rafmacalaba/datause-encoder-data huggingface dataset:
# 3.2 What are the impacts of innovation outputs on productivity?

The third, and final, stage of the CDM model addresses the key question: the relationship between innovation (output) and productivity. To this end, we estimate the impacts of innovation on productivity controlling for firm characteristics, including physical and human capital and input costs. Innovation measures are based on the innovation outputs predicted from the second step (Table 4), to address endogeneity concerns. In Table 5 column 1, we control for overall innovation, whereas in column 2 we control for both product and process innovation but distinguish between the two, to capture any heterogeneous effects. In columns 3 and 4, we separately control for product and process innovation.

The results suggest that innovation has a positive impact on productivity. Firms that produce innovation generate 37% more output per worker than non-innovative firms (Column 1), and 27% more if we only look at product innovation (Column 3).<sup>10</sup> However, the association between innovation and productivity is not significant if we restrict the estimation to process innovation only (Column 4) or together with product innovation (Column 2). These findings for East Asia are in line with those for Latin America documented in Crespi et al. (2016) and Europe (Hall, 2011).

_Table 5 The impact of innovation on labor productivity in China and ASEAN5_

|||**Laborp**<br>|**roductivity**<br>||
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Materials|0.537***|0.540***|0.540***|0.541***|
||(0.0243)|(0.0240)|(0.0192)|(0.0237)|
|Capital|0.0826***|0.0860***|0.0872***|0.0846***|
||(0.0125)|(0.0129)|(0.0146)|(0.0126)|
|Human capital|0.00700|0.00836|0.00865|0.00810|
||(0.00610)|(0.00611)|(0.00626)|(0.00631)|
|Employment|0.0219|0.0268|0.0291|0.0247|
||(0.0167)|(0.0193)|(0.0194)|(0.0198)|
|Manager experience|-0.0261***|-0.0253***|-0.0251***|-0.0258***|
||(0.00259)|(0.00272)|(0.00254)|(0.00266)|
|Firm age|0.0211***|0.0195***|0.0192***|0.0199***|
||(0.00322)|(0.00321)|(0.00274)|(0.00325)|
|Innovation|0.314**||||
||(0.146)||||
|Product innovation||0.194|0.240*||
|||(0.215)|(0.135)||
|Process innovation||0.0628||0.220|
|||(0.228)||(0.170)|
|N|2,607|2,607|2,609|2,607|

*** p<0.01, ** p<0.05, * p<0.1. Ordinary least squares. Bootstrapped standard errors in parentheses. The sample includes data from these surveys: China (2012), Indonesia (2015), Malaysia (2015), Philippines (2015), Thailand <u>(2016), Vietnam (2015).</u>

> 10 These results are robust if we measure labor productivity using total factor productivity (TFPR) estimated based on the approach put forward by De Loecker (2013).

10
of a body of water, a land border, or a sea border. The estimated regression has the form: where _yit_<sup>_k_</sup> _−_ 1<sup>isadummyequalto1forthe</sup><sup>_k_’thdecileof</sup><sup>_yit−s_,and</sup><sup>_y_</sup> _it_<sup>_kn_</sup> _−_<sup>1</sup> _s_<sup>and</sup><sup>_y_</sup> _it_<sup>_kn_</sup> _−_<sup>2</sup> _s_<sup>arethesameset</sup> decile dummies for _yit_<sup>_n_1and</sup><sup>_y_</sup> _it_<sup>_n_2.Weonlyestimateregression(5)fortheshortestlag</sup><sup>_s_,i.e.,using</sup> 1975 for 1990, 1990 for 2000, and 2000 for 2014. Coefficient estimates for the buildup variables of interest are presented in graphical form in Figure 5. Results by and large confirm earlier findings from Table 8. They also provide some evidence of a stronger lag effect for higher deciles of _yit−s_ , suggesting more persistence for high buildup. Interestingly, we see that the lagged effect of buildup in neighboring cells tends to taper off at high buildup values, suggesting some form of substitution effect perhaps.

