFWorld Bank's debt sustainability analysis (DSA).

In 1996, the IMF and the World Bank launched the Heavily Indebted Poor Countries (HIPC) initiative which consisted in providing debt relief to countries with unmanageable debt burdens. In 2004, Senegal was granted 850 million U.S. dollars in debt service relief, a large fraction of which was implemented immediately. Senegal's external debt declined substantially, reinforcing a trend that started in 2000 (top panel of Figure 5). Foreign debt 20
# **Introduction**

Paid work without legal contract is a phenomenon closely related to such fields of economic and social studies as shadow economy, tax evasion, trust in and efficiency of institutions, labor demand and labor supply, self-employment, worker mobility, labor market flexibility, social exclusion, social security, and many others. Understanding determinants of the size of informal workforce is thus important both for policy making and for design of institutional reforms. Yet research in this field, especially in European context, has been limited due to lack of data.

This paper employs data from rounds 2-4 of ESS (European Social Survey, 2004-2009) to analyze institutional and other macro determinants of prevalence of informal dependent employment, as well as informal self-employment, across 30 European countries. The measures on prevalence of informal employment used in this paper are those from Hazans (2011a); they include 74 observations on 30 countries. Importantly, for most countries, our data cover the early stage of the economic crisis of 2008-2010; thus the impact of macro factors during different stages of economic cycle is accounted for in our analysis.

The paper aims to contribute to two strands of the literature. Firstly, many scholars have studied determinants of the size of informal economy and informal workforce (e.g., Johnson et al., 1997; Schneider and Enste, 2000; Friedman et al., 2000; Djankov et al., 2003; Loayza et al., 2005; Schneider, 2005; Perry et al., 2007; Kucera and Roncolato, 2008; Bajada and Schneider, 2009; Loayza et al., 2009; OECD, 2009; Torgler and Schneider, 2009; Dreher et al., 2009; Dreher and Schneider, 2010; Schneider et al., 2010; Feld and Schneider, 2010; Fialova and O. Schneider, 2011). Regarding informal employment, an apparent gap (which we are trying to fill) in this literature is lack of multi-country analysis for Europe based on direct survey data on work without legal contract rather than proxy measures of the size of informal dependent workforce<sup>1</sup> . As a theoretical contribution, we provide arguments for the effects of many factors traditionally viewed as promoting informality to have a potential for a reversal. Accounting for joint determination of informal wage employment, informal selfemployment, and unemployment is another distinctive feature of our analysis. We also provide empirical support for the positive impact of income inequality and shares of minorities and population with immigrant background on employment informality in Europe.

Secondly, there is a growing literature, following the seminal contribution by Layard et al. (1991), on the impact of institutions on labor market performance. We refer to Blanchard

> 1 Previous multi-country survey-based studies on undeclared work in Europe (Riedmann and Fischer, 2008; Williams  and Renooy, 2008; Feld and Schneider, 2010) have focused on undeclared activities and earnings rather than on informal employment relationship.

2
41 emigration were assumed to be only half as high as Statistics Canada has estimated, net international migration could not be as high as previously proposed by the World Bank. I would recommend rounding the official estimate of 308,000 off to 310,000 for 1980-85 and adopting a sex ratio of 90, which is somewhat higher than the official estimate but much lower than the previous World Bank estimate.

Because of the rapid increase of immigration in the last two years, a somewhat higher figure for net migration is necessary for the next five-year period I would recommend an assumption of net immigration of 370,000 for 1985-1990. This estimate is based on the official low migration assumption, augmented by some additional immigration. After 1990, T would recommend a gradual decline in the level of net immigration to 350,000 in 1990-1995 and 340,000 in 1995-2000. Again these recommendations would be consistent with the low migration assumptions of the official projections with a small amount of additional immigration. Although there is a reasonable chance that net immigration will continue to rise substantially over time, it is equally likely that a reoccurrence of economic difficulties could have a temporary inhibiting effect on immigration. Since immigration is more subject to fluctuations in economic conditions in Canada than in the United States, considerable caution needs to be exercised in projecting a continuing increase in immigrant arrivals. I would also recommend a continuation of female dominance in the flows, but with some movement toward more of a sex balance over time (i.e., sex ratios of about 90 in 1985-1990, 95 in 1990-1995, and 100 in 1995-2000).

