rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Becker, Bettina (2013). “T</mark> <u>he Determinants of R&D Investment: A Survey of the Empirical Research,” Discussion Paper Series</u> <mark>2013_09, Department of Economics, Loughborough University.</mark> Becker, Bettina. 2015. “Public R&D Policies and Private R&D Investment: A Survey of the Empirical Evidence.” Journal of Economic Surveys 29(5): 917–42.

Bernard, A. B., & Jensen, J. B. (1999). Exceptional exporter performance: Cause, effect, or both? _Journal of International Economics, 47_ (1), 1-25.

Bloom, N., & Van Reenen, J. (2007), “Measuring and Explaining Management Practices Across Firms and Countries,” _Quarterly Journal of Economics_ 122 (4): 135 – 1408.

Bloom, Nicholas, Heidi Williams and John Van Reenen. 2019. “A Toolkit of Policies to promote Innovation” Journal of Economic Perspectives 33(3): 163–184.

Blundell, R., Griffith, R., Van Reenen, J. (1999). Market share, market value and innovation in a panel of British manufacturing firms. Review of Economic Studies 66: 529-554.

Broadberry, S. and Crafts, N., 2001, ‘Competition and Innovation in 1950’s Britain,’ _Business History_ , 43, pp. 97–118.

Brown, J.R., Fazzari, S.M and Petersen, B.C. (2009) Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. _Journal of Finance_ 64: 151-185.

Brown, J.R., Martinsson, G. and Petersen, B.C. (2012) Do Financing Constraints Matter for R&D? _European Economic Review_ 56: 1512-1529.

Brouwer, E., Kleinknecht, A. (1999). Keynes-plus? Effective demand and changes in firm-level R&D: an empirical note. Cambridge Journal of Economics 23, 385-391.

Bustos, Paula (2011). “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms,” American Economic Review 101 (1): 304– 340.

Coad A, Pellegrino G, Savona M (2016a) Barriers to Innovation and Firm Productivity. Econ Innov New Technol 25(3):321–334. <u>https://doi.org/10.1080/10438599.2015.1076193</u> Coad A, Segarra A, Teruel M (2016b) Innovation and firm growth: Does firm age play a role? Research Policy 45(2):387–400 Cohen, W.M. (2010). Fifty years of empirical studies of innovative activity and performance. In Hall, B., Rosenberg, N. (Eds). Handbook of the Economics of Innovation vol 1, 129-213. NorhHolland.

36
emissions (Table 9). For example, less than one-third of energy (coal) is needed to produce alternative bricks compared to clay-fired bricks. This less energy consumption translates into avoided emissions of 77% to 80% of CO2 and over 90% of PM2.5 emissions when alternative bricks are used for building construction. Further, there is also a much lower social cost of PM2.5 associated with alternative bricks than with clay-fired brick production. It is estimated that the social cost of alternative bricks is NRs 0.29 for CSEB, and NRs 0.26 for HCB compared to NRs 6.81 for clay-fired bricks from ZZK. These energy and environmental benefits associated with alternative bricks are important in promoting them in the country.

**Table 9: Avoided coal, CO2, and PM2.5 emissions, and social cost of alternative building materials wrt clay-fired brick from selected kiln technologies**

||Avoided energy (%)|Avoided e|missions(%)|Sil f PM25 NR|
|---|---|---|---|---|
||Coal|CO2|PM2.5|oca cost o . (s)|
|CSEB|69|77|92.6|0.29|
|HCB|72|80|93.4|0.26|

Sources: (Fordham, 2021; Maithel et al., 2017) and authors’ calculations.

