rafmacalaba/datause-encoder-data huggingface dataset:
and are interacted with community characteristics . The system of equations used in the original Günther and Harttgen (2009) implementation is described by equations 3, 4, and 5. ZZcc Substituting equations 4 and 5 into equation 3 yields equation 6 in which the constant and slope terms vary with community characteristics ( 11 ) and community unobserved factors ( ). However, if and 11 are assumed to be zero and no commune-level characteristics are included, the system γγ ZZccXXcch uu1ccXXcch reduces to equation 1. 1cc<sup>14</sup> uu γγ l 00 + 01 + 01 + 11 (6) A comparison of equations 1 and 6 provides useful insights. First, although the specification does not (yycch) = γγ γγ ZZcc �γγ γγ ZZcc�XXcch + uu0cc + uu1ccXXcch + εεcch A comparison of equations 1 and 6 provides useful insights. First, although the specification does not include random slopes, the estimation of region-specific models allows, in practice, coefficients to vary entirely across regions, equivalent to interacting every coefficient with a regional dummy.<sup>15</sup> Second, the estimated variance of expenditure across households and communities in Günther and Harttgen (2009) is modeled as a function of household and community characteristics using a linear model described in equation (7).<sup>16</sup> Third , when imputed to the census, the area-level effects are only available for those geographical areas included in the sampling of the household survey.<sup>17</sup> Considering these points, the robustness check section compares the variance estimates in both models showing that, despite the estimated differences, both methods provide comparable results on the share of the vulnerable population and the ranking of the country’s departments.

> 14 It is possible to include (commune-level) characteristics interacted with household characteristics in the empirical best models to recover ( ). Including commune-level characteristics ( ) is optional because it is subject to the availability of statistically representative commune-level variables, which are usually derived from the census. 15 cc cch cc Even if the same covariates are used as candidates in the Lasso regression, model selection is implemented ZZ XX ZZ independently, which might lead to region-specific models.

> 16 The analysis obtains empirical best estimates of the random location effects, ) (Rao and Molina 2015). It also modeled for heteroskedasticity in the idiosyncratic portion of the residuals (Elbers, Lanjouw, and Lanjouw 2003). 17 0cc 2 Estimates for the idiosyncratic and covariate shocks are modeled based on the equations (uu 0 1 and 2 0 1 3 _._ In the case of the vulnerability map, an alpha model was implemented for the 0cc cc variance of the idiosyncratic errors because the literature suggests this helps reduce bias and noise in the estimates. uu = ττ + ττ ZZ cch cc cch cc εε = θθ + θθ ZZ + θθ XX ZZ

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# Household Welfare and Poverty Dynamics in Burkina Faso: Empircal Evidence from Household Surveys

### by

Hippolyte Fofack, Celestin Monga and Hasan TuluyI

## _JEL classifications: C14; D31; D63; R12_

**Keywords:** poverty dynamics, income inequality, poverty-growth elasticities, Spearman coefficient of rank correlation.

Hippolyte Fofack was with the Poverty Reduction Group when this research was initiated and is currently with Macroeconomics 3 Division in the Africa Region, Celestin Monga is the Country Economist for Burkina Faso in Macroeconomics 4, and Hasan Tuluy is Country Director forBurkina Faso, in the Africa Region of the World Bank. This paper was prepared as an input to the Burkina Faso Poverty Reduction Strategy Paper. The authors are very grateful to the Burkinabe's Institut National de la Statistique et de la Demographie (INSD) for access to the record data of the 1994 and 1998 household survey. The authors gratefully acknowledge the support of the Office of the Director, and the comments of Jeni Klugman.
addition to tangible innovation inputs‐ such as technology, equipment, and physical production facilities, innovation requires intangible assets such as human capital, scientific and creative capital and, more importantly, managerial and organizational capital. These inputs require in turn specific innovation activities depending on the degree of novelty and technology sophistication of the innovations introduced. Firms invest in training in order to increase their human capital available. In addition, firms can also invest in R&D, software and digitalization or copyrights, patents and licenses, and equipment in order to increase their scientific or innovative capital. Finally, innovation also requires organizational capital through adoption of new business models, design and prototyping or corporate alliances and networks. There is, however, uncertainty about the outcomes that can be achieved with the combination of inputs as innovation outcomes depend on each firm’s ability, the sector and country context, and the enabling environment and policy framework.

