rafmacalaba/data-use-mentions-extended huggingface dataset:
# **IV. Reforming Latvia’s National Innovation System**

As the benchmarking analyses suggest, it is unlikely that Latvia will achieve its goal of becoming an innovative economy unless Latvian officials take steps to promote the technology upgrading of traditional Latvian industrial sectors and also generate closer links between (i) the domestic enterprise sector and the domestic R&D sector, (ii) the domestic R&D sector and the international R&D sector, (iii) domestic enterprises and global technology markets, and (iv) SMEs and dynamic large enterprises, both inside and outside of Latvia. None of these linkages currently exist, at least not in sufficiently strong form. Even worse, the enterprise sector and R&D sector seem to be developing in total isolation from each other. As a result, on the rare occasion when innovation does occur, it has no connection with the output or technical capacity of the domestic R&D system. And decisions about what R&D to conduct are taken without any regard to the needs of industry. The net effect is an R&D system which is an overhead expense rather than a resource for generating wealth.

# <u>A. Enterprise Sector – Little Innovation and Less R&D</u>

The sad but true situation is that Latvian enterprises are not very innovative. According to a recent survey, only 19% of Latvian enterprises conducted any innovative activity,<sup>30</sup> defined as the introduction (or intended future introduction) of a new product or production process. This is much lower than in EU countries or in NMS’s like Estonia (36%) and Lithuania (27%).

An overwhelming preponderance of the innovative activity that does occur takes place in large firms which are more than four times as likely to innovate as small or medium size enterprises. Moreover, although they comprise only 3% of the total sample, large enterprises accounted for 52% of the total spending on innovation
housing is estimated as the yard size of these units. Informal settlements areas are taken from the 2011 census. Finally, amenities are taken from various sources (see Pfeiffer et al., 2019). For the estimation of the model’s parameters, we also use property price data extracted from the City of Cape Town’s geocoded data set on property transactions for 2011, as well as data on dwelling sizes made available to us by the City of Cape Town.

As described in Pfeiffer et al. (2019), three sets of parameters need to be estimated. The first set consists of the minimum lot size, the size of the backyards of informal dwellings, and agricultural rents, and are directly derived from the available data without solving the model. The second set consists of the housing production function parameters, the utility function parameters, the wages, and the amenities, that had been calibrated in Pfeiffer et al. (2019) using partial relations from the model. Finally, we spatially calibrate the disamenity parameters for informal housing and informal settlements by running the entire model in order to replicate the population distribution by housing type from the 2011 Census.

## 4.2. Validation and baseline

We performed some validation checks to assess the validity of our modeling approach. All results can be found in Appendix A. First, at the aggregated level, we checked that the number of households per income class and per housing type simulated by the model in 2011 matches the data from the Census (Appendix A1). Then, we compared simulated population spatial distribution for each housing type in 2011 with the data from the Census (Appendix A2). Finally, we compared simulated rents and housing prices data (Appendix A3).

Regarding housing types, our simulations fit the data: we slightly overestimate the number of households living in formal private housing and informal housing in backyards, and underestimate the number of 7
“other MoSD” transfer were significantly higher than the bread compensations should be, and consequently we preferred to simulate the transfer amount according to the administrative procedures.

33 For a programme reaching such a large proportion of the population, there is little value in trying to simulate the exact targeting as most of the bottom half of the distribution will receive it.

34 Water and electricity subsidy allocations and reform scenarios are not final; the figures presented here still need reconciliation with administrative records.

35 Electricity figures include a preliminary adjustment to match the share of households in each consumption block reported in administrative data.
and 2021 vs. 2019 (%)_

<!-- Start of picture text --> a. Bogota b. Buenos Aires<br>Workdays Saturday Sunday Workdays Saturday Sunday<br>5% 5%<br>0% 0%<br>-5% -5%<br>-10% -10%<br>-15% -15%<br>-20% -20%<br>-25% -25%<br>-30% -30%<br>-35% -35%<br>Areas with Rest of areas Areas with Rest of areas Areas with Rest of areas Areas with Rest of areas<br>shopping shopping shopping shopping<br>centers centers centers centers<br>2020 2021 2020 2021<br>Source: Team’s estimate based on CDR data<br><!-- End of picture text --> In Buenos Aires, total daily trips to areas associated with commercial activity in 2020 fell slightly more than in the city as a whole, although this was not the case of areas with shopping centers on weekdays, in which case the trips declined by 32.5 percent compared to 32.8 percent to the rest of areas. In 2021, the trend observed in 2020 was reversed: total daily trips to areas associated with commercial activity recovered faster than trips in the city overall. Of particular note are Sundays, when non-home-based trips with destination in areas with shopping malls was only 6.6 percent below the 2019 level, while non-home-based trips to other areas of the city were still 24.6 percent below.