5.4 International trade implications of coal replacement with domestic biomass in the brick industry One of the key advantages of replacing imported coal with domestically supplied biomass fuels would be savings on import bills. Figure 8 shows the significant amount of coal imports that can be saved through switching from coal to domestic biomass pellets. The savings are calculated with an increasing percentage of coal substitution by domestic biomass pellets from the conventional kilns (FC BTK and ZZK) in 2021. It is estimated that the annual coal import savings range from NRs 2.1 billion to NRs. 6.1 billion if 10% of the coal is substituted by biomass pellets (Figure 10). Since all coal is imported in Nepal, these figures translate to about 6.5% to 18.9% of total coal import cost savings in 2021 (NTIP, 2023). It is important to note that the higher the percentage of coal substituted by biomass pellets, the higher the fuel cost savings. For example, if all coal is replaced by domestic biomass pellets for brick making from FC BTK and ZZK combined, close to two-thirds (NRs 20.6 billion) of total coal import costs can be reduced. Although all FC FTK and ZZK may not be able to run 100% on biomass pellets in the country for many practical reasons, 27
<!-- Start of picture text --> .4 .6 .8 1 1.2<br>.4 .6 .8 1 1.2 Trade Openness<br>Trade Openness Source: Authors’ calculations<br>(a) Product Herfindahl is low (b) Market Herfindahl is low<br>.4 .6 .8 1 1.2<br>.4 .6 .8 1 1.2 Trade Openness<br>Trade Openness Source: Authors’ calculations<br>(c) Product Herfindahl is medium (d) Market Herfindahl is medium<br>.6 .7 .8 .9 1 1.1<br>.6 .7 .8 .9 1 1.1 Trade Openness<br>Trade Openness Source: Authors’ calculations<br>(e) Product Herfindahl is high (f) Market Herfindahl is high<br>8 15<br>6<br>10<br>4<br>Growth Volatility Growth Volatility 5<br>2<br>0<br>0<br>10<br>10<br>8<br>6<br>5<br>Growth Volatility 4 Growth Volatility<br>2<br>0<br>0<br>10 15<br>8<br>10<br>6<br>Growth Volatility 4 Growth Volatility 5<br>2<br>0<br>0<br><!-- End of picture text --> Figure 1: Plots of standard deviation of GDP per capita growth against trade openness, with each row of the left (right) column capturing country-year observations from low, medium, and high levels of product (market) concentration, with fitted (navy) regression lines, excluding outliers identified by the Hadi (1992) multivariate detection procedure. The pattern of a negative (positive) relationship between volatility and openness when diversification is high (low) is evident for both classes of diversification measures.

is at this point only suggestive in nature, it indicates that product diversification may be more important than market diversification in shielding an economy from the adverse impact of external shocks.

10
# **2 Rural households operating under social prescriptions**

Our context is that of households in an agriculture-based rural economy, in which members maximize their utility over consumption and leisure. Households’ utility depends, inter alia, on how their members conform (or do not conform) to behaviors that are considered socially appropriate. The purpose of the framework is to i) illustrate the links between traction power, agricultural production and labor supply in this setting, and ii) predict what happens to household members’ labor supply in the face of gendered social prescriptions related to traction power.

## **2.1 Setup**

Our framework incorporates Akerlof and Kranton (2000)’s formalization of the insight that identity–including gender identity and the social prescriptions that accompany it–is a motivation for behavior, and draws on Kevane and Wydick (2001)’s model of time allocation under social norms.

In our simple setup, farm households maximize utility U subject to income, production technology and time constraints. Men own farm capital _K_ , including traction oxen, and female household members divide _T_ units of work time between farm work, _Tf_ , off-farm work _To_ , and domestic work _Td_ . The household has a production function _A_ ( _K, Tf_ ) that corresponds to output on the husband’s farm, and a function _H_ ( _K, Td_ ) that corresponds to domestic production, the value of which also depends on the household’s farm capital. Off-farm production, _To_ , is valued by the market at price _p_ . We assume that _ATT <_ 0, _AKK <_ 0, and _ATK >_ 0, and similarly for _H_ ().<sup>2</sup> The labor supply of the husband is assumed inelastic and its allocation is fixed across activities for the purposes of this simplified model, where we are interested in examining female household members’ labor supply.

