rafmacalaba/data-use-mentions-extended huggingface dataset:
against those of African peers**

|**Indicator**|**Unit**|**South**<br>**Sudan**|**Low-**<br>**income**<br>**countries**|**East**<br>**Africa**|**Middle-**<br>**income**<br>**countries**|**Resource-**<br>**rich**<br>**countries**|
|---|---|---|---|---|---|---|
|Classified road density|Km per 1,000 sq-km of arable<br>land area|15|88|101|278|57|
|Paving ratio|% of primary network paved|2|71.6||32|82.1|
|GIS rural accessibility|% of rural population within 2<br>km of an all-season road||23|21|32|20|
|Paved road traffic|AADT|0|1,341|1,549|3,798|1,408|
|Unpaved road traffic|AADT|53|39|47|75|54|
|Classified network condition|Percentage in good or fair<br>condition|0|86|59||80|
|Overengineering|Percentage of main road<br>network overpaved relative to<br>traffic flows||30||18|15|
|Paved network condition|% of paved roads in good or<br>fair condition|0|86|79|82|68|
|Unpavednetworkcondition|% of unpaved roads in good or<br>faircondition|30|56||58|61|

_Source:_ Derived from AICD calculations.

_Note:_
of residence. The STI-MAR contains the same information on those who have requested asylum in Brazil and is also maintained by the Federal Police. IBGE publishes population estimates of Brazil by municipality every year. It should be noted here that the population estimates of Venezuelan from the two sources may be an overestimate due to double counting and lack of updating of STI-MAR.

# **4.2 Summary Statistics**

## **4.2.1 Education**

The Venezuelan refugees and migrants, 20% of whom are below 20 years and with majority located in Roraima, have the potential to strain the education system. However, according to the education census of 2020, only about 37,738 or 45% of the Venezuelan school age children have been enrolled in school. The gross enrollment rate among the 0-5 years old cohort is about 18% while it is about 74% in the 6-14 years old cohort and only 40% in the 15-19 years old cohort. The Brazilians enrollment rate is much higher across all age groups. Figure 4a reveals that gross enrollment rate in the fundamental level (grade 1 to grade 9) is only about 74% among Venezuelans, compared to 100% among Brazilians, while in high school, the enrollment rate is about 40% among Venezuelans, compared to 80% among Brazilians. The drop-out rate between fundamental and high school seems to be the higher among Venezuelans than the Brazilians.<sup>26</sup> Among Brazilians enrolled in school, 33% are white, while only 15% of the Venezuelans enrolled in school are white. Among the white Venezuelans in school, 65% are in fundamental level and 11% are in high school, while among the non-white Venezuelans, 70% are in fundamental level and 11% are in high school. Given that about half of the Brazilian population is white, the relatively low presence of Brazilians white in the regular schooling needs to be explored further. Although race data on all Venezuelan refugees and migrants are not
The education and journals variables follow the same pattern as GDP, positive at very low levels of development, peaking at a level of educational attainment/journal publication rate typical of a country with GDP per capita around $4000, and then declining. The patent and barriers to entry variables are more interesting. For developing countries in the sample (levels of income per capita less than $14,000), the patents variable has a negative relationship with discovery, and the barriers to entry has a positive relationship. This is consistent with the results above, in that even when GDP is excluded, variables that increase both _π_ and _α_ are having the opposite effect on discovery activity, suggesting that their negative effects via the appropriability parameter are outweighting their positive effects on first-mover profits.

The main conclusions arising from these estimations are therefore that discovery activity appears to have a positive relationship with export growth, and by extension policies that increase exports, that discovery in differentiated products may have a positive relationship with the traditional concepts of technological innovation, and that market failures arising from imitation may inhibit the frequency of discovery.

# **7. Summary and Directions for Future Research**

We began this investigation from the viewpoint that discussions of the emergence of new production, or discoveries, are not informed by systematic empirical analysis. We attempt to fill this void by studying the empirical properties of discovery activity through three theoretical perspectives. This is accomplished by using worldwide disaggregated export data.

