rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4. Health inequalities in the demand-for-health framework**

<!-- Start of picture text --> Health (H)<br>Mec\cal care (M) Corsumption (C)<br>Camunpticn (C)<br><!-- End of picture text --> One simple and obvious prediction of this framework is that increases in income inequality will result in higher levels of health inequality. This is shown in **Figure** 5, where the poorer individual starts on the budget line BCAO and the richer starts on BCco. Suppose then income is taken from the richer of the two and given to the poorer, and this continues until they end up with the same income-the average income before the redistribution. This puts them both on the budget line BCBO, with the same health HBO. Eliminating income inequality-but keeping average income unchanged-has eliminated health inequality, since it is only in their incomes that these individuals have been assumed to differ. So, controlling for other relevant influences on health inequality, we would expect to find that countries with unequal income distributions also have unequal distributions of health across income groups.

_12_
Now consider a direct effect, of magnitude _ρ >_ 0, of mother’s height on child’s height, so _hc_ = _gc_ + _ρhm_ + _ec_ . To avoid unilluminating complications of infinite regress, ignore this mechanism in mothers’ generation, so _hm_ = _gm_ + _em_ .<sup>25</sup> Now mother-child height correlation is which will be greater than in the case without _ρ_ because of the additional link between generations. Therefore, if conditions are such that _ρ_ is quantitatively important in a poor country – perhaps due to rapid change across generations in environmental conditions – but not in a richer country, the intergenerational height correlation could be greater in the poorer country.

Figure 8 plots mother-child height correlations from the Indian and African DHS samples used in this sample, and for the U.S. from the National Longitudinal Study of Youth 1979. NLSY data used span several calendar years; each child’s height is used in the year he or she is five years old. The correlation computed from the NLSY, 0.41, is comparable to other correlations computed from developed country data (Livson et al., 1962). It is also consistent with the formula above, if _ω_ = 0 _._ 5 and _σe_<sup>2issmall.</sup> Two conclusions emerge from the figure. First, the correlation computed for Indian children – although greater than that in the African data – is much below the correlation for U.S. children, suggesting that any _ρ_ , a direct causal effect of mother’s height on children’s height, may not be large. Second, the correlation between mothers’ and children’s height is lower, within both India and Africa, in villages with more open defecation, which is consistent with an important role for _σe_<sup>2andwithMartorelletal.’s(1977)conjecturethat</sup> environmental variation will be more important relative to genetics in poorer places.

> 25Perhaps environmental conditions were so consistently bad in past generations that intrauterine restriction on fetal growth due to large intergenerational differences in environmental conditions were not a binding constraint.

25
Figure 1 displays the relation between female (left panel) and male (right panel) labor force participation and inequality. The two do not show a common trend; while the female one is, as expected, concave, whereas the one for males is almost linear: thus, increasing levels of male labor force participation goes hand in hand with increasing levels of inequality.

Figure 2 plots only the FLFP’s results of Figure 1, evidencing the different clusters of countries characterized by a similar FLFP/inequality gradient. From left to right in the top row, the developing countries being focused on, notably those in the MENA, SA and LAC regions, are visible. MENA and SA countries are characterized by similar (and low) levels of FLFP but also by relatively low inequality levels; in the inverted U-shaped curve they are positioned in the bottom left-hand part. LAC countries show, on average, higher levels of FLFP; however, at the same time they also display higher inequality, and their position on the curve is just below the turning point. This preliminary evidence shows that these three groups of countries have a relatively lower FLFP, and that marginal increases in FLFP tend to increase inequality, and also that the increase is much stronger among MENA and SA countries but more attenuated for LAC countries.

