rafmacalaba/datause-encoder-data huggingface dataset:
# **II Estimating the Innovation Function**

# _Data_

Our dataset consists of a panel of 49 developed and developing countries from the 1960s to the present. We first review the measures of innovation output and input.<sup>8</sup> _Innovation Output:_ A large literature uses patents as an imperfect measure of innovation output although the limitations of the measure are well known (see for example, Griliches 1990, Trajtenberg 2001, Jaffe and Trajtenberg 2002). Of perhaps greater concern for the present work is that patents granted by national agencies are not comparable due to differences in national standards, costs of applying for patents, levels of intellectual property protection, pecuniary benefits from patenting, and other country-specific institutional features. To ensure comparability we follow Jaffe and Trajtenberg (2002), Branstetter (2001), Furman, Porter and Stern (2002) and use the number of patents granted by the United States Patent and Trademark Office (USPTO).<sup>9</sup> In the absence of a global patenting agency, the US remains the principal locus of patenting activity and the USPTO offers reliable panel data for the period 1963 to 2000 for a large number of countries. Granted patents are assigned by country of origin based on the country of residence of the first inventor.

That said, countries may differ systematically in their propensities to apply for patents in the US. Those with a large volume of exports to the US have a greater interest in patenting any invention that may be embodied in their exports.<sup>10</sup> Similarly, country endowments and economic structure may also impact the ratio of innovations to patents: manufacturing in general may generate higher patents per innovative dollar than natural resource based sectors. To control for these effects we include in our specifications the volume of merchandise exports to the US, drawn from the IMF Direction of Trade Statistics, and Leamer’s (1984) index of natural resources endowments, net exports of

> 8 See Lederman and Saenz (2003) for a complete discussion of the data.

> 9 The U.S. PTO demands that the invention be “novel and nontrivial, and has to have commercial application” (Jaffe and Trajtenberg 2002, 3-4). 10 See Trajtenberg (2001).

5
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure contribution to annual per-capit economic growth In CentralAfrican countries**

<!-- Start of picture text --> Percentage points<br>a. Infrastructure’s contribution between 2001-05 b. Potential contribution<br>1.6 4.50<br>1.4 4.00<br>1.2 3.50<br>1<br>3.00<br>0.8<br>2.50<br>0.6<br>2.00<br>0.4<br>1.50<br>0.2<br>1.00<br>0<br>-0.2 0.50<br>-0.4 0.00<br>Roads Power ICT<br>Roads Power ICT<br>Percentage points Percentage points<br><!-- End of picture text --> Source: Calderón 2009.

Poor infrastructure is a handicap for business in Cameroon. Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 42 percent of the productivity gap faced by Cameroonian firms, the remainder being due to poor governance, red tape, and financing constraints (figure 2a). Firms’ perception of infrastructure as a bottleneck to growth in Cameroon is typical of countries in francophone Africa. Customs clearance is the infrastructure constraint that weighs most heavily on the country’s firms, with power deficiencies in second place (figure 2b).

**Figure 2. Infrastructure deficits constrain firms’ productivity**

<!-- Start of picture text --> a. Degree to which infrastructure is perceived by firms as an<br>obstacle to growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> Benin<br>Senegal<br>Mali<br>Cameroon<br>Mauritania<br>Burkina Faso<br>Niger<br>0% 20% 40% 60% 80% 100%<br>Infrastructure Others<br><!-- End of picture text --> b. Degree to which infrastructure is perceived by firms as an obstacle to growth, by subsector (%) <!-- Start of picture text --> Mali<br>Senegal<br>Mauritania<br>Niger<br>Cameroon<br>Burkina Faso<br>Benin<br>0% 20% 40% 60% 80% 100%<br>Electricity Customs clearance Transportation ICT Water<br><!-- End of picture text --> _Source:_ Escribano and others 2010.

4
of equality.<sup>10</sup> Between 2002 and 2010, there was not a marked increase in segregation. However, since 2012, there was a steady rise in the spatial inequality in the distribution of the poor. Provinces with a cumulative share of about 50% of the total population had about 30% of the poor population in 2002, whereas only about 3% of the poor population in 2020. Over the years, poor provinces such as Dien Bien and Lai Chau saw their share of the poor population increase disproportionately. Despite a rapid decline in average poverty levels, we find that the remaining poor were increasingly segregated in certain provinces in the country.

