rafmacalaba/datause-encoder-data huggingface dataset:
Estimating the return to firms’ product innovation is not entirely new. Previous studies have tried to measure the sale and employment return to firms’ innovation, but mostly are limited in a single country. Earlier studies focus on the manufacturing sector in developed countries, such as Van Reene (1997) for the UK, Greenan and Guellec (2001) for France, Hall et al. (2008) for Italy, Guadalupe et al (2012) for Spain. Recent studies start to quantify the return in developing countries, Benavente and Lauterbach (2008) for Chile, Aboal et al. (2011) for Uruguay, and Crespi and Tacsir (2012) for four Latin American countries.

The main contribution of this paper is the second step, where we show the return to firms’ product innovation positively correlates with countries’ institutional quality. In other words, in countries with lower levels of institutional quality, the return to firms’ product innovation is lower. This is an interesting result because this suggests that poor levels of institutions depress the return to innovation, and therefore discourage firms to innovate. Related to our findings, Goni and Maloney (2014) find that at the country level, the rates of return from R&D expenditures follow an inverted U: they rise with distance to the frontier and then fall thereafter, potentially turning negative for the poorest countries.

The comparison of firms’ return to innovation across countries in this study is made possible thanks to the World Bank‘s Enterprise Surveys (ES) -- a firm-level survey of a stratified representative sample of firms. It covers a large set of countries. This survey has been conducted since 2002 and typically answered by business owners and top managers. The survey covers a broad range of business environment topics, including access to finance, corruption, infrastructure, crime, competition, and performance measures<sup>3</sup> . Firms are chosen by a stratified random sampling technique, where the strata are firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms).

In our paper, we focus on product innovation. A firm is understood to innovate if it introduced a new product or service or upgraded an existing product or service. In our data, only firms in the Latin America (LAC) and Eastern Europe and Central Asia (ECA) regions are surveyed about their product innovation. We estimate the percentage change in sales per worker _within_ a firm if it has introduced or upgraded the products or services in the 3 years prior to the survey. The idea is that if a firm innovates, its sales and sales per worker should increase. Ideally one should look at firms’ profit as the best measure of the return. Unfortunately that is not possible in our study because data on

> 3 Methodological details can be found at the link below

> ~ <u>http://www.enterprisesurveys.org/ /media/FPDKM/EnterpriseSurveys/Documents/Methodology</u>

3
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Billari et al. (2019) find that broadband internet increased fertility in Germany and they linked this result to women’s choice of telework and part-time work. We do not expect this channel to be at play in Jordan as internet adoption did not impact women’s employment. Guldi & Herbst (2017), on the other hand, find that broadband diffusion helps to explain the decline in teen birth rate in the United States due to greater access to information.

We analyze the change in the marriage rate for women who were not married in 2010. IV results appear in Table 8. Column 1 shows the estimates for the sample of not married women in 2010, while columns 2 and 3 present the results when restricting the sample to low- and high-educated not married women in 2010. In both cases, we find that internet adoption or continuation between 2010 and 2016 reduces the marriage rate in about 0.6 and 0.9 percentage point for each one percentage point of increase in internet adoption. We next investigate whether internet adoption affected the birth rate by using as an outcome variable the number of 5-year-old or younger children in 2016 that each woman had. Results appear in columns 4 to 8 for the entire sample and for subgroups of women according to educational level and marital status. The estimates indicate a reduction in the number of children for the entire sample and for low-educated and not married of women of approximately 0.5 children over a 6-year period when moving from not having to having internet.

# **5.1.3 Comparative exercise**

In order to further evaluate the importance of changing social norms as a mechanism to explain the increase in female labor force participation, we explore the impacts of internet adoption in a country where barriers for women are lower in comparison to Jordan. We explore the case of Chile where more than half of working age women have been participating in the labor market since 2000 (CEDLAS and The World Bank (2019)). This figure differs greatly from Jordan’s statistics that showed that women’s labor force participation was below 30% during 2010-2016 (Table 1).

We use individual longitudinal data from the Chilean Panel CASEN for the period 200613
Finally, Jakubovsky and Sakowski (2006) find a small positive effect of school size and an inverse u-shaped effect of class size.