# New Zealand

In New Zealand, the Department of Statistics publishes annual migration statistics based on information from forms filled out by each person entering or leaving the country, except for crews, transit passengers, and members of the Armed Forces on military exercises (Farmer, 1986). International migrants are divided into two categories: (1) permanent, and (2) long-term migrants who state that they are leaving New Zealand for 12 months or more but not permanently. New Zealand immigration laws recognize several special arrangements with residents of certain countries. Persons born in the Cook Islands, Niue, and the Tokelau Islands are New Zealand citizens who are allowed unrestricted entry to New Zealand (Trlin, 1987). Moreover, under a reciprocal agreement regarding trans-Tasman mobility, there is free migration between Australia and New Zealand in both directions.

As can be seen from Table 18, arrivals of permanent settlers and longterm migrants have been fairly constant since 1979. The annual number of permanent settlers arriving has ranged from 9,960 to 12,595. Arrivals of long-term migrants varied between 30,676 and 33,259 during the same period. Nearly 80 percent of long-term migrants were New Zealand residents who were returning after a long absence. There was a slight preponderance of males among permanent and long-term migrants. The sex ratio of permanent and longterm migrants between 1980 and 1984 was 105.1.
||Poverty (NSO)|
|---|---|
|(Intercept)|-0.45205 **<br>(0.21987)<br>|
|ln<br>~~l~~pop12|-0.03781 ***<br>(0.00734)|
|ln<br>~~r~~ugged|0.02051 **<br>(0.01005)|
|ln<br>~~s~~lope|-0.06148 ***<br>(0.01745)|
|ln<br>~~f~~orloss|0.07558 ***|
||(0.01169)|
|agr<br>opp<br>~~c~~st|0.0085 ***|
||(0.00328)|
|rain<br>~~c~~rop<br>~~s~~h|-0.02675<br>|
||(0.01844)|
|dist2major<br>~~r~~iver|-0.00131 ***<br>(0.00016)|
|ln<br>~~t~~t50k2010|0.02608 **|
||(0.01045)|
|ln<br>~~n~~tl<br>~~2~~010|-0.02033 **|
||(0.00968)|
|ln<br>~~n~~tl<br>~~1~~996|-0.03566 ***|
||(0.01279)|
|bio1|-0.00406 ***|
||(0.00077)|
|bio4|-0.00021 ***|
||(4e-05)<br>|
|pet|0.00147 ***<br>(0.00016)|
|s2|0.02357<br>(0.24747)|
|rho|0.75524 ***<br>(0.00165)|
|Observations|765|
|adj. _R_<sup>2</sup>|0.558|

Table 4: Parameter estimates of SEM model with _τ_ = 0 _._ 54 t-statistics in parentheses: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01

# **4.2 Comparing poverty maps**

Before we inspect the estimates of poverty at the small area level, let us first evaluate the estimates at the national and the regional levels. Since the IHS3 survey is designed to be representative at these levels, the survey direct estimates provide a natural benchmark to compare our model-based estimates against. The ELL and SEM estimates are 23
Business registries managed by courts are less efficient, costlier for businesses, and due to their discretionary nature increase uncertainty for investors. However, we are not aware of any papers that measure the effects of reforming business registration from a judicial to an administrative procedure on firm creation. This paper aims to fill this gap by studying a reform in Serbia that transferred business registration from regional courts to a new centralized administrative agency in 2005. Although this reform only eliminated one procedure in the registration process, going from 11 to 10, it implied a large drop in the number of days required to register a business, going from 51 to 15 (World Bank, 2006). The World Bank’s 2006 Doing Business called this reform one of the “boldest reforms” to the business environment that took place between 2004 and 2005 (p. 2).

Moving business registration away from courts may have large effects on firm creation since courts can entail significant bottlenecks and inefficiencies (see Chemin, 2009, and Kondylis and Stein, 2018). They may also pose opportunities for corruption. In fact, prior to recent reform efforts, the great majority of citizens and lawyers in Serbia perceived the presence of corruption in the judicial system (World Bank, 2014). Moreover, business registration at court was highly discretionary (Jersild and Skopljak, 2007). We thus expect the 2005 reform in Serbia to have relatively large effects on firm registration.

To measure the effects of the reform, we obtained data on the number of new firms from the Serbian Financial Accounts Registry for all years from 2003 to 2011. This data set covers formal companies (not sole proprietors) that submitted their financial statements to the registry.

We employ a difference-in-difference strategy that compares the number of new firms before and after the 2005 reform across districts based on the level of distrust in regional courts (as measured in the 2006 Life in Transition Survey). That is, we test whether the regions where a large fraction of individuals report distrust in courts saw a larger increase in new firms after the reform than districts with less distrust in courts. To verify whether this is a valid identification strategy, we show that the level of distrust in courts is not correlated with pre-reform changes in the number of new firms over time.

