rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE 6.4: Household Level - Probit Regressions**

Source of Data: Households' Survey Dependent variables: MONOPOLY_DISC = Household discouraged in at least one monopoly institution (0,1) NON-MONOPOLY_DISC = Household discouraged in at least one non-monopoly institution (0,1) Marginal Effects evaluated at mean values are reported

||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|---|---|---|---|---|---|---|---|---|
|||Non-||Non-||Non-||Non-|
|**Dependent variable:**|Monopoly|Monopoly|Monopoly|Monopoly|Monopoly|Monopoly|Monopoly|Monopoly|
|Middle Income|0.051|-0.008|0.051|-0.008|0.05|-0.008|0.054|-0.005|
||[1.62]|[0.29]|[1.62]|[0.28]|[1.60]|[0.30]|[1.70]*|[0.17]|
|High Income|-0.066|-0.068|-0.064|-0.067|-0.048|-0.058|-0.058|-0.067|
||[0.97]|[1.16]|[0.94]|[1.16]|[0.70]|[0.99]|[0.84]|[1.14]|
|Secondary Education|0.1|0.12|0.111|0.124|0.097|0.12|0.096|0.119|
||[2.09]**|[2.65]***|[2.31]**|[2.74]***|[2.02]**|[2.63]***|[1.99]**|[2.61]***|
|University Education|0.117|0.143|0.134|0.149|0.121|0.146|0.108|0.143|
||[2.27]**|[2.84]***|[2.62]***|[2.96]***|[2.36]**|[2.89]***|[2.09]**|[2.83]***|
|female|-0.059|0.016|-0.064|0.015|-0.059|0.016|-0.058|0.016|
||[2.23]**|[0.67]|[2.42]**|[0.63]|[2.25]**|[0.68]|[2.19]**|[0.68]|
|age|0.017|-0.001|0.017|-0.001|0.017|-0.001|0.016|-0.001|
||[2.40]**|[0.20]|[2.49]**|[0.17]|[2.47]**|[0.17]|[2.37]**|[0.16]|
|age2|0|0|0|0|0|0|0|0|
||[2.18]**|[0.28]|[2.27]**|[0.31]|[2.23]**|[0.30]|[2.17]**|[0.31]|
|Dishonesty institutions - Number|0.004|0.001|||0.004|0.001|0.004|0.001|
||[2.94]***|[0.62]|||[3.16]***|[0.75]|[2.94]***|[0.73]|
|Dishonesty institutions - Average|||-0.006<br>[0.42]|0.006<br>[0.49]|||||
|Trust in State Institutions|-0.004|-0.016|-0.015|-0.021|-0.004|-0.016|-0.002|-0.018|
||[0.36]|[1.48]|[1.20]|[1.86]*|[0.35]|[1.50]|[0.15]|[1.64]|
|Knowledge report corruption|0.044|0.027|0.043|0.026|0.049|0.029|0.045|0.024|
||[1.66]*|[1.14]|[1.63]|[1.09]|[1.84]*|[1.24]|[1.69]*|[1.03]|
|Member of association|0.05|0.027|0.052|0.028|||0.052|0.028|
||[4.64]***|[2.97]***|[4.82]***|[3.05]***|||[4.80]***|[3.01]***|
|Member of at least one association|||||0.106<br>[4.08]***|0.066<br>[2.87]***|||
|region==Lima|||||||0.002|-0.063|
||||||||[0.06]|[1.99]**|
|region==Costa|||||||0.106|-0.07|
||||||||[2.43]**|[1.93]*|
|region==Sierra|||||||0.018|-0.06|
||||||||[0.43]|[1.69]*|
|Observations|1558|1558|1558|1558|1558|1558|1558|1558|
|Pseudo-Rsq|0.04|0.02|0.03|0.02|0.03|0.02|0.04|0.02|

Absolute value of z statistics in brackets * significant at 10%; ** significant at 5%; *** significant at 1% 34
Policy Research Working Paper 7493

