rafmacalaba/datause-encoder-data huggingface dataset:
it is used throughout our analysis to deflate nominal R&D investment.

# **6.2 Other Variables Used in the Analysis**

In the empirical analysis we employ US annual data for the period 1959-2007. We use annual frequencies because R&D investment and total employment of R&D performing companies are reported only at annual frequencies. Moreover, data on R&D investment and employment are available only after 1959 and 1958, respectively. The former are obtained from the BEANSF R&D satellite account while the latter are from the NSF annual survey.<sup>22</sup> Our sample excludes the turbulent period after 2007.

Total hours worked in each sector are defined as the number of employed multiplied by average hours worked during the reference year. While data on aggregate average hours worked are available, data on individual hours that correspond to workers employed in the R&D sectors are not reported. In our benchmark specification, _HRt_ is computed as employment in the R&D sector multiplied by per capita hours in the nonfarm business sector divided by a population measure that refers to population over 16 years old (US Census Bureau).<sup>23</sup> To compute hours in the consumption-good sector, we first compute employment in the sector as employment in the nonfarm business sector minus employment in the R&D sector. Then, _HCt_ is computed as employment in the sector multiplied by per capita hours in

> 22The NSF reports data on domestic employment by R&D performing companies which does not include universities and government. Although there are various statistics for employment from NSF surveys, there are difficulties in constructing an aggregate measure of R&D employment series. First, there are no complete data for all years of our sample and second, it is unclear which of the participants in the surveys are actually involved in performing R&D activities. Given those issues and since R&D investment by universities and government constitutes, on average, only 20 percent of total R&D investment we approximate aggregate employment for R&D by the domestic employment of R&D performing companies.

> 23Altig, Christiano, Eichenbaum and Linde (2011) compute their measure of aggregate per capita hours in the same way. Nonfarm business hours and employment are published by the Bureau of Labor Statistics.

30
this investment because of moral hazard (the tenant’s output share is no greater than 50 percent in the vast majority of our cases).

To help explain the decision to take a plot on fi xed rent versus on share, and thus to improve the precision of our IV estimates, we add 6 instruments to our earlier set of 12 (speci fi cally, interactions between two of the plot owner asset variables and three village-level variables: proportion of land sharecropped, proportion of land rented, and gini coefficient for landownership).<sup>35</sup> But even this augmented instrument set does not explain the fi xed rent decision very well, relatively speaking; the F -statistic for the excluded instruments in the fi xed rent equation is only 27.3, compared to 60.6 in the leasing equation. Nevertheless, whether we use instruments (row (9)) or not (row (8)), we can reject with near certainty the hypothesis that plots leased on fi xed rent receive the same amount of investment as owned plots ( p-value = 0.0002 in row (9); < 0.0000 in row (8)). Far less certain is the comparison between fi xed renters and share tenants. The uninstrumented results in row (8) suggest that rented plots actually receive less FYM than sharecropped plots (p-value = 0.022). After instrumenting, the fi xed rent coefficient is practically unchanged but becomes insigni fi cant. If we ignore the endogeneity of leasing decisions (as seems justi fi ed), and return to the fi xed effect tobit estimator discussed earlier, we obtain the results reported in row (10). Here we again fi nd a negative but insigni fi cant fi xed rent effect (p-value = 0.303).

The curious fi nding that share-tenants do not invest any less than fi xed rent tenants, conditional on tenancy duration, might be explained by a monitoring argument. Given moral hazard in current production effort, it may pay for a landlord to be involved in and to supervise his share-tenant’s production activities. Indeed, share-tenants in Pakistan are typically heavily supervised (see Nabi, 1986; Jacoby and Mansuri, 2004), either directly by their landlord or by hired labor managers (kamdars). If there are economies of scope in supervision, then the cost of monitoring a tenant’s investment effort (at least to some degree) is lower in sharecropping than in fi xed rental arrangements. Provided that this monitoring effect outweighs the moral hazard effect, share tenants may invest more than fi xed rent tenants, although an econometric investigation of this hypothesis is beyond the purview of the present paper. Our main result here is that tenants of either type, unequivocally, invest less than owners.

> 35 The fi rst two of these village level variables are computed directly from the full survey sample, whereas the land gini is based on a census of landownership for the entire population of each village. Since these 6 new instruments hardly add any explanatory power to the fi rst-stage regression for the overall leasing decision, we did not use them in earlier IV speci fi cations.