When measuring innovation, the emphasis can be on measuring inputs and innovation activities and/or innovation outcomes. The earlier literature on innovation measurement focused on a specific set of innovation inputs that were more easily quantifiable, such as R&D or the technological intensity used. These early efforts were subsequently complemented with the framework provided by the Oslo manual, currently the main reference for innovation surveys, which mainly focuses on measuring innovation outcomes such as new products and/or processes, product and process improvements or patents at the firm level (OECD/EUROSTAT, 2015). A third generation of synthetic innovation indicators, such as the OECD STI scoreboard, was developed later on. These indicators combine innovation inputs and outputs in order to facilitate cross‐country benchmarking and comparisons (OECD, 2015). However, to date, the Oslo manual framework, implemented originally through the Community Innovation Surveys (CIS) in the EU, has dominated innovation measurement in most countries, to the extent that even countries that do not conduct CIS have adopted the same questions at least in relation to the measurement of innovation outcomes.

One challenge in measuring innovation outcomes is the subjective nature of many of the questions used in the surveys. The Oslo manual defines innovation as “the implementation of a new or significantly improved product (good or service) or process, a new marketing method, a new organizational method in business practices, workplace organization or external relations”. Most surveys use this definition to identify innovations, by directly asking firm managers and owners whether they have implemented any “new” products, processes, marketing methods, or organizational practices or “significant” improvements in existing ones in the last three years. In this context, the answer to this question is problematic since 5
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Table 4: **Self and Social Image - Credit Card** **<u>(Experiment</u> 3)**

||Plati<br>upg|num<br>rade|Ben<br>upg|efts<br>rade|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Self-Afrmation|-0.1491|-0.1548|0.0049|-0.0350|
||[0.0981]|[0.1060]|[0.0670]|[0.0615]|
||(0.192)|(0.142)|(0.743)|(0.604)|
|Mean (neutral)|0.3|26|0.1|09|
||[0.0|72]|[0.0|46]|
|Controls|No|Yes|No|Yes|
|N|77|76|90|90|
|R2|0.0285|0.1811|0.0001|0.2380|

Notes: Column 1 presents the results of a regression of a dummy variable equal to one if the client accepted the platinum upgrade offer on a dummy for self-affirmation treatment. The regression presented in column 2 includes income, credit limit, female, muslim, and Jakarta as covariates. The regressions presented in columns 3 and 4 present results using a dummy variable equal to one if the client accepted the benefits upgrade offer. Robust standard errors in brackets. Permutation test p-values in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

41
support. In urban areas of Indonesia and Korea, by contrast, fewer than 35 percent of men and 15 percent of women have access to pensions.<sup>17</sup> Another unique feature of pension eligibility in Korea lies with an imperfect correspondence between mandatory retirement age and age at which pension-eligible retirees may start receiving pension benefits. The National Pension scheme, the largest of the three main sources of pensions in Korea, does not begin paying benefits until age 60, yet a significant share of employees face mandatory retirement at age 55 (Cho and Kim, 2005). This imperfect correspondence likely increases incentives for those employees facing mandatory retirement to look for new career opportunities, including in the self-employed sector.

Across rural areas of China, Indonesia and Korea, both men and women remain actively employed until much later in their lives than urban residents. In rural areas of all three economies, agricultural production on the family farm continues to be a significant source of employment for older workers, and this is necessitated by the fact that rural residents tend to accumulate less wealth over their working lifetimes. In addition, older workers in rural China and Indonesia are far less likely to have access to pensions than their urban counterparts. Both the CHARLS and the 2005 Population Census suggest that, of rural residents over 60, roughly 5 percent of men and less than one percent of women have pension support. Similarly, in rural Indonesia, the Indonesia Family Life Survey (IFLS) shows that only 8 and 3 percent of men and women over 60 have access to pensions.

