rafmacalaba/datause-encoder-data huggingface dataset:
the _Plus_ modality ( _p_ -values = 0.746, 0.183, 0.442, 0.517, 0.539, and 0.575). In spite of quite noisy estimates due to the sample attrition and the reduced sample size—parents in the control group cannot be part of this analysis by design—the evidence does show a systematic pattern. Over a two-month period, mentors in the _Plus_ modality met one time more with parents at school and 0.7 times more at home compared to those in the _Original_ modality (sample means in the _Original_ group are five and three, respectively). The GLS-weighted index shown in the third column documents that the quantity of parent-mentor interactions increased by 0.36 standard deviations under the _Plus_ modality, which is significant at the 10 percent level. Columns 4 and 5 of Panel A show marginally significant estimates on two measures of the quality of the interactions between parents and the mentors: (i) an indicator variable for whether the mentors have informed parents about their children’s learning difficulties, (ii) and whether the mentors provide concrete advice to the parent on how to tackle these difficulties. The effect sizes are large for both outcomes, implying a 14 percent increase in the probability of informing parents relative to the respective sample means in the _Original_ group (70 percent). The estimated coefficient for the GLS-weighted quality index is 0.25 standard deviations, which is significant at the 90–95 percent level depending on the inference procedure.

Panel B in Table 4 shows the effect of the _Plus_ modality on different competencies, or “parenting style,” that the mentors report to have promoted during their encounters with parents. This information was collected during a follow-up interview at the end of the field experiment. Of a total of 126 mentors between the _Original_ and _Plus_ modalities, enumerators were able to interview 107 of them. The attrition of survey participation of mentors is unrelated to the treatment assignment ( _p_ -value = 0.514). For further details on the survey of mentors, please refer to Appendix A.2. Mentors with enhanced training are more inclined to foster attitudes that are centered on educative parenting styles, such as communicating with the child (first column), as well as learning activities (second column). The overall educative style GLS-weighted index (third column) shows a sizable and significant effect (across the three inference procedures) of the _Plus_ modality, with an increase of 0.49 standard deviations in the promotion of educative parenting styles to parents during the home visits. Other aspects of the parent-child relationship that are focused on emotional practices do not seem to systematically vary across the two program modalities.

The evidence presented in this section points toward cross-modality variation in the quality of both the parent/mentor interactions and parent/child interactions as a potential mechanism behind the observed difference in children’s outcomes. While we cannot separately quantify 17
situation in which the present value of government disbursements does not exceed the present value of revenues given specific macroeconomic forecasts and assumptions about fiscal policy. However, to take into account the importance of external and internal risks in Burundi, the paper will also draw on sensitivity analysis, stochastic simulations, and scenario analyses to gauge the impact of various risks on the fiscal projections and consequences of alternative policy actions.

**In so doing the paper seeks to contribute to the existing literature in several ways.** Earlier studies on Burundi have highlighted the country’s vulnerability to external shocks related to commodity exports, rain-fed agriculture, and foreign aid.<sup>6</sup> Internally, uncertainty about the implementation of the government’s fiscal reform is also a key risk.<sup>7</sup> But these studies have not quantified the size and impact of the risks on the country’s fiscal sustainability. This paper contributes to filling this gap by estimating the size of the fiscal risks, by applying both the standard inter-temporal framework and stochastic simulation. The paper also complements other fiscal sustainability studies undertaken by the World Bank using similar approaches in countries such as Botswana, Cape Verde, Malaysia, Nigeria, Russia, and Turkey—and is one of the first application of these approaches to a low income country. Finally, by examining the fiscal risk arising from volatility in rainfall, the paper is also one of the first to study the impact of climate change on a country’s fiscal sustainability.

**The paper is organized as follows.** Section 2 presents the baseline analysis, which will examine the medium-term consequences of Burundi’s current fiscal strategy using the estimated outturn for the preceding year’s budget as the starting point and assuming unchanged policy in the short term (2013–14). The medium term macro-fiscal framework (2013–17) will be aligned with the latest projections available from the World Bank and the IMF’s country policy dialogue. Section 3 carries out risk analysis, which will examine the sensitivity of fiscal outcomes obtained under the baseline by subjecting them to various country-specific shocks (also known as fiscal risk analysis). The calibration of the shocks—e.g., their types, sizes, and transmission mechanisms— will be informed by the historical data.  Section 4 then carries out the policy scenario analysis, which will examine different fiscal strategies for Burundi with a view to identifying policies that will help keep the country on a sustainable path and make it more resilient to shocks. Section 5 concludes.

