rafmacalaba/datause-encoder-data huggingface dataset:
adversely affect them compared to domestic firms, which likely have better ongoing relationships with the local governments in charge of various permits.

# **Further analysis of how foreign-owned firms and domestic firms respond to the BE**

We further gauge how foreign-owned firms (“Foreign”) and domestic firms respond to the BE differently by linking a firm’s _individual_ answers in the Enterprise Survey to the _country-level_ indicators of the business environment based on either the Global Competitiveness or _Doing Business_ surveys. This exercise, when combined with our earlier findings on the links between the business environment and firm performance, allows us to understand whether foreignowned and domestic firms respond differently to the BE due to differences in their endowment and/or returns. To this end, we allow this link to differ by whether the firm is foreign owned (“Foreign”). That is, Responsei i = α1 ⋅BEk + α2 ⋅BE × Foreign + X β1 + Wi β2 + θ + η + εi i . (2) We do this one aspect at a time to avoid multicollinearity. For instance, we regress a firm’s ii ii ii ii ii ii response to the Enterprise Survey indicator of Corruption Obstacle to the Global Competitiveness indicator on Corporate Ethics, conditioning on country and firm controls, industry and year fixed effects. The results are presented in table 8.

_Government Protection–corruption_ . Foreign-owned and domestic firms are similarly hampered by a low level of the Corporate Ethics measure, as shown by the similar magnitudes of the coefficient of the Global Competitiveness Corporate Ethics indicator in explaining the Enterprise Survey’s Corruption Obstacle indicator (0.151 versus 0.122). This is consistent with our earlier finding that foreign firms do not differ significantly from domestic firms in the effect of corruption on their firm performance.

_Government Protection–safety_ . Foreign-owned firms appear to be much more sensitive to physical safety. When the Global Competitiveness Police System score is lower—that is, when the host country’s physical safety is worse—foreign-owned firms increase their security costs significantly more: 33 percent more (that is, -0.004 versus -0.003). This is consistent with the notion that because the return to physical safety is higher for foreign-owned firms (as demonstrated by a higher return from reducing Security Costs for foreign firms for log labor productivity, LP), they increase their investment in safety more when the local environment is worse.

_Human Capital_ . Similarly, foreign-owned and domestic firms do not differ significantly in their perception of skill obstacles (the Enterprise Survey’s Skill Obstacle mesure) when facing 20
# **B.  POLICY RIGIDITY AND THE 2002 CRISIS**

15. In sharp contrast with the 1998-1999 adjustment, Brazil’s fiscal policy did not react to the shocks in early 2002. This policy rigidity compounded uncertainty arising from other sources and led to asset price changes that complicated the situation even more. The government’s commitment to maintain a constant primary surplus seemed to falter as the primary balance declined during the first semester (Figure 2) amidst a heated political debate on the stance of future fiscal policy.

**Figure 2 Primary Fiscal Balance of the Public Sector 2000-2003 (as a percentage of GDP)**

<!-- Start of picture text --> 4.8<br>4.4<br>4.0<br>3.6<br>3.2<br>2000 2001 2002 2003<br><!-- End of picture text --> Source: Bacen, Boletin Estadistico, several issues 16. The rigidity of fiscal policy may have been at the root of the 2002 crisis. Inflexibility was the result of both structural factors and transitory circumstances. The structural inflexibility of the budget exists in both expenditures and revenues: on the expenditure side, rigidity arises from constitutional mandates and entitlements, while on the income side, it emanates from revenue sharing with the states and earmarking. More than 70 percent of primary expenditure is composed of social security benefits and wages (Velloso, 2002), while more than 80 percent of federal government revenues are earmarked or subject to revenue sharing (Ministerio de Planejamiento, 2003). This legacy severely limited the options of fiscal adjustment to cutting capital expenditures or raising revenues, both of which have limits. International experience shows that successful fiscal adjustments, that is, those that are permanent, rely more on current expenditure cuts than on revenue hikes or capital spending reduction (Alesina and Peroti, 1996). Given that, by 2001, the primary surplus had already reached 3.8 percent of GDP, with capital expenditures dropping to historically low levels and public sector revenues reaching extraordinarily high echelons, it was difficult to increase public savings without compromising the quality of the adjustment.