Kallai and Maniu (2004) estimate educational production functions for Romania. They found a _positive effect of school size but no effect of class size on students’ test scores_ . Porta (2011) using PISA data for Romania also finds that the _smallest schools (first quintile) perform less well than bigger schools_ , but that the size of this effect is small and significant only for reading scores. A similar study for Serbia (MacDonald et al, 2009) found similar _small but significant effects for reading, math and science scores_ . Hermann (2004b) finds a negative effect of primary school size on the likelihood to entering a better secondary school in Hungary.

Summarizing, the large literature on the effects of class size and school size suggest that while class and school size might have an effect, these effects are unlikely to be large. For transition countries, school size, however, appears to be more significant than class size as a determinant of educational performance and, increasing school size appears to be, if any, beneficial for educational performance. At the same time, the literature also suggests that effects vary from one country to another, pointing to the need for more country-specific studies.

# **III. The Educational System of Ukraine - Some Background**

In Ukraine the state is the main provider of education services, at all levels including general secondary education<sup>14</sup> (GSE). Public schools constitute 99 percent of general secondary institutions educating more than 99.5 percent of students. According to the data of the State Statistics Service of Ukraine the school network in the 2009/2010 school year consisted of 20.368 institutions: 2.177 level I schools (grades 1-4); 5.397 level I-II schools (grades 1-9); 12.312 level I-III schools (grades 1-12)<sup>15</sup> ; 82 level II-III schools; 389 special schools for children with disabilities (boarding schools) and 11 schools of social rehabilitation. Schools for „talented‟ students include 588 gymnasiums 386 lyceums 46 collegiums as well as the so called „specialized‟ schools with more in-depth training in particular subjects.

> 14 In Ukraine general secondary education encompasses primary and secondary education.

> 15 Including gymnasiums, lyceums and collegiums.

10
To our best knowledge, the first published application of a time-varying weight matrix was used by CPRX (2011), who investigated the impact of China’s economic growth on the Latin American countries.

Since this weight represents the closeness of the economic activities between the countries, the ideal weights should properly reflect this magnitude. In the literature of GVAR, either one of two candidates is often used. One is the trade weight, which is constructed by using the bi-directional trade flow data. The other is the financial weight, which represents the of funds between the countries.

As we have examined in Section 2, China’s trade linkages with the rest of the world have drastically increased after China joined the WTO in 2001. Although the financial linkages are deepening, the trade linkages continue to define China’s economic impact. Besides, the quality of data used for constructing the trade weight is more reliable than that of financial weight, and these data are available from the 1980s.<sup>12</sup> Thus we construct _ωij_ ( _t_ ) by using the three-year averages of bi-directional trade flow data, obtained from the IMF’s Direction of Trade Statistics.

# **3.3.2 Adding two commodity price indices**

In the literature, the standard GVAR models are estimated with only one global variable, i.e. the crude oil price, which is the representative of commodity “energy”. According to Table 2, which reports the shares used for calculating the World Bank’s Commodity Price Index, the share of crude oil in the energy index is 84.6%. The importance and the influence of crude oil price fluctuations on the macroeconomic variables of countries, such as the US, are reported by numerous researchers. Just to name a few, Hamilton (1983, 1996, 2003), Hooker (1996), and Cunado and de Gracia (2005).

Besides “energy”, the World Bank publishes two more commodity indices, i.e. “nonenergy commodities” and “precious metals” (See Table 2); and the “non-energy commodi-

> 12For more discussion, see CPRX (2011).

13
states. This is consistent with the perception that India suffers from a major problem regarding factor mobility(see for instance Munshi and Rosenzweig, 2016). Importantly, the results appear to be the same for a mostly immobile factor such as land and buildings as for supposedly more mobile factors such as employment or other fixed assets.<sup>8</sup>

# **4. An exploration of the service sector**

In this section we duplicate the analysis of Duranton _et al._ (2015) for the service sector. This is a useful exercise for a number of reasons:

- Can we confirm that factor misallocation affects output misallocation?