The picture is more heterogenous for developed countries, as shown by the quadrants in the lower row of Figure 2 show. For two of three country groups the gradient is negative, and positive for the remaining group. These three groups are the Scandinavians, with very high FLFP levels (the highest in the developed world) and low inequality; these countries are known for their very efficient and egalitarian fiscal policies and strong state support for working mothers. With a much lower level of FLFP (10 percentage points below the Scandinavians), but higher levels of inequality, include the following countries; the UK, the Netherlands, and Ireland, followed by the Euro-Mediterranean group, where FLFP is closer to that of Latin American countries (and in some cases lower, as in the case of Italy and Greece), but the inequality levels are much lower because they enjoy much more developed redistributive fiscal systems.

In Figure 3 the same relation is displayed separately by income levels with female participation. Here the FLFP concave relation is more pronounced for the middle-income group, while for those in the lower income group the relation is almost linear and sloped upward, and for those in the high income set the relation is almost linear but sloped downward. Notably both Kosovo and Turkey, despite being categorized as middle-income countries, show strong similarities with countries in the MENA and SA regions in terms of FLFP and inequality.

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THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE figures for 2009 show that the total number of seats grew by about 50 percent compared to 2007 levels. In particular, the international seats more than doubled between 2007 and 2009 (figure 8a). Connectivity is recovering, evident in the increase in city pairs being served within Africa. Between 2001 and 2007, the city pairs served dropped from 11 to 3. Preliminary numbers show that in 2009 city pairs went up to 6 (figure 8b).

**Figure 8. Evolution of seats and city pairs in the Central African Republic**

<!-- Start of picture text --> a. Seats b. City pairs<br>160,000 12<br>140,000 10<br>120,000<br>8<br>100,000<br>80,000 6<br>60,000<br>4<br>40,000<br>20,000 2<br>- 0<br>2,001 2,004 2,007 2009 (Est) 2,001 2,004 2,007 2009 (Est)<br>Total International Intercontinental excluding flights between NA and SSA Total International Intercontinental<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems Carriers in the CAR are using modernized air fleets. Air France and Libya’s Air Afriquia, the only air carriers in the CAR, have modernized 100 percent of their fleets, and as of today, the CAR is served by the newest fleet in the region (table 5).

**Table 5. Air transport indicators, benchmarked against select other countries**

|Country|Central<br>African<br>Republic|Chad|Congo,<br>Dem.<br>Rep. of|Cameroon|Niger|Congo, Rep.<br>of|
|---|---|---|---|---|---|---|
|Domestic seats (seats per year)|—|—|327,988|105,742|—|443,634|
|Seats for international travel within Africa (seats per year)|20,661|109,074|468,217|472,089|128,414|351,882|
|Seats for intercontinental travel (seats per year)|23,842|88,608|193,414|398,034|41,717|117,962|
|Seats available per capita|0.010|0.018|0.016|0.051|0.012|0.24|
|Herfindahl-Hirschmann Index—air transport market (%)|50.26|36.35|22.65|10.18|18.97|30.79|
|Percent of seat-km in newer aircraft|100|99.5|74.7|91.8|94.3|73.3|
|Percent of seat-km in medium or smaller aircraft|23.5|93.6|39.3|31.9|44.9|50.5|
|Percent of carriers passing IATA/IOSA audit|0|0|0|0|0|0|
|FAA/IASA audit status|No audit|No audit|Failed|No audit|No audit|No audit|

_Source:_ Bofinger 2009. All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of worldwide traffic, but a higher percentage of African traffic is not captured by the data. _Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent. FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit.

— = Data not available.

17
public expenditure only – it is an issue of overall investment in physical as well as human infrastructure. Some existing studies, such as Jimenez (1994), Devarajan et al. (1996), also point out this research gap. There might not be a generic answer to this question. The optimal mix of investments between the physical infrastructure and human capital could vary across countries, depending on their specific situations. It would, therefore, be interesting to investigate this variation and provide insights to policy makers.