> 6 For example, Lim, C. and L. Rugwabiza (2009), World Bank (2011), and IMF (2012).

> 7 For example, World Bank (2008) and IMF (2011).

6
status, giving support to the income-based approach. Section 6 reports the evolution in size and in income shares of the middle class between 2003 and 2015. Section 7 sets out the conclusions.

# **2. Data Issues**

This analysis is based on data from the Household Budget Survey of Kazakhstan (HBS) conducted periodically between 2003 and 2015 by the Statistics Agency of the Republic of Kazakhstan (www.stat.gov.kz) and harmonized by the World Bank Poverty and Equity Group for Europe and Central Asia (ECA). Survey samples were generally 12,000 households, except in 2010, when the sample was doubled to 24,000. Households are currently interviewed quarterly about sources of income and amounts received, but the evolution of the sampling design introduced inconsistencies into our analysis. For example, in earlier surveys a household provided data for only one quarter, so annual income is extrapolated by multiplying household income by four. Later, each household was contacted twice, in either quarters 1 and 2, or 3 and 4.

The households in the samples are associated with sampling weights. Sampling designs are summarized in Table 1. The surveys are stratified using a geoadministrative division of the country into 16 regions further divided into their urban and rural areas (see Table 2). The rural-urban subsample sizes, which were similar in the surveys for 2013 through 2015, roughly reflect the distribution of population in the region. Because the cities of Astana and Almaty are entirely urban areas, there are 30 rather than 32 strata. Within a stratum there are at most two distinct values of the sampling weight.

**Table 1: KHBS Survey Sampling Designs**

|Year|Households|Strata|Quarters|
|---|---|---|---|
|||(Regions)|per household|
|2003|12,000||4|
|2006|12,000|30(16)|1|
|2010|24,000|30(16)|2|
|2013|12,000|30(16)|4|
|2014|12,000|30(16)|4|

4
# **Product Innovation by Incumbent Firms in Developing Economies: The Roles of Research and Development Expenditures, Trade Policy, and the Investment Climate**

Daniel Lederman<sup>∗</sup> The World Bank Keywords: Product innovation, R&D, investment climate, developing countries JEL Classification: O14, L25

> ∗ Lederman is Senior Economist with the Development Economics Research Group of the World Bank. Address correspondence to: <u>dlederman@worldbank.org, 1818 H Street NW, Washington, DC 20433; (t) 202-473-9015, (f) 202-522-1159. The author</u> gratefully acknowledges the impeccable research assistance provided by Daniel Chodos and Javier Cravino. Roberto Alvarez, Pablo Fajnzylber, Rodrigo Fuentes, Alvaro González, José L. Guasch, J. Humberto López, and Marcelo Olarreaga provided insightful comments on earlier drafts. Financial support from the World Bank’s Latin America and Caribbean Regional Studies Program is gratefully acknowledged. The views expressed in this paper do not represent the views of the World Bank or of its member countries. All remaining errors are the responsibility of the author.
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normal crop yields in the preceding year and inequality as measured by the mean log deviation of household consumption in the community. We also include controls for some changes in household characteristics (including size and composition, and the maximum education) and some changes in community characteristics over time. However, we do not control for changes in whether the house is built with bricks and land inheritance amounts, which are clearly affected by access to electricity. For the same reason, we do not control for changes in village access to facilities. Finally we include a dummy variable to indicate whether the household split between the survey rounds.

There are endogeneity concerns about some of our controls. For example, following Khandker et al. (2012) we control for the household’s maximum years of education when explaining the schooling attainments of children and we control for the change in this variable. Over a long period of time, as in our study, if electrification influences children’s schooling then one might expect it to also influence the gain over time in the maximum years of schooling. Similarly, there are concerns about the possible endogeneity of changes in household size. We will test the effect of dropping these controls. However, we acknowledge that there is always a trade-off between dropping variables to address endogeneity concerns and omitted variable bias.

