rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8642

# **Abstract**

How do high-growth firms affect the rest of the economy? This paper explores this question using Hungarian administrative microdata. It finds evidence of stronger productivity growth for firms supplying and operating in industries with more high-growth firms. The surge of high-growth firms’ demand for intermediate inputs could explain this positive vertical spillover. Firms with intermediate productivity levels seem most likely to benefit from this effect. The results hold irrespective of the level of spatial aggregation.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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/research. The authors may be contacted at fdenicola@worldbank.org or swtan@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 the series is to get the findings out quickly, even if the presentations are less than fully polished. 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._ Produced by the Research Support Team
weighted to adjust for non-response and other sampling errors. See **Table 0** for the response rates by treatment.

The survey was implemented using the World Bank’s _Survey Solutions_ software for Computer Assisted Personal Interviews (CAPI). This allowed for daily upload of completed interviews by the field teams, as well as frequent data download and review by the NBS and OCGS headquarters teams to ensure the quality of the data being collected, while providing feedback to the enumerators for further quality control.

# **3.2. Study design**

Three approaches to collecting household food consumption data – 14-day diary, 7-day recall with short items list, and 7-day recall with long items list – are compared in this study. To better understand the differences in household food consumption data collection, this methodological study replicated the HBS 2017/18 (14-day diary) and the NPS (7-day recall with short list) methodologies, as well as a variant/combination of the NPS (7-day recall with long list). Given that the HBS is the survey that NBS and OCGS use to produce official poverty statistics, the diary methodology served as the benchmark and households assigned to this group served as the control group, and the two 7-day recall arms are compared to the diary. In addition, all households in the study received a Food Away from Home (FAFH) module implemented at the individual level to capture expenditure on food consumed outside the home, such as from restaurants, joints, etc. Households were randomly assigned to each study arm in all clusters to ensure non-contamination (Beegle et al. 2012; Brzozowski et al 2017). The design details of each method are described below.

7
0<br>0.05<br>0<br>Scale 0‐1 (1=no diverisifcation)<br>Scale 0‐1 (Lower = Higher Diversification)<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**Table 4: Export Value of Selected Products (% of total exports), Togo, 1980‐2006.**

<!-- Start of picture text --> product code 1980 1985 1990 1995 2000 2005 2006<br>71 Coffee and coffee substitutes 11.0% 14.3% 6.0% 12.0% 5.6%<br>72 Cocoa 13.9% 7.7% 12.0% 22.0%<br>223 Oils seeds and oleaginous fruit, wh 2.4%<br>263 Cotton 12.6% 25.0% 26.7% 22.7% 10.5% 7.2%<br>271 Fertilizers,crude 46.7% 51.2% 52.1% 28.9% 18.1% 10.1% 14.1%<br>333 Petrol.oils,crude,& c.o.obtain.from 9.8%<br>334 Petroleum products,refined 18.5% 5.0% 2.5% 18.1% 13.6% 15.3%<br>661 Lime,cement,and fabricated construc 8.5% 20.3% 12.2%<br>667 Pearls,precious& semi‐prec.stones,u 2.3% 8.2%<br>Grand Total 92.4% 90.8% 88.0% 85
The survey data confirm that the pandemic had a profound impact on people’s well-being, particularly in the beginning of the pandemic in early 2020. Subsequent recovery is observed for many well-being indicators from mid-2020 onwards. While no single variable performs well in predicting changes in well-being, various combinations of indicators from Google search terms, mobility, and satellite data have considerable explanatory power. These predictive models explain the largest share of the variation when measuring the prevalence of income declines and work stoppage since the onset of the crisis in the initial wave of the survey. This is partly due to the large variation across different regions and countries in economic disruption caused by the initial impact of COVID-19. However, the real-time indicators are also correlated with predetermined regional characteristics, like the overall level of development, that were themselves correlated with the intensity of the shock. When predicting variation within regions across time, however, real-time indicators maintain substantial explanatory power. For example, the indicators can explain about a quarter of the variation in the prevalence of income declines across regions, and about 15 percent of the variation in current employment, income gain, food security, and the share of households with children that stopped school. The real-time indicators considered here are significantly stronger predictors of the prevalence of reported income declines across urban regions, where the indicators explain 55 percent of the prevalence of income declines across regions. The models suggest that Google search terms are more powerful at distinguishing differences in the prevalence of reported income declines across urban regions than rural regions. This is consistent with the fact that most Google search queries are originating from urban areas, due to greater connectivity.

