rafmacalaba/data-use-mentions-extended huggingface dataset:
As mentioned earlier, the vast majority of emerging countries have far below 1000 patents and the mean is a mere fraction of that found in the advanced countries which never shows any zeros.

Table 7 breaks the sample into OECD and developing countries.<sup>18</sup> We again report the results for our two preferred estimators although we now have clearer reasons to choose one over the other. Of the 6.5 % of zero patent values in the sample, all are found among the developing countries. Hence, in theory, the LFM estimator should be preferable for this sample and the GMM for the advanced countries. In practice, there is little difference in the estimates for the OECD-.95 for the LFM and 1.04 for the GMMoffering somewhat stronger evidence for constant returns in the advanced countries.

The suspicion that, for some reason, the higher returns to scale arise from using aggregate as opposed to private sector R&D spending turns out not to be justified. Using a reduced sample for the OECD of countries with complete data on R&D done by the productive sector versus the non productive sector (including government) still generates an elasticity very close to unity for both aggregate- and productive-sector investment.

Since our aggregate sample spans a wide variety of sectors and countries which cannot be seen as the aggregate counterpart to the US manufacturing firm level data, asserting that the gap between this finding and the bulk of the micro findings captures spillovers is probably too heroic. Nor does a finding of constant returns to scale in the production of patents necessarily imply the same is true of knowledge generally. Still, the robustness to technique, sample and proxy for variable of the CRS finding for the OECD sample is suggestive that there we cannot rule out scale economies emerging at the aggregate level that may offset the increasing difficulty and/or patent race effects that might be found at the level of the firm.

The results from the developing countries are strikingly different, however, with the LFM for
number of observations is 4,762. The summary statistics is presented in Table 5. The sample average of monthly power consumption is about 350 kWh with a wide range from less than 1 kWh to over 4,000 kWh per month. The metering rate is about 88 percent. The average marginal rate faced by residential customers is about 8 lek per kWh. Note that all the monetary variables are normalized to the 2005 constant term. In theory, the Nordin’s difference variable should be positive under the increasing block tariff regime. However, it can be negative in the real data, because of possible false data reports on bill payments and censored data on electricity
the PNAD is much more disaggregated than the 16 food groups used for the INPC – POF concordance, and given in Table 1. An additional concordance was needed, which maps each sector-specific agricultural activity reported in the PNAD to one of the 16 food groups whose prices we follow. The full concordance takes fourteen pages to describe and is available from the authors on request. An excerpt corresponding to the first four INPC food categories is reported in Annex Table A1 for illustrative purposes.
data is often accessible, matching it with less accessible industrial census data can be challenging, which limit the broader applicability of their method.13 To address this difficulty, we estimate _κ_ based on (2), allowing for a more direct calculation without requiring matched census data:

> 11Given that not all domestic materials are used in the production of exports, to obtain the part of foreign content embedded in domestic materials used for export, we multiply the estimates with _κ_ , which is the share of imports used for exports. The assumption is that the share of foreign content embedded in domestic materials used for exports and the share of imports used for exports are the same. Likewise for domestic content embodied in imports, given that not all imports are used for the production of exports, to obtain the domestic content embodied in imports that are used for exports, we multiply the estimates with _κ_ .

> 12Please refer to Branstetter and Feenstra (2002) for detailed discussions about the trade regimes of China. 13Please refer to Brandt et al. (2017) for a discussion on merging difficulties, particularly for the post-2007 period.

9
addition to Puerto Rico and cross-border outages affecting Canada. Furthermore, the dataset details the location, the start and end date and time, as well as the cause of the power outage. In addition, the number of affected customers is reported, though unknown in about a third of the cases. The database records a total of 2,169 power outage events between 2000 and 2017. In cases when the start and/or end date of an outage event is not recorded the outage duration cannot be inferred – thus these observations are dropped from the sample for all analyses pertaining to outage durations.

The US dataset identifies 130 unique causes of power outages, and a majority of outages record combinations of multiple causes. These causes can be categorized into natural shocks and non-natural causes. For 19 outage events the causes are unknown. Natural shocks fall into six main categories: Storms, severe weather, extreme temperature, wild fire, flooding, and other. Non-natural causes include operational and technical disruptions, including planned load-shedding and un-planned technical failures, as well as external shocks such as vandalism or fuel supply shortages.

## 2.1.2. Europe

The European Network of Transmission System Operators for Electricity reports significant outages that occurred during the period 2000-2016 in the 26 countries of the Council of European Energy Regulators (European Commission, 2018). A _significant disruption_ is defined as (1) causing a long disruption –typically

> 4 The data was retrieved from https://www.entsoe.eu/data/power-stats/ 5 Data was collected by the Bangladesh Rural Electrification Board (http://www.reb.gov.bd/).

