rafmacalaba/data-use-mentions-extended huggingface dataset:
the consequences of regulatory barriers against entrepreneurship are seen, not in young firms, but in older firms, who grow more slowly and to a smaller size. Desai et al. (2003) uses the same database and a cross-country approach and find complementary evidence that entry regulations have a negative impact on firm entry. Scarpetta et al. (2002) using firm-level data from OECD countries to analyze firm entry and exit finds that higher product market and labor regulations are negatively correlated with the entry of small and medium sized enterprises (SMEs) in OECD countries.

A number of studies have also used firm surveys to study the determinants of firm growth. Using a survey of manufacturing firms in Cote d'Ivoire, Sleuwaegen and Goedhuys (2002) find that younger firms grow faster than older firms, but larger entrants experience greater growth opportunities that improve over time. Yet a shortcoming of most studies in low- and middleincome countries is their dependence on survey data. The reliance on survey data poses limitations since firm entry is typically not observed, the data is biased towards surviving firms, and time series are short. Mead and Liedholm (1998) rely on survey data from five Eastern and Southern African countries (Botswana, Kenya, Malawi, Swaziland, and Zimbabwe) as well as the Dominican Republic to examine the magnitude and determinants of firm births, deaths, and expansions of micro and small enterprises (MSEs). The authors find that the annual rate of new MSE start-ups averages over 20%, and that the vast majority of new firms were one-person establishments. Shiferaw (2007, 2009) and Bedi and Shiferaw (2009) are notable exceptions to the majority of African firm-level studies that depend on survey data and instead utilize manufacturing census data. However, in their studies of Ethiopian establishments the authors still face stock sampling (survivorship bias) problems and are limited to the manufacturing sector.

Another important question in the literature is the determinants of firm survival. For example, evidence from Portuguese manufacturing firms suggests that drivers of
, renewables, electricity, biomass) energy use into the power, industrial, transport, and residential sectors, projecting it forward by: i) GDP forecasts; ii) assumptions about the income and own-price elasticity of demand for fuels; iii) assumptions about the rate of technological change affecting energy efficiency; and iv) changes in international energy prices, with pre-existing fuel taxes and levies being held constant in real terms. The impacts of mitigation policies on fuel use and emissions depend on i) their effect on future energy prices; ii) fuel switching within the power generation sector; and iii) price elasticities of electricity and other fuel demand across sectors.

The tool is parameterized using fuel use and emissions factors by country-sector from the International Energy Agency (IEA, 2021). Data on energy taxes, energy subsidies and prices by product

> 11 CPAT is forthcoming and will be available for public use at: https://www.worldbank.org/en/topic/climatechange and - <u>https://www.imf.org/en/Topics/climate change/CPAT</u>

9
GMB for those who manage to find (paid agricultural) work in their home villages in The Gambia.<sup>22</sup> The next two columns of Table 4 examine impacts on internal migration to the capital city of Banjul. This is the most common form of migration, with 41 percent of the control group having spent at least a month in Banjul during our 18-month follow-up period, and 10.7 percent being there at the time of the follow-up survey. The Senegal treatment is found to have reduced the likelihood of having gone to Banjul at some point during the follow-up period by 7.6 percentage points, and the vocational training treatment by 3.6 percentage points. Both these interventions therefore appear to have spurred some longer-term migration to Senegal and crowded out some temporary internal migration. Our wage data show the average monthly earnings of those in Banjul at the time of the endline survey were 5,088 GMB, which is almost 50 percent higher than income in their home villages, but more than 1,000 GMB (20 percent) lower than the income earned by those working in Senegal.

The last three columns then examine whether the treatments changed backway migration. Column 6 examines the number of concrete steps taken towards backway migration, such as saving the money needed, asking someone for help finding a job or accommodation, and mapping out the route and making a travel plan. The control mean is for 1 step out of 5 to be completed, and the vocational training treatment reduces this by 0.15 steps. However, this effect is no longer significant when adjusting for multiple hypothesis testing across all backway migration outcomes. We then see small (0.1 to 0.4 percentage point) reductions, which are not statistically significant, on the likelihood of attempting the backway, and of making it to Europe. However, the control mean is only for 1.1 percent of individuals who have made a backway migration attempt, and only 0.6 percent who have made
# **4.2 Panel estimates**

Table 3 presents estimates of the relationship between economic freedom and economic growth in a panel of data from 1975 to 2005. The average economic growth is measured over 5 year intervals (as the index of Economic Freedom of the World Index is only available every 5 years during 1975-2000). Data on economic freedom is available for 121 countries. Like in the cross-section estimates, the dependent variable is explained by the initial levels of the various exogenous variables (EF, CPR, and ER), the changes of those variables in the previous periods (to avoid the problem of endogeneity), and a similar set of control variables.<sup>18</sup> The panel data estimates are consistent with the cross section estimates. We find a robust positive relationship between the initial level of economic freedom and its growth in the previous period. In addition we find a similar relationship between economic growth and civil and political rights. Both the initial level and changes in the civil and political rights in the previous period contribute positively to the economic growth. This relationship is robust to the inclusion of the indicator of economic freedom. In contrast to the cross section estimates we find this time a robust negative relationship between the initial level of entitlement rights and economic growth in all specifications, while we still do not find any statistically significant impact of the change in entitlement rights on economic growth.