# **_6.6 Long-Run Impacts of Infrastructure Investment_**

Panel data studies such as Calderón and Servén (2010) pick up the combined effects of infrastructure adjustment processes over time for countries with differing initial economic and institutional levels of development. Such studies can be used to assess statistically both the impact effects of changes in infrastructure development and longer-term effects. However, such research does not in itself provide insight on how infrastructure affects development over the longer-term. Experimental studies (e.g., Lenz et al. 2017) can provide insight on short-term impacts of infrastructure changes for certain users but have less to say about the longer-term impacts across the economy. Long-term structural models can provide that information, but such models are more complex to build and estimate, and the literature contains only a few exceptions (see, e.g., Roberts et al. 2012).

# **7. Conclusion**

Developing countries are facing a crucial question: what to prioritize when the financial resources needed for economic development are limited. Should they invest more in physical infrastructures, such as roads, rail, electricity supply systems, or should they focus on services, such as health care and education? Researchers have attempted to address this question through empirical analysis over the past three decades. This paper aims to review the literature and summarize the insights coming from the existing studies. Both types of studies -- those analyzing the impacts of public investment in general, and those examining the effects of physical infrastructure, in particular, are considered.

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<!-- Start of picture text --> Workplaces Grocery and Pharmacy<br>fo)<br>N° fo}“ /~~ A<br>oO oO<br>© t<br>2020m1 2020m4 2020m7 2020m10 2021m1 2020m1 2020m4 2020m7 2020m10 2021m1<br>Date Date<br>Residential Retail and Recreation<br>w<br>Ns SN oOoy QQ GS=_———<br>S ! N<br>[~~<br>WSs 8<br>io SS= $ Zz<br>2020m1 2020m4 2020m7 2020m10 2021m1 2020m1 2020m4 2020m7 2020m10 2021m1<br>Date Date<br>——— East Asia and Pacific ————_ Europe and Central Asia<br>————__ Latin America and Caribbean ——————._ Middle East and North Africa<br>——— North America ——— South Asia<br>————— Sub-Saharan Africa<br><!-- End of picture text -->
application of the decree described in Section 5, and estimate equation (6) via 2SLS using the following first stage: After pre-trial starts _i,j,t ×_ Fast pre-trial _i,j_ = _γ_ 1After pre-trial starts _i,j,t ×_ After decree _ij_ For this IV estimate to be consistent, we need a first stage ( _Cov_ (After pre-trial starts _i,t ×_ Fast pre-trial _i,_ After pre-trial starts _i,t×_ After decree _i_ ) = 0; Panel C, Figure 3 and col 4, Table 8) and we need to assume exogeneity of the instrument ( _Cov_ ( _εi,j,t,_ After pre-trial starts _i,t ×_ After decree _i_ ) = 0), that is, that the decree application did not affect short-term firm outcomes through other channels than the increase in probability of cases completing pre-trial in under four months.

# **6.1.2 Results**

# _Pre-trial entry_

Do firms experience an economic slowdown at the outset of a court case? Estimating (4) produces striking results (Panel A, Figure 9): entering pre-trial is associated with a sharp reduction in firm revenues as reported in their VAT filing for the month immediately following the first pre-trial hearing (Figure 9). One concern with the interpretation of this finding is that a firm could end up in court as a result of its economic struggles. However, results from the event study support a causal interpretation of this drop in economic activity, as the absence of pre-trends rejects such reverse causality.<sup>26</sup> Similarly, difference-in-differences estimates from (5) suggest an 8.2% average monthly decline in firm revenues for the first six months after entering court (p-value _<_ 0.01; col 1, Table 8).<sup>27</sup> This sharp impact of starting

> 26Robustness is further discussed in Section 6.3.

> 27We split the sample between plaintiff and defendant and estimate a 6% and a 10.4% average monthly decline in firm revenues, respectively (not reported; Figure A11 plots the event study coefficients from estimating (4) separately across sides); we fail to reject that the impact is similar across sides of the dispute (p-value = 0.30). Thus we pool across sides for power.