# **Abstract**

The subject of which firms are the key employers—and which of these create or destroy jobs at a faster rate—is eminently important for academics and policy makers. The relative importance of small versus large firms and old versus young firms has in particular been extensively debated and studied. Nevertheless, the results often hinge on the questions that are asked. Moreover, the categorical definitions used to define firm size and age, and the nature and coverage of the data used have important effects. This paper lays out the relevant definitions and metrics that are central to the debate, reviewing the main findings to date on the subject (with particular emphasis on results in developing economies). The paper adds updated results for 117 developing economies using the World Bank’s Enterprise Survey Data, finding that (i) small and medium enterprises and older establishments are the dominant employers in the nonagricultural private sector labor force in developing economies, and (ii) net job creation is negatively correlated with establishment age and, although the effect of size is also negative, its significance is sensitive to the definition and methods used.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dfrancis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
WHEN IS THE GOVERNMENT _TRANSFER_ MULTIPLIER LARGE?

25 out by the same amount (Equation 3.6) but only government purchases shift the aggregate supply curve (via the neoclassical wealth effect).<sup>30</sup> As in Proposition 1, government purchases increase the desired labour supply of the Ricardian household (because they are worse off due to higher taxes), but transfers have no effect on aggregate supply, because the extra labour supply of Ricardian households (who are worse off) exactly offsets the reduced labour supply of the HtM HH (who are better off). When the aggregate demand curve is downward sloping (for example with a high persistence shock, with _ρ_ = 0 _._ 9 and a Taylor Rule in the left-hand panel of Figure 3.1), the increase in aggregate supply from a government purchases shock increases the purchases multiplier above the transfer multiplier.

<!-- Start of picture text --> Agg supply & Demand; High Persistence Agg supply & Demand; Low Persistence<br> (Baseline; Supply=red, Demand=blue ) (Baseline; Supply=red, Demand=blue )<br>0.2 0.2<br>0.15 0.15 A2<br>Tr Tr<br>0.050.1 A1 G 0.050.1 B2 G<br>0 O1 B1 0 O2<br>−0.05 −0.05<br>−0.1 −0.1<br>−0.15 −0.15<br>−0.2 −0.2<br>−1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1<br>% Change Output % Change Output<br>Inflation Inflation<br><!-- End of picture text --> Figure 3.1. Aggregate demand and supply when the purchases multiplier is larger (LHS: high persistence of fiscal policy ( _ρ_ = 0 _._ 9) and when the transfer multiplier is larger (RHS: low persistence of fiscal policy ( _ρ_ = 0 _._ 5)). See footnote 29 for details.

However, when the Disposable Income effect dominates the Taylor Principle effect, the aggregate demand curve inverts and the transfer multiplier will be larger than the purchase multiplier (Proposition 3b).<sup>31</sup> This always happens when monetary policy is constrained by

> 30In Online Appendix 2.1, we show that the simple model A1-A9 in Box 1 is _equivalen_ t to assuming perfect substitutes across labour types with a union as in Galí _et al_ (2007). In Online Appendix 2.2 we assume instead that the labour of the two households are imperfect substitutes (with elasticity of substitution _θ_ ), _without_ a union. This reduces the transfer multiplier, and substantially complicates the analysis, though doesn’t change the results much so long as the elasticity of substitution between labour inputs is not too high. Imperfect substitutes means that the aggregate demand curve shifts by _less_ for a transfer than for a government purchase, because some of the income from the transfer is spent on leisure instead of consumption (this doesn’t happen in the Cobb-Douglas model because lower labour supply for a household is offset by higher wages, keeping labour income constant). As a result, the AD curve has to be even more inverted for the transfer multiplier to be greater than the purchase multiplier.

> 31The slope of the supply curve is always positive. It increases with (1 + _φ_ ) and decreases with _κ_ — this means that the supply curve is flatter (more Keynesian) with a higher Frisch elasticity or more sticky prices.
regional level more than tripled from 210 million tons in 1980 to 745 million tons in 2005 with a robust average annual growth rate of 5.2 percent.