As in Akerlof and Kranton (2000), an individual’s utility is a function of their social category and the extent to which their own and others’ actions correspond to behavior indicated by social prescriptions P. In our rural setting of northern Cˆote d’Ivoire, one example of such a ‘benevolently sexist’ social prescription is that women should not undertake activities perceived as risky, including handling large livestock (as discussed in Section 1). Since this social prescription does not regard women’s farm work overall, but specifically farm work when oxen are used ( _K_ =1), we modify Kevane and Wydick (2001)’s formalization of this norm’s impact on behavior by focusing on women’s deviation from average oxen use by other women in their community:

> 2 While an expansion of farm capital such as traction oxen increases the productivity of domestic production, e.g., through easing water/fuel transport, this impact may be offset by increased chore responsibilities such as animal care. We thus assume that _HTK_ , though positive, is close to zero.

4
McKenzie, David. 2003. “Asymptotic theory for Heterogeneous Dynamic PseudoPanels.” Journal of Econometrics, 120: 235-262.

McKenzie, David and Hillel Rapoport. 2007. “Self-Selection Patterns in Mexico-US migration: the role of migration networks.” World Bank Working Paper no. 4118.

Moffitt, Robert. 1993. “Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-Sections.” Journal of Econometrics, 59: 99-123.

Schoeni, Robert. 1997. “New Evidence on the Economic Progress of Foreign-Born Men in the 1970s and 1980s.” The Journal of Human Resources 32(4): 683-740.

Schoeni, Robert. 1998. “Labor Market Outcomes of Immigrant Women in the United States: 1970 to 1990” International Migration Review 32(1): 57-77.

Verbeek, M. and T. Nijman. 1992. “Can Cohort Data Be Treated as Genuine Panel Data?” Empirical Economics, 17: 9-23.

Warren, R. and E. Kraly. 1985. “The Elusive Exodus: Emigration from the United States.” Population Trends and Public Policy Occasional Paper no. 8, Population Reference Bureau. Washington D.C.

Warren, Robert and Jennifer Marks Peck. 1980. “Foreign-Born Emigration from the United States: 1960-1970.” Demography 17(1): 71-84.

33
# **1. Introduction**

Among the geographic regions of the world, Sub-Saharan Africa has had the highest incidence of poverty since around 1990. Furthermore, progress against poverty has long been slower than in other regions. This has changed in the new millennium. Sub-Saharan Africa has enjoyed a sustained period of economic growth, as well as robust poverty reduction since the late 1990s. In addition to its high incidence of poverty, Sub-Saharan Africa has, in the past, been singled out for the significant share of households headed by women. The literature to date has generally suggested that female-headed households tend to be poorer.

This raises the question of what has been happening to the prevalence of female-headed households (FHHs) and their living standards during the recent period of sustained growth and declining poverty. Hypotheses can be advanced either way. On the one hand, high poverty can constrain the prospects of escaping poverty, such as when caught in a poverty trap. On the other hand, poor people may face relatively high economic returns to the new opportunities unleashed by growth; the poor are also likely to have benefited disproportionately from the expansion of social protection in the region (World Bank 2012). To complicate matters, the group of people living in FHHs may be fundamentally changing over time. Hence our interest in the two broad questions addressed by the paper: Have we seen more or less female headship during the recent period of aggregate reduction in poverty? And is poverty also falling for FHHs, or are they being left behind?

The paper provides new evidence on FHHs for Sub-Saharan Africa (Africa hereafter) drawing on the micro data from all suitable national household surveys. All the available Demographic and Health Surveys (DHS) for Africa are used to describe the current prevalence and characteristics of FHHs, and to examine changes in their prevalence over the last 25 or so years covered by the data. The macro, population and demographic factors that correlate with the incidence of FHHs, and changes in that incidence, are also explored. With these supplementary data we investigate the roles of rising education levels, and changing social norms, as well as the roles of circumstances and exogenous shocks, such as economic, health and conflict crises, and the breakup of traditional systems of patriarchal marriage and family norms.

Using a set of high-quality household consumption surveys for 24 countries with comparable surveys for at least two dates, the paper goes on to examine how FHHs have fared during the recent improvements in living standards, and provides a breakdown of the total change in poverty into that contributed by male- versus female-headed households. To examine potential heterogeneity among FHHs, the paper extends this analysis to different types of FHHs.

