rafmacalaba/data-use-mentions-extended huggingface dataset:
, and increase output per worker by 0.20 percentage point. We obtain similar results by using the logarithm of the annual number of _utility_ patents granted by the USPTO to a resident of a specific country to represent domestic innovation. The statistically significant positive coefficient of 0.18 indicates that a one percent increase in the number of utility patents is associated with a 0.18 percentage point increase in annual growth of real GDP and also growth of output per worker. It is shown in Appendix B that we attempted to account for the differences in the quality of patents by using utility patent citation data available from Jaffe and Trajtenberg (2002). However, we obtained results that were theoretically implausible and have decided to exploit the information contained in the citation data at a later date.

The next domestic innovation variable that we examine is the logarithm of the annual number of published scientific and technical journal articles (Reg 2.3 and Reg 2a.3). We see that the estimated coefficient is positive and statistically significant and the value of 0.22 indicates that a one percent increase in the number of journal article tends

> 45 We also take a look at two other indicators, namely log of the ratio of gross foreign direct investment to GDP, and log of the manufactures trade share in GDP.  However, as presented in Appendix B, the estimated coefficients for these 2 variables were statistically not significant.
as floods, droughts, landslides, forest fire etc. that can lead to direct loss of lives and severely affect livelihoods, a large proportion of Nepalis also face sharp variation in income simply as a result of variation in monsoon rainfall. Now if credit markets were to work well and if households were able to insure against these shocks, one would expect consumption and living standards to not be affected by much. But as shown recently by Tiwari et al. (2017), households in rural Nepal in particular, are far from being shielded from such risks. In fact, by their estimates, the level of exposure is such that a monsoon rainfall that is 10 percent below the historical norm can lead to a 0.12 standard deviation decline in the height-for-age for children.

Using the three rounds of the living standards data and applying the vulnerability of falling into poverty method developed by Lopez-Calva and Juarez-Ortiz (2011), we define a “vulnerability line” as the level of consumption below which a particular household has larger than 10 percent probability of falling back into poverty. In other words, all households that are above the poverty line but below this vulnerability line are categorized as vulnerable. Why do we do this instead of anchoring the definition of vulnerability along a more absolute concept, such as say, a multiple of the poverty line as it is sometimes done? We do it in order to align ourselves to Sen’s (1983) statement that “poverty is absolute in the realm of capabilities but relative in the realm of _functionings_ ”. Extending that notion to vulnerability, this approach takes an absolute stance on what it means to be vulnerable (when a household’s probability of falling into poverty is larger than 10%) but a “relative” position on how much income or consumption is required to achieve that capability.

An additional benefit of the methodology we use is that it allows us to get at the notion of the middle class almost as an immediate byproduct. The vulnerability line thought of as
However, it must be noted that the above numbers based on GLSS are likely to be underestimates of the actual size of informal sector, as many activities are small and fall outside the standards definitions of economic activity and will not be considered as important by households and hence not divulged to enumerators during household or even enterprise surveys. It is an accepted fact that non-farn enterprises always underreport profits as their concept of profits mostly does not take subsistence consumption into account which tend to form a large component of revenue in most households level activities. In the absence of any other meaningful tracking of informal sector activity it can be argued that GLSS type household data can be used to construct reasonable estimates of informal activities in agricultural and nonagricultural sector.

In Ghana's case under household agricultural activity food crops predominate export crops as the main item of consideration in informal sector activities. In certain agro-climatic regions like, rural forest and rural Savannah a large majority of output and value added in crop production comes from infornal activities. In crops maize and cassava are followed by grains and starches, from all of which large proportion is consumed by households. Cocoa production accounts for a very low proportion of total household informal activity income. In cases such as livestock production more than two thirds of income is non-cash and also a large portion is self consumed.

**Table4.4: Source of Household Income by Quintile**

|**Source**|**First**|**Second**|**Third**|**Fourth**|**Highest**|**Ghana**|
|---|---|---|---|---|---|---|
|wage|7.2|9.6|12.1|14.0|17.2|13.6|
|NF self emp income|1.7|0.2|0.5|1.
two.