We next expand the model to include dummies for different road categories. The estimated regression has the form: where: _rit−s_ is a vector of road availability dummies, one for each road type, at time _t − s_ in cell _i_ ; _rit_<sup>_n_1</sup> _−s_<sup>isavectorofaverageavailabilityofdifferenttypesofroadsinneighborhood</sup><sup>_n_1,and</sup> _rit_<sup>_n_2</sup> _−s_<sup>isthesameforneighborhood</sup><sup>_n_2.Wefocusourattentiononoutcomeyears1990and2000</sup> because for each of them we have comparable road data approximately 14 years earlier, i.e., in 1976 and 1986. Road data for 2008 and 2014 are not comparable to either of these or to each other. Coefficient estimates for _γ_ 1 _, γ_ 2 and _γ_ 3 are presented in Figure 6, for each of the four road categories and each of the two outcome years, 1990 and 2000. Coefficient _γ_ 1 gives the predicted % point increase in buildup if there is a road in the cell 14 years before. Coefficients _γ_ 2 and _γ_ 3 give the predicted % point increase in buildup if neighborhoods N1 and N2, respectively, go from no cell with a road to all cells with a road. We note a lot of similarity between the two regressions in terms of relative magnitude of the coefficients: the presence of a class 1 road in the cell or nearby predicts more buildup 14 years later. For the other three categories, the presence of a road in the cell predicts slightly higher buildup later on, but the presence of a road in the more distant 19
27 indirectly by more than it would raise it directly for given starting level of uncertainty. In sum, in those circumstances the MAT could in fact lower rather than raise the MERTI The final conclusion is that two a priori plausible presumptions in fact seem to be incorrect, at least in the sample we looked at. First, because of the high variance of capital intensity accross sectors, the MAT does not reduce sectoral distortions; the standard deviation of the MERT with MAT is higher than without. Second, although it is true that high variance leads to a higher marginal impact of the MAT, it is not true that high risk firms tend to be hit harder by the MAT. The explanation is that high-risk firms tend to also be high rate of return firms, and the latter element reduces the impact of a MAT. In our Brazilian sample, the latter effect in fact dominates. So a concern that the MAT would discriminate against the most innovative but riskiest firms seems unwarranted.

References:

- Atkinson, and J. Stiglitz (1980), Lectures in Public Economics, New York: McGraw Hill

- Auerbach, A. (1990), "The Cost of Capital and Investment in Developing Countries: Tax Incentive Evaluation Research Project Discussion Paper No. 1, The World Bank, Washington, D.C., January

- Estache, A. (1990), "Minimum Taxes on Business Activities: A Brief Introduction to Design Issues", mimeo, January

- Graetz, M.J. and E. Sunley (1988), "Minimum Taxes and Comprehensive Tax Reform", in Aaron, H.J., H. Galper and J. A. Pechman, Uneasy -

- Compromise Problems of a Hybrid Income-Consumption Tax, Washington, D.C., The Brookings Institution.

- King, M.A. and Fullerton, D. (1984), The Taxation of Income from CaRital: A Comparative Study of the United States. The United Kingdom. Sweden and West Germany, Chicago: The University of Chicago Press.

- Knuth, D. (1981), Seminumerical Algorithms, Addison-Wesley, Reading, Massachusetts.

- Lyon, A. (1989), "Understanding Investment Incentives under Parallel Tax

- Systems: An Application to the Alternative Minimum Tax", University of
This specification is supported by Leser’s (1963) work who compares different forms of Engel curves and shows that assuming constant prices, an estimation based on log of income as used by Working [1943] provides a good fit, particularly for cross sectional data.

To estimate a quadratic demand system, a QUAIDS, adds considerable complexity. The quadratic term in income in equation 19 then depends on prices: previously prices only entered as shifters of the intercept _α_ . To allow for heterogenous coefficients, Attanasio et al. [2011] suggest to introduce interactions of the regional dummies with the linear and quadratic terms in total expenditure (in our case by income) in addition to the regional dummies. If we were to introduce interaction terms based on the sampling strata, 998 additional parameters would have to be estimated.

Instead, we maintain the hypothesis that regional differences in prices only affect the intercept and allow for a nonlinear relation between the expenditure share _si_ and income _m_ . We choose this approach (which is not identical to QUAIDS) because the specification has great similarity to the later applied RDD specification and eases the comparison of results.<sup>69</sup> We estimate equations 20 and 21 on the subsample of untreated households to avoid contamination by BFP treatment. We estimate these equations separately for each commodity (i) by OLS under the assumption that differences in relative prices are captured by regional dummies. In this case, the adding-up restrictions are satisfied, without the imposition of cross-equation restrictions (homogeneity and symmetry) related to the price elasticities.

When estimating equations 20 and 21 we assume that log income (m) and its square are not correlated with the residuals _ε_ . However, this is a strong assumption. There are three main reasons why income (or expenditure) may not be exogenous: a) non separability; b) transitory shocks and c) measurement errors.