# **3. Relationship between economic growth, inequality and poverty**

# **3.1. Factors affecting provincial poverty**

We further analyze the VHLSS data to find which factors were highly correlated with provincial poverty levels. Following the existing literature that examines the effects of income and inequality on poverty (Bourguignon, 2003; Kraay, 2006; Chen and Ravallion, 2010; Ferreira, 2010; Marrero _et al._ , 2024), we estimate the following province fixed effects (FE) model 1L 2 ′ (1) In equation (1), _Pi,t_ ii,tt _i,t_ is a poverty index of province iitt iitt _i_ in year iitt tt _t_ , _Yi,ti,t_ is per capita expenditure, ii iitt _Gitit_ is the PP = αα+ ββ LL(YY ) + ββ GG + δδXX + ττ + uu + εε In equation (1), _Pi,t i t_ , _Yi,ti,t Gitit_ is the Gini index, _Xit_ is a vector of explanatory variables including high school completion rates, population density (in logarithmic form), shares of the urban population and the ethnic minority population, share of the population with a high school diploma, and different types of investments such as state spending and investment spending. These are the control variables that are widely

> 10 The Gini index satisfies the properties of symmetry, scale invariance, and the regressive transfer principle, and provides a consistent ranking of the distributions whenever the segregation curves do not intersect. The dissimilarity index does not satisfy the principle of regressive transfer. See Dhongde (2017) for a detailed discussion on these properties.

11
# **Table 4. Determinants of FDI at the District Level: Dependent Variable = Foreign Ownership Weighted by Firm Size**

||(1)|
|---|---|
|ln(L)|0.022|
||(1.542)|
|ln(firm age)|-0.121***|
||(-4.783)|
|ln(GDP PC)|0.053|
||(1.597)|
|ln(city distance to port +1)|-0.008|
||(-1.391)|
|city phone density|0.045*|
||(1.891)|
|ln(city avg wage)|-0.018|
||(-0.330)|
|dist.avg share of coll grad in L|0.164|
||(1.554)|
|city share of good air days|0.159*|
||(1.893)|
|dummy: open city|0.095**|
||(2.098)|
|ln(dist.avg of days passing customs)|-0.170***|
||(-4.549)|
|dist avg of priv firm access to loans|-0.141***|
||(-3.029)|
|dist avg of priv firm ETC|-0.602|
||(-0.417)|
|dist avg of foreign advantage in ETC|0.034***|
||(3.594)|
|dist avg of priv firm contract enforcement|-0.020|
||(-0.325)|
|dist avg of foreign advantage in contract enforcement|-0.011|
||(-0.441)|
|dist avg of priv firm tax burdens|-0.611|
||(-0.871)|
|dist avg of foreign advantage in tax burden|0.024|
||(0.904)|
|party secretary young|0.154**|
||(2.085)|
|party secretary old|0.150|
||(1.437)|
|PS young*ln(avg PS tenure)|-0.145***|
||(-2.611)|
|PS middle*ln(avg PS tenure)|0.017|
||(0.393)|
|PS old*ln(avg PS tenure)|-0.086|
||(-1.093)|
|Observations|909|
|R-squared||

Note. The standard errors are clustered at the city level. *, **, and *** represent statistical significance at the 10, 5 and 1 percent levels.

29
routine preventative health clinic visits, was only collected in the final survey round, so we are unable to examine whether the variable exhibits balance in the baseline round (pre-intervention) across the treatment and control groups.<sup>7</sup> Household attrition was very low between the baseline and one-year follow-up survey (1.42 percent), and increases slightly when comparing the baseline and two-year follow-up survey (4.62 percent). In Table 3, we explore the relative differences between attritor and nonattritor households. Column 1 presents means of the household-level characteristics from the baseline survey for the sample of households that were followed from the baseline to the twoyear follow-up survey (non-attritors). Column 2 presents means for the sample of attritor households, and column 3 presents the average difference in characteristics between attritors and non-attritors, as well as a test of whether the difference is statistically significant. Results suggest that attrition is not likely random, as attritors are more likely to come from smaller households, with fewer adults, are younger, have fewer wives, and are more likely to be female headed households. However, what is more relevant for our analysis is whether attrition differs across treatment and control groups. In columns 4 to 7 of Table 3, we present difference-in-differences regressions for each characteristic comparing the difference between attritors and non-attritors in each specific treatment arm with the same difference between attritors and non-attritors in the control group, and we find few significant differences.