**Figure 1: Trend of Transport Sector CO2 Emissions in Asia**

<!-- Start of picture text --> China India Indonesia Bangladesh Mongolia Sri Lanka<br>Korea Thailand Malaysia Pakistan Philippines Vietnam<br>350 30<br>300 25<br>250<br>20<br>200<br>15<br>150<br>10<br>100<br>5<br>50<br>0 0<br>Mt of CO2 Mt of CO2<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 1980 1983 1986 1989 1992 1995 1998 2001 2004<br><!-- End of picture text --> Note: These graphs are based on total transport sector CO2 emissions including those from international aviation, pipeline transport and all non-specified categories, and excluding emissions from electricity, which is assigned to the power sector. Hence, emissions presented in these graphs may differ from the data presented in Tables 2 and 3 where we exclude international aviation and pipeline transport, and include CO2 emissions from electricity used by the transport sector. Source: IEA (2007a) Despite the increase in transport sector emissions in absolute terms, the share of the sector in the national total in China and India are significantly smaller than that in most Asian countries. Table 1 presents total national CO2 emissions and the sectoral emission mix for the abovementioned 12 Asian countries. Transport sector shares of total national CO2 emissions have increased in Republic of Korea, the Philippines and Vietnam; decreased in India, Indonesia, Pakistan and Sri Lanka; and remained more or less stable in Bangladesh, China, Malaysia, Mongolia and Thailand. Because the transport, power and industry sectors are the main contributors to national CO2 emissions, changes in the magnitude of the emissions from the other two sectors, particularly the 6
Table 3 Log price and FIR: Spread heterogeneity

|Dependent varia|ble: log <br>Hi|price<br>gh spread|Low s|pread|
|---|---|---|---|---|
|FIR X Post|0.461|***<br>0.462|***<br>0.100|0.100|
||(0.136)|(0.135)|(0.088)|(0.088)|
|Bond FE|Yes|No|Yes|No|
|Month FE|Yes|No|Yes|No|
|Bond-month FE|No|Yes|No|Yes|
|Observations|53,364|53,364|53,774|53,774|
|N. of bonds|497|497|498|498|
|N. of countries|62|62|51|51|
|N. of clusters|1,249|1,249|895|895|
|F (frst stage)|594|2,333|451|963|

Note: This table presents 2SLS estimates of bond log prices on the FIR measure, instrumented by _Z_ , across rebalancing dates. The sample is divided into high-spread bonds in Columns 1 and 2, above the median stripped spread, and low-spread bonds in Columns 3 and 4, below the median. The sample period, the construction of five-day window, and the 2SLS procedure are identical to those described in Table 2. The estimation includes coefficients for _Post_ and _FIR_ but are not reported for brevity. Standard errors are clustered at the country-month level. *, **, and *** denote statistically significant at the 10%, 5%, and 1% level, respectively.

precisely with the role of our _β_ coefficient in Equation (10). Based on the estimates in Table 2 (last column), the inverse demand elasticity is _−_ 0 _._ 33, implying a demand elasticity of _−_ 3. These estimates are in the ballpark of other estimates in the literature for other financial markets and assets (Appendix Figure 5).

As shown in Section 2, the previous estimates do not capture a structural elasticity because the default risk and expected payoffs of bonds may be affected by the shocks. Thus, part of the documented price reaction could be driven by these endogenous responses and not by a downward-sloping demand curve (as shown in Equation (6)). One way to assess the magnitude of these endogenous effects is to quantify how changes in the FIR affect a country’s default risk. To this end, we use credit default swaps (CDS) as a proxy for default risk. We find that increases in the FIR tend to decrease CDS spreads (see Appendix Table 5). The estimates imply that, for the median CDS spread in the sample (about 190 basis points), a 1 p.p. increase in the FIR decreases CDS spreads by 1 _._ 5 basis points.

20
the _SAH_ as a smooth but unknown function of real expenditure with the control variables entering linearly in parameters.<sup>7</sup> We use the STATA program for estimating the PLM written by Lokshin (2006). While this method allows a relatively flexible representation of the income effect, it has the disadvantage that it ignores the fact that our dependent variables come in the form of ordinal qualitative variables for which it cannot be assumed that the difference between (say) “very good” and “good” is the same in terms of health status as the difference between “good” and “average”. To address this concern, we follow past work in the literature in interpreting the answers to the subjective health question as an ordinal, categorical, summary of unobserved true health status, which is assumed to be a continuous variable that is determined by a vector of specific health attributes and an i.i.d. error term. In modeling the income effect on health we include a cubic function of real expenditure. Assuming level comparability of _SAH_ (conditional on covariates) across persons and that the error term is normally distributed we can use an Ordered Probit (OP) to model the responses. We estimate similar models for _SAW_ , where the latent continuous variable is interpreted as individual utility. We will allow for level noncomparability of subjective scales by adding controls for likely socio-economic covariates of reporting bias, following Thomas and Frankenberg (2000). We also allow for a latent individual effect using panel data, though this requires modifications to the econometric specification, for which we use the method of Ravallion and Lokshin (2001).