For identification purposes, we also use data on the location of power generating plants obtained from the 2004-5 CO2 Baseline Database for the Indian Power Sector, published by the Central Electricity Authority of India. This public database has detailed information on each power generating plant in India, across different types of power (hydropower versus thermal, for example), and generating capacity. The data include the date at which each power plant became active. Using the GPS locations of the REDS survey villages, we calculated the straight-line distance from each village to the nearest power plant for 1965 (as a benchmark before the REDS survey, during the time when rural electricity generation capacity was beginning to expand), and 1975.

# **5. Specification and identification**

We postulate two distinct ways that a rural household can benefit from electrification. First, it can gain from village electrification, even if it does not have electricity itself, though we allow for the possibility that the extent of that gain may depend on whether the household itself 19
<u>Table 1: Poverty rates in Africa by age group ($1.90 a day, 2011 PPP)</u>

|<br>**Age groups**|<br>**Per capita**<br>**approach**|<br>**FAO/WHO**<br>**equivalence**<br>**scale**|<br>**Square-root**<br>**equivalence**<br>**scale**|
|---|---|---|---|
|_0-4 _|52.8|46.4|43.8|
|_5-9 _|50.1|46.6|43.0|
|_10-17_|48.8|46.8|40.2|
|**_0-17_**|50.4|46.6|42.1|
|**_+18_**|39.0|39.9|37.0|
|**_All_**|44.5|43.2|39.5|

<u>Source: World Bank's African Poverty Database</u> Tables 2 and 3 show the distribution of poverty among adults and children according to respectively the number of children in the household and the household size. In the per capita approach, poverty increases naturally with the number of children in the household. The same trend may be observed in the case of the FAO/WHO equivalence scale, except that its slope is less marked. Importantly, the results show that proper accounting for economies of scale tends to increase the poverty rate of children in households with fewer than three children, while it reduces it in those with three or more children. As a result, the poverty gap according to the number of children decreases. The adult poverty distribution follows the same pattern. This in turn leads to the reduction of poverty gaps between the different categories of households. It appears also that, with households with more than five children, poverty tends to decrease. The same patterns may be observed when poverty is broken down by household size.

<u>Table 2: Poverty rates for children and adults by number of children in household</u>

|<br>**Number of**|<br>**Per capita**|<br>**approach**|<br>**FAO/WHO e**<br>**scal**|<br>**quivalence**<br>**e**|<br>**Squar**<br>**equivale**|<br>**e-root**<br>**nce scale**|
|---|---|---|---|---|---|---|
|**children**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|**_Children (0-_**<br>**_17)_**|**_Adults_**<br>**_(+18)_**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|
|_0 child_|-|16.7|-|26.5|-|30.2|
|_1 child_|24.2|26.4|33.0|34.0|36.8|34.6|
|_2 children_|36.2|37.2|39.6|40.0|40.8|38.4|
|_3 children_|46.0|45.9|45.2|44.9|43.7|41.2|
|_4 children_|52.5|51.3|48.3|47.1|44.9|41.9|
|_5 children_|58.9|57.5|52.0|50.6|45.8|42.6|
|_6 children_|63.2|60.9|51.4|48.6|38.9|34.8|

<u>Source: World Bank's African Poverty Database</u> 11
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA were calculated as the product of the firm’s consumption of self-generated electricity and the difference between the costs per kWh of self-generated electricity and of electricity from the public grid.<sup>32</sup> The benefits of own generation were computed as the product of the share of firms owning a generator and the marginal benefit of owning a generator, as estimated from the regression analysis discussed in the previous section.<sup>33</sup> Both costs and benefits of owning a generator are expressed as percentages of sales. The resulting difference between the benefits and costs of own generation (the benefit-cost margin) was tested for statistical significance from zero using the Student’s _t_ test. The results are summarized in tables 7a–c below.

In five countries (Benin, Egypt, Eritrea, Morocco, and Zambia), the costs of own generation significantly outweigh the benefits (table 7a). Only in Mali and Mauritius do the benefits outweigh the costs. In most cases the negative difference between benefits and costs is not statistically significant from zero, implying that investment in in-house generation allows firms to break even.