24
**Table 9: Relationship between the employment rate of older women (55-64) and the employment rate of younger women by age group**

||(1)<br>15-24|(2)<br>25-34|(3)<br>35-44|(4)<br>45-54|
|---|---|---|---|---|
|Employment rate of old women (55-64)|0.416***|0.738***|0.777***|0.849***|
||(0.056)|(0.057)|(0.052)|(0.034)|
|Poverty rate|0.005|0.070|-0.076|-0.180***|
||(0.103)|(0.102)|(0.084)|(0.051)|
|Share of employment in the|-0.027|-0.266***|0.004|-0.025|
|agriculture sector|(0.073)|(0.059)|(0.048)|(0.039)|
|Share of employment in the|0.231**|-0.353***|-0.159**|-0.213***|
|industrial sector|(0.112)|(0.091)|(0.069)|(0.066)|
|Share of younger women|-0.275|0.325*|0.327|-0.548*|
|(15-24/25-34/35-44/45-54)|(0.202)|(0.182)|(0.280)|(0.293)|
|Share of population with a primary|0.192***|0.197***|0.092**|0.066|
|education or less|(0.067)|(0.052)|(0.042)|(0.043)|
|Natural log of GRP per capita|6.703***|2.424***|-0.141|-0.627|
|(constant 2010 IDR)|(0.985)|(0.829)|(0.824)|(0.571)|
|Growth in GRP per capita|-0.308***|-0.070|-0.003|-0.084|
||(0.103)|(0.102)|(0.059)|(0.069)|
|Number of observations|272|272|272|272|
|R-squared|0.422|0.648|0.791|0.865|
|Year dummies|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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# **Table 7a: Predicted change in the probability of selecting each animal from uniform** **<u>climate scenarios for small farms</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|Baseline probability|2.20%|15.40%|29.60%|18.27%|34.53%|
|Increase temp 2.5°C|0.17%|-3.27%|2.13%|4.03%|-3.07%|
|Increase temp 5°C|0.36%|0.09%|1.27%|7.57%|-9.29%|
|Decrease rain 15%|0.02%|1.69%|-1.69%|1.48%|-1.50%|
|Increase rain 15%|-0.07%|-0.95%|1.79%|-1.39%|0.62%|

**Table 7b: Predicted change in net income per animal from uniform climate scenarios for** **<u>small farms (US$)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline income|151.38|140.98|6.55|12.89|0.86|
|Increase temp 2.5°C|-51.12|1.34|-1.63|0.18|-0.18|
|Increase temp 5°C|-80.99|7.90|-2.24|3.78|-0.18|
|Decrease rain 15%|18.70|3.37|-0.43|0.48|0.03|
|Increase rain 15%|-13.60|-3.14|0.66|-0.30|-0.01|

**Table 7c: Predicted change in number of each animal per farm from uniform climate** **<u>scenarios for small farms (animals/household)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline number|1.48|2.92|7.09|5.83|31.10|
|Increase temp 2.5°C|-0.16|-0.02|-0.66|-0.77|-8.15|
|Increase temp 5°C|-0.05|-0.00|-1.17|-1.64|-12.13|
|Decrease rain 15%|-0.03|0.03|-0.06|-0.21|1.35|
|Increase rain 15%|0.08|-0.02|0.06|0.20|-1.01|

**Table 7d. Predicted change in expected income from uniform climate scenarios for small farms** **<u>(US$)</u>**

||**Mean**<br>**(US$/farm)**|**Total**<br>**(billions US$)**|**% change**|**Bootstrap**<br>**lower 95 %**|**Bootstrap**<br>**upper 95%**|
|---|---|---|---|---|---|
|Expected income|102|6.0||||
|Increase temp 2.5°C|-13|-0.8|-12.7%|-24|3|
|Increase temp 5°C|-1|-0.1|-0.9%|-8|6|
|Decrease rain 15%|6|0.4|5.9%|1|13|
|Increase rain 15%|-3|-0.2|-2.9%|-6|4|