Our use of objective indicators of economic welfare, based on household expenditures and personal incomes, as regressors raises concerns about their possible endogeneity. The most likely way this could happen appears to be through work effort, based on the argument that latent

> 7 The parameters on the control variables are estimated by first ordering the data in terms of real expenditures and then taking differences to eliminate the nonparametric sub-function; then one runs a nonparametric regression of the _SAH_ scores net of the controls on real expenditure.  (We used Fan regressions for the nonparametric function.) For a review of PLMs and related topics see Yatchew (1998).

10
**INTROOUCTION LXXXV**

<!-- Start of picture text --> Fgum Al Asumed Life Expectancy Figure A2 Assumed Life Expectancy<br>Trend, Len Developed Countres, and Trend, More Developed Countres, and<br>U.N and Prevows World Bank Trends U.N. and Previous World Bank Trends<br>72 ~~~~~~~~~~~~~~~~~~~~~~~~83 -<br>71- - 2 -,<br>70 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~9-<br>661<br>65 -<br>62 7<br>us9-Ig.05 SI30 ,02O0 2 20005 on io 15 M2ts 2020-2 tsss. 1 19.5 1995-^ 20 805 25-10 2010.15 2015-20 20&2.5<br><!-- End of picture text --> Countries in the pretransition stage are assumed to start a fertility transition<sup>in the</sup> quinquennium after combined male and female life expectanc.y reaches 50 years, but in no case later than 2005. This life expectancy threshold is slightly lower than the threshold of 53<sup>years previousl)</sup> shown to be a universal precondition, though not a sufficient condition, for<sup>the start of fertility</sup> transition (Bulatao and Elwan 1985). Until a transition starts, total fertility is assumed<sup>to be constant.</sup> The one exception is where sterility is a significant factor; sterility is then assumed to<sup>decline linearly</sup> to 6 percent over three quinquennia, with each percentage point drop in sterility<sup>raising total fertility</sup> by 0. 11 points (Frank 1983).

In the transition stage, the rate of fertility decline is based on the rate in the preceding quinquennium, assuming transition had already started in that period. With previous<sup>annual change</sup> represented by Am, annual change during the transition is set at (-.05 + .5 ATM).<sup>However, limits</sup> are set on this change: it must be at least -.073 points and at most -.210 points,<sup>which may be taken</sup> to define slow and rapid fertility decline. (Previous analysis in Bulatao and Elwan<sup>1985 defined rapid</sup> decline **as** an annual total fertility change of -.2164 points, which is consistent.)<sup>If the formula does</sup> not apply because transition has just started, -n average annual decline in total fertility<sup>of 0.102 points</sup> is imposed. Figure A3 shows the patterns of fertility decline implied by<sup>this average decline and by</sup> the slow and rapid limits. In the early part of the transition stage, the sterility adjustment<sup>is applied</sup> if appropriate to raise total fertility slightly.

In the late-transition and posttransition stage, fertility approaches replacement level,<sup>either</sup> from slightly above or slightlv below it. Four alternative patterns, represented in<sup>Figure A4, Pre</sup> applied in this stage.

*eplw Generally, a geometric function is imposed on total fertility decline<sup>from one point above</sup> replacerent level to replacement level, with this decline assumed to take<sup>15years.</sup>
# **_Capital outflows from Sub-Saharan Africa have decreased in recent years, but the stock of flight capital abroad remains high_**

Capital outflows from Sub-Saharan African countries averaged $8.1 billion annually from 1990 to 2005.<sup>20</sup> Capital outflows from Sub-Saharan Africa increased until 2002 but have declined in recent years (figure 3). The cumulated stock of outflows from Sub-Saharan African countries was $178 billion in 2006, nearly 30 percent of GDP—down from a high of 51 percent of GDP in 2002. Capital outflows increased faster from middle-income and resource-rich SubSaharan African countries in the 1990s, reaching 59 percent of GDP in 2002 (figure 3).<sup>21</sup> Figure 3. Capital Outflows from Sub-Saharan Africa have declined recently <!-- Start of picture text --> % of GDP<br>SSA_MI<br>60<br>SSA_LI<br>40<br>20<br>LICs (excl. India)<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank 2007a.