Most of the other metrics suggest that in Bogota e-commerce did not reduce shopping related travel during the pandemic. The evolution of demand in times of the day that concentrate shopping travel according to the 2019 survey is contrary to what would be expected in case there was a substitution effect of trips through increased e-commerce adoption. The drop in travel to
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# CHAPTER III

# BINARY COMPARISONS OF REAL OUTPUT AND PURCHASING POWER BRAZIL/USA AND MEXICO/USA IN 1975

# Introduction

This study applies the "industry of origin" approach to 3 countries, namely Brazil, Mexico and the United States. The sample covers 17 industries representing 20 to 39 per cent of manufacturing output. This chapter explains the methodological and empirical problems which we encountered, and the procedures we used to overcome them. We have tried to make our procedure completely transparent to facilitate the task of those who wish to criticise, replicate, augment, truncate or otherwise modify it.

The year 1975 was chosen as the basis for comparison, so that the results can be compared with ICP Phase III (1982). One of the major problems in comparisons with the United States is that the nearest US census figures refer to 1977, so that price and volume adjustments had to be made to bring the US estimates to a 1975 base. This problem does not arise in the binary Mexico/Brazil comparisons which are presented in chapter IV.

# Scope of the Production Census and Definition of the Products

Detailed analysis of production census material was the basis for comparing the manufacturing sector. Census data have a distinct advantage over other sources. Large amounts of information on gross output values and quantities, input values and (sometimes) quantities, and employment are available from a single source covering the same establishments. The reliability of the information is backed up by legal penalties for non- or inaccurate reporting. A higher degree of internal consistency can, therefore, be assumed than when making use of different sources to compare sectors. Census material, however, is not perfect or always ideally suited for our purposes, as will be shown below.

With regard to the scope of the production census, it should be emphasized that physical quantities and output values are not specified sepazrately fOr all individual commodities of an industry. For some items only value figures are provided, and some items are not specified individually at all.
Policy Research Working Paper 9645

# **Abstract**

Does a global value chain framework provide additional insights into the question of whether foreign direct investment is beneficial to host countries? The literature has found mixed results on whether foreign direct investment provides positive spillovers over and above mere financing. But the studies have focused on one country, or studies with an international focus tend to abstract from intersectoral linkages. By examining this question in the context of global value chains, this paper provides a much better understanding of the association as well as general validity. It harmonizes three major panel data sets: 1) the Multi-Regional Input-Output table for international input-output linkages, 2) the FDI Markets reports for greenfield foreign direct investment, and 3) the World Bank Enterprise Surveys for firm performance measures. The paper produces a rich panel data set from 2011 to 2017. The findings show that foreign direct investment has a positive effect on labor productivity in sectors and firms within those sectors. Moreover, global value chain participation plays a key role in shaping the foreign direct investment effects. Sectors with lower global value chain participation benefit more from foreign direct investment: doubling the foreign direct investment in those sectors results in an 8 percent productivity gain. The positive effect seems to be due to the increased competition created by foreign direct investment. Foreign direct investment spillovers also take place through domestic and foreign backward linkages, which means that foreign direct investment also has positive inter-sector and cross-border spillovers.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at vmercerblackman@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
Despite a continued significant policy emphasis on social assistance, little is known about the impact of these assistance programs on welfare measures and labor markets in Turkey.<sup>1</sup> The purpose of this study is to fill this gap in the literature, particularly with regard to the relationship between receipt of benefits and labor market activity. Although Alcan, Can, and Pektaş (2015) provide some empirical evidence that social assistance beneficiaries are more likely to transition into the labor force and are less likely to transition into the formal sector, this study explores these issues in more detail covering a longer time period, and using disaggregated social assistance variables which permits the analysis of different types of benefits. We used the _Survey of Income and Living Conditions_ , an individuallevel annual panel data set, in our empirical analysis of the period between 2006 and 2013. The panel nature of the data set allowed us to study the transitions in the labor market statuses of individuals. We used the multinomial logit model for this purpose and estimated the role of social assistance programs in labor market transitions.