All the control variables are statistically significant in most specifications and have the same signs as in the cross section estimates presented in Table 2. Column 7 of Table 3 presents the results based on the fixed effects estimation. The only difference in this specification is that the growth of economic freedom in the previous period does not seem to affect economic growth in the current period. The overall fit of the fixed effects regression is poor and it does not seem to be the appropriate way to model the economic growth since the heterogeneity of the data set is driven by cross-country variation and not by time series
>the</sup> minimum observed level of labor efficiency in the urban sector. The presence of these parameters in equations [3] and [4] imply that non-NEG, as well as NEG, forces will be important in determining the geography of both urban and rural wages.

# _3.2. Methodology for Estimating  Impacts_

Our methodology consists of five stages, involving the two key wage equations – equations [3] and [4] – and an assessment of the fit of the model's short-run equilibrium solution to Chinese data for 2007. In the **first stage** , we use GIS techniques to construct two _N_  _N_ travel time matrices, _TIME After_ and _TIME Before_ . The _i_ - _j_ th element of the matrix _TIME After_ ,<sup>_t_</sup> _ijAfter_ , measures the optimal travel time by road between regions _i_ and _j_ based on a digital representation of the 2007 Chinese road network, which includes the NEN. Likewise _TIME Before_ measures optimal travel times excluding the NEN, thus providing the basis for our counterfactual scenario. For both networks, travel time is invariant to the direction of travel, _x x x_ i.e. _t ji_  _tij_ where _x_  { _Before_ , _After_ }. For simplicity, we also assume that<sup>_t_</sup> _ii_  0 for  _i_ . In 13
public sector have been very unstable, with the real earnings having fallen in the late 1980s. Does the fall in earnings in the formal sector imply that the large wage differential in favor of the formal sector observed in the 1 970s and early 1 980s has largely disappeared. In other words is the labor market closer to an equilibrium situation in which the formal sector wages are more in line with the supply price or alternate earnings of labor? This question has to be addressed in terms of the urban informal and rural sectors.

Another related issue is whether the fall in real earnings was caused by a fall in real wages or by a fall in employment or both. The data analysis tells us that there has been a fall in employment in the formal sector, but we don't have the wage information of the self-employed. Similarly, secondary data (Ghana Statistical Yearbook) also provides the evidence that there has been a fall in overall employment, more so in the public sector (Fig 4). Thus, we can say that fall in employment was one of the factor resulting in the fall in real earnings. The effect of the trend of real wages on the trend of real earnings also depend on the wage determination process in the formal sector-through unions or the government. Government labor market policies either through minimum 26
the source of a tiny fraction of all jobs found by workers: 9 percent of jobs in Albania, 5 to 6 percent of jobs in Jordan and Morocco, and down to less than 1 percent of jobs obtained in Mexico.

**Table 1: Main methods used by employed workers to find jobs**

||||Percent wh|o used meth|od to find job|**Sierra**||
|---|---|---|---|---|---|---|---|
||**Albania**|**Jordan**|**Mexico**|**Morocco**|**Romania**|**Leone**|**Turkey**|
|Friends and Relatives|28.8|41.0|53.0|70.9|37.2|46.0|59.7|
|Direct Application to Employers|52.7|37.1|32.0|40.4|36.4|41.1|22.9|
|Newspaper ads|0.5|8.5|8.9|0.1|15.7|5.0|4.5|
|Internet|0.3|1.0|1.6|1.5||0.0|5.0|
|Public or Private Employment Agency|9.1|6.2|0.6|5.5|2.5|2.2|2.7|
|Other|8.5|6.2|3.9|12.5|8.2|5.8|5.2|
|SampleSize|14653|904|117924|712|36542|504|3302|

_Source:_ Albanian data from Quarterly Labor Force Survey 2019; Jordan is
durable assets were gathered at the household level, we use such information to explore PROSPERA’s long-term impacts on welfare. Individuals in the critical age groups described were asked several additional questions on income and work-related topics. An important limitation of the ENCEL 2017 data is that non-durable consumption (crucially including food consumption) is not available. This somewhat constrains our welfare analysis, especially for a poor population context, where food expenditure represents a high proportion of total expenditures. However, as an additional exercise, we propose an imputed measure on non-durable consumption expenditure, which includes food, personal products, and clothing, adapting the methodology of Blundell et al. (2005) and Attanasio and Pistaferri (2016) to the available data and context (see Appendix Section C). It is important to remind the reader that while durable consumption items are typically bigger ticket items, their purchase is infrequent, and for poor households the total expenditure on durables is typically about 20% of total expenditure, so that providing evidence on non-durable expenditure seems rather important.