4
not find evidence of a significant link between the amount of labor farmers allocate to their plots (including both family and hired labor) and participation in extension services (see Table A12 in Appendix D). Thus, our data seem to provide no empirical support for this concern.

# **5 Conclusion**

In this paper, we study demand for advice in an agricultural setting. We first lay out a novel channel to explain farmers’ demand for agricultural extension services. Second, we provide empirical evidence that is consistent with implications of the model. The empirical work makes use of a large recently collected panel dataset of farmers from three countries in Sub-Saharan Africa.

The theoretical insights we provide on the determinants of farmers’ demand for agricultural advice and extension services center around the idea that participation in extension programs is only worthwhile if farmers devote sufficient attention to demanding, listening to, and implementing received advice. Based on this feature, we model farmers as rational decision makers facing a limited capacity to process information, causing attention to be a scarce resource. Our model gives rise to several interesting predictions. First, farmers’ decision to request extension services will depend on the amount of attention they are willing to devote to their agricultural production process, as opposed to other areas of life. Second, negative shocks to household income or wealth can result in larger demand for agricultural advice. Importantly, this holds even for shocks which do not directly affect optimal farming practices and thus do not constitute an immediate reason for farmers to request agricultural advice.

Overall, the model suggests a specific channel through which limited attention affects agricultural input choices and productivity, based on (imperfect) demand for advice. The derived insights complement the existing literature in this context and can contribute to explanations for low demand for extension service. Specifically, the implications of our model complement the insights of Banerjee and Mullainathan (2008), who argue that “people may not be able to fully attend to their jobs if they are also worrying about problems at
427<br>(0.003)**|
|Fraction of pre-primary institutions with computer|0.849<br>(0.002)|0.370<br>(0.002)|-0.479<br>(0.003)**|

_Notes:_ Standard errors are in parentheses. ** indicates p _<_ .01.

_Sources:_ Author’s calculations based on school- and teacher-level data from the 2008 Census of Schools (Censo Escolar).

Table 2: Number of Municipalities, by Tercile of Income Per Capita and Tercile of Net Per-Child Receipts (Net Per-Child Benefits) from FUNDEF Education Finance Reform, 1998

||Lowest Receipts|Middle Receipts|Highest Receipts|
|---|---|---|---|
|Poorest|51|372|710|
|Middle|664|695|369|
|Richest|638|286|273|
|**Total**|1353|1353|1352|

_Notes:_ The total number of municipalities in the table is smaller than the national total, due to missing data on per capita municipal income or per-capita FUNDEF receipts data. _Sources:_ Author’s calculations based on data from Tesouro Nacional and IBGE (1998).

41
sample of household monthly electricity billing records for the period July 2013 to June 2014 (the same period during which the PSLM data were collected).<sup>6</sup> The DISCO databases have a record of basic household characteristics and monthly electricity consumption, but they do not have any information on the household’s welfare level. To obtain a good proxy for welfare level, we merged the billing data with the National Socio‐Economic Registry (NSER), a near‐census of households in Pakistan conducted in 2011. The NSER has data for 27 million households in Pakistan on occupation of the head, household size, assets and dwelling characteristics. The government collected and used these variables to create a proxy means test (PMT) score for each household for targeting of BISP.<sup>7</sup> The National Database and Registration Authority (NADRA), which manages the NSER, generously agreed to merge the two databases for us, using the citizen’s national identity card number (CNIC) as a common identifier.<sup>8</sup> As well as looking for direct matches between CNICs in the two databases, NADRA also used its ‘family folder’ system to look for family links between CNICs in each database as illustrated in Figure 3. Around 45 percent of the 325,926 records in the DISCO database were successfully matched to the NSER data. This seems low, but is actually quite impressive considering the NSER database was put together in 2011, and many households that did not have a CNIC in 2011 may have since obtained one and recorded it in the DISCO database. Another major reason why records did not match was that many CNICs in the billing data were missing or invalid. As robustness check, we compared the matched and unmatched samples and found that they did not differ substantially (see Appendix Table A2). We are therefore relatively confident that the omission of these households does not skew the results of the analysis. Even though we have
# **Life Expectancy at Birth and Lifetime Education and Earnings**

## **1. Introduction**

Improvements in nutrition, education, transportation, sanitation, and knowledge of diseases have had a dramatic effect on life expectancy worldwide. Vallin and Meslé (2009) have estimated that various improvements in knowledge and technology increased potential life expectancy at birth by 0.33 year per year from 1885 to 1960 and by 0.2 year per year thereafter. A woman born in 2000 is expected to live 33 years longer than a woman born in 1885.<sup>1</sup> This 63% increase in expected length of life has been accompanied by improved health status and enhanced physical abilities which, in turn, should have had profound effects on life-cycle investments in skills, the application of skills to the labor market, and the ability to enjoy life.