Of the real-time indicators, Google mobility data and search data for food, jobs, assistance, exercise, and religion were generally most predictive, though relative importance varies from indicator to indicator. Google search variables accounted for all of the explained variation of the prevalence of total income decrease, both in the first wave and over time, and at least 40 percent of the variation of all
Earnings in the Colombian labor market are, not surprisingly, closely related to a worker’s level of education. Figure 2 pictures the average monthly earnings across age cohorts by level of education. It reveals large disparities in earnings between workers with different levels of education. In particular, there is a noticeable wage gap between workers with tertiary education and other workers. Age strongly amplifies the wage difference. Further, there is an almost linear relation between earnings and age for the education and age-groups considered. The large earning gap could suggest a large potential for adult formal lifelong learning.

**Figure 2 - Average Monthly Wage by Age and Education**

<!-- Start of picture text --> 2,500,000<br>2,000,000<br>1,500,000<br>1,000,000<br>500,000<br>0<br>20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59<br>Less Primary Primary Secondary Tertiary<br>Pesos<br><!-- End of picture text --> Source: Encuesta de Hogares, 2000 Note: The range on the y-axis spans from US$ 0 to US$ 1,200 calculated with the average exchange rate in 2000 of Col$ 2,088 per US$.

Few adults are enrolled in education institutions. Figure 3 illustrates enrollment by education level. For each age-cohort, the bar indicates the net enrolment rate. It is calculated as a share of adults in the given age-cohort that attends the indicated level of education out of the potential number of workers that qualify for that level (persons that have completed the level below).

5
of Enterprise Surveys. Indicator Surveys conducted in mostly Caribbean economies targeted only 150 firms due to small country size. Variation in bribery measure across location-sector cells is low in these countries. There are eighteen countries in this sample compared to 29 countries in the whole region. Excluding these countries shrinks the sample size to 5,586 observations, which is only about one thousand observations fewer than in the entire region. Bribery continues to distort sales growth and more severely for small and young firms (Panel B, Table 9). The differential effect of bribery for firms in the 25<sup>th</sup> and 75<sup>th</sup> percentiles is smaller, 18.5 percent in 18 countries compared to 24 percent for all countries.

# <u>B. Definition of Bribery</u>

Table 10 show results when considering a different definition of bribery. Results are shown for version 2 of the Bribery Indicator where refusals to respond to bribery survey questions are 20
More importantly, despite Latvia’s rapidly rising trade **<u>deficit</u>** <u>, in recent years the forestry</u> sector generated a rapidly increasing trade **<u>surplus</u>** <u>. Indeed, without the forest sector</u> surplus, Latvia’s overall trade deficit would be nearly 50% greater.

**Figure 13 Trade Balance of Latvia**

<!-- Start of picture text --> Trade Balance of Latvia<br>1200 2400<br>1000 2000<br>800 1600<br>600 1200<br>400 800<br>200 400<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Export of Forest Sector Import of Forest Sector<br>Export of Latvia Import of Latvia<br>mln LVL mln LVL<br>Foreign trade of Latvia<br>Foreign trade of forest sector<br><!-- End of picture text --> _Source_ : Swedish National Board of Forestry, 2003 However, the structure of Latvia’s forestry exports and forestry trade surplus cast serious doubt on the long term sustainability or desirability of these trends. Approximately 70% of Latvia’s total forestry sector exports consist of sawnwood, roundwood, firewood, and plywood – products that require minimal processing and which generate limited value added in Latvia. Thus, although Latvia has significant potential to generate more value added in the forestry and wood products sectors, it is only latent potential. Latvia is not yet have the capacity to exploit this potential.<sup>20</sup>

> 20  According to representatives of the Latvian forestry and wood sectors, building this capacity will require action on several fronts including: (i) Training architects and engineers to work with wood and wood products.  These skills and this know-how is currently absent in Latvia.  (ii) Reviving,
Taken together, the evidence reported in Figure 7 suggests that there is considerable scope for future productivity gains from further factor reallocation across ownership forms, given the productivity advantages of the private sector and the relatively small share of labour and capital currently allocated to it. Adding this to reasonable estimates of pure productivity growth in the 1 to 2 percent range, suggests to us that scenarios in which China's productivity level gains on the world average over the next 20 years are reasonable ones to consider.