# **3. Empirical Identification Strategy**

The key question we want to answer is whether cash transfers increased the frequency of routine preventative visits to health facilities by young children in recipient households. The experimental design provides a strong identification strategy and allows us to attribute any

> 7 However, we are able to test baseline balance for other health outcomes for which we do have three rounds of data, such as the probability the child was sick during the past month, health clinic utilization, and illness duration, and all of these exhibit balance across randomized treatment and control groups in the baseline.

10
# **[Table 3 about here]**

# **5.2 Opportunity-Benefit Incidence Analysis (Opp-BIA)**

Three patterns of education spending—the distribution of monetized benefits accruing to children attending public schools in Liberia per beneficiary (―gross unitary public transfer‖), the distribution of private out-of-pocket household spending incurred as a result of attending schools per beneficiary (―unitary private spending‖), and the resulting difference between public benefit and private contribution towards school attendance, ―net unitary benefit‖, again on a beneficiary basis—are now compared (Figure 2). World Bank (2010) reports total resources in the public education system of US$ 77.2 million, accruing from three sources: US$12.2 million by the government, US$27 million by households and US$38 million by international donors. Gross unitary public transfers are obtained by dividing government‘s and donors‘ educational spending by level of education among enrolled students: US$ 6.9 per student enrolled in primary education and US$ 84.5 per student enrolled in secondary education. Unitary private spending is obtained directly from household‘s reports on their actual spending on children attending school. These patterns of average public resources spent per student and households‘ average private contributions towards education are compared against the distribution of household wealth<sup>8</sup> and the distribution of educational opportunities, proxied by the estimated probability of attending school conditional on their circumstances (see Table 1 above). The former generates traditional BIA, while the latter an ―Opportunity BIA‖.

> 8 There is no direct interpretation of the values of the wealth index. The wealth index is constructed with principal factor analysis (PCA) of information on assets in the household. The source data are a series of dummies where 1 is assigned if the household has the asset (television, car, radio, etc). We construct z-scores of the data and project them onto a new basis using the eigenvectors that decompose the covariance of the data. Intuitively, we are using the information of asset ownership to rank households according to their ownership of goods, giving more weight to the information that explains the largest part of the variance in the asset dummies.

21
In the city of El Alto, there have been substantial gains in access to water and sanitation services. The water coverage rate continued to grow at the same rapid rate as before the reform, gaining 20 percentage points in the period 1997-2000 and thereby attaining universal coverage. While sewerage coverage which had been more or less static at around 40% during the earlier part of the decade, increased by 10 percentage points to reach 53% by the year 2000.

Comparison with other urban areas suggests that coverage increases for the water service in La Paz and El Alto were in fact comparable to those found elsewhere in the country where there was no private sector participation (Figure 2.6). However, with respect to the sewerage service, the coverage rate in La Paz and El Alto rose by more than 15 percentage points between 1994-99, whereas coverage in other urban areas increased by less than 10 percentage points over the same period. (Barja and Urquiola, 2001) Electricity coverage appears to have been more or less static following the reform, however there are significant discrepancies between data sources as to the overall level of coverage. According to Electropaz data, coverage in El Alto stands at around 75%. However, according to survey data, coverage in El Alto is around 95%. While such a difference could be explained in terms of illegal connections, Electropaz does not believe that this is a major problem in El Alto. One possible explanation is that households in El Alto that lack official connections are still able to obtain electricity by wiring-up to a neighbors system and purchasing electricity from them. In this case, the electricity is not being stolen from the network but merely purchased from an intermediary. Electropaz reports that the main obstacle to further expanding coverage in El Alto is the fact that new settlements cannot be legally connected until they have their urbanization plans approved by the municipality; a process that is often subject to lengthy delays.

Once again, there is some discrepancy between the telephone coverage estimates resulting from COTEL data and those derived from household surveys. The COTEL data shows that fixed line coverage has been stagnant at just under 20%. While the household survey data indicates that coverage of fixed plus cellular telephones has increased by about 10 percentage points since the reform from a pre-reform base of around 10%.