If there is non-separability between labor supply and the utility function, income might be correlated with taste shifters which are captured by the residuals. This issue also applies when total expenditure is used as an explanatory variable. In this case, it might be related to intertemporal preferences (e.g. net savers might be more likely to spend more or less on specific goods). Another issue related to income is the presence of transitory shocks. For example, if a

> 69We also estimated equation 21 strictly following Attanasio et al.’s (2011) approach by including 26 regional dummies and their interactions with a linear and quadratic term in income. The results are robust with respect to the interpretation of the nature of goods as luxuries or necessities around the eligibility threshold.

31
These absolute and relative trends, however, are not consistent throughout Asia. In contrast to the promising results in East Asia, the percentage of malnourished children in South Asia has declined only slightly, from 52 to 43 percent between 1990 and 2009, despite the area’s economic growth and reduction in poverty. Even more disturbing (and inexplicable) are the figures on child malnutrition according to socioeconomic level. Although it is well known that shortages of nutritious food, poor diet and hygiene, lack of access to sanitary facilities, and the resulting high incidence of diarrheal diseases contribute to very high rates of malnutrition in poor children (about 60 percent in 2009), it is much more difficult to explain why as many as 40 and 26 percent of South Asian children in the fourth and fifth wealthiest household quintiles of the distribution, respectively, are also malnourished (UN 2012). Yet another finding emphasizes the heterogeneous nature of the situation in Asia. In the 17 countries of East Asia and South Asia in which the United Nations monitors progress toward the goal of eliminating hunger, only two (Myanmar and Vietnam) have achieved the goal of reducing their 1990 malnutrition levels by half (UN 2012). Of the others, five (Cambodia, China, the Philippines, Sri Lanka, and Thailand) are on track to reach the target, but five more have made insufficient progress (Bangladesh, Indonesia, Lao People’s Democratic Republic, Mongolia, and Nepal), and three have not made any progress or have lost ground (India, the Democratic People’s Republic of Korea, and Pakistan).<sup>3</sup> All of these absolute and relative numbers point to two conclusions. First, progress in the reduction of extreme poverty (and, though it is not emphasized here, economic growth) is not in perfect alignment with progress in nutrition, at least when measuring poverty in terms of income and consumption. Second, Asia is not at all homogenous in terms of reducing poverty and meeting nutritional targets: East Asia—particularly China—has made a disproportionate positive 5
**Table S1.E: Results for specifications with dummy variables for web, email and perceived consequences of disputes**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Web|email|reput_D1|reputD_2|reputD_3|reputD_4|reputD_5|pcf_repD|
|supbuy|0.0550|-0.125|-0.0650|0.00583|-0.0266|0.0706|-0.0304|-0.107|
||(0.113)|(0.118)|(0.121)|(0.0965)|(0.0935)|(0.130)|(0.0832)|(0.180)|
|samesup|-0.0689*|0.00586|-0.0190|0.0244|-0.0518|-0.0270|-0.0229|-0.0649|
||(0.0392)|(0.0524)|(0.0484)|(0.0464)|(0.0434)|(0.0486)|(0.0447)|(0.101)|
|samecus|0.123*|-0.0631|0.0385|0.00236|-0.0690|0.000110|0.0355|0.0340|
||(0.0676)|(0.0733)|(0.0403)|(0.0684)|(0.0560)|(0.0499)|(0.0741)|(0.147)|
|same_sec|-0.0402*|-0.0165|-0.00501|-0.00482|0.00721|-0.00622|0.00769|-0.000615|
||(0.0205)|(0.0146)|(0.0141)|(0.0122)|(0.0103)|(0.0131)|(0.00907)|(0.0247)|
|ldist|-0.0251***|0.00777|0.00192|-0.0107***|-0.0100**|-0.000809|-0.0157***|-0.0310***|
||(0.00299)|(0.00656)|(0.00201)|(0.00316)|(0.00469)|(0.00224)|(0.00432)|(0.00890)|
|ad_fmage|-0.000781|-0.00109**|-6.10e-05|-0.000841|-0.00204***|0.000619|-0.000481|-0.00268|
||(0.00110)|(0.000534)|(0.000213)|(0.000912)|(0.000638)|(0.000539)|(0.000841)|(0.00177)|
|ad_meduc|-0.00391|0.0684***|-0.00364|-0.00498|-0.0127*|-0.000439|-0.00838|-0.0173|
||(0.0138)|(0.0200)|(0.00223)|(0.00639)|(0.00771)|(0.00543)|(0.00636)|(0.0139)|
|ad_mexper|-0.000455|-0.000425|-0.000157|-0.000943|0.00174|0.000806|0.00176|0.00239|
||(0.00122)|(0.000638)|(0.000390)|(0.00160)|(0.00177)|(0.00114)|(0.00209)|(0.00426)|
|ad_mfemale|0.0465|0.0439**|-0.00101|0.00926|0.0263|0.00943|0.0201|0.0209|
||(0.0332)|(0.0202)|(0.00737)|(0.0316)|(0.0361)|(0.0183)|(0.0357)|(0.0729)|
|ad_lemp|0.0766***|0.113***|0.00243|-0.00226|0.00724|0.00813|0.00801|0.0109|
||(0.0194)|(0.0150)|(0.00408)|(0.00951)|(0.0103)|(0.00889)|(0.0114)|(0.0214)|
|Constant|<br>0.265***|<br>0.167***|<br>0.497***|<br>0.407***|<br>0.283***|<br>0.446***|<br>0.246***|<br>1.135***|
||(0.0404)|(0.0420)|(0.0133)|(0.0401)|(0.0425)|(0.0281)|(0.0431)|(0.0996)|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 4
comparison to women in Group A, there is no such difference between women in Group A and Group B. It is possible that women in Group C are stay-at-home mothers who spend the least amount of time on market work and, therefore, they are able to spend some time on other activities after completing their work around the house.