- Can we confirm the importance of factor misallocation on output per worker?

- Did policies affect factor misallocation?

- Is land as important for services as it is for manufacturing?

## **_4.1 Data and descriptive statistics_**

Beginning with the 57th round during July 2001 to June 2002, the nsso conducted a survey of the unorganized enterprises belonging to the service sector and this survey was repeated as part of the 63rd round of National Sample Survey (nss) during July 2006 June 2007. The 57th round covered broadly all unorganized service sector enterprises engaged in the activities of hotels and restaurants; transport, storage and communication; real estate, renting and business activities; education; health and social work and other community, social and personal service activities. The 63rd round additionally covered financial intermediation services which was not so in the 57th round while specific sectors such as non-mechanised transport activities related to transport via railways and activities of business, employers and professional organizations were covered in 57th round but excluded from coverage in 63rd round. Neither survey covered the service sector enterprises

> 8While land is immobile, this is not the case for buildings before their location is decided. We must also keep in mind that we are talking about manufacturing land which represents only a small proportion of total land. Its supply should be arguably somewhat flexible unlike the total supply of land. As an additional point, note also that the immobility of land might be driving the immobility of the other factors. While this is true to some extent, it is hard to believe that land and other factors are perfect complements.

18
# Figure 3

<!-- Start of picture text --> Hungary: NBH Refinance, Deposits of the Enterprise Sector,<br>& Bank Credits to te Enterprise Sector<br>(Year-end, In billion Fonnts)<br>Toutl<br>Credit<br>(485 bF)<br>Total<br>Total Credit<br>Credit Total (391 Wb) - 400<br>(360 bF) (36ObP) ~~~~~(341 Credit bF) . NBHlB<br>NBH Returance Dpst<br>Refinance rtgit (202bF) tgiu 0 NBH 0 NBH n n - . . : aef= -( W X: t -,.0-~~~~287 300<br>Refinance RfnneDpst<br>JD",ceta (202 bP) D1b 25<br>_ , __ , . _ _ . _ , . _ _ _ _ , . . . _ 0<br>1987 1988 1989 1990<br>LI Large Banks Small 8 Medium Banks * Savings Banks & SFIs<br>Source: National Barit of Hungary<br><!-- End of picture text --> <!-- Start of picture text --> Nominal Refinance, Deposit, & Credit Growth, 1987 to 1990<br>Refinance Deposits Total Credit Consumer Prices<br>Total 14% 69% 35% 75%<br>Large Banks -11% 35% 1%<br>Small & Medlum Banks 1559% 396% 261%<br>Savings Banks & SF1s 204% 158% 1033% Soure: Natonal Bank of Huna<br><!-- End of picture text --> Shares & Borrow-Back Ratio, 1987 versus 1990 <!-- Start of picture text --> Refinance Deposits Borrow-Back Ratleo<br>1987 1990 1987 1990 1987 1990<br>Large Banks 93% 72% 84% 67% 43% 58%<br>Srmall & Msdum Banks 1% 100% bSo 18% 41% 57%<br>Savings Banks & SFIs 7%h 18% 10% 15% 304% 69%<br>Total 47% 59%<br>Source Natonal Sank ot Hungary<br><!-- End of picture text --> 8a
# **A note on the economic cost of climate change and the rationale to limit it below 2°C**