# **Table 7a Cost-benefit analysis of  own generation, by country**

Percent

|Country|Investment<br>costs|Own-<br>generation<br>costs|Total costs|Reduced<br>sales losses|Benefit-cost<br>margin|
|---|---|---|---|---|---|
|Algeria|3.12|—|—|0.26|.|
|Benin|0.43|1.62|2.05|0.22|–1.83<br>**|
|Egypt|2.34|0.86|3.20|0.21|–2.99<br>***|
|Eritrea|1.54|0.41|1.95|0.48|–1.47<br>**|
|Kenya|0.14|—|—|0.80|—|
|Madagascar|0.28|—|—|0.21|—|
|Malawi|0.09|0.78|0.87|0.52|–0.35|
|Mali|0.31|0.17|0.48|0.56|0.08|
|Mauritius|0.17|0.01|0.18|0.54|0.36<br>***|
|Morocco|0.06|0.39|0.45|0.18|–0.27<br>**|
|Senegal|0.32|0.50|0.82|0.70|–0.12|
|Senegal<br>††|0.98|—|—|0.13|—|
|South Africa|0.10|0.09|0.19|0.13|–0.06|
|Tanzania|0.43|1.01|0.94|0.47|–0.47|
|Uganda|0.45|0.49|0.94|0.45|–0.49|
|Zambia|3.02|0.95|3.96|0.26|–3.70<br>**|

†† Survey of informal sector.

*** statistically significant at 1 percent level.

** statistically significant at 5 percent level.

— = data not available.

Under less restrictive assumptions than those used so far, the benefits of own generation can compensate for the losses even for countries where the difference between benefits and costs is negative and statistically significant. First, the fixed costs of own generation can be sunk or depreciated

> 32 Total consumption of  self-generated electricity was estimated using the “electricity approach” discussed in appendix 2.

> 33 This measure probably represents a lower bound estimate of the own-generation benefits given the various considerations not related to reliability of power supply as described in section 4 of this paper.