Looking at the differences between mothers of young children, women in Group B spend more time on market work than those in Group C, but both groups spend an equal amount of time on unpaid work per day. The Wald test indicates that there is no statistically significant difference in the marginal effects of unpaid work between these two groups (see Appendix C, Table C1). This finding suggests that mothers of young children who have access to childcare take advantage of that support and participate in the labor market, or alternatively, it is because they have to work that these women have childcare arrangements in place. Regardless, access to childcare does not relieve any burden of unpaid work on these mothers, who spend as much time on domestic chores and care obligations as mothers of young children who do not have access to childcare and are likely stay-at-home mothers. As a result, working mothers seem to be the most overworked group in terms of shouldering the double burden of paid work and unpaid work, despite having access to childcare support.

Finally, we perform robustness checks using the same three subsamples used for testing the association between childcare availability and women’s labor market outcomes. In the first subsample, we limit the age of women to 25–49 years to explore how women allocate their time during their prime working age years. The second and third subsamples focus on mothers with young children aged 0–12 years and 0–8 years, respectively, to explore the change in time allocation across paid work and unpaid work by children’s age.

As shown in Table 5, for women in Group B and C, having children aged 0–5 is associated with less time being spent on market work compared to the reference group. The Wald tests confirm that the differences in marginal effects of time spent on market work by women with children aged 0–5 with and without childcare are not statistically significant (see Appendix C, Table C3). In other words, women with children aged 0–5 spend more time in unpaid work in general, regardless of the availability of childcare support. That women with young children spend the same amount of time to take care of household responsibilities irrespective of their labor market status suggest the presence of a gendered division of labor in the sampled urban areas of Dhaka.

20
**Table 6. Sensitivity of NPV and ERR:** _Annual Incidence without GDEC_ **Project Horizon: 1987-1998. Percent of Work-Year Lost Per Case: 12.5% (5 weeks out of 40).**

|Growth in||||
|---|---|---|---|
|Annual Incidence||||
|-1.5%|_NPV_<br>_ERR_|**@3%**<br>@10%|$5,185,570<br>$4,460,382<br>15%|
|0%|_NPV_<br>**_ERR_**|@,3%<br>**@10%**|**$7,235,744**<br>**56,257,928**<br>29%|
|1.5%|_NPV_<br>ERR|**@3%**<br>@10%|$9.617,654<br>$8,346,331<br>**43%**|

# _Project Horizon: The Case of Sudan_

So far in our analysis we have assumed the project horizon to be 1987 through 1998. However, by the end of 1998, the disease is projected to be eradicated from all countries _except_ for Sudan, given the precarious political situation there. In this subsection, we focus specifically on the eradication program in Sudan. Assuming it takes anywhere from 3 to 5 to 7 years to ensure eradication there _beyond_ 1998, we derive the economic returns specifically for that country. Table 7 summarizes the results.<sup>2 8</sup>

**Table 7. Sensitivity of NPV and ERR:** _Project Horizon_ **Percent of Work-Year Lost Per Case: 12.5% (5 weeks out of 40).**

|Project Horizon for<br>Eradication in Sudan||||
|---|---|---|---|
|3 years (1999-2001)|_NPV_<br>_ERR_|**@3%**<br>@10%<br>30|$999,971<br>$696,083<br>%|
|5 years**(1**999-2003)|_NPV_<br>_ERR_|_@3%_<br>@10%<br>**10**|$83,245<br>-$107,681<br>**%**|
|7 years (1999-2005)|_NPV_<br>_ERR_|**@3%**<br>@10%<br>0|-$716,339<br>-$808,738<br>%|

According to Table 7, the economic returns from eradication in Sudan are consistent with the returns from the campaign for other countries as long as eradication is achieved within 3 years beyond 1998 (i.e., by the year 2001). The longer it takes for eradication efforts to be successful, the lower are the projected economic returns -- going down to 0% by 2005, and negative thereafter.