17. Fiscal policy rigidity was also due to the short-term effect of the October presidential elections. The government’s coalition had weakened because of internal disputes in anticipation of the presidential election Additionally, corruption allegations in 8
Subsequently, we use the primary data from these 190x100 and 190x61 matrices, for 2000 and 1990 respectively, to make out-of-sample predictions for those destination countries for which data are missing. Taken together, the raw and imputed data comprise 190 countries in 1990 and 2000 (denoted by _j_ = 1 _, ..., J_ ) and include stocks of migrants aged 25 and above. This cutoff is chosen so as to omit students and children since our focus is upon labor migration. The full data set in turn facilitates, for the first time, a global analysis of human capital mobility over time using nuanced and improved Brain Drain indicators as described in Section 4.

# **2.1 Migration Data for OECD Countries**

Our starting point when constructing our matrices is the Docquier, Lowell, Marfouk (DLM) data set, which comprises a collection of census and register data by country of birth, education level and gender for OECD countries in 1990 and 2000. The original DLM data set omitted data for member states that subsequently joined the OECD in 2010 however (namely Chile, Estonia, Israel and Slovenia) and so in this paper, we augment the original OECD data from the DLM data set with census data pertaining to these newer members. As a result, our set of OECD countries includes all 34 current members for both 1990 and 2000. Data sources for these four destination countries are presented in Table A1.

DLM enumerates stocks of migrants living in a destination country at the time of the census, as opposed to flows that are observed between two points in time. For reasons of consistency and comparability, the four methodological choices made in DLM guide our 9
9 These require that consumption or income be converted into the unit used for the poverty line. The World Bank uses poverty lines in international dollars, so expenditures have to be converted to 2011 prices using the country’s consumer price index (CPI) and then into 2011 international (PPP) dollars. The International Comparison Program is an important source of data on PPPs. CPI and PPP series are available from the World Bank and IMF databases.

## <u>Computation</u>

The computation of the first two measures – expenditure in absolute terms and dispersion – is straightforward, the only complication being that Q90/Q50 is undefined if median out-of-pocket expenditures are zero, which is quite possible. The computation of the budget share is also straightforward, the only complication being what to do if the denominator is negative, which is possible if it is household income. Progressivity is easily computed as the difference between the Gini coefficient (for income or consumption) and the concentration index for out-of-pocket expenditures, both of which can be computed straightforwardly in Stata using any user-developed section that computes inequality measures such as CONINDEX for Stata (O'Donnell et al. 2015) and INEQ for R (Zeileis and Kleiber 2014). The incidence of and inequality in catastrophic expenditures can both be computed using the Stata module FPRO (Eozenou and Wagstaff 2018), as can the headcountand poverty gap-based measures of impoverishment. Further guidance on the computation issues is provided in O’Donnell et al. (2008).

# **New global estimates of out-of-pocket health expenditures – data**

In the rest of the paper we present new global estimates of out-of-pocket health expenditures for each of the measures listed in Table 1. We present in this section details of the data we use.

Our data are drawn from the 2019 version of the World Bank’s Health Equity and Financial Protection Indicators (HEFPI) database (Wagstaff et al. 2018a; Wagstaff et al. 2019). In generating the database, we identified potentially suitable surveys by searching the above-mentioned microdata catalogs and multi-country survey collections. In some cases, the surveys were not accessible to us. In others, the data were accessible, but the surveys turned out to lack key information. Sometimes the data sets we analyzed were different adaptations of the same survey – for example, as mentioned above, Romania’s HBS has been harmonized by Eurostat and the World Bank’s ECAPOV exercise. We identified 1,948 potentially suitable surveys or adaptations thereof from 182 countries. We analyzed and obtained out-of-pocket expenditure estimates for 1,000 of these surveys, covering 154 countries.