37
data elsewhere, we do so for informational purposes only, and any comparisons must consider this caveat.

# **Data description**

Table 2 presents key descriptive statistics of the survey data. Between 2015 and 2019, the survey collected an average of 204,000 observations annually for men and 240,000 for women. However, in 2020, the year most impacted by the pandemic, the number of observations for both groups dropped sharply to 123,000 and 142,000, respectively. By 2021, the data collected had partially recovered, but the observations remained below pre-pandemic levels (188,000 for men and 219,000 for women).

Demographic variables such as average age, marital status, and residential area show a slight difference between the groups, while poverty is more prevalent among women. For both groups, the average age is 36 years, and 22% of men live in rural areas, while on average, 19% of women do. Additionally, 56% of men are married or living in a union, and 58% of women are. Finally, 31% of men live in low-income households, while 37% of women do. A further breakdown of these categories reveals that 21% of the population are young people between 18-24 years old, approximately 42.6% are between 25-44 years old, and 36.5% are between 45-59 years old.

In terms of children in the household, women live with more children than men. On average, for every man, there are 0.3 children aged 0-3 years in the household, 0.2 children aged +3-6 years, and 0.40 children aged +6-12 years. In comparison, women have, on average, 0.3 children aged 0- 3 years in the household, 0.2 children aged +3-6 years, and 0.45 children aged +6-12 years. Moreover, 21% of men live in households with children aged 0-3 years, while 26% of women
||(0.01)|(0.02)|(0.01)|(0.01)|
|---|---|---|---|---|
|**Country fixed effects**|No|No|No|Yes|
|**R2**|0.59|0.39|0.65|0.59|
|**N**|194,850|22,060|85,074|301,983|

# **5. Conclusion**

<mark>This paper examines within-sector resource misallocation using firm-level data for three large Southeast Asian countries: Indonesia, Malaysia, and Vietnam. We build on Hsieh and Klenow (2009) and integrate the methodological correction proposed by Bils, Klenow and Ruane (2020) to explicitly account for the presence of measurement error. Our results document the level of (in)efficiency in these economies that have been playing an increasingly important role in the world economy. We find that the degree of measurement error affecting the estimated allocative efficiency varies by country and changes over time.</mark> <mark>Second, the e</mark> vidence suggests that resource misallocation across firms within a sector is large, albeit declining over time. Private domestic firms and firms with higher productivity face distortions that prevent them from growing. Foreign firms in Indonesia and Vietnam, and SOEs in Vietnam face lower “taxes”, that is, enjoy preferential conditions that make them larger.

Taken together, these results provide valuable insights on the areas where policy makers' attention could focus to improve resource allocation and raise firms' productivity. These lessons can be relevant for developing East Asian economies looking for ways to sustain their economic growth.

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size. This is precisely the challenge described as the “Penrose effect” which refers to managerial capabilities and the absorption of technology as the key binding constraint limiting firm growth (Penrose, 1959). Such a short-term trade-off between high growth in employment and productivity has been observed in Sweden over the period 1997-2005 (Daunfeldt et al., 2010). A report by the UK Department for Business, Enterprise and Regulatory Reform (BERR) (2008) also confirms that high-growth may not necessarily imply high productivity.