**28** Annual costs for eradication in Sudan were assumed to be between 3 to 3.5 million in real terms.

12
and poverty-reducing patterns of economic growth in rural India. This overview paper provides the overall framework for a more detailed study of the factors that constrain access to land in selected states, and considers potential policy instruments and mechanisms to reduce those constraints. Four main sections follow this introduction. Section 2 presents the analytical framework, followed in section 3 by a description of the context within which land relations in India are played out. Policy approaches to improving access to land are likely vary from state to state according to differences in agrarian structure, conditioned in turn by history and political economy. In light of shortcomings in available data, preliminary evidence is presented on the distribution of rural land, and on the relationship between farm size and productivity.

Section 4 outlines five key issues that are suggested to be among the most important constraints on access to land for the rural poor and socially excluded, and which warrant further analysis in selected states. These issues are: restrictions on land-lease markets, fragmentation of holdings, the widespread failure to translate women's legal rights into practice, access to and encroachment on commons, and transaction costs associated with land transfers.

The final section summarizes the main policy options considered here for enhancing access to land by the rural poor and other socially excluded groups. Since land is a state subject in India, it is not possible to suggest specific policy options except with reference to particular states. A companion paper presents an exploratory, state-level analysis of social exclusion and land administration in the state of Orissa (Mearns and Sinha 1998). This pilot study aimed to apply the overall framework developed in the present paper in a specific context, and to field-test the methodology so that it may be used as a 'template' for further studies in selected states of India. In order to produce trustworthy and meaningful findings relating to the issues identified here as priorities, careful triangulation is required between existing survey data, village studies, and primary fieldwork adopting methods of institutional and stakeholder analysis. The intention is that on the basis of several state-level studies, each applying a common framework to a common set of research questions, a broad strategy may be identified for improving access to land for the rural poor in India.

3
# **6 Conclusion**

Politically connected businesses are not a rarity, nor are they limited to SOEs. Their incidence cuts across political systems, regions and levels of development. Our paper has focused primarily on economies in East and Central Europe, Russia and, as a comparator, Spain. Using a new and unique dataset that identifies Politically Exposed Persons (PEPs) and their links to companies, politicians and political parties, we are able to identify the broad scale of the phenomenon in each of the countries. Yet, the originality of the paper lies principally in our ability to identify the links between individuals, politicians, political parties and different types of firms, as well as the complex configurations of the resulting networks in each of the countries. These networks in turn could be expected to shape behaviour.

Drawing on the tools of network analysis, we show that each of these countries is characterized by a giant component or Big Island. The second largest component in all instances is small. Nevertheless, we find evidence that their networks have small-world properties, with high clustering and short path length due to a relatively small number of bridging connections. In the network space, however, there are also very different configurations that reflect, inter alia, differences in political and other institutions. Given the region’s recent past, it is perhaps not surprising that State-Owned Enterprises (SOEs) are prominent and tend to have positions in the network with relatively high betweenness or centrality. There is a clear difference in this regard when compared with Spain.

Matching the information on connections to firm level data from _Orbis_ , we demonstrate that there is a positive and robust correlation between the levels of sales, output, wages as well as a performance variable – the return on assets – and being connected. We cannot pin down causality, given the limitations in our data, but it seems reasonable to assume that the likely direction of association is from connections to outcomes. However, the principal interest from our data analysis concerns how network features can influence these associations. We find clear evidence that the location in a network, the extent of ties and betweenness or centrality is often positively, and significantly, associated with firm level indicators for the scale of activity. Network variables are also seen to be significant in an estimation that has – – a performance indicator Return on Assets on the left-hand side. In other words, not only are networks likely to be important in shaping how connections arise and propagate (something that we cannot directly measure) but they are also important in shaping the returns to connections. Of course, the configuration of networks, as well as their respective paybacks, is likely to be materially affected by other factors, including political institutions, resource endowments and neighbourhood. We are exploring these features in continuing research for a far larger group of countries and regions using the same dataset.

22