Ben-Porath (1967) and Becker (1993) posited that increased length of life would have an unambiguously positive effect on investments in human capital. Both models limited agents to choices of working versus acquiring additional human capital, ignoring the possibility that individuals would consume more leisure rather than spending more time working or learning. Heckman (1976) extended the model to allow both time and financial investments in human capital production and to allow agents to choose labor supply and the consumption of goods and leisure over the life cycle. Still, his model predicts that increased life expectancy at birth would cause individuals to increase also their lifetime human capital production.

> 1 Vallin and Meslé (2009) focused on women’s life expectancy to avoid the effects of war and the higher probability of violent or accidental deaths in their analysis of vital statistics.  Potential life expectancy is based on the highest country life expectancy in each of the years they evaluated between 1750 and 2000.

2
and unskilled labor nominal wages will remain constant as these factors experience high levels of unemployment.<sup>10</sup> The only factor that would change for semi- and unskilled labor is employment.

We assume investment is savings driven. The marginal propensity to save for all nongovernment institutions will be fixed, while capital formation is flexible. The level of savings determines investment. Nell (2002) examines the long-run exogeneity between saving and investment in South Africa and finds that private savings is strongly exogenous to private investment in the period 1977 to 2001. This implies that the savings level will determine investment. (Nell,2002:26).

When analyzing tax reforms, we assume government revenue does not change. Instead, other tax rates adjust when we remove or reduce the VAT.

> 10 Depending in the choice of numeraire, real wages may also be held constant when the nominal wages are held constant. For example, when the consumer price index is the numeraire, real wages are held constant. In this version of the model, the producer price index is the numeraire. Tax changes can affect the consumer price index.

11
br>2000q1 2001q1 2002q1 2003q1 2004q1date2005q1 2006q1 2007q1 2008q1 2009q1 wage-employed with ss self-employed<br>wage-employed without ss not in labor force<br>share individual with ss share household ss covered other informal employment<br>source: Labor Surveys source: Labor Surveys<br>.4<br>.3<br>.2<br>.1<br>0<br>.45<br>.4<br>.35<br>.3<br>.25<br><!-- End of picture text -->

> 6 There are three fifths of individuals at the end of 2004 that rollover to ENOE in 2005. However, in view of some methodological changes implemented to the ENOE relative to the ENE which could affect observations overlapping the two panels, we chose to discontinue rollover of individuals into 2005. In our estimates, differences in methodology across the two datasets are accounted for with either a survey dummy  or with household fixed effects. 7 About 370,000 individuals per quarter.

> 8 Mexico has 2,456 municipalities in 2009 and the data covers 1,272, however we eliminated municipalities that: 1) do not have at least 50 observations once; 2) do not have at least 30 observations which appear at least 4 consecutive quarters.

13
decade. In most cases, missed payments start piling up years before a debt restructuring is arranged, suggesting that the typical default spell on Chinese debts may be particularly protracted.

Yet in many cases there is no evidence of prior arrears, despite the fact that the government restructured its debts with China amid a situation of financial distress. One possible explanation is that some of these debt restructurings were preemptive, meaning that no payments were missed before the debt exchange. Preemptive restructurings are not unusual in the context of external bank or bond restructurings, but “strictly preemptive” cases with no missed payments account for only 13% of cases between 1970 and 2010 (Asonuma and Trebesch 2016). According to DRS data, in the case of China, almost half of the restructurings occurred with no prior arrears. This points to alternative explanations for the absence of arrears.

Horn et al. (2021) document a substantial hidden debt problem, with DRS aggregates underreporting liabilities to China by about 50%. If individual loans are missing altogether from the DRS data in the first place, arrears on those loans will be equally absent. Thus, a “hidden debt’ problem becomes a “hidden default” problem. In addition, Gelpern et al. (2021) and others have shown that Chinese lending contracts are special in several dimensions and make it less likely that a payment default is recorded. In particular, contracts involving China’s state-owned banks regularly ask loan recipients to pay project revenues into escrow accounts in China, or to repay loans through proceeds from commodity exports. In case of late payments, Chinese lenders may first seize the cash collateral in the established escrow account, so that no arrears occur – despite a formal payment default. Similarly, a default by a commodity exporter may not show up, because missed commodity deliveries are not recorded as a default and, accordingly, not formally registered.