Table 5 summarizes the relationship between the QAG panel GPE ratings and status or outcome ratings. It shows that operations seen as ̳somewhat responsive‘ or better to GPE issues outperformed those with limited effort or being unresponsive to GPE issues.

**Table** **<u>5: Summary – QAG attention to GPE and project performance ratings</u>**

||**Implementation or outcome**<br>**rating MS or better**|**Implementation o**<br>**rating MU or**|**r outcome**<br>**worse**|
|---|---|---|---|
|_Very or somewhat responsive to GPE_<br>_total PSG_|9|1||
|of which<br>DPLs ILs|5<br>4|0|1|
|_Limited effort or unresponsive to GPE_<br>_total PSG_|4|3||
|of which<br>DPLs ILs|1<br>3|0|3|

The robustness of this finding is limited given the small sample size, sub-sectors of public sector reforms covered, as well as the variance in the status of the ratings (some operations are ongoing, others have been closed).<sup>44</sup> Within these limitations, the data indicates that ignoring GPE issues is associated with a higher risk of poor project performance and that when greater attention is

> 44 More continuous monitoring is probably valuable – including additional years and operations – to generate a more robust sample over time, and one that potentially allows analysis also for specific lending instruments and subsectors.

36
21.7, R2 = 0.93 0.5<br>3 0.0<br>2<br>1<br>0 India's GDP as a share of World GDP<br>India's PCY as a share of US's PCY (constant 2010<br>US$)<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br><!-- End of picture text --> Note: Data are from Central Statistics Office, and World Development Indicators. In panel C, 10-year rolling averages of growth rate are for the current year and the preceding nine years. Years refer to respective fiscal years in Panels A-C and calendar years in panel D. *** p<0.01, ** p<0.05, * p<0.1.

**Figure 2: Growth rates have accelerated and become more stable across sectors**

2 A: A consistent acceleration in growth has 2 B: ...but is evident in industry not been observed in the agriculture sector...

6
0.50|0.67|0.33|
|Deviation in No Rain Days|0.50|0.42|0.92|0.33|0.33|0.33|
|% Rainy Days|0.58|0.58|0.75|0.50|0.58|0.42|
|Deviation in % Rainy Days|0.50|0.42|0.92|0.33|0.33|0.33|
|Longest Dry Spell|0.08|0.58|0.17|0.25|0.25|0.42|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Tanzania.

Table E6: Share of Significant Rainfall Coefficients in Uganda

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.33|0.75|0.58|0.58|0.58|0.42|
|Median Daily Rainfall|0.33|0.08|0.50|0.33|0.50|0.50|
|Variance of Daily Rainfall|0.42|0.75|0.67|0.50|0.67|0.33|
|Skew of Daily Rainfall|0.33|0.42|0.42|0.17|0.42|0.50|
|Total Rainfall|0.33|0.75|0.67|0.67|0.58|0.50|
|Deviation in Total Rainfall|0.33|0.58|0.50|0.58|
et al., 2021) instead would result in a poverty rate of 16.2% in India implying a poverty rate of 9.8% globally amounting to a net impact of 130 million additional poor. The preferred approach in this paper also employs the CPHS but with the adjustments suggested by Roy and van der Weide (2022). This results in a poverty rate in India of 13.4% implying a poverty rate of 9.3% globally and a net pandemic impact of 90 million additional poor in 2020 globally.

> 3 Given that 25% of the fieldwork for the 2019/20 round took place in 2020, we assign it the time 2019.25. To find the growth between the 2019/20 survey and 2020, 2019.25−2020, we first calculate per capita household final consumption expenditure (HFCE) at 2019.25 as a weighted average of the 2019 and 2020 HFCE, with gg 75% of the weight given to 2019. Upon that, we calculate the growth in HFCE from 2019.25 to 2020. A similar procedure is used with the growth from 2020 to the 2020/21 round.