21
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# _E. Determinants of aggregate productivity (TFPQ)_

Table 3 suggests that all three of the predictions of the Melitz‐Ottaviano model about the determination of aggregate industry productivity apply to the production of cinder blocks but not to any of the three food processing industries. The table displays estimates of parameters of the Arellano‐Bond specification of productivity in a plant as an augmented AR (1) process of TFPQ in log units by industry. In the table, market size measured by nighttime luminosity of the city where the establishment is located. Estimates of parameters the model for cinder blocks production are presented in the last column of the table, where TFPQ rises as market size increases but declines with any rise in sunk costs of entry or in transport costs. The decline in TFPQ that an increase in license fees would imply per the column is consistent with the model’s prediction that productivity would fall with any increase in sunk costs of entry. The model would explain this outcome in terms of rising sunk costs having the effect of reducing entry and exit rates in the industry. Similarly, the negative elasticity coefficients of the terms in transport costs is consistent with the prediction of the model that rising transport costs would reduce aggregate productivity. The model would explain this outcome by higher transport costs reducing final demand product substitution possibilities for customers of the industry.

But results contrast sharply with what we see in data on grain mills and bakeries as shown in column 3 (for grain mills) and column 4 (for bakeries) of the same table. In each of these cases TFPQ declines as the market expands but rises if sunk costs of entry or transport costs increase.<sup>6</sup> Broadly speaking, these findings concur with what is reported in Table 4, where we apply the same estimator to the Arellano‐Bond specification but this time while measuring market size by the population size of the city of location of the plant rather than by the city’s nighttime luminosity. In Table 4, all three predictions of the Melitz‐Ottaviano model hold up in the data on

> 6 It is worth point out that the case of the cooking oil industry as reported in second column of Table 3 lies in between these two extreme cases.  This a case where TFPQ rises with market size and falls with rising entry costs, as in the case of the cinder blocks industry, but increases with rising transport costs as in the case of grain processing and bakeries.  That said these the estimates for cooking oil production are based on based on what would by all counts too small a sample of observations and should not therefore be given much weight.

16
# Appendix D. Data Collection Timeline

Figure D.1 illustrates the timeline of the data collection. It denotes the timing of the three rounds of the GHS with post-planting and postharvest visits in the autumn and spring, respectively, for each wave.

The telephone survey on conflict was conducted with 717 GHS households that were part of the GHS panel in wave 3, visit 2. It collected recall data on conflict events at the annual level from 2010 to 2016 and for spring 2017. The blue bar in the figure denotes the conflict intensity over time.

Figure D.2, panels a and b, show the merge of the outcome variables and the conflict recall periods for the consumption aggregate as well as food security, respectively. We have merged the outcome variables with the conflict data from the previous year; in cases where there is more than one year between two visits of the GHS, we have used data from several years.

For consumption, we have data from three waves that have two visits each. The consumption aggregate is the median of the consumption level of those two visits. Hence, we have a measure for consumption at three points in time. Conflict events that occurred _during or before each wave_ are the main independent variable. The conflict events that took place before wave 1 (2010–11) are those that occurred in 2010; conflict events between wave 1 and wave 2 (2012–13) occurred in 2011 and 2012; and conflict events between wave 2 and wave 3 (2015–16) occurred in 2013, 2014, and 2015. Therefore, events that occurred in 2016–17 are dropped from the analysis because they have occurred mostly after the end of the last visit of data collection in spring 2016 (figure D.2, panel a).

For the food insecurity analysis, a six-round panel of the GHS, the first round (visit of wave 1, that is the post-planting visit in 2010) was excluded from the analysis because no conflict information existed for 2009. For the postharvest visit of the GHS (spring 2011), we include events in 2010. For the GHS wave 2, visit 1 (autumn 2012), events in 2011 were included; and for wave 2, visit 2 (spring 2013), events in 2012 were included. For autumn 2015, events in 2013 and 2014 were included; and for spring 2016, events in 2015 were included (figure D.2, panel b).

xv
- Fujita, Masahisa, Paul R. Krugman, and Anthony Venables (1999) _The Spatial Economy_ , Cambridge, MA: MIT Press.