Our out-of-pocket expenditure estimates vary in how they are computed. In some cases, the survey asks only in the consumption or expenditure section about out-of-
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32
|**Appendix 1 : List of variables**|
|---|
|gy (year T) (year t)<br>Geometric average growth rate of real GRDP per capita from year t<br>and year T. The base year for the real GRDP data is 1993|
|Ln_PCY(year t)<br>Log value of real GRDP per capita.|
|GAW (year T) (year t)<br>Weighted average growth of neighboring districts from year t and<br>year T.|
|PRIM(year t)<br>Share people ever/being in primary school per total population<br>(Susenas)|
|SECJ(year t)<br>Share people ever/being in secondary junior high school per total<br>population (Susenas)|
|SECH(year t)<br>Share people ever/being in secondary senior high school per total<br>population (Susenas)|
|sh_urban(year t)<br>Share of people who live in urban area (Susenas)|
|shagr<br>Share of agriculture sector to total GRDP|
|shmin<br>Share of mining sector to total GRDP|
|shman<br>Share of manufacture sector to total GRDP|
|shenr<br>Share of electricity, gas, water supply sector to total GRDP|
|shcon<br>Share of construction sector to total GRDP|
|shtrd<br>Share of trade, restaurant, hotel sector to total GRDP|
|shtrs<br>Share of transportation and communication sector to total GRDP|
|shfin<br>Share of financial sector to total GRDP|
|shser<br>Share of service sector to total GRDP|
|MIGAS2<br>Dummy variable for district that has oil & gas as main sector in all<br>years|
|jarak_prop / big<br>Distance to provincial capital / one of the five big cities (Jakarta,<br>Surabaya, Medan, Batam and Makassar)|
|dlaut_prop / big<br>Dummy if need to use boat to get to provincial capital / one of the five<br>big cities (Jakarta, Surabaya, Medan, Batam and Makassar)|
|TELPHH(year t)<br>Share of households with telephone connection in year t (Podes)|
|SH_road_asphalt (year t)<br>Share of villages with asphalt road in year t (Podes)|
|NTP (year t)<br>Farmer’s Terms of Trade year t (base year 1993)|
|D_island<br>Dummy for big island group|
|GOVEXP (year t)<br>Ratio of government expenditure to total GRDP|
|ELF<br>Ethnic Linguistic Fragmentation|
|RF<br>Religion Fragmentation|
|Demind (year t)<br>Dummy if head of district is democratically indirectly elected|

29
**Table 3: Poverty Measures in 2005 and 2008 with Corrected Standard Errors***

||||**2**<br>|**005**<br>|||||**20**<br>|**08**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||P|0|P|1|P|2|P|0|P|1||P2|
||Est.|St.Er.|Est.|St.Er.|Est.|St.Er.|Est.|St.Er.|Est.|St.Er.|Est.|St.Er.|
|Urban|13.2|3.3|2.7|1.1|1.0|0.5|8.6|1.8|1.8|0.5|0.6|0.2|
|Rural|31.1|2.9|5.8|0.8|1.6|0.3|26.5|2.8|5.5|0.8|1.8|0.3|
|**All Egypt**|**23.4**|**2.2**|**4.5**|**0.7**|**1.3**|**0.3**|**18.9**|**1.8**|**4.0**|**0.5**|**1.3**|**0.2**|

_Note: all measures in percentages, P0-headcount, P1-poverty gap, P2- severity of poverty (squared P1). Table uses actual household size in 2005 and 2008 to weight data together with households sampling weights. The frontier regions are excluded from regional disaggregation due to their small sample size, yet they are included in other national averages. Source: Authors estimates based on HIECPS 2005-2008._ Indices sensitive to distribution show less impressive poverty reduction. Poverty gap (P1), and severity of poverty (P2) capture the degree of poverty as experienced by the poor. Table 3 demonstrates that assessment using changes in P2 shows no reduction in poverty, with a clear worsening in rural areas. Indeed during this period the ranks of the extreme poor – those consuming less than the cost of the subsistence food basket - swelled by 1.1 million, entirely due to the sharp increase of incidence of extreme poverty in rural areas. Hence, growth in the period 2005-2008 was not beneficial to the extreme poor, i.e. it was not pro-poor in a strict sense.

Table 4 further illustrates the bias against the poor of the growth during 2005-2008 by listing several inequality indices and dissecting the distribution. This is done by presenting percentiles of distribution: for example, p25/p10 is ratio of consumption between those who are at the 25th percentile and those who are on the 10th percentile (that is, poorer). Table 4 shows a widening of distribution at the top (p75/p50 and p90/p50 measure) and at the bottom (p25/p10 measure) – the latter exclusively in rural areas.

11
# **_2.2 Human Capital, Persistence, and Finance_**

# **2.2.1 Human capital investment**

We begin with wage earnings, which account for about 70% of the persistence of total earnings across generations in the United States and elsewhere. We first emphasize the role of finance in shaping the persistence of inequality through human capital accumulation. Then, we discuss recent work on how financial market imperfections affect cross-dynasty wage differences even among individuals with the same human capital.