Rural Korea is a somewhat different matter. Efforts to bring rural residents into the National Pension scheme established in 1988 have led to pension coverage rates in rural areas that do not differ significantly from urban areas, indeed 34 percent of older rural men in the 2006 KLoSA wave report receiving pensions. Nonetheless the continued labor supply of rural men is viewed as an important factor contributing to high rates of economic activity among older Koreans (Lee 2009). In spite of availability of pensions, levels of support are not sufficient to permit retirement of the rural elderly. Lee (2009) suggests that the out-migration of the young has left elderly who remain behind with both a lack of young labor and insufficient wealth to cease productive

> 17More specifically, in Korea, 34 percent of urban men have access to pensions and 15 percent of urban women, and in Indonesia, 27 percent of urban men have access to pensions and 10 percent of urban women. Figures on pension coverage are drawn from the 2006 Wave of the Korean Longitudinal Study of Aging (KLoSA) and the 2007 wave of the Indonesia Family Life Survey (IFLS).

10
In the Krugman model, all varieties in a given sector have the same price and are shipped to all destinations, so we could drop the destination index. In the Melitz model, however, the measure of the number of firms is bilateral, and firm level prices and quantities in (ii) and (iii) above are for the representative firm as defined by Melitz (2003). Bilateral expenditure shares are given by the product of the gross price of the representative firm times its quantity times the number of firms, i.e., Given the expenditure shares, the Sato-Vartia weights for our sub-indices are: Our matrix of bilateral trade data at the country level does not contain any zeros. That is, we have _stirs_  0 , for all _i, r, s_ and _t_ . Since we track expenditures based on sales of the average firm, we have _wirs_  0 for all _i, r, s,_ d, and we do not need an exception for zero expenditures.<sup>27</sup> We have  _wits_ = 1 . _t_ Analogous to the Sato-Vartia index in a single sector model, we define a Sato-Vartia index for the sector i in region s of the change in prices charged by the firm. Our ideal price index for the change in the prices charged by firms for the goods available in both periods is:

> 27 In Feenstra’s approach, he allowed for the possibility of some good to be sold in only one of the periods by defining the Sato-Vartia index over the set _I_ = _It_ − 1 _It_ where<sup>_I_</sup> _t_<sup>is the set of goods with expenditures in period t;</sup> so I is the set of goods with expenditures in both periods. If there were no exports to some destinations for some good i, in either the benchmark or counterfactual scenario, noting that _s_ 1lim _irs_ → 0 + _wirs_ = 0 and similarly for _s_ 0 _irs_ , we could define the weight as zero if the share is zero in either the benchmark of counterfactual. Then if a new variety appears (or an old variety disappears) in the counterfactual equilibrium, this variety would not impact our SatoVartia index.

48
organizations. The process of commercialization often requires an additional engagement from the researcher that is inconsistent with his or her career objectives. Researchers in faculties and public institutes are traditionally rewarded based on academic accomplishments, such as publication of scientific papers, more than, for example, obtaining or licensing patents or collaborating with the business sector. However, in Russia scientists seem to massively engage in a (limited) commercialization process.

More than half of Russian TTOs pay their personnel using the university/research institution pay scale and incentives, bonuses, and success fees related to performance. Research in other countries has shown that career opportunities for university technology licensing officers are limited and often short, which implies difficulties in recruiting and retaining appropriate talent (Phan and Siegel 2006). This is especially true for technology transfer officers with broad-based commercial skills that may be better used elsewhere. In Russia, a significant share of TTOs declares to give their personnel performance-related payment to complement the institutional pay scale, even though they are public employees (Figure 9).

Russian TTOs perform several other activities besides technology transfer. Only 40 percent of TTOs had technology transfer as their main activity. Other main activities are coordination of R&D (13 percent), consulting (11 percent), and training (11 percent).

As a result, proceeds from technology transfer are marginal. Russian TTOs’ budgets come mainly from the parent institution, averaging 40 percent. Other important sources are consulting fees (18 percent) and fees received as a broker for university/research institution contract research (10 percent). License royalties and equity in new companies represent 7.3 percent and 3.6 percent of the average TTO budget, respectively (Figure 10).