Stéphane Hallegatte<sup>a,b</sup> , Patrice Dumas<sup>a</sup> , Jean-Charles Hourcade<sup>a</sup> a Centre International de Recherche sur l’Environnement et le Développement b Ecole Nationale de la Météorologie, Météo-France
|(5)<br>tion manufacturing|0.0151***<br>[0.00521]<br>-0.0196**<br>[0.00943]|293<br>0.506<br>IPUMS<br>OLS<br>district<br>ogous to the baseline specifcation<br>raverse a given length of dirt road<br>rtest routes. In columns 3-5, the<br>rimate city is more paved than the<br> coastal primate countries in 1992,<br>n 2 it is the log of the lights digital<br>when applicable. In column 3, the<br>anzania (2002), and the dependent<br>ance(Primate) is the road network<br>utof is light=5.5. Robust standard<br>the ten, fve, and one percent level,|
|---|---|---|
|(4)<br>ln(light+5.5)<br>frac|0.584**<br>[0.275]<br>0.0633<br>[0.366]|272<br>non-primate cities<br>tobit<br>city<br>52<br>Columns 1 and 2 are anal<br> of the time required to t<br>, used in calculating sho<br>it’s path to its country’s p<br>2 non-primate cities in 15<br>opulation, while in colum<br>atellite-years within a year<br>83), Senegal (1988), and T<br>g in manufacturing. Dist<br>of kilometers. The tobit c<br> *** mean signifcance at|
|(2)<br>(3)<br>ln(light)<br>ln(population)|0.280***<br>[0.106]<br>-0.640***<br>[0.207]<br>-0.0791<br>[0.135]|4,879<br>272<br>0.258<br>all<br>non-primate cities<br>tobit<br>OLS<br>city<br>city<br>2<br>263<br>ion, with country fxed efects. <br>ferently. Dirt factor is the ratio<br> the same length of paved road<br>a dummy indicating that the un<br>lumns 3 and 4, the sample is 27<br>ependent variable is the log of p<br>n the city, and averaged across s<br>its in Ghana (2000), Guinea (19<br>d population over age 10 workin<br>ountry, measured in thousands<br>s 1 and 2, are in brackets. *, **,|
|(1)<br>ln(light)|-0.653***<br>[0.213]|4,879<br>all<br>tobit<br>city<br>1.5<br>263<br>arate regress<br>calculated di<br>d to traverse<br>of interest is<br>ountry. In co<br>umn 3, the d<br>ss all pixels i<br>nistrative un<br> the employe<br> city in the c<br>ty in column|
||1(paving _>_ median)<br>dist(primate)*_Poil_<br>distance(Primate)|Observations<br>R-squared<br>sample<br>model<br>unit<br>dirt factor<br>left censored cases<br>Each column is a sep<br>with route distances <br>and the time require<br>independent variable<br>average within that c<br>the initial year. In col<br>number, summed acro<br>sample is census admi<br>variable is fraction of <br>distance to the largest<br>errors, clustered by ci<br>respectively.|

35
1 WPS4134 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 11</mark>

# Health Insurance for the Poor: Initial Impacts of Vietnam’s Health Care Fund for the Poor

## by

## Adam Wagstaff<sup>a</sup>

a Development Research Group, The World Bank, Washington DC, USA

#### **Abstract**

Vietnam’s Health Care Fund for the Poor (HCFP) uses government revenues to finance health care for the poor, ethnic minorities living in selected mountainous provinces designated as difficult, and all households living in communes officially designated as highly disadvantaged. The program, which started in 2003, did not as of 2004 include all these groups, but those who were included (about 15% of the population) were disproportionately poor. Estimates of the program’s impact—obtained using single differences and propensity score matching on a trimmed sample—suggest that HCFP has substantially increased service utilization, especially inpatient care, and has reduced the risk of catastrophic spending. It has not, however, reduced average out-of-pocket spending, and appears to have negligible impacts on utilization among the poorest decile.

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: <u>awagstaff@worldbank.org.</u>

**Keywords:** Vietnam; health insurance; financial protection; impact evaluation.

**Acknowledgements:** My thanks to Jim Knowles and Magnus Lindelow for helpful comments on an earlier version of the paper.

### World Bank Policy Research Working Paper 4134, February 2007

The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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.
Current account deficits and surpluses can be approached in many different ways. The traditional narratives on the US-China imbalances are usually analyzed for each of the two countries from three perspectives: (i) a domestic perspective based on national income and product accounts; (ii) an international perspective based on trade flows; and (iii), an international perspective based on flows and holdings of financial assets (Mann, 2002).