This relationship between productivity and high growth could be further nuanced based on whether productivity is measured in growth or in levels. Using data from 2001-2010, Du et al. (2013) find that firms in the United Kingdom are more likely to achieve high-growth when they exhibit higher TFP growth. By comparison, when productivity is measured in levels, the relationship with HGF incidence is negative. The rationale for this difference is that it is usually the larger firms that have higher productivity levels (Van Ark and Monnikhof, 1996) and hence their growth is limited by the large base. It could also be the case that a threshold level of productivity is needed before firms begin to strategize their expansion capacity, which may imply lower growth in the short term.<sup>13</sup> Additionally, the metrics used for assessing high-growth could also be crucial. One could argue that growth in employment is less sensitive to firm productivity but depends more on industry characteristics. Thus, revenue may be a more natural metric for computing the HGF status (Du and Temouri, 2015). However, if it is the more productive firm that expands into international markets (Melitz, 2003) or engages in foreign investments (Helpman et al., 2003), then productivity is likely correlated with higher employment growth. From the perspective of this paper, both employment and revenue metrics are critical, although, given that the focus of developing country policy makers on jobs, HGFs computed using employment metrics is more relevant.
<br>Macroeconomic indicators<br>(GDP growth, current<br>account balance, terms of<br>trade, loan to deposit<br>ratio...)|2000-2009 (main<br>focus)||<br>Need for more concessional financing to moderate the<br>tradeoff between stimulus and sustainable debt levels.<br>Importance of distressing in the banking sectors through<br>liquidity support and deposit guarantees|
|Turkey|Misch & Seymen<br>(2019)|<br>Financial Crisis Surveys<br>Data, change of sales of<br>firms, additional firm<br>specific controls (number<br>ofemployees, share of|2009-2010|Cutting the value added tax and special<br>consumption tax in the first quarter of<br>2009|Positive and robust effects of consumption tax cuts on the<br>change of firm sales|

38
may occur in the context of conflict.  Some of the factors identified in the crime literature, including inequality, may also be associated with conflict, although often through different mechanisms.  Whereas crime could be argued to be largely based around individuals (with varying degree of organization), instances of local conflict appear to be best defined in group/communal terms (Barron _, et al._ 2004b).  The measurement of crime also faces issues of potential underreporting/reporting bias, although phenomena such as property or personal crime incidents can often be defined as more discreet events.  While the Indonesian data from PODES also contains evidence of local crime (it asks whether stealing, robbery, plundering, torture/violence, arson, rape, use of narcotics and murder took place in a village in the past year, and whether levels increased, decreased, or remained the same) we consider an examination of this to be beyond the scope of this paper.

6
The weak correlation between the two variables also holds when the five categories of food insecurity are grouped as either (i) moderate or better, or (ii) high or very high vulnerability to food insecurity (Figure 6). When total agricultural spending (not in per capita terms) and vulnerability categories are compared, results remain very similar, thus confirming that scale considerations do not appear to drive these patterns (Figure 7).

**Figure 6. Per Capita Agricultural Expenditure and Probability of Being in Each VAM Category Grouping**

<!-- Start of picture text --> Total PC AGR Expenditure and Prob of being in each VAM cat<br>Prob VAM 1, 2, 3 Prob VAM 4 or 5<br>.5 .6 .7 .8 .9 1 .5 .6 .7 .8 .9<br>low07 high07<br>PC exp_2007 Fitted values PC exp_2007 Fitted values<br>600 600<br>400 400<br>PC exp_2007<br>200 200<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database. Note: each figure depicts the association between the estimated probability of vulnerability across municipalities pertaining to the respective vulnerability group, that is, low (categories 1 to 3) and high (categories 4 and 5)

**Figure 7. Total Agricultural Expenditure and Probability of Being in Each VAM Category**

<!-- Start of picture text --> Tot Agr Exp and prob of being in each VAM cat<br>Prob of VAM 1, 2, 3 Prob of VAM 4 or 5<br>.5 .6 .7 .8 .9 1 .5 .6 .7 .8 .9<br>low07 high07<br>Tot Exp_2007 Fitted values Tot Expl_2007 Fitted values<br>15000 15000<br>10000 10000<br>Tot Exp_2007
, Jon (2012), Market-based Instruments for International Aviation and Shipping as a Source of Climate Finance. World Bank Policy Research Working Paper WPS 5950. Washington DC: The World Bank, January 2012.

> 31 The EU’s enforcement of its inclusion of non-EU airlines in the EU-ETS has currently been put on hold, until the end of 2013, awaiting ICAO’s decision on a possible global scheme for handling airlines’ carbon emissions.