Our benchmark estimates implied that in comparison to non-beneficiaries, social assistance beneficiaries are significantly more likely to be unemployed or working informally and less likely to be formally employed. Among the unemployed, the probability of moving to informal employment is found to be significantly larger for beneficiaries than for non-beneficiaries. Another interesting result is that social assistance beneficiaries are more likely to enter the labor force in search of work, suggesting that benefits are not associated with increased dependency.

This is not the first study to supply evidence on the interaction between social assistance and labor market outcomes. There has been a substantial effort in the empirical literature to identify the effects of social benefits on labor market outcomes in order to provide guidance to policy makers on optimally designing benefits programs. Given the relatively more generous nature of social safety nets in developed countries and the limited data availability in the developing world, most of the early literature has been focused on developed country experience, particularly in
While further exploration of these trends is still necessary, these initial findings with SPOT5 not only demonstrate the utility of coarser imagery but also its limitations. Lastly, conducting inter‐city comparison provides valuable insight into the changing shape of African cities.

# CONCLUSION

In order to provide an assessment of the morphology of African cities, this study began by examining the spatial distribution of population within several African cities, and the structural relationship population density has with transportation. It then took a closer look at the composition of the built environment by conducting a semi‐automated classification of each city using high resolution imagery. This classification broke the city down into 9 land cover classes, such as irregular residential, regular residential and commercial/industrial. This classified layer was then analyzed across space as one leaves the city center towards the periphery with variations in the composition highlighted. In conclusion this paper presents a study that relies heavily on remotely sensed data that provide insight into the internal spatial structure of a city. In an exploration of additional sources of imagery, the algorithm was also used on coarser images for 16 cities, suggesting the advantages and costs of moving to alternative sources of imagery.

# Annex

## **Spatial Indicators**

To further characterize the urban form of these 4 cities, several spatial indicators were calculated to measure each city’s structure. Of concern was the amount of sprawl in each city, the degree of clustering in various land cover classes, and the relative distances between residential land and commercial/industrial land. Similar to the population density gradients discussed at the beginning of this paper, these indicators reflect the degree of monocentricity, homogeneity, and compactness in each city.

31
Figure 29. Cumulative government and private capital stock (2004-2014) <!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>Government capital stock Private capital stock<br>Constant 2017 thousand international US$ Equatorial Guinea Gabon Botswana Angola São Tomé and Príncipe Mauritania Congo, Republic of Nigeria Sudan Ghana Cameroon Central African Republic Zambia Côte d'Ivoire Togo Liberia Tanzania Niger Madagascar Mali Uganda Guinea Chad Mozambique Sierra Leone Mauritius South Africa Eswatini Cabo Verde Namibia Comoros Lesotho Eritrea Kenya Senegal Benin Gambia, The Burkina Faso Guinea-Bissau Zimbabwe Rwanda Malawi Ethiopia Burundi<br>Congo, Democratic Republic of the<br><!-- End of picture text --> _Note: Sum of annual general government capital stock per capita and private capital stock in constant 2017 international dollars, between 2004 and 2014. Countries to the left correspond to resource-rich countries and countries to the right correspond to non-resource-rich countries. Source: authors’ calculations using IMF's Investment and Capital Stock Dataset, 2021._

# **The legacy of the boom**

The legacy of the boom for Africa’s resource-rich exporters has proven very mixed. As commodity prices fell in 2014 onwards a challenging period was entered where revenues and exports slumped. Some have weathered the end of the boom well, including managing to sustain impressive rates of growth. Tanzania is an example of this – recording 6.5% during the boom, and 6.3% after the boom. By contrast, a group of countries, and especially those rich in oil and gas, have faced growth slumps induced by the price crash and weak price recovery. Here examples include Ghana, Nigeria and Angola, with boom and postboom growth measured as 7.1% to 5.0%, 6.7% to 1.0%, and 8.2% to -1.0% respectively.

Overall, the
the only country among the three that enacted a major expansion of the beneficiary base to respond to the crisis.