22
**_Table 6 Starting informally and subsequent performance: turnover_**

||**(1)**<br>**All**|**(2)**<br>**firms**|**(3)**<br>**Young**|**(4)**<br>**firms**|**(5)**<br>**Sub-Saha**|**(6)**<br>**ran Africa**|**(7)**<br>**Middle Ea**<br>**A**|**(8)**<br>**st and North**<br>**frica**|**(9)**<br>**Sou**|**(10)**<br>**th Asia**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Y = Log (sales)**|**Female**<br>**participati**<br>**on**|**No female**<br>**participatio**<br>**n**|**Female**<br>**participatio**<br>**n**|**No female**<br>**participatio**<br>**n**|**Female**<br>**participatio**<br>**n**|**No female**<br>**participatio**<br>**n**|**Female**<br>**participati**<br>**on**|**No female**<br>**participation**|**Female**<br>**participatio**<br>**n**|**No female**<br>**participation**|
|Began operating<br>informally|-0.181**|-0.229***|-0.165|-0.235*|-0.264*|-0.365***|-0.357|-0.356***|-0.665***|0.042|
||(0.088)|(0.062)|(0.273)|(0.125)|(0.156)|(0.093)|(0.306)|(0.117)|(0.173)|(0.139)|
|Female top<br>manager Y/N|-0.097*|-0.029|-0.371**|-0.186|-0.142|-0.142|-0.432***|-0.349|-0.173|0.666*|
||(0.050)|(0.081)|(0.149)|(0.142)|(0.133)|(0.173)|(0.164)|(0.224)|(0.115)|(0.355)|
|Log of age of firm|0.129***|0.097***|0.118|0.105|0.214**|0.157**|0.061|-0.103|0.085|-0.016|
||(0.045)|(0.032)|(0.164)|(0.093)|(0.085)|(0.067)|(0.199)|(0.085)|(0.106)|(0.104)|
|Log of size|0.969***|0.970***|0.901***|0.977***|0.918***|0.933***|0.709***|0.888***|0.977***|0.982***|
||(0.027)|(0.021)|(0.104)|(0.047)|(0.056)|(0.042)|(0.114)|(0.055)|(0.102)|(0.052)|
|Part of a larger<br>firm Y/N|0.276***|0.122**|0.488**|0.209|0.224|0.201*|0.242|0.008|0.135|0.098|
||(0.081)|(0.059)|(0.239)|(0.129)|(0.171)|(0.107)|(0.346)|(0.148)|(0.162)|(0.096)|
|Offers formal<br>training Y/N|0.259***|0.091**|0.181|0.135|0.300***|0.150*|0.229|-0.103|-0.132|0.027|
||(0.054)|(0.041)|(0.152)|(0.096)|(0.110)|(0.089)|(0.205)|(0.123)|(0.197)|(0.112)|
|Top manager<br>experience in<br>|0.002|0.001|0.019*|0.000|0.017***|0.003|0.003|0.015***|0.008|0.003|
|sector (years)|(0.003)|(0.002)|(0.010)|(0.006)|(0.006)|(0.005)|(0.008)|(0.006)|(0.011)|(0.004)|
|Direct exports<br>10% or more of<br>sales Y/N|0.050<br>(0.076)|0.328***<br>(0.053)|-0.018<br>(0.324)|0.457***<br>(0.140)|0.074<br>(0.172)|0.200<br>(0.122)|0.069<br>(0.319)|0.272*<br>(0.139)|0.121<br>(0.210)|0.416**<br>(0.165)|
|Foreign<br>ownership Y/N|0.312***|0.402***|0.363**|0.411***|0.434***|0.571***|0.382|-0.122|-0.400|-0.171|
||(0.083)|(0.059)|(0.183)|(0.117)|(0.161)|(0.100)|(0.461)|(0.204)|(0.799)|(0.427)|
|Has checking or|||||||||||
|<br>savings account<br>Y/N|0.260***|0.293***|0.437**|0.393***|0.234|0.351***|-0.352|0.383***|0.396|0.051|

21
to counterbalance any shortfall in pension benefits from the social security system. Thus the growth of domestic demand is likely to be paced by the expansion of GDP which will be driven almost exclusively by increases in factor productivity.

Fourth, as Europe is discovering, once economies start to stagnate, there is a strong incentive for young and talented knowledge workers to emigrate. Some 400,000 Europeans with advanced degrees have settled in the U.S. and three out of four Europeans receiving tertiary level education in the U.S. do not return to their own countries. More than half of Japanese recipients of doctorates in science and engineering fields from US institutions do not intend to go back home and the stay rate in the U.S. has been raising (National Science Board 2004).<sup>23</sup> The number of Japanese who are longterm residents and permanent residents abroad has been steadily increasing to reach more than 900,000 people in 2003. By 2006, this number will be more than one million (Japan Ministry of Foreign Affairs 2004). Most of these are individuals with high levels of education and skills. Demographic and economic changes in Japan could reduce the relatively small number of foreign knowledge workers that come to Japan and could further contribute to a larger net outflow of highly skilled Japanese workers to East Asia and the U.S. in search of better opportunities.<sup>24</sup> One can argue that these trends are caused by the stagnation of industry in Japan. A reforming Japan that is able to revive 23 In 1990-93, 43% of doctorate recipients in science and engineering field intended to stay in the US. This rate has increased to 44% and 55% in 1994-97 and 1998-2001 respectively. Although the number of Japanese doctorate recipients in S&E fields are small with only 655 in 1998-2001 (National Science Board 2004).

24 Currently, the number of foreign workers in Japan is just 0.15 percent of the workforce as against 3 percent in Taiwan, 9 percent in Germany, and 11 percent in the U.S. (Lorange and Turpin 2004).

14
|Year|Price|Year|Price|Year|Price|
|---|---|---|---|---|---|
|1861|73.5|1882|55.5|1901|40.5|
|1862|71|1880|45|1903|46.6|
|1864|73.5|1890|52|1904|40.5|
|1870|93|1892|44|1908|40|
|1872|105|1894|37|1909|40|
|1875|57|1896|34|1910|40.5|
|1881|58|1900|38|||

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25