2
caveat to our survival results, however, is the reliance on time series variation, which leads to weak identification.

New research on the survival of formal firms in low-income countries has important implications for development strategies. In order for the private sector to act as an „engine of growth‟ and advance the development process, it is necessary for firms to survive and grow. It is also important for policy makers to understand and consider the impact of government policies on private sector survival and growth.

An important caveat is that our analysis covers all formally registered firms, but excludes firms that operate informally, even though the informal economy encompasses an estimated 72% of jobs in Sub-Saharan Africa (OECD, 2009).<sup>3</sup> Yet it remains important to understand job dynamics among formal sector firms, since the inability of economies to create legal jobs is one of the causes of the expansion of the informal economy. Data from the World Bank Enterprise Surveys of informal firms in three African countries suggests that most informal firms have less than 2 employees and unlikely to contribute to job creation. For instance, 46% of informal firms in Cote d‟Ivoire (as compared to 39% in Madagascar and 31% in Mauritius) start not because the owner sees a good business opportunity, but because the (largest) owner could not find alternative employment opportunities (“unwilling” entrepreneurs). Furthermore, 81% of firms in Cote d‟Ivoire (as compared to 72% in Madagascar and 49% in Mauritius) operate within household premises and few have paid non-family employees (World Bank, 2010b).<sup>4</sup> Furthermore, in general, labor productivity and TFP are higher on average in formal firms than in large informal firms, which in turn have higher productivity than small informal firms (Amin, 2009). The literature also highlights other potential advantages of formal sector participation for large firms, including police and judicial protection (and less vulnerability to corruption and the demand for bribes), access to formal credit institutions, the ability to use formal labor contracts, and greater access to foreign markets (Schneider and Enste, 2000). Firms that choose to remain informal may be unable to realize their full growth potential. Therefore, “high-growth” entrepreneurship, which is most likely to lead to formal job creation and

> 3 The formal sector represents around 60% of GDP in Cote d‟Ivoire (OECD, 2004; Berthelamy and Bourguignon, 1996).

> 4 The Mauritius statistics are statistically different from those of Cote d‟Ivoire at the 1% level; the Madagascar statistics are not significantly different from those of Cote d‟Ivoire even at the 10% level.

3
Høyland et al. to be ―almost surely‖ in the top 10 of the DBI, while Congo, Zimbabwe, Chad and the Central African Republic are almost surely among the 10 countries doing worst. However, most rankings in the middle 80% look far more uncertain. Høyland et al. (2010, p. 15) conclude: ―In contrast to the key message of the precise ranking published in the Doing Business report, it is clear that the index does not do a very good job in distinguishing between most of the regulatory environments in the world. While the rankings, after taking uncertainty into account, clearly distinguish the best economies from the worst, it does not distinguish particularly well between the economies that are somewhere in between.‖ Turning to the HDI, Høyland et al. find that no country has more than a 75% chance of being in the top 10 in terms of this composite index, though we can have more confidence about which countries have very low HDIs. Similarly to the DBI, there is great uncertainty about the middle rankings. For example, Georgia has a DBI rank of 18, but Høyland et al. find that the 95% CI is that the true ranking lies between 11 and 59. To give two more examples, Saudi Arabia has a DBI rank of 23 but a 95% CI of (12, 63), while for Mauritius, with a DBI rank of 27, the CI estimated by Høyland et al. is (16, 77).

In the light of their findings, Høyland et al. argue that it would be more defensible for these composite indices to try to identify a few reasonably robust country groupings than these seemingly precise but actually rather uncertain country rankings. Of course, there will always be a degree of arbitrariness about such groupings; for example, the 2010 edition of the HDR uses quartiles. However, Høyland et al. provide defensible country groupings for the HDI (and DBI) for various ―certainty thresholds,‖ given by one‘s desired confidence that there is a difference between the top and bottom ranked country within a given group.