23
FIGURE 1 Contribution of the global factor to inflation: Literature <!-- Start of picture text --> Percent<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>CPI CPI CPI CPI CPI CPI PPI Cost<br> (A, 99) (Q, 19) (Q, 22) (Q, 36) (A,64) (Q, (Q, 30) shock<br>104) (Q, 30)<br>1970- 1980- 1961- 1985- 1950- 1980- 1995-2011<br>2017 2008 2008 2007 2009 2012<br>Hakkio (2009) Parker<br>(2017) (2017)<br>(2011) Auer et al.<br>Neely and<br>This study Ciccarelli and Mojon (2010) Mumtaz et al. Rapach (2011) (2018) Auer et al.<br><!-- End of picture text --> Source: Hakkio 2009; Ciccarelli and Mojon 2010; Mumtaz, Simonelli, and Surico 2011; Neely and Rapach 2011; Auer, Levchenko, and Sauré 2017; Parker 2018.

Note: Numbers in parentheses indicate the number of countries included. The figure for “This study,” for consistency with other studies, shows the average contribution of the global factor to inflation variation. A = annual data set; CPI = consumer price index; PPI = producer price index; Q = quarterly data set.

17
- Kanbur, R. and Venables, A. J., editors (2005). _Spatial Inequality and Development_ . Oxford University Press.

- Kurosaki, T. and Khan, H. (2006). Human Capital, Productivity, and Stratification in Rural Pakistan. _Review of Development Economics_ , 10:116–134.

- Lipton, M. (1975). Urban bias and food policy in poor countries. _Food Policy_ , 1(1):41–52.

- Manski, C. F. (1993). Dynamic Choice in Social Settings: Learning from the Experience of Others. _Journal of Econometrics_ , 58:12136.

- Mazumder, B. (2005). Fortunate Sons: New Estimates of Intergenerational Mobility in the United States Using Social Security Earnings Data. _The Review of Economics and Statistics_ , 87(2):235–255.

- Mazumder, B., Rosales-Rueda, M., and Triyana, M. (2019). Intergenerational Human Capital Spillovers: Indonesia’s School Construction and Its Effects on the Next Generation. _AEA Papers and Proceedings_ , 109:243–249.

- Neidhofer, G., Serrano, J., and Gasparini, L. (2018). Educational inequality and intergenerational mobility in Latin America: A new database. _Journal of Development Economics_ , 134(C):329–349.

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multiple output from the teacher influence models. As pointed out above, if the chooser is attempting to optimize then new information about the production function should change inputs and outcomes. In this section we argue that the converse is also true: if the optimizing model were really correct then one should _not_ see patterns of input use and outcomes respond to different degrees of incentives. Greater local financing or control of schools, differing degrees of competition, or differing amounts of union power should not affect technical efficiency. But the evidence suggests that these factors do have an important effect both on the allocation of expenditures and the efficacy of outcomes. The following section discusses three sources of evidence relating to various sources of differences in parental power: the degree of local control, teacher unions, and private schools.

_Local and parental contributions._ A first source of evidence is the impact of direct parental participation and contributions. Modest amounts of direct contributions of parents should have large effects on performance only to the extent that some inputs (infrastructure, equipment, instructional materials) are so dramatically under-funded by the publicly allocated budget that their marginal products per dollar are very high. If marginal products were equalized (even according to an optimizing model with multiple outputs) then the relatively small contributions should only have a proportionately small effect. Evidence of substantial impact by parent's groups, or of local control, or of the proportion of resources from local sources is itself a sign of a failure to maximize outputs in the overall system of expenditure allocation.