> 4 In this comparison, we only switch the estimate for India to per capita GDP growth, while keeping the approach for all other countries as in the baseline. If instead we used per capita GDP growth for all countries, the global poverty rate would be 9%. For poverty at various poverty thresholds using distribution-neutral projections, see Table B.6.

39
6 long-term income-generating investment projects commissioned by the government, but financed by the private sector, which is to be repaid from the proceeds from the operation of the project. It is not clear to what extent these projects represent a fiscal liability, even though they do not involve immediate fiscal outlays. Moreover, the "BLT" arrangements also raise questions about the extent of private participation involved, other than in the construction itself, and consequent implications for competition in the sector. The amounts involved are significant: for 1997, the Mexican government had programmed 24 billion Pesos (equivalent to 24 % of the 1997 public investment budget) in "proyectos financiados" in the hydrocarbons, electricity and highway sectors. These projects are expected to represent an increasing share of public investments devoted to incomegenerating projects.

# **C. Public Investment and Economic Growth: Time Series Analysis**

_Crowding out or crowding in?_ The discussion in Section A indicated that the impact of public investment on economic growth can be positive or negative, depending on the circumstances under which it is carried out. The preceding overview indicates that these underlying circumstances have varied significantly in Mexico over the last four decades, so that its impact is likely to have varied accordingly over time. Even so, Mexico's experience in this regard is shown below to broadly mirror the international experience described in Easterly et al (1994), which found that public investment has tended, on balance, to exert a crowding out effect on private investment.

The equations in Table 3 describe the results from regressing private investment **(JP)** on the consolidated public sector expenditures (EG), which are decomposed into public investment (IG) and government consumption expenditures (CG), on consolidated public sector revenues (RG) and GDP (Y), using simple least squares.<sup>7</sup> The regressions are based on annual data from 1970 to 1996 from the national income accounts. These series are non-stationary, so the regressions
# **1. Introduction**

Development monitoring and policy evaluation efforts have traditionally put a high weight on progress against poverty, defined in terms of household consumption or income as measured in surveys.<sup>2</sup> It is well recognized that household consumption or income aggregates need to be normalized for cost-of-living differences, including differences in household size, although there are differing views on how the normalization should be done. It has also come to be recognized that neither income nor consumption (even with seemingly appropriate normalizations) can be considered a sufficient statistic for welfare. Standard survey methods allow a fairly complete accounting of the market goods consumed (including own-farm products). However, there are important non-market goods that are typically excluded, such as access to public services for health care or schooling. And household aggregate income or consumption reveals nothing directly about distribution within the household.

There have been attempts to define development indices that allow for these ―nonincome‖ factors. An early example was Morris’s (1979) ―Physical Quality of Life Index‖, which is an average of literacy, infant mortality and life expectancy. A now famous example is the ―Human Development Index‖ (HDI), which aggregates life expectancy and education, along with income.<sup>3</sup> A recent example is the ―Multidimensional Poverty Index‖ (MPI) of Alkire and Santos (2010). This combines 10 components, two for health (malnutrition, and child mortality), two for education (years of schooling and school enrollment), and six for ―living standards‖ (including both access to services and proxies for household wealth).<sup>4</sup> All such composite indices confront the problem of setting the trade-offs (relative weights) across their dimensions. This is essentially _ad hoc_ in all these measures.<sup>5</sup> And there are objections that can be raised. For example, Ravallion (2012a) points out that
However, gender gaps in access to formal employment and market income still persist in Brazil. The proportion of women with formal jobs increased from 41.5 percent in 1999 to 48.8 percent in 2009, but it is still lower than that of employed men, which stood at 53.2 percent in 2009. At the same time, women in formal sector employment work less than men–an average of 36.5 hours a week in 2009, against 43.9 hours for men. Even though there has been progress in the share of women employed in the non-agricultural sector, their comparative advantage in education has not been reflected in relative market wages–in spite of the average higher skill level of the female labor force. In 2008, women’s wages were 84 percent of men’s and the gap increases at higher levels of education.<sup>5</sup> Among those with 12 or more years of schooling, women earned merely 58 percent of men’s salaries. Brazilian women, even those working full time, continue to bear the brunt of time allocated to family chores (Bruschini (2007)); in 2008, they devoted an average of 25.1 hours per week to caring for their families and housekeeping, whereas men devoted an average of only 10 hours per week to such tasks. The unemployment rate for females consistently exceeds by an average of 4-5 percentage points the unemployment rate for males; the gap is up to twice as high for those aged 15-24. According to the Gender Inequality Index (GII) introduced by the United Nations, in 2011 Brazil’s rank is only 80 out of 187 countries, with a score of 0.449–same as in 2008.<sup>6</sup> The Gender Gap Index (GGI) developed by the World Economic Forum since 2006 gives similar results; in 2011 Brazil was ranked 82 out of 135 countries, with a score of 0.668–compared to 0.654
paper, this review mainly focuses on LSMS<sup>2</sup> surveys, a household survey program by the World Bank’s Development Data Group that provides technical assistance to national statistical offices (NSOs) in the design and implementation of multi-topic household surveys It also mentions other sources such as Household Budget Surveys, Demographic and Health Surveys, and Afrobarometer surveys to some extent.