# _Scenarios for China's Current Account and NFA Position_

We now put all the pieces of the analysis together to come up with estimates of China's net foreign asset position in 2025, and of the pattern of current account surpluses or deficits required to reach this position over the next 20 years. We consider three scenarios. Scenario 1 is basically a continuation of recent trends. China’s savings rate has reached 25 percent of PPP GDP; the economy has been growing at 6 percent per capita according to the Penn World Tables; and the rest of the world’s productivity advantage over China declined from a ratio of 1.2 at the beginning of reform to about 1.0 today. So, one possible scenario is a continuation of these trends (Scenario 1). We think a more likely scenario is that there is some slowing of China’s growth rate, for example to 5 percent per annum. If reform continues, there will be ongoing productivity improvements as resources shift out of agriculture to services and industry, and out of the state sector toward the private. So, in Scenario 2 we have somewhat slower aggregate growth as result of diminishing returns, but a continuation of the historical trend in aggregate TFP and a reduction in the savings rate to about 20% of PPP GDP. As discussed above, some of the current high savings reflects distortions and institutional weaknesses, so that continued reform would result in lower overall savings. In our final Scenario 3, we consider a stagnation in reform
**Figure A.3. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to Target Survey Sample TA 1 for Different Sample Sizes, using 2020/21 Poverty Line, Normal Linear Regression Model, Tanzania**

<!-- Start of picture text --> Model 1 Model 2<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(71) (142) (213) (284) (356) (427) (498) (569) (640) (711) (71) (142) (213) (284) (356) (427) (498) (569) (640) (711)<br>Model 8 Model 9<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(71) (142) (213) (284) (356) (427) (498) (569) (640) (711) (71) (142) (213) (284) (356) (427) (498) (569) (640) (711)<br>% of target survey % of target survey<br>10% (464) 20% (930) 30% (1393) 100% (4644)<br>35 40<br>30<br>30<br>25<br>20<br>20<br>15<br>10<br>10<br>40<br>35<br>30<br>30<br>25<br>20<br>20<br>15<br>10 10<br><!-- End of picture text -->

**Note:** The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects
Table 4: Descriptive Statistics: Rural model

||**2004/05**|**2009/10**|**2011/12**|**2014/15**|
|---|---|---|---|---|
|Log HH per capita expenditure<br>Mean|4.18|4.25|4.40|.|
|HH size|||||
|1 or 2|0.05|0.06|0.06|0.06|
|3|0.08|0.09|0.09|0.09|
|4|0.16|0.19|0.19|0.20|
|5|0.19|0.20|0.21|0.21|
|District HH size|||||
|Share 1 or 2|0.05|0.06|0.06|0.06|
|Share 3|0.08|0.09|0.10|0.10|
|Share 4|0.17|0.19|0.20|0.21|
|Share 5|0.20|0.20|0.21|0.21|
|HH age structure|||||
|Share 0-14|0.38|0.35|0.34|0.33|
|Share 15-24|0.16|0.16|0.16|0.16|
|Share 25-34|0.15|0.15|0.15|0.15|
|Share 35-49|0.17|0.19|0.19|0.20|
|Share 50-64|0.10|0.11|0.11|0.11|
|District avg HH age structure||||
|Task Name|Task Description|
|---|---|
|DCP: Direction, Control, and Planning of<br>Activities|Measures nonroutine cognitive tasks, intended to<br>capture interactive, communication, and<br>managerial skills. This variable captures the<br>extent to which the occupation involves<br>direction, control, and planning of activities. It<br>takes on high values for occupations requiring<br>interpersonal and managerial tasks.|
|GED-MATH|Measures quantitative or analytical reasoning<br>skills.|
|STS: Set limits, Tolerances or Standards|Measures routine cognitive tasks. Measures<br>adaptability to work requiring the setting of<br>limits,tolerances or standards.|
|FINGDEX: Finger Dexterity|Measures routine manual activity. FINGDEX is<br>an abbreviation for finger dexterity.|
|EYEHAND|Measures non-routine manual task requirements.<br>EYEHAND is an abbreviation for eye, hand,<br>foot coordination.|

Notes: Our measure of routine-ness is defined as the sum of the cognitive and manual routine measures divided by all the measures. Routine = (sts + fingdex)/(sts + fingdex + math + dcp + eyehand)

# E. Price of Investment, Total Factor Productivity, and the Real Price of Shipments

The price of investment, which varies by industry and year, is taken from the NBER’s productivity database. Total factor productivity is computed by the NBER for all years through 1996, and was updated through 2002 using data provided to the authors by Wayne Gray. The real price of shipments is taken from this database as well.

# F. Bureau of Labor Statistics Imports and Export Prices

Our data on prices of imports and exports are recorded by the Bureau of Labor Statistics using the Standard Industrial Trade Classification scheme and are weighted by purchases of goods within each industry. These are converted to the 3-digit SIC coding scheme, and then made compatible with the
used mobile money in the last three months. Although 80 percent of respondents reported saving some money, only 8 percent saved in a mobile money account.

The surveys included modules on remittances, asking if households had received money from somebody outside the household during the past six months. For the households that reported receiving money, the surveys then asked for details of the transactions with the person they received money from most frequently. At baseline, only 15 percent of respondents reported that they received money via any channel with 7 percent receiving the transfer via mobile money. Almost all other transfers were hand or bus delivered by somebody in the households or a friend. Conditional on receiving money, the average transportation cost for picking up the money was 7,400 shillings. This amount corresponds to about 20 percent of monthly household expenditures per capita (median of 32,859 shillings, mean of 41,995 shillings).