Responses 2 and 3 are hypothetical benchmarks as they assume perfect identification of the poor – something that is only possible under perfect means testing – an ideal that we simulate to establish a “first-best” response. In response 2 we identify in the survey all poor households _before_ the shock and distribute the same fixed budget to all of them; and in response 3 we do the same but consider instead all households identified as poor _after_ the shock hit. The payments simulated here are added to any pre-shock transfer that households may have reported. It is important to note that due to data limitations this simulation does not include other types of transfers that were implemented in response to the COVID-19 shock, such as wage subsidies or new unemployment benefits. Doing this would have required extreme assumptions about how transfers to firms would eventually be channeled to households and about the households affected by shock-related unemployment. It is also important to note that unemployment insurance programs in these countries have very low coverage and thus are not well captured in household surveys.

Response 4 reflects a pragmatic choice – i.e., a universal, flat transfer that divides the fixed budget equally among all households, independently from their income and from whether they suffered an income shock. This scenario captures the absence of any information on which to target resources during an emergency.

Responses 5 and 6 are more realistic versions of 2 and 3. Specifically, pre-shock poverty status is estimated using a proxy-means test indicator (PMT scores) that ranks households’ welfare 12
However, the literature studying the impacts of groundwater irrigation on agricultural outcomes is scant. In one study in Mali, Burney, Woltering, Burke, Naylor, and Pasternak (2010) find the introduction of solar-powered irrigation pumps is associated with a 1.9 ton increase in the production of crops, which results in a 17% reduction in food insecurity in treatment villages. The scarcity of research is attributable to low rates of groundwaterirrigation adoption in the region. We contribute to filling this gap in the literature by simulating the potential gains in agricultural production through groundwater irrigation across the region.

This study makes three important contributions.

First, we create a novel database for SSA that combines agricultural data with a granularity of 291,798 grids with aquifer data composed of 8,099 grids. This approach allows us to combine information on agricultural production, productivity, and climatic and soil conditions with information on groundwater availability for each agricultural grid.

Second, we provide quantitative evidence on agricultural productivity gains associated with groundwater access. Our simulation framework estimates the gains in agricultural production through groundwater irrigation on existing rain-fed croplands. We reach these estimates by determining the proportion of existing crop water deficits in agricultural grids that can be mitigated through groundwater irrigation. We find the median gains in production range between 27.97% and 129.42% depending on the crop of interest and methodology used to predict crop yield gains.

Third, this is the first study to incorporate sustainability conditions on groundwater extraction. We do so by simulating production gains only in agriculture grids with a sufficiently low ratio of groundwater extraction to recharge. Furthermore, our analysis is confined to calculating gains solely from renewable groundwater sources within the region.

The remainder of this paper is organized as follows. Section 2 provides a brief background on the need for groundwater irrigation in SSA. Section 3 details the data used for the simulation. Section 4 provides statistics on groundwater availability in the region and the current state of agriculture. Section 5 describes the methodology used for the simulation. Section
- Auxiliary nurse midwife: a dummy variable that takes a value of one if the village has any Auxiliary Nurse Midwives (ANMs).

- Female doctor: a dummy variable that takes a value of one if the village has a female doctor.

- Share of constituencies won by women in a district: The fraction of state assembly constituencies in a district in which a female candidate won in the most recent state election before the woman-level outcome variable survey.

- Share of close male-female elections won by women in a district: Fraction of state assembly constituencies in a district where a woman won in a close election against a man in the most recent state election before the woman-level outcome variable survey.

- Share of male-female close elections in a district: Fraction of state assembly constituencies in the district that had a close male-female election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female election: A dummy for whether a district had at least one male-female election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female close election: A dummy for whether a district had at least one male-female close election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female close election won by a woman: A dummy for whether a district had at least one male-female close election in which a woman was the winner, in the most recent election before the woman-level outcome variable survey.

- Age: The age of the respondent.

- Rural: A dummy variable equal to one if the respondent lives in a rural location.

- Hindu: A dummy variable equal to one if the respondent reports that her religion is Hindu.

- Muslim: A dummy variable equal to one if the respondent reports that her religion is Muslim.

- Christian: A dummy variable equal to one if the respondent reports that her religion is Christian.

- Sikh: A dummy variable equal to one