Several studies have identified a number of factors that encourage capital flight from Africa (see for example, Ajayi 1997; Boyce and Ndikumana 2001; Collier, Hoeffler, and Pattillo 2001; Hermes et al. 2002; Ndikumana and Boyce 2002; Powell, Ratha, and Mohapatra 2002; Salisu 2005; World Bank 2004). Some of the main determinants of capital flight include macroeconomic instability, political instability, external borrowing, risk-adjusted rates of return differentials, and financial development, among others. Consistent with the view of outflows as a portfolio diversification choice (Collier, Hoeffler, and Pattillo 2004), the stock of cumulated capital outflows appears to be negatively related to the country performance rating including corruption, economic management and transparency (figure 4).

> 20 See Powell, Ratha and Mohapatra (2002) and World Bank (2002) for the construction of capital outflows as the difference between sources and uses of funds in the International Monetary Fund Balance of Payments Statistics. 21 Average annual capital outflows from Nigeria have been in the range of $2.5 billion since the late 1980s.

14
1997, Johnson, Kaufmann and Zoido-Lobaton, 1998a,b, Loayza, Oviedo and Serven, 2005).<sup>4</sup> The modern micro literature on informal labor markets (e.g., Maloney, 2004) suggests that we should look at the formal and informal sectors in an integrated way (as opposed to a segmented view of the labor market) and emphasizes the role of the informal sector as a source of unregulated labor to fi rms. This is the basis of the economic reasoning underlying our work.

The paper is organized as follows. In section 2 we describe the Brazilian labor market, the enforcement of regulation in Brazil, and a simple model that guides our empirical work. Section 3 describes the data. In Section 4 we analyze the relationship between enforcement, informal employment and fi rm performance. In section 5 we present estimates of the importance of labor fl exibility for fi rm productivity and wages. Section 6 concludes.

# 2. Background

The Brazilian labor market has a large number of unregistered workers. According to the Brazilian National Statistical Institute (IBGE), there were 36 million wage earners in the private sector in 1999, 40% of which were informal workers.<sup>5</sup> Brazil also has one of the most highly regulated labor markets in the world.<sup>6</sup> In a recent paper Botero, Djankov, La Porta, Lopez-de-Silanes and Shleifer (2003) document that Brazil has the highest score in an index of strictness of employment law. Since we expect to fi nd higher levels of non-compliance with the law in the presence of strict regulation (Loayza, Oviedo and Serven, 2005), it is not surprising that the level of labor informality in Brazil is so high. In this section we brie fl y 4Most of the cross country studies fi nd that income tax and social security burden are important determinants of the informal economy. The larger is the difference between total cost of formal and informal labor, the greater the incentive to hire informal workers (e.g., Loayza, 1997 and Reinhard, Hofreither and Schneider, 1989). Loayza, Oviedo and Serven (2005) show that labor regulations are positively related to the size of the informal sector but that the strength and efficiency of government institutions are negatively related to it.

> 5Informal workers here include self-employed workers, non-paid workers and the employers and employees in small fi rms. In our empirical work we de fi ne informal workers those that do not have their labor contract registered in a work permit (we ignore self-employment).

> 6In the early 1980s informal workers only accounted for 30% of the workforce. During the 1990s, there was a large increase in informality following several market oriented reforms (like privatizations and trade liberalization) and the new federal constitution in 1988, which greatly reinforced job security regulation. Barros and Corseuil (2001) describe the current rules governing worker’s rights and the organization of unions.

5
<!-- Start of picture text --> 40<br>20 BDI<br>KEN<br>QWeaA...-......54--------= A. (------------------------BWA_-__----------<br>Ta AGO<br>-20<br>CIV<br>a<br>(LIF PE IR Al NC CR<br><!-- End of picture text --> <!-- Start of picture text --> @ Electricity production from oil sources (% of total)<br>Fossil fuel energy consumption (% of total)<br>Electricity production from oil, gas & coal sources (% of total)<br># Renewable energy consumption (% of total final energy consumption)<br>Renewable electricity output (% of total electricity output)<br><!-- End of picture text -->