In sum, we lack an encompassing and precise empirical basis to measure the start and end dates as well as the size
|Observations|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1. The instrument combines panel variation on the development of new technologies across US cities during the 1975-2000 period with historical settlement patterns for migrants and their ancestors from countries that are recorded in the 1980 Census of Populations. The F statistics in Panels A and B are 4.7 and 11.6, respectively.
to be correlated with redistributive behavior in the literature).<sup>28</sup> To construct indicators of the second dimension we use information from three survey questions that participants answered after the experiment, asking about participants’agreement with the following statements: (1) People are poor in Jordan because of bad luck or injustice (rather than laziness or lack of willpower); (2) Successful careers are a matter of luck and connections (rather than hard work)<sup>29</sup> ; and (3) A just society should make people’s incomes more equal.

Tables 7 and 8 present the results. Table 7 is similar to Table 4 but focuses on distance from the poor rather than age. Results show that distance from the poor does not appear to mediate the effect of the transparency-enhancing treatment, with the interaction term of distance from the poor and treatment being not significant in most specifications. Table 8 focuses on social norms and adds to our baseline specification controls for the scope for giving using the three attitudinal questions described above (Columns 1 to 3) and a composite index, produced with polychoric principal-components analysis, for the importance of redistributing (Column 4). When controls for indicators of being in favor of redistribution are included, results on the effect of the interaction term between treatment and trust on giving remain largely unchanged. This suggests that the relationship of interest is not being driven by differences in these indicators.

Finally, in Table 9 we check the robustness of our results to a different age threshold for youth. Table 9 is similar to Table 4 but considers youth to be those aged 18 to 34. Results on the interaction term between trust and treatment are robust to this alterantive

# 6 Conclusion

This paper analyzes income redistribution preferences and the effect of program design and enhanced transparency on willingness to give up a personal endowment. It uses data from a behavioral experiment conducted on a nationally representative sample of the Jordanian middle class. In contrast to opinion surveys,
-93-

TABLE 8.1 Confrontation of Our PPPs for Manufacturing with the Exchange Rate and with the PPPs of ICP for 1975

||Brazil/USA<br>(Cr./US$)|Mexico/USA<br>(Ps./US$)|Mexico/Brazil<br>(Ps./Cr.)|
|---|---|---|---|
|Our PPPs for Manufacturing<br>(weighted<br>by major branch)|7.42|12.77|1.62|
|ICP (Augmented Binary) PPPs for GDP|5.40|7.17|1.48|
|Exchange Rate|8.13|12.50|1.54|
|Our Exchange Rate Deviation Index<br>for Manufacturing|0.91|1.02|1.05|
|ICP Exchange Rate Deviation Index<br>for GDP (Augmented Binaries)|0.66|0.57|0.96|

Source: Our PPPs for Brazil/USA and Mexico/USA from table 3.17 and for Mexico/Brazil from table 4.10; ICP augmented binaries from Kravis, Heston and Summers (1982), pp. 225, 272 and 313. In fact the preferred ICP PPPs are multilaterally weighted, but we have shown their augmented binaries here because they are conceptually closer to ours. The multilaterally weighted PPPs of ICP were not very different, i.e. 5.20, 7.40 and 1.43 respectively for 1975 (see Kravis, Heston and Summers, 1982, p.177); Exchange rates from IMF; The exchange rate deviation index is the ratio of the PPP to the exchange rate.

TABLE 8.2

|Confrontation<br>of Our PPPs for Manufa<br>from the**ICP 1 5**Au|cturing<br>with<br>gmented Binary
# **1. Introduction**

The relationship between school inputs and education outcomes is of fundamental importance for education policy and has been the subject of hundreds of empirical studies around the world (see Hanushek 2002, and Hanushek and Luque 2003 for reviews of US and international evidence respectively). However, while the empirical public finance literature has traditionally paid careful attention to the behavioral responses of agents to public programs<sup>1</sup> , the empirical literature estimating education production functions has rarely accounted for household re-optimization in response to public spending. This is a critical gap because (a) household responses to education policies will mediate the extent to which different types of education spending translate into learning outcomes, and (b) parameters of education production functions are typically not identified if household inputs respond to changes in school-level inputs (see Urquiola and Verhoogen 2009 for one such example in the context of class-size).

We develop a dynamic model of household optimization that clarifies how increases in school-provided inputs translate into learning outcomes. We then test the main predictions of the model in two very different countries – Zambia and India – using unique matched data sets of school and household spending, and panel data on student achievement. A key contribution of this paper is our ability to measure household spending changes and student test-score gains in response to both _unanticipated_ as well as _anticipated_ changes in school funding. The former measures the production function effect of increased school funding (a partial derivative holding other inputs constant), while the latter measures the policy effect (a total derivative that accounts for re-optimization by agents).

The theoretical framework of a dynamic forward-looking model provides a useful guide to the key issues. In this framework, households' optimal spending decisions will take into account all information available at the time of decision making. The impact of school inputs on test scores depends then on (a) whether such inputs are anticipated or not and (b) the extent of substitutability between household and school inputs in the education production function.