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26
This paper makes two contributions to the literature exploring how outcomes at birth are affected by prenatal shocks. First, rather than considering occasional shocks, we consider the effects of extreme weather shocks, whose incidence and frequency are increasing as a result of climate change (IPCC, 2007). Second, we examine the effects of weather shocks on weight and gestational length, but expand the analysis to other birth outcomes. Two of the aforementioned studies examined the effects on other outcomes and found detrimental effects: the 2001 New York attacks lowered the five minute Apgar score, which is a summary measure of the newborn’s health based on her Appearance, Pulse, Grimace, Activity and Respiration (Eccleston, 2011) and the hurricane in Texas increased the probability of abnormal conditions of the newborn such as being on a ventilator more than 30 minutes (Currie and Rossin-Slaterc, 2013). These studies pertain to particular regions in the U.S., but our study is in a developing country setting and we use administrative, nationally representative birth records, which enhances the external validity of our findings.

Ours is not the first study looking at the wellbeing impact of weather shocks, but it is the first one considering fetal exposure to both extremes of rainfall (drought or flood) and temperature (extremely cold or hot) focusing on health outcomes at birth in a developing country context. Past empirical literature in this area focuses on the effects of after-birth exposure to one type of weather shock on outcomes in childhood and adulthood. This literature has found that excess rainfall during the first two years of life improves the survival probabilities of children in rural India (Rose, 1999), but has detrimental effects on children’s height, weight, language capacities and cognitive development in rural Mexico (Aguilar and Vicarelli, 2011; Conroy et al. 2011). Positive rainfall shocks at birth improve the health, educational attainment and labor market outcomes of women in rural Indonesia, but not of men (Maccini and Yang, 2009). Exposure to a drought leads to growth faltering in childhood in Zimbabwe only if experienced in the second year of life (Hoddinott and Kinsey, 2001; Hoddinott, 2006), but has no effect on height-for-age among children in rural Mexico (Conroy et al. 2011). High temperature days increase annual mortality, particularly among rural populations in India (Burgess et al., 2014). Both extremely cold and hot days lead to higher ageadjusted mortality rates in the U.S., but have no effect on infant mortality in the U.S. (Deschênes and Moretti, 2009; Deschênes and Greenstone, 2011) or on children’s height-for-age in rural Mexico (Conroy et al., 2011). The effects of weather shocks experienced by pregnant women on the health of their offspring are less explored by this literature.

4
respondent. However, the latter could just be picking up a spatial autocorrelation in income measurement errors, giving spurious social effects.

The measurement of comparison-group welfare also poses a problem. In the nearuniversal approach in the literature, the researcher must make an _a priori_ judgment about the relevant comparison group. Yet this can vary greatly from person to person. It has been argued that ―... comparisons are most salient if individuals perceive the reference person or group as in some way similar to themselves‖ (Kahneman and Varey, 1991, p. 140). But that is based on the individual’s own judgment, given idiosyncratic informational and social factors that a researcher would have a very hard time observing in any systematic way. Neither psychological nor economic theories of relative deprivation offer much insight into what constitutes the relevant comparison group. The researcher must make some potentially strong identifying assumptions.

There are also concerns about survey design and implementation effects, including sensitivity to how questions are asked and the context. It is known that precisely how, and in what circumstances, one asks subjective questions matters to the results. In survey data for Britain, Taylor (2006) finds that those interviewed on a Friday report significantly greater job satisfaction and less mental stress than those interviewed mid-week, controlling for other observed factors. Also for Britain, Conti and Pudney (2011) find that seemingly small changes in how questions on satisfaction with work were asked led to large changes in the answers obtained, particularly for women. Moreover, they find that the measurement errors are not ignorable, in that the distortions influence findings on the determinants of job satisfaction. They also find evidence of a ―put a good show for the visitor effect‖ (Conti and Pudney, 2011, p.1093)— whereby there is an upward bias in reported welfare in open oral interviews relative to when the questionnaire is completed in private—and a ―not in front of the children effect‖ (p.1093), whereby there was another upward bias when children were present at the interview. These biases were found to be larger for women. Bertrand and Mullainathan (2001) give other examples of how the ordering and wording of subjective questions can alter responses.