Our results suggest that the reform increased the number of new firms by up to 34%. We also look at employment, survival and growth rates of these new firms, but find no significant effect of the reform on the average number of employees or employment growth rates of new firms. We do, however, find that the reform increased the one- and two-year survival rates of new 3
developing world by providing an economic analysis of backyarding, developing a theoretical model whose predictions are then tested empirically.<sup>1</sup> By improving our understanding of the backyarding phenomenon, the paper also helps to guide the formulation of government housing programs in the developing world, mainly by the exposing consequences of giving poor households more housing than they can reasonably use.

An appreciable share of South Africa’s population, particularly the urban population, lives in backyard dwellings. According to Statistics South Africa, the number of households living in either formal or informal dwellings in backyards has increased from 1.135 million in 2011 (7.3% of the country total), to 1.835 million in 2016 (12.5% of the total). Backyarding is predominantly an urban phenomenon, with 84.2% of households that live in backyard dwellings residing in urban areas (as defined by Statistics South Africa as of 2011). Households living in backyards constituted 8.9% of urban households in 2011, rising to 13.4% by 2016. In contrast, the number of households living in informal settlements declined both in absolute and relative terms, from 9.8% of urban households in 2011 to 8.7% in 2016.

The durability of housing capital limits the adjustment of urban densities as population growth raises housing demand, and backyarding can be viewed as an efficient way of overcoming this limitation. With the durable housing stock fixed in the short run, backyarding allows an incremental increase in housing supply without the need to redevelop existing structures at higher densities, an adjustment that might take decades to unfold. The backyarding phenomenon may also indicate that yard areas in government-subsidized housing, where backyarding is most common, were too large from an overall efficiency perspective, with backyarding providing a correction to this original misallocation of resources.<sup>2</sup> These ideas could be illustrated formally, but doing so is beyond the scope of the paper (the argument is clear intuitively in any case).

The theoretical model developed in the paper depicts the homeowner’s backyarding choice, where the amount of yard area rented to backyard households is chosen.<sup>3</sup> In making this choice, the homeowner trades off rental income against the loss of yard space for his own consumption. A higher land rent raises the “price” to the homeowner of a unit of yard-space consumption (the forgone income from renting it), generating the usual income and substitution effects of 2
**Figure 3: Government Education Spending as a Share of National Income Has Risen Fastest in Low-income and Lower-middle-income Countries**

_a. Public education spending as percentage of b. Public education spending as percentage of GDP by income group classifications as of 2017 GDP in low-income countries by graduation status_ <!-- Start of picture text --> 6<br>6<br>remained LIC between 1999 and 2017<br>graduated between 1999 and 2017<br>HICs<br>5<br>5<br>UMICs<br>4 LMICs 4<br>LICs<br>3 3<br>1998–2001 2002–05 2006–09 2010–13 2014–17 1998–2001 2002–05 2006–09 2010–13 2014–17<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF online databases. _Note:_ World Bank income group classifications in 1999 are used to group countries and are as follows: LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country.

Overall, countries that graduated out of low-income status between 1999 and 2017 devoted a larger share of GDP to public education spending than those countries that remained in the low-income group (Figure 3). Between 1998 and 2013, countries that graduated from low-income status spent approximately 4.1 percent of GDP on education compared to 3.5 percent for countries that did not graduate.<sup>4</sup> However, this gap has narrowed over time because of a decline in the spending share among graduating countries and an increase in the share among non-graduating countries. Between 2006 **–** 09 and 2014 **–** 17, government education spending as a share of GDP declined from an average of 4.4 to 4.1 percent among graduating countries but increased from an average of 3.7 to 4.1 percent among non-graduating countries.

# **_Government Education Spending Has Grown in All Regions in Absolute Terms, But Trends in Spending as a Percentage of GDP Have Varied_**

Real public education spending growth has been most rapid in East Asia and the Pacific, Latin America and the Caribbean, and Africa. While public education spending has grown in real terms in all geographic regions since the late 1990s, the rate of growth differs enormously (Figure 4). For example, since the late 1990s, spending in East Asia and the Pacific has more than trebled and in Africa it has increased by 120 percent. Trends in other regions were more muted. For example, public education spending in the Middle East and North Africa increased by only 12 percent over the same period.

> 4 Between 1998 and 2013, low-income countries and countries that graduated from that status allocated between 15 **–** and 16 percent of total government expenditure to education. In 2014 17, education as a share of total government expenditure declined to 14 percent for countries that graduated but increased to 17 percent for non-graduating countries.

8