**Figure 3.8: Geographical comparison of gains in water and sewerage coverage**

<!-- Start of picture text --> 100<br>90<br>Water (all urban)<br>80<br>Water (La Paz/El Alto)<br>70<br>Sewerage (all urban)<br>60<br>Source: Barja and Urquiola, 2001. Sewerage (La Paz/El Alto)<br>50<br>Distribution of new connections. A second key question is how these new connections<br>40<br>were distributed between rich and poor? Using household survey data it is possible to 1989 1994 1999<br>break down access rates across income groups, in the years surrounding the reform<br>Coverage rate (%)<br><!-- End of picture text --> 16
Unfortunately, whereas the second term is easy to estimate from the use of flood maps combined with flood depth – damage curve functions, the first term is impossible to compute without relying on a fully fledged urban economics model as we have described and used throughout this study. It however can be proxied for, through several simplifications. We report below how the changes in central housing rents because of resilience investments can be approximated and provide calculation details in the Appendix. 0 1 1 2 2 + ⋯. (17) 0 RR RR In equation (17), 1 , 2 , ..., represent fractions of the total urban population living in grid cells 1, 2,..., so NNRR ≈1 + bbff MM + bbff MM that = <u>i</u> , where is the population residing in grid cell RR , and is the total population in the urban rr ff ff area. And ii 1 , 2 , ... represent the difference in flood depreciation parameters divided by the initial ii ff NN aa ii NN complete unitary capital costs, so that MM MM = <u>� NR �</u> ~~.~~ ρρffRR−ρρff MMii �ρρffNNRR+ρρ+ii� If we combine (16) and (17) and aggregate over the whole urban area, we can compute the ratio of aggregate land values with and without flood mitigation works, and as: 1 RR NNRR AALLAA AALLAA <u>NR</u> bb (18) II II aa RR ≈�� ffii �aa � AALLAA ρρ+ ii+ ρρ ii ii AALLAANNRR ii=1 ρρ+ ii+ ρρffiiRR �1 + bb�ffii=1 MM With the formula in equation (18) it is possible to proxy for the impacts of flood mitigation investments without developing and calibrating a fully-fledged urban economics model and with only a limited set of data. The data needed to inform equation (18) are the construction function parameter , the real interest rate , the depreciation rate of structures (commonly assumed to be 0.01), the flood depreciation rates, bb i.e. the percentage of damages that floods inflict on structures with and without resilience investments ii ρρ which can be retrieved as in this paper through the use of before and after flood maps combined with flood depth - damage curve functions, and finally the distribution of population in urban areas at the grid 24
exposures. However, in Emerging Europe, consolidated exposures are substantially above locational exposures, reflecting the important role of foreign-owned banks in the domestic banking sector of this region.

**Table 3.4j. International Debt Securities, Percent of GDP**

||2002|2007|2009|2010|
|---|---|---|---|---|
|Euro Periphery|41|156|220|227|
|Emerging Latin America|25|15|12|10|
|Emerging Asia|12|9|10|9|
|New Member States|10|9|13|14|
|Candidates and Potential Candidates|9|5|7|7|
|Eastern PartnershipCountries|2|4|6|6|

_Source_ : World Bank, Joint External Debt Hub. Note: This table reports international debt securities, all maturities.

**Table 3.4k. International Debt Securities, Nonbanks, Percent of GDP**

||2002<br>|2007<br>|2009<br>|2010<br>|
|---|---|---|---|---|
|Euro Periphery<br>|31<br>|112<br>|167<br>|172<br>|
|Emerging Latin America<br>|24<br>|14<br>|11<br>|9<br>|
|Emerging Asia|10|8|8|8|
|New Member States|9|7|11|13|
|Candidates and Potential Candidates|12|6|6|6|
|Eastern Partnership Countries|2|2|4|5|

_Source_ : World Bank, Joint External Debt Hub. Note: This table reports international debt securities, nonbanks.

**Table 3.4l. Liabilities to Banks, Consolidated, Percent of GDP**

||2002|2007|2009|2010|
|---|---|---|---|---|
|Euro Periphery|73|130|125|114|
|Emerging Latin America|17|12|13|11|
|Emerging Asia|14|15|15|16|
|New Member States|25|58|61|57|
|Candidates and Potential Candidates|18|45|38|39|
|Eastern PartnershipCountries|4|9|11|11|

Source: BIS. Note: This table reports liabilities to BIS banks, consolidated total.

**Table 3.4m. Liabilities to Banks, Locational, Percent of GDP**

||2002<br>|2007<br>|2009<br>|2010<br>|
|---|---|---|---|---|
|Euro Periphery|100|182|199|184|
|Emerging Latin America|17|11|11|10|
|Emerging Asia|16|17|15|15|
|New Member States|17|49|52|49|
|Candidates and Potential Candidates|11|26|27|25|
|Eastern Partnership Countries|3|9|10|8|

Source: BIS. Note: This table reports liabilities to BIS banks, locational total.

# **Finding 3.1:  External Debt Liabilities**

- <mark>The rapid growth in external liabilities has led some countries to seek official assistance</mark> (Latvia, Hungary, Romania, Ukraine).