**Table 1. Variety of instruments for household-level data collection**

|**Instrument**|**Sponsor**|
|---|---|
|Censuses|UNFPA|
|Income Expenditure Survey (IES) /Household Budget Survey (HBS)|Central Banks,IMF,NSOs|
|Labor Force Survey (LFS)|ILO|
|Demographic and Health Survey (DHS)|USAID|
|Multiple Indicator Cluster Survey (MICS)|UNICEF|
|Core Welfare Indicator Questionnaires (CWIQ)|UNDP, Department of<br>International Development,<br>World Bank Africa Region|
|Welfare MonitoringSurvey (WMS)|Stat Norway|
|Statistics on Income and LivingConditions(SILC)|Eurostat|
|Comprehensive Food Securityand VulnerabilityAnalysis(CFSVA)|WFP|
|Integrated, Multi-topic Surveys<br>• Living Standards Measurement Study (LSMS)<br>• Integrated Survey (IS)<br>• Family Life Survey (FLS)|World Bank, RAND, NSOs|

# **2.1. LSMS Survey Collection and Characteristics**

## **2.1.1. LSMS Research Collection**

To capture the up-to-date survey questions for understanding household transport characteristics, the paper focuses on the LSMS surveys conducted in 30 countries after 2010 (see Table 2). These surveys mainly fall into two categories; 12 in of 30 country surveys are from the LSMS collection included in the World Bank Microdata Library. The other 18 country surveys are from other multi-topic household surveys, most of which are used as the data sources for PovcalNet<sup
of science.” _The Economic Journal_ , 127 (October): F200‐F208.

McCullough, B.D., McGeary, K.A., and Harrison, T.D., 2006. “Lessons from the JMCB Archive,” _Journal of Money, Credit, and Banking_ , 38(4): 1093‐1107.

Meyer, M.N. and Chabris, C., 2014. “Why psychologists’ food fight matters.” _Slate_ , July 31, 2014. Available at: <u>https://slate.com/technology/2014/07/replication‐controversy‐in‐psychology‐bullying‐file‐drawer‐effect‐blog‐</u> ‐ <u>posts repligate.html (Accessed: February 16, 2019).</u> Miguel, E., and Kremer, M., 2004. “Worms: Identifying impacts on education and health in the presence of treatment externalities.” _Econometrica._ 72(1):159‐217.

Miguel, E., and Kremer, M., 2014. “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities. Guide to Replication of Miguel and Kremer (2004)” Available at: ‐ ‐ <u>http://emiguel.econ.berkeley.edu/assets/miguel_research/46/PSDP REP__2014 11.pdf</u> (accessed March 6, 2018)
tion we observe with the ACT subsidy.