Most households in the sample engage in agriculture. At baseline, only about 11 percent of the sample worked outside farming, with about 5 percent being self-employed.

# **4.2 Airtel Transactions Data**

We obtained monthly data from Airtel on mobile money transactions for June 2016 to November 2017, including seven types of transactions: sending a peer-to-peer (P2P) transfer, receiving a P2P transfer, cash-in, cash-out, bill pay, airtime top-up and data top-up.

To analyze these data, we had to map them to our study clusters, which was not straightforward. We have the phone number associated with each transaction, but we do not have information on who owns the phone number or where the owner is located. The location can be approximated by the location of the cell phone tower 11
the causal effect of stunting on early childhood development, stunting, which is more easily measured, is frequently used as a stand-in for developmental delay in cross-sectional population health (e.g. Jeong et al., 2019; and Kang et al., 2018). However, there is an ongoing debate about the relationship between child linear growth (height gain) and child development, especially based on the evidence from cross-sectional data. On the one hand, some researchers argue that there is a causal relationship between linear growth or stunting and childhood development, and especially their cognitive ability (Sudfeld et al., 2015a and 2015b). On the other hand, studies such as Tran et al (2019), based on data from 178,393 children aged 36 to 59 months from 55 countries, conclude that the “association between growth and development in early childhood appears to be primarily a cooccurrence because the magnitude of the association varies among settings from no association in higher-income countries to a moderate level in low-income countries, and that.....overall, growth is not a sensitive and therefore suitable indicator of child development.” 3
13 total food expenses, total nonfood non-durable good expenses, estimated use value of durable goods and an actual or imputed rental value of housing.22 Second, data from the IFPRI sl:udy are used to calculate weekly food expenditures by individual food - both subsidized and nonsubsidized - for each household.

This paper disaggregates the population of the 1997 IFPRI sample into quintile groups, which are based on population quintiles ranked by total per capita expenditures. Since an important par; of this analysis focuses on the nutritional benefits received by different expenditure quintile groups, it should be noted that a potential bias arises when the same informaticn on calorie availability (from food expenditures) is used to classify households by expenditure level (food plus nonfood expenditures). For example, households that randormly overestimate food expenditures will also overestimate calorie availability and will tend to be filtered towards the top expenditure quintile groups. As a result, the gap in calorie availability (calorie supply) between high and low expenditure **23** quintile groups can be grossly overstated.

To avoid such potential bias, this paper calculates a "fitted" total expenditure per capita variable for each household by: (1) regressing ncnfood expenditures, household size, and dummny variables for five different geographic regions on food expenditure;<sup>2 4</sup> (2) calculating "fitted" food expenditures from the regression coefficients; and (3) adding "fitted" food expenditures to observed nonfood expenditures.25 In the analysis fitted total expenditures were then used to classify the population by expenditure quintile, separately for urban and rural Egypt.<sup>26</sup> Throughout the analysis the population belonging to the lowest expenditure quintile group are considered to be "poor," and those belonging to the highest quintile
city center, 35% of its land was detected as residential, and roughly 25% of its land consists of commercial/industrial land (i.e. large buildings). Perhaps even more interestingly, almost 40% of the land within that 5km core is not built upon (i.e is bare soil, or vegetation). Main bodies of water (rivers and ocean) have all been masked out. Upon closer inspection of the image, this non‐built‐up land consists primarily of parks, the airport, vacant plots, and some farmland. Figure 20 shows some high resolution imagery displayed behind the SPOT5 land cover classification. The ‘un‐built’ land class has been made transparent, for better visualization, while the commercial/Industrial land has been colored purple and the residential land orange.

<!-- Start of picture text --> Residential<br><!-- End of picture text --> Source: Authors calculation from SPOT5 A certain amount of error will always exist when distinguishing between various land cover classes. Particularly when local land use data are not available and distinctions are being made by imagery analysts. For example, when built‐up land is divided into residential and commercial/industrial, large apartment buildings will often be labeled as commercial/industrial. However this error decreases significantly when the classifier is trained to identify vegetation or bare soil, not merely because the natural environment has distinct spectral signatures (i.e. vegetation has a high Near Infrared value) but also because identifying features such as trees, grass, and bare soil involves less subjectivity. Figure 20 highlights how accurately the classifier is able to detect ‘un‐built’ land, providing evidence of how un‐ built areas are not being equated to un‐used or vacant land.

On average for these 16 cities, the land within 5 kms of the CBD consists of 33% unbuilt land, 42% residential and 24% commercial/industrial. Landlocked cities,