31
Mattoo, Rathindran and Subramanian (2006) analyze the effects of trade and investment openness for the financial and telecommunications sector on growth in a cross-sectional analysis. Controlling for other determinants of growth, they find that countries that fully liberalized the financial services sector grew, on average, about 1 percentage point faster than other countries. Fully liberalizing both the telecommunications and the financial services sectors was associated with an average growth rate 1.5 percentage points above that of other countries. Focusing on a sample of transition economies, Eschenbach and Hoekman (2006) find that services-related policies play an important role in attracting FDI. They explore the impact of financial and infrastructure services policy reforms on per capita income growth using time-series data covering the 1990-2004 period. Controlling for other potential explanatory variables, they find that improvements in services policies – infrastructure and finance – have a large and statistically significant positive impact on per-capita growth.

The positive association between more efficient service sectors and overall economic performance implies that services policy reforms will help reduce poverty – as sustained economic growth is the most powerful instrument to reduce poverty. Underlying this aggregate, indirect relationship are many channels through which better services can directly improve economic outcomes for poor households, for example, by enhanced consumption of health and education. Another channel is by enhancing the benefits of trade reforms. Numerous country studies have concluded that greater trade opportunities can raise incomes but only if poor households produce products for the market. This may require active intervention to help households switch from subsistence to cash crops and improve productivity—through extension services, better access to credit and transportation services, and investments in infrastructure.<sup>3</sup> Domestic supply constraints are the main reason for the lack of trade growth and diversification in many of the poorest developing countries. Without action to improve supply capacity, reduce transport costs from remote areas, increase farm productivity and more generally improve the investment climate, trade opportunities cannot be fully exploited and the potential gains from trade will not be maximized. The needed reforms span numerous areas, but

> 3 See, e.g., the country studies in Hertel and Winters (2006) and Hoekman and Olarreaga (2007).

4
# **3. Survey Methodology and Profile of Survey Respondents**

To gain a perspective on how the actors in the three innovative sectors in the Singapore economy view the challenges that they face in pursuing or responding to innovation in the course of their business operations in Singapore, we conducted a small sample, explorative survey covering 45 firms drawn from the three sectors. This section provides a description of the survey instrument used and the sampling method adopted. The survey instrument employed is a semi-structured questionnaire that we designed jointly with the World Bank. The questionnaire covers a total of 80 questions, a large number of which are open ended in order to obtain a richer range of responses of various issues. Through these open ended questions, we tried to elicit qualitative information on various aspects of the respondents’ business operations including:

- a) Determinants of location choice

- b) View on skills and preferences of sources for skills

- c) Type of investments needed to extract value from creative capital

- d) Firm level actions to improve efficiency and innovation capacity

- e) Desired external factors to improve efficiency and innovation capacity

- f) Views on prospects of IP industries

- g) Views on relative strength of other cities in comparison to Singapore

From the open-ended responses provided by the respondents, we tried to identify commonalities and developed coding to group them into the identified categories.

The questionnaire was completed through a combination of assisted self-administration by respondents and face-to-face interviews<sup>1</sup> . To facilitate completion, the questionnaire was given to respondents ahead of the interview sessions. Each interview took around 60 to 90 minutes to complete, although a number of interviews lasted longer than two hours.

The sectoral composition of respondents is shown in the **Table 3.1** . Of the 45 firms covered in the survey, 16 are in high-tech manufacturing, 15 are in the knowledge intensive business services sector (KIBS) and 14 are in creative content industries. While the sample size is too small to achieve any realistic proportionate sampling, an attempt was consciously made to cover the spectrum of relevant players in each of the three industries in terms of spread of sub-industries (ensuring that most if not all relevant sub-industries of importance in Singapore are covered), size distribution (ensuring that there is a

> 1 One interview was conducted over the telephone

21