Changes in mismatch rates relative to the Control need to be interpreted with caution, given that more patients visit clinics on _PV_ and _DV_ days. More generally, we cannot extrapolate our findings in the voucher treatments to mismatch rates we would observe in a long-term equilibrium with subsidized ACTs, since the composition of patients visiting the clinic would likely change. However, we have no evidence of differential selection in _PV_ and _DV_ , and the analysis indicates that patient-driven demand in our sample significantly worsens the treatment-illness match.

One weakness of our expected match measure is that it assumes no private information on the part of doctors. Doctors may have access to private signals ̃ – – in particular malaria tests that improve upon _π_ when making prescription decisions.<sup>33</sup> This would bias our expected match measures downwards, because private signals would improve the match of treatment to illness _con-_ ̃ _ditional_ on _π_ . Assessing the consequences for estimated treatment effects is more difficult, since this depends on what drives “marginal” prescriptions in the different experimental conditions. For example, suppose all marginal prescriptions in _PV_ were driven by malaria negative patients. In this case, we would falsely estimate a positive effect on _mp_ (since marginal patients ̃ will have _π >_ 0) and underestimate the negative effect on _mn_ .

Appendix Table B16 uses the home-tested RDT subsample to get an idea of how much private information skews our expected match estimates. We find strikingly similar treatment effects on the expected and actual match for the home-tested subsample.Control group means are also similar, at 0.48 for the expected match and 0.51 for the actual match. Analogous numbers for purchases are 0.56 and 0.61. While these numbers are consistent with some private information, the impacts on match estimates are very modest, likely because providers often do not use, or do not comply with malaria tests.

# **5 Conclusion**

A critical challenge for developing country health systems is
**Figure 7:  The Human Capital Index**

<!-- Start of picture text --> Human Capital Index<br>Singapore<br>Korea, Rep.<br>Germany<br>United States<br>Russian Federation<br>Vietnam China<br>Turkey<br>ColombiaThailand<br>PhilippinesIndonesia<br>Kenya Morocco<br>India<br>Malawi Benin South Africa<br>Mozambique Côte d'Ivoire<br>Chad<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>1<br>.8<br>.6<br>.4<br>Productivity Relative to Benchmark<br>.2<br><!-- End of picture text --> Notes: This figure reports the Human Capital Index. The vertical axis measures productivity relative to the benchmark of complete education and full health. A value of x on the vertical axis means that the productivity as a future worker of a child born today is only x�100 percent what it would be in the benchmark of complete education and full health. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for 157 countries. Selected countries are labelled for illustrative purposes.

16
15 Note that for all simulations, the 1991/92 HBS data is taken as corresponding to calendar year 1992, and likewise the 2000/01 data is taken as being from 2001. Consequently, the projections apply the annual growth rates starting in 1993, the year following the first survey. The simulations are carried forward one additional year past the second survey, to 2002. An alternative choice would be to assign the two surveys to calendar years 1991 and 2000, respectively, and apply the growth rates from 1992 onwards. Several versions of the analysis (not shown) were run with this assumption, and results were similar.

# **5.1 Simulation with Uniform National Growth Rates**

The simplest version of the analysis is to apply the national GDP per capita growth rates, as shown in Table 2, uniformly to all households. Headcount rates from this simulation are shown in Appendix Table A2 and plotted in Figure 4 below. Poverty levels for the two years corresponding to the surveys, 1992 and 2001, are shown in italics. Headcount rates from the 2000/01 HBS are also appended to the end of table for comparison purposes. The simulated poverty rates for 2001 match the survey data surprisingly well. The survey estimate of 0.357 is well within the confidence interval of the simulated value, 0.343. The simulated headcount rates for “other urban areas” and rural areas also match up well with the survey estimates. This simplest form of the simulation, however, does not reproduce the steeper decline in poverty in Dar es Salaam.

**Figure 4**

<!-- Start of picture text --> Simulated changes in poverty<br>using national growth rates<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>