The EPI has been subjected to numerous sensitivity tests, reported in Saisana and Saltelli (2010). They find that the rankings for 60 of the 163 countries ―...depend strongly on the original methodological assumptions made in developing the Index and any inference on those countries should be formulated with great caution‖ (p.3). For the other 103 countries, the ranking was considered reasonably robust, although this only means that the actual EPI rank lies within a confidence interval that could span up to 20 positions in the country ranking.<sup>34</sup> Probing some of the data provided on the websites for recent mashup indices also helps give us an idea of their sensitivity to different weights. For example, I find that Finland‘s ranking

> 34  Also see the results on the EPI reported in Foster et al. (2009).

23
# **Small Enterprise Growth and the Rural Investment Climate: Evidence from Tanzania**

Tidiane Kinda CERDI-CNRS, Université d’Auvergne tidiane.kinda@ecogestion.u-clermont1.fr Josef L. Loening* World Bank, Washington D.C. jloening@worldbank.org Keywords: Rural labor markets, rural investment climate, enterprise growth, Tanzania. JEL Classification: L10, O12, D92.

_* Corresponding author. This paper emerged from the Tanzania Pilot Rural Investment Climate Assessment. We would like to thank James Keough for exceptional research assistance, the Tanzania National Bureau of Statistics for being able to make extensive use of the survey, participants in the 2007 Dar es Salaam Rural Investment Climate seminar, as well as Klaus Deininger, Henry Gordon, Songquing Jin, Mary Hallward-Driemeier, Hans Hoogeveen, Donald Larsson, Abdoul Mijiyawa, Robert Townsend, Kees van der Meer and Michael Wong for useful discussions and comments. The views expressed in this paper are our own and do not necessarily reflect those of the institutions we are affiliated with. Financial support from the Norwegian ESSD and the Netherlands’ BNPP Trust Funds is gratefully acknowledged._
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**Table 17. Family and repro health outcomes (ITT), disaggregated by** **<u>gender, 2010, 2011 and 2012 cohorts</u>**

|||Pooled 20<br>IPS|10‐2012 Cohorts<br>W model||Difference between|
|---|---|---|---|---|---|
||Baseline mean for men|Baseline mean for women|Men|Women|men and women|
||[Std Dev]|[Std Dev]|(1)|(2)|(3)|
|Desired number of children|2.141|2.219|0.098**|‐0.068**|0.167***|
||[0.704]|[0.800]|(0.043)|(0.027)|(0.051)|
|Pregnant (1= Yes), if married|0.044|0.035|‐0.012|0.017|‐0.029|
||[0.205]|[0.184]|(0.019)|(0.013)|(0.023)|
|Number of children|0.857|1.281|0.011|‐0.027|0.039|
||[1.197]|[1.366]|(0.018)|(0.017)|(0.025)|
|Ever argue with spouse/|0.772|0.812|0.001|‐0.002|0.003|
|partner, if applicable (1=Yes)|[0.420]|[0.390]|(0.050)|(0.025)|(0.056)|
|Use any type of contraception|0.818|0.742|0.031|0.013|0.018|
|(1=Yes), if sexually active|[0.386]|[0.437]|(0.037)|(0.028)|(0.046)|
|HIV Knowledge score (0‐3)|1.244|1.090|‐0.044|0.057|‐0.101|
||[0.739]|[0.772]|(0.057)|(0.055)|(0.079)|
|Household food insecurity (0‐|0.607|0.521|‐0.058|‐0.062*|0.003|
|4)|[0.787]|[0.762]|(0.052)|(0.036)|(0.063)|
|Weekly protein consumption|0.273|0.388|0.080*|‐0.025|0.105*|
||[0.446]|[0.487]|(0.046)|(0.035)|(0.058)|
|Household receives|0.202|0.263|‐0.004|0.014|‐0.019|
|remittances (1=Yes)|[0.402]|[0.440]|(0.038)|(0.031)|(0.048)|
|Household received more|0.078|0.119|‐0.013|0.029|‐0.042|
|remittances than last year|[0.268]|[0.324]|(0.027)|(0.025)|(0.036)|
|(1=Yes)||||||
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

40