- <mark>However, the overall external liability position exaggerates the debt problem for most</mark> countries in the sample, in view of the importance of equity financing.

- <mark>The high level of foreign reserves in most countries also provides a buffer against external funding</mark> <u><mark>problems.</mark></u>

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# **Appendix Table IV:  List of Surveys used in the Sample**

|**ccode**|**Country name**|**year**|**Survey Name**|
|---|---|---|---|
|AFG|Afghanistan|2003|National Risk and Vulnerability Assessment 2003<br>|
||||Inquerito aos Agregados Familiares sobre Despesas e Receitas 1999-|
|AGO|Angola|1999|<br>2000|
|ALB|Albania|2002|Living Standard Measurement Survey 2002|
|ARG|Argentina|1994|Encuesta Permanente de Hogares 1994|
|ARG|Argentina|2001|Encuesta Permanente de Hogares 2001|
|ARM|Armenia|1999|Integrated Survey of Living Standards 1999|
|ARM|Armenia|2003|Integrated Survey of Living Standards 2003|
|AUS|Australia|1989|Survey of Income and Housing Costs (SIHC)|
|AUS|Australia|1994|<br>Survey of Income and Housing Costs (SIHC)|
|AUT|Austria|1994|Austrian Microcensus|
|AUT|Austria|2000|Austrian Microcensus|
|AZE|Azerbaijan|1995|State Statistical Survey 1995|
|AZE|Azerbaijan|2002|State Statistical Survey 2002|
|BDI|<br>Burundi|1998|<br>Enquete Prioritaire 1998|
|BEL|Belgium|1992|Socio-Economic Panel (SEP)|
|BEL|Belgium|2000|Panel Study of Belgian Households (PSBH) / BE ECHP|
|BEN|Benin|2003|Indicateurs de Base du Bien-etre|
|BFA|Burkina Faso|1994|Enquete Prioritaire 1994|
|BFA|Burkina Faso|2003|Indicateurs de Base du Bien-etre|
|BGD|Bangladesh|1991|Household Income-Expenditure Survey 1991|
|BGD|Bangladesh|2000|Household Income-Expenditure Survey 2000|
|BGR|Bulgaria|1995|Integrated Household Survey 1995|
|BIH|Bosnia and Herzegovina|2001|Living Standard Measurement Survey 2001|
|BLR|Belarus|1998|Income and Expenditure Survey 1998|
|BLR|Belarus|2002|Income and Expenditure Survey 2002|
|BOL|Bolivia|1993|Encuesta Integrada de Hogares 1993|
|BOL|Bolivia|2002|Encuesta Continua de Hogares - Condiciones de Vida 2002|
|BRA|Brazil|1995|Pesquisa Nacional per Amostra de Domicilios 1995|
|BRA|Brazil|2001|Pesquisa Nacional per Amostra de Domicilios 2001|
|BTN|Bhutan|2003|Bhutan Living Standards Survey 2003|
|CAN|Canada|1991|Survey of Consumer Finances (SCF)|
|CAN|Canada|2000|Survey of Labour and Income Dynamics (SLID)|
|CHE|Switzerland|1992|Income Distribution Survey (HINK)|
|CHE|Switzerland|2000|Income Distribution Survey (HINK)|
|CHL|Chile|1990|<br>Encuesta de Caracterizacion Socioeconomica Nacional 1990|
|CHL|Chile|1996|Encuesta de Caracterizacion Socioeconomica Nacional 1996|
|CHL|Chile|2003|Encuesta de Caracterizacion Socioeconomica Nacional 2003|
|CIV|Côte d'Ivoire|2002|Enquete Niveau de Vie des Menages 2002|
|CMR|Cameroon|1996|<br>Enquete sur les Niveau de Vie 1995|
|CMR|Cameroon|2001|Deuxieme Enquete Camerounaise aupres des Menages 2001|
|COL|Colombia|1995|Encuesta Nacional de Hogares - Fuerza de Trabajo 1995|
|COL|Colombia|2000|<br>Encuesta Nacional de Hogares - Fuerza de Trabajo 2000|
|CPV|Cape Verde|2000|<br>Recesamento Geral da Populacao e Habitaco 2000|
|CRI|Costa Rica|1995|Encuesta de Hogares de Propositos Multiples 1995|
|CRI|Costa Rica|2001|Encuesta de Hogares de Propositos Multiples 2001|
|DEU|Germany|1994|German Social Economic Panel Study (GSOEP)|
|DEU|Germany|2000|German Social Economic Panel Study (GSOEP)|

27