# 2.2.1 <u>The National Accounts Lenses</u>

The first approach typically uses national accounts to describe how patterns of domestic savings and investment are linked to trade and current account balances. It starts with the identity reflected in equation (3a) that domestic production equals total spending plus the trade balance. The sources of savings in any given economy can therefore be said to correspond to the demand for financial capital. To examine the national accounts identity from the perspective of the sources and uses of funds, one must disaggregate foreign and domestic variables, and public and private variables: private savings plus capital inflows (foreign savings) through the current account or trade deficit must equal private investment and the budget balance: This formulation helps make the point that the US trade deficit reflects a higher level of spending than its domestic production. As a consequence, fiscal deficits fuel current account deficits through their effect on national saving. This is the wellknown twin-deficits hypothesis: when a government increases its fiscal deficit—for instance by launching a fiscal stimulus package, by cutting taxes—domestic residents use some of their new income to consume more, causing national saving to decline. This trend in saving requires the country either to borrow from abroad or reduce its foreign lending, unless domestic investment decreases enough to offset the saving shortfall. Thus, a larger fiscal deficit is typically accompanied by a wider current account deficit.

The story seems quite logical. Yet, empirical evidence has been hard to find in the US case, not least because the link between fiscal and current account deficits is not as straightforward as the accounting identities would suggest. While the US fiscal and current account deficits seemed to move in parallel during the first half of the 1980s, things have changed substantially since the early 1990s, raising doubts about the twin-deficit hypothesis: The US fiscal deficit rose from 2.7 to 5 percent of GDP in 1980-1986 while the current account deficit increased from 0 to 3.5 9
_7_ _The Optimality ofBeing Efficient_ First, suppose bidders have flat demands and are ex ante symmetric: _Flat demands_ (constant marginal values). _av(t,qi)/aqi_ = _0,_ for qi _e [O,ki],_ so marginal value is _vi(t)._ _Symmetry._ For all i, j, _vi(..,ti,...,tj,...)v(...,t,...,ti,...)and Fi = Fj = F._ In this case, we can restate the regularity conditions as: _iValue regularity._ A higher type has a weakly higher value: ti _> t => vi(t)_ 2 _vj(t)._ _MJR regularity._ A higher type has a higher marginal revenue: ti _> tj_ > _MRi(t) > MRj(t)._ PROPOSITION 1. _In a symmetric, flat demands model satisj5ing both value and MR regularity, then the seller can maximize revenues by awarding the good to those with the highest values._ PROOF. From Theorem 1, the seller wants to assign the good to those with the highest marginal revenue, but by MR regularity, the highest types have the highest marginal revenues, and by value regularity the highest types have the highest values. Hence, assigning the good in order of marginal revenue also assigns the good in order of value. Moreover, MR regularity implies that _qi(t)_ is weakly increasing in _ti,_ which is sufficient for _q(t)_ to be consistent with incentive compatibility.

From Myerson's (1981) single-good analysis, it is clear that the symmetry assumption is essential to Proposition 1. Can we relax the flat demands assumption to downward-sloping demands? The optimal selling procedure assigns the good based on the aggregate marginal revenue curve; whereas, an efficient auction assigns the goods based on the aggregate demand curve. With flat demands, the assignments based on aggregate demand and marginal revenue are identical, assuming ex ante symmetry. However, with downward-sloping demands, this typically is not be the case.

How the revenue-maximizing assignment distorts the efficient assignment depends on the - distribution of private information. Suppose the bidders have separable inverse demands, _pi(t,qi) = vi(t) gi(qi), where dgildqi >_ 0 for all qi. Further suppose that the intercept vi(t) satisfies the symmetry and value regularity assumptions of Proposition 1, and that the bidders' types are drawn independently from the distribution F. In this setting, any distortion depends on the hazard rate on types,j(ti)/(l-F(ti)).

PROPOsrpION 2. _In the symmetric model with downward-sloping demands, assigning the good to those with the highest values maximizes revenue if the hazard rate on types is constant. However, if the hazard rate is increasing (decreasing), the optimal auction distorts the efficient assignment by shifting quantity awayfrom (toward) low types._ PROOF. From Theorem 1, the seller's expected revenue from an allocation q(t) = _(ql(),...,qn(t)),_ where the lowest type bidders get 0, is <!-- Start of picture text --> Y. fol g[ (xi(t) fiX) (Ft() dx)]<br><!-- End of picture text -->