**Figure 9: TTO personnel payment system, 2010**

**Figure 10: Sources of TTO Figure 11: Additional services income, 2010 provided by TTO or parent institution, 2010**

<!-- Start of picture text --> 8%<br>20.4 21%<br>20% 39.5<br>13% 51%<br>54% 18.4<br>18% 15%<br>9.9 7.3<br>0.4<br>3.6<br>0.5<br>from parent institution<br>univ. pay scale and<br>bonus license royalties incubator and prototyping services<br>equity in new companies<br>only univ. pay scale<br>only incubator services<br>broker for research<br>rent of space<br>only bonus<br>only prototyping services<br>rent of equipment<br>other payment system consulting fees no additional services<br>other<br><!-- End of picture text --> Source: World Bank/MOES survey, 2011.

Russian TTOs reliance on public support is not unusual but the income from technology transfer activities should increase in the future. Some level of public support to the TTO in the initial years may be 24
our estimation results are similar whether zero-valued links are considered (PPML) or not (OLS), if not larger in the latter specification. For example, the OLS regressions show that South-to-South portfolio investment grew on average at 8.8 p.p. per year faster than North-to-North investment, whereas the PPML regressions estimate an implied growth differential of 5.5 p.p. For FDI, this growth differential is estimated at 7.3 p.p. in the OLS regressions and 4.4 p.p. in the PPML regressions. Similar patterns emerge for the South relative to the North as source and destination of international investment. In section 4, we analyze in depth the dynamics of international investment along the extensive margin.

As discussed in section 2, when merging UNCTAD and CDIS data, there was a large increase in the number of reported counterparties in 2009, especially for South countries (appendix, section A.3). Hence, the regression estimates in table 3 might overestimate FDI growth over our sample period because of improvements in coverage. Table 4 reports a robustness test eliminating the possible effect of the change in coverage in 2009. In particular, we add to all the specifications a dummy that equals 1 from 2009 onward interacted with region-to-region dummies, thereby assigning all the growth in FDI positions from 2008 to 2009 to the change in coverage. These new estimations still show that South-to-South investment grew faster than North-to-North investment and the South expanded faster than the North as source and destination of international investment.

# **4. Evolution of International Financial Connectedness**

# **4.1. Bilateral Links and the Extensive Margin**

In this section, we examine the growth of the South through the pattern of links between countries. We analyze the share of active links in reported links, within and between the North and South.<sup>16</sup> We

> 16 In network analysis, this simple measure of connectedness is known as density. Active links are defined as those with a positive value of international investment. Inactive links are those with a zero value. The total number of reported links is

20
**Figure 8    Firm-level labor productivity decomposition for Total Economy**

<!-- Start of picture text --> FHK Decomposition Shares - Total Economy<br>Labor Productivity - Five-Year Differencing, Real Gross Output<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>Within Between Cross<br>Entry Exit Firm Turnover(i)<br>Chile Estonia Latvia Portugal West Germany<br><!-- End of picture text --> <!-- Start of picture text --> Chile: 1985-1999. Estonia: 2000-2001. West Germany: 2000-2002.<br>Latvia: 2001-2002. Portugal: 1991-1994.<br>Excluding Brazil and Venezuela.<br><!-- End of picture text -->

# NB:

Within = within firm productivity growth between = productivity growth due to reallocation of labor across existing firms entry = productivity growth due to entry of new firms exit = productivity growth due to exit of firms

**Figure 9    Firm-level labor productivity decomposition for Manufacturing**

<!-- Start of picture text --> FHK Decomposition Shares - Manufacturing<br>Labor Productivity - Five-Year Differencing, Real Gross Output<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Within Between Cross<br>Entry Exit Firm Turnover(i)<br>Argentina: 1995-2001. Chile: 1985-1999. Colombia: 1987-1998. Estonia: 2000-2001.<br>Finland: 2000-2002. France: 1990-1995. West Germany: 2000-2002. Korea: 1988 & 1993.<br>Latvia: 2001-2002. Netherlands: 1992-2001. Portugal: 1991-1994. Slovenia: 1997-2001.<br>Taiwan: 1986, 1991 & 1996. UK: 2000-2001. USA: 1992 & 1997.<br>Excluding Brazil and Venezuela.<br>Argentina ChileColombia Estonia Finland France Korea LatviaNetherlands PortugalSlovenia Taiwan UK USAWest Germany<br><!-- End of picture text --> 36