Building on Becker and Tomes (1979, 1986), researchers examine the decision by parents to invest in the human capital of their children and how this determines the persistence of relative incomes across generations. Let h(i,t) = h(e(i,t), s(i,t)), where e(i,t) is the dynastic endowment of ability and s(i,t) is investment in human capital accumulation (schooling). Further assume that ability endowments and schooling are complementary inputs into human capital production, so that ∂<sup>2</sup> h/ ∂ e ∂ s > 0. Thus, it is socially efficient for kids with high ability endowments to receive the most schooling. If one assumes that ability endowments across dynasties are mean-reverting, then ability can be specified as e(i,t) = ρ e(i,t-1) + ε (i,t), where 0 ≤ ρ < 1, and ε (i,t) is the random component of individual ability. The children of high ability parents tend to have greater abilities than children from low ability parents, but the relative difference in ability tends to shrink from generation to generation.

With perfect credit markets, high ability people get schooling irrespective of parental wealth. Human capital, h(i,t), is only a function of ability, e(i,t), and the economy attains the socially efficient allocation of schooling, s(i,t). That is, an individual’s economic opportunities are determined solely by her abilities, not by parental wealth. Since ability regresses to the mean and individuals can borrow to finance education, initial dynastic wealth differences tend not to persist.

Imperfect credit markets increase the persistence of cross-dynasty differences in human capital, with corresponding implications for the persistence of cross-dynastic differences in income and wealth. Even if ability tends to regress toward the mean, there will be a slower reduction in cross-dynasty human capital differences if access to schooling is constrained by parental wealth so that s(i,t) is a function of a(i,t-1) and h(i,t) = h[e(i,t), a(i,t-1)]. Children from 7
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Zambia already spends a sizeable $0.7 billion per year to meet infrastructure needs (table 11). (Due to the nonavailability of financial statements from the ZAMTEL, the state-owned telecommunications incumbent, these figures represent a lower limit for the level of infrastructure spending in the country.) About 65 percent of the recorded total is allocated toward capital expenditure and 35 percent toward operating expenditures. Two-thirds of total spending is domestically financed, coming from the pockets of Zambian taxpayers and users of utility services. Focusing on infrastructure investment alone, about half is funded by the public budget and the remaining half by a range of external financiers—primarily private investors and official development assistance (ODA) partners. Zambia receives relatively little infrastructure investment from countries outside the Organisation for Economic Co-operation and Development (OECD). Private finance goes almost entirely to the ICT sector, while ODA is evenly split between the transport and WSS sectors.

**Table 11. Existing financing flows for infrastructure,** **<u>average, 2001–06</u>**

|Sector||||$m per year||||
|---|---|---|---|---|---|---|---|
||O&M<br>Public sector|Public sector|<br>ODA|Capital expenditure<br>Non-OECD<br>financiers|PPI|Total CAPEX|Total<br>spending|
|ICT|n.a.|n.a.|1|0|89|90|>90|
|Power|99|70|2|8|0|81|180|
|Transport|99|85|52|6|3|145|245|
|WSS|35|67|47|1|9|123|158|
|Total|233|224|99|15|101|439|>673|

_Source:_ Briceño-Garmendia, Smits, and Foster 2008. _Note:_ Due to nonavailability of ZAMTEL financial statements the data represent a lower bound on total spending. PPI = private participation in infrastructure; OECD = Organisation for Economic Co-operation and Development; n.a. = not applicable.

25
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

|**Table 3: Doing Busines**|**s Rankings for**|**Sub-Saharan**|**Africa**|
|---|---|---|---|
||**Mean***|**Minimum**|**Maximum**|
|General rank|135.4|27|178|
|Trade regulation rank|129.9|16|178|
|Entry regulation rank|125.3|8|178|
|Land registration rank|123.2|26|173|
|Bankruptcy regulation rank|121.3|26|178|
|Labor regulation rank|116.4|10|176|
|Contract enforcement rank|115|33|176|
|Access to credit rank|112.8|13|176|
|Licensing regulation rank|112.5|9|178|
|Investor Protection rank|111|9|175|
|Taxation regulation rank|104.6|11|176|
|*Mean over 46 SSA countries.<br>Source: DoingBusiness||||

31
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