_Table 4_ gives descriptive statistics for the original and new households employed in our analysis using 1997 and 2017 data. For new households, the 1997 information corresponds to that of their respective household of origin. The comparison between 1997 and 2017 shows an

> 10These individuals added _by mistake_ are not a random sub-sample of all the individuals out of the group of interest, but correspond to those for whom age was not available in 1997-2000 and were mistaken for newborns.

12
needs to know how much of this volatility is transmitted to domestic markets and passed to consumers. If a country is well integrated into world trade and there are no policy interventions, international prices are fully transmitted to domestic markets. Evidence on price transmission is mixed. For example, Mundlak and Larson (1992), using a data set for 58 countries over 1968 – 1978, estimate that international agricultural prices are almost completely transmitted to domestic markets. A disaggregation of prices into single commodities shows that the transmission of wheat prices, for example, is lower than the average for agricultural commodities. Moreover, the authors find that the main part of domestic price instability is caused by volatile international prices. However, Baffes and Gardner (2003), investigating eight 41
approach under a constant pension level) and the allocation of capital more efficient (compared with the constant tax rate, government pre-funding approach).

In addition, tax cuts in exchange for reductions in future pension benefits are likely to be fair from the perspective of inter generational equity. The current generation would enjoy lower taxes without burdening the future generations as much. This hypothesis could be tested by applying the assumptions described above to the New Zealand generational accounts (Auerbach, Baker, Kotlikoff 1995).

# **5. Conclusions**

The fiscal position of the government of New Zealand, like that of other OECD governments, will begin to feel the effects of an aging population as the cost health care and pension payments rise. A drop in productivity growth or an increase in social spending relative to the baseline case could exacerbate the negative effect on fiscal balances.

The financing of future liabilities presents major policy challenges. With tax rates maintained at current levels the government could pre-fund the future increases in its liabilities through 2050. Such pre-funding would mean that by the 2020s-30s the government would be managing net financial assets worth 80 percent of GDP, however. Under a balanced budget approach the government could cut taxes in the next 10 to 15 years but would have to increase taxes by even more twenty years later.

Sensitivity tests suggest that tight control over social spending will be necessary to reduce the fiscal burden of the aging population and the fragility of future fiscal balances. Cross-country experiences suggesting a negative correlation between public pension and private saving levels imply that reduction in the level of NZS benefits could bring about significant gains on the economic as well as the fiscal side. Transfer of responsibility for health care and pension provision to the private sector could allow for tax cuts without necessitating large tax increases later. Thus, the benefit of tax smoothing could be achieved at tax rates that are lower than the model's baseline rates, without relying on the government to accumulate and manage large financial assets.

49
financial losses that CGSs could incur in adverse climate-related scenarios.

**Exposure data is collected through a survey among AECM members while vulnerability data has been obtained from a range of other sources.** Outstanding guarantees with sectoral and spatial breakdowns have been collected from 29 CGSs that together administered EUR 278 billion of outstanding guarantees as of December 31, 2020. Our sample covers 84 percent of the total outstanding guarantee volume of AECM members, which totaled EUR 330 billion in 2020 (AECM 2020). However, not all CGSs provide breakdowns of their guarantees into sectors and regions with equal granularity. For 1-digit sectors (NACE1) and 2-digit sectors (NACE2) our sample covers 82 percent and 35 percent of the total outstanding guarantee volume of AECM members, respectively. For regions at the second administrative level (NUTS2), our sample covers 42 percent of the total outstanding guarantee volume of AECM members. All exposure data is as of end-2020 and thus includes volumes under the extensive COVID-19 support programs. For the risk assessment we map CGSs’ exposure data to vulnerability data to assess the amount of exposure at increased risk. We obtain vulnerability data from a variety of sources and at different levels of aggregation. Table 1 provides a summary of the collected vulnerability data.

> 5 See https://www.ngfs.net/ngfs-scenarios-portal/.