Such problems are not confined to subjective questions. Seemingly objective questions can entail similar biases. My favorite (undocumented) example is from a colleague who was doing field work piloting a questionnaire and found a markedly higher literacy rate for a given population and date when asking ―are you literate?‖ than when using a direct test by showing one 18
**Figure 2. Post-conflict recovery in per capita GDP, in selected post-conflict countries**

|Angola|-1.4||10.6<br>|1990-2000<br>2001-08*<br>End of civil war in 2002|
|---|---|---|---|---|
|Rwanda|-0.3|6.2||End of civil war/<br>genocide (1990-94)|
|Ethiopia|-0.0|5.7||End of border conflict in<br>2000|
|DRC|-8.1|2.2||End of "second Congo<br>war" 2003|
|Burundi|-2.9|1.0||End of civil war in 2000|

_Source_ : World Bank World Development Indicators and IMF World Economic Outlook.

**After the conclusion of the initial peace accord in 2000, the government, with the help of the international community, introduced economic reforms to stabilize the economy and began the process of reconstruction.** To restore the fiscal base and improve tax collection, the Burundi Revenue Authority (OBR) was established in 2009. To strengthen budget transparency and integrity, a number of reforms were enacted, including creation of a court of audit, adoption of modern budget laws, and introduction of a single treasury account. Significant progress has also been made in health and education. For example, the elimination of school fees in 2005 has boosted primary enrollment to nearly 100 percent; and introduction of free health care for children under five and for pregnant women have contributed to the country’s progress toward Millennium Development Goals.<sup>2</sup>

**However, there were setbacks.** Despite the return of steadier growth and increases in external grants, the fiscal deficit (including grants) persisted, averaging 3–4 percent of GDP. As the government increased spending on health, education, and security, the size of the civil service rapidly expanded—caused by the “absorption of former rebels in lawful security forces” and later by hiring in the health and education sectors. Spending on wages and compensation, as a result, shot up by more than 80 percent in real terms in 2005–2010. The government’s revenue raising efforts, in the meantime, remained erratic, with occasional reverses, such as the failure to

> 2 IMF Country Report No. 11/269.

3
**<u>Table 1: Farmer-Level Characteristics</u>**

||**Total**|**Irrigation S**<br>**Kpong**|**cheme**<br>**Weta**||**Crop Estab**<br>**Mode (Kpo**<br>**Broadcast**|**lishment**<br>**ng only)**<br>**Transplant**||
|---|---|---|---|---|---|---|---|
|**Household & main cultivator characteristics**||||||||
|Female headed household (%)|18|18|17||16|20|*|
|Female cultivator (%)|33|33|34||32|33||
|Age (years)|45.4|44.0|49.0|***|44.8|43.2|**|
|Education (years)|8.7|9.3|7.1|***|9.0|9.7|**|
|Can Read and Write (%)|67|70|58|***|65|75|***|
|Farming Main Occupation (%)|89|89|89||90|88||
|No. HH Members|5.9|5.7|6.2|***|5.8|5.9||
|**Household assets and land**||||||||
|Physical (non-land) assets (GHC)|4556|3674|6680|***|3022|4329|***|
|Livestock (GHC)|427.5|415.2|457.6||474.7|356.1||
|No. of parcels|3.08|2.69|4.03|***|2.63|2.74||
|No. of irrigated parcels|1.59|1.72|1.29|***|1.54|1.89|***|
|HH Ag. land irrigated w rice (%)|70|83|38|***|80|86|***|
|**Income in 2012 major season**||||||||
|Cultivator had any wage inc. (%)|19|20|16||20|20||
|Anyone in HH had any wage inc. (%)|31|33|28||33|33||
|Household wage income (GHC)|268|304|181||265|342||
|Received non-farm enterprise income (%)|53|49|61|***|48|51||
|HH non-farm enterprise inc. (GHC)|930|864|1091||701|1026||
|Total income from agricultural parcels|3015|3847|975|***|3124|4566|***|
|Net remittances and other inc. (GHC)|-48|-64|-9|*|-58|-69||
|Total net household income(GHC)|4166|4952|2239|***|4032|5865|***|

_Source:_ Own computation from World Bank Kpong and Weta irrigation scheme survey. Asterisks denote significance of t-tests for equality of means between the preceding columns: *** p<0.01, ** p<0.05, * p<0.1.

17