Jimenez and Paqueo (1996) using data from the Philippines find that schools with a small component of local financing (under _5_ percent) are incredibly less productive than 29
_1987-2018 247.2 75.9 3.0 5.9_ _15.1_ Source: Authors’ calculations based on data from DOSM A more detailed analysis of the three sub-periods from 1987 to 1997, 1997 to 2007, and 2007 to 2018 shows that the growth in output per capita for all three sub-periods can to a large extent be attributed to within-sector productivity growth. Over all three sub-periods, within-sector productivity growth consistently contributed more than 60 percent of overall growth in output per capita. The contribution of within-sector labor productivity to growth in output per capita was the highest in the decade from 1997 to 2007, when it contributed 103.2 percent of growth. Thich means that over this sub-period all other components combined had a negative effect on the growth in output per capita. This was due to a large negative contribution to the growth in output per capita from the change in the employment rate discussed above. The change in the demographic structure had a significant positive and between-sector labor productivity growth a small positive contribution.<sup>6</sup> In the decade from 1987 to 1997, Malaysia benefited the most from the reallocation of labor, both as a share of this component among all components of growth in output per capita and relative to its share in other subperiods (see Figure 16). During the decade from 1987 to 1997, structural change contributed RM 672.3 million or 6.8 percent of overall growth in output per capita. Thus, between 1987 and 1997 a significant amount of labor moved from relatively less productive to relatively more productive sectors. During the other sub-periods, the contribution of structural change to growth in output per capita. was smaller both in absolute terms and relative to other components. In the decade from 1997 to 2007, between-sector shifts only contributed 1.5 percent of growth in output per capita while between 2007 to 2018 its contribution was in fact negative. This means that between 2007 and 2018 labor on average shifted to less productive sectors. These findings are consistent with
measures|
|Number of clusters|108|108|108|108|108|108|108|108|108|
|Mean dependent|0.348|0.267|0.267|0.104|0.0289|0.0289|0.430|0.395|0.395|

* p<.10  ** p<.05  *** p<.01

_Notes_ . The dependent variable “Out of village” in columns (1) to (3) and (7) to (9) indicates whether the respondent lives outside the village where we initially surveyed their household (their natal village most of the time). The dependent variable “Out of Pakistan” in columns (4) to (6) indicates whether the respondent lives outside of Pakistan. Out of the 2,360 women in the sample, only 11 reported residing outside of Pakistan. Therefore, we do not run these regressions for the sample of women. The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the paper-based test and the tablet-based computer-adaptive test (leaving out items that less than 50 respondents answered and items that less than 5% or more than 95% of respondents solved correctly). The socioemotional (SEM) skills index is computed using principal-component factor analysis on the Big-Five items, Grit items, BART, and GoNoGo scores. The sample for columns (1) and (4) is all men surveyed. The sample for columns (2), (3), (5), and (6) is all men who answered the direct version of the questionnaire (for whom we have skills measures). The sample for column (7) is all women. The sample for columns (8) and (9) is all women who answered the direct version of the questionnaire
.  These data weaknesses should be born in mind when considering the econometric results.

# **3.2.2. Econometric Issues**

Panel data generally allow major opportunities for carrying out investigations that are not possible with single-year cross sections or single-country time series, but these give rise to a number of issues which need taking into account for estimation. In our case, with data on 28 countries for 21 years, we have a large and long panel. Because of missing observations, it is an unbalanced panel.

> 18 See Domah, Pollitt, and Stern (2002) for full details.  We are very grateful to Preetum Domah for permission to use the information from his survey in this paper.

16
also call for a series of other data improvements in longitudinal household surveys such as those of the LSMS-ISA. These surveys, in fact, exhibit a clear potential given their intertemporal nature (Carletto and Gourlay, 2019), which also allows them to track over time, and include in the subsequent waves, households that relocate within the country, as well as individual members who split off from previously selected households. A possible additional tool to enhance this traceability is the design and implementation of an “associate module” meant to keep track of “ _people who are not members of the surveyed households but who are somehow associated with them, either because they contribute to the household standard of living and/or because they moved out of the household, and/or simply because they are close relative”_ (Beauchemin, 2020). A complementary solution could be to proxy this information by collecting histories and retrospective data in panel waves covering years between surveys. In any case, the data gaps on migration require one to track people over time and across space, either directly or indirectly. Finally, a third route could consist of establishing a

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