7
individual participates (or does not participate) in the sector, but also whether that individual receives (or does not receive) wages for that job. The labor participation model relies on the utility maximization �� approach developed by McFadden.<sup>12</sup> Assume that the utility (��� ), for individual � of household _h_ , associated with labor status _j=0,...,J_ , and level of education _L_ , can be expressed as a linear function of observed individual and household characteristics (���� ) and unobserved utility determinants of the

> 10 This section is based on Olivieri, S. et.al (2014)

> 11 Notice that although we could estimate specific models for salaried and non‐salaried workers based on the microdata from the household survey, we would not be able to use these models because this information, generally, is not available from the macro side. Macro‐economic projections are calculated mainly for aggregate economic sectors such as agriculture, industry and services instead of wage or self‐employed, formal and informal sectors.

> 12 McFadden (1974).

5
**Figure 2**

Employment and the labor force (1964-94) <!-- Start of picture text --> 7.8 a a a r a a a a a<br>4.3<br><!-- End of picture text --> Source: CSO and African Regional Database (World Bank). Note: The series are log.

After a period of civil war Zimbabwe achieved majority rule in 1980 and a new labor legislation was launched. The Riddell Commission that framed the early 1980s legislation argued that the industrial labor market was asymmetric in the bargaining position of managers (of European origin) and workers (of African origin). The new legislation was justified to redress a basically non-competitive (monopsonistic) bargaining structure in the labor market. After 1980, trade unions experienced an increase in memberships and grew in coverage and total employment and output increased. Furthermore, minimum and maximum wages were introduced in recognition of workers needs. However, they were abolished again in 1990 except for agricultural and domestic workers.

In 1990 the government started to deregulate labor markets as part of the general reforms causing a change in the structure of employment. The aims were to reduce the size of the public sector and to reduce public intervention in labor markets. Now, the labor codes are more flexible than in the 1980s as employers can set wages and contracts, retrench, and dismiss employees without consulting the Ministry of Labor (Knight; 1996).

5
#

A primary challenge with estimating the effect of public investment on private is the issue of endogeneity of government investment. This issue occurs because public investment is a fiscal policy tool often used to help stimulate the economy during economic downturns. There is a strong possibility that during economic downturns where private investment is low, governments may increase public investment to stimulate the economy. Consequently, an estimated negative relationship between public and private investment is likely to be driven by the negative shock to the economy. This means that there will be a negative correlation between the error term and public investment, thereby, inducing a downward biased OLS estimate of _α_ . On the other hand, if government investment is procyclical (that is, increases during economic booms), which is historically the case for several developing countries, then during good times such as booms in commodity prices, government investment will rise (Galeano et al., 2021). Procyclicality would create an upward bias on the OLS estimate of _α._ Additionally, and perhaps a more critical issue of endogeneity is measurement errors in investment data. The presence of measurement errors in the independent variable can lead to attenuation bias. This may imply a positive relationship between the error term and public investment, inducing an upward bias in the OLS estimate of the crowding-in parameter. Generally, this endogeneity issue that causes government investment to be contemporaneously correlated to the error term, _εit_ , will render the OLS estimation inconsistent.

Evidently, failure to address the issue of endogeneity can lead to inaccurately estimating the size, and potentially the sign of _α_ . The natural strategy to employ to address the endogeneity is an instrumental variable estimation. We, therefore, utilize the identification strategy of Kraay (2012) and Kraay (2014). While focusing on the output multiplier and government spending, these studies exploit two conditions: (1) The studies argue that a significant fraction of government spending in developing economies is financed by loans from official creditors, and (2) these loans typically disburse over a period
Table 2. Simple correlations between various inequality measures and inequality concepts

|||Average<br>|OWW<br> wage by occ<br>|upation<br>|
|---|---|---|---|---|
|||Gini_oww|Stdev|Absdev|
|UTIP<br>(average<br>wage by<br>industry)|Theil|0.45**<br>(513)|0.48**<br>(513)|0.41**<br>(518)|
|OWW|Gini_oww||0.96**<br>(723)|0.81**<br>(723)|
||Stdev|||0.85**<br>(723)|

Note: Number of observations given between brackets. Each country/year represents one data point, that is for each country/year, there is one inequality statistic. The level of significance (null hypothesis: correlation = 0): ** = less than 1%, * = less than 5%. Shaded are cells with correlation coefficients calculated between various inequality measures from the same database.

Figure 2. Inter-occupational (Gini) and inter-industry (Theil) inequality <!-- Start of picture text --> 0 .1 .2 .3<br>Galbraith: Theil of inter-ind. w ages<br>.8<br>.6<br>.4<br>gini of wages, x1wu<br>.2<br>0<br><!-- End of picture text --> Note: Calculated from 513 observations from 79 countries Total number of observations is 723 (from 103 countries) for inter-occupational inequality and 2160 (from 141 countries) for inter-industry inequality.

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