rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Differences in total factor productivity account for roughly half the differences in income across countries and are generally associated with differences in technological progress (e.g. Hall and Jones, 1999). These differences are also large between firms within a single country (Hsieh and Klenow, 2007). Innovation is a key to technology adoption and creation and studying the determinants of innovation is a crucial first step in understanding how firms catch up to the technology frontier, and for designing policies to enhance growth and development. However, the existing empirical literature on innovation has two main gaps. The first is that data limitations have largely restricted analysis to the role of firm characteristics in innovation, leaving out any role for the characteristics of the firm owner. Given the central role of the entrepreneur in the innovation process, it is important to understand whether firm characteristics alone are sufficient to capture the role of the owner. The second limitation is that existing studies of innovation have not examined innovation in microenterprises and small firms. Such firms account for the overwhelming majority of firms in developing countries, and it is thus of great interest to see whether and how such firms are innovating.

This paper uses a new representative survey of over 2800 firms in Sri Lanka to empirically examine the determinants of innovation in micro, small, and medium firms. The survey contains detailed measures of innovation, allowing us to consider the four main types of innovation identified by the OECD’s Oslo Manual (OECD, 2005): product, process, marketing and organizational innovations. The survey collected detailed information on the socioeconomic background, ability levels and personality traits of the enterprise owner, enabling us to examine the role the owner plays in innovation.

We develop a parsimonious model of firm innovation which combines the idea in Klette and Kortum (2004), where innovation allows firms to produce new products, with that in Cohen and Klepper (1996a), where innovation lowers the unit costs of production. Both firm and owner characteristics are allowed to influence the efficiency of innovation in this model. The model then delivers predictions for the interplay between the different types of innovation, firm size, competition, and firm and owner characteristics, which we can take to the data.

- 2 -
As mentioned above, improvement of transportation energy intensity has also had a tremendous impact on emission growth in India. However, the drastic improvement in transportation energy intensity between 1995 and 1996 (see Figure 5c) is the product of a statistical adjustment in the reporting of diesel consumption rather than the result of any real change (Zhou, 2007). It is thus likely that both CO2 emission growth and transportation energy intensity for India ought to be revised downwards from 1980 to 1995. Similarly, an abrupt change in diesel consumption is reported for the Philippines between 1993 and 1994 (see Figure 5i). While no documentation could be found, it is highly likely that this is another instance of a statistical adjustment to the energy consumption data, and CO2 emission growth and transportation energy intensity for the Philippines ought to be revised upwards from 1980 to 1993.

Figure 5g presents an indexed time-series chart of the decomposition results for Mongolia, the only country where transport sector CO2 emission changes were primarily driven by changes in transportation energy intensity. Transportation energy intensity in Mongolia declined drastically after 1988, but especially from 1990-91 onwards, without Soviet assistance after the demise of the Soviet Union. Although Mongolia suffered a devastating recession after the collapse of the Soviet Union, its primary trading partner, its economy recovered and started growing again after 1994. However, its transportation energy intensity remained low as the country continued to suffer from chronic energy shortages. This explains the net reduction in CO2 emissions from the transport sector as 35
|Constant|-1.563***<br>(0.301)|(0.125)<br>1.721**<br>(0.668)|(0.148)<br>1.526**<br>(0.731)|0.617**<br>(0.243)|(0.100)<br>-2.880***<br>(0.661)|(0.118)<br>-2.977***<br>(0.724)|
|---|---|---|---|---|---|---|
|Observations|5255|4672|4143|5255|4672|4143|
|Work conditions|N|Y|Y|N|Y|Y|
|Wages|N|Y|Y|N|Y|Y|
|Region FE|Y|Y|Y|Y|Y|Y|

# Source: Authors’ estimation

# Note: Columns (3) and (6) exclude self-employed informals.

This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their likelihood of being satisfied or dissatisfied with their life, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

63 Informal Employment and Worker’s Wellbeing in Russia / June 2019
A key distinguishing feature of the compulsory schooling policy in Turkey is that it alters the sheepskin effects of schooling (i.e., the signaling effects of schooling; Spence 1973) through its redefinition of the number of years of schooling required to obtain various diplomas. Acquiring a primary school diploma requires the completion of grade 5 before the policy, but the completion of grade 8 after the policy. The completion of grade 8 would give the students a secondary school diploma before the policy. Hence, the sheepskin effects of the completion of both grade 5 and grade 8 fall with the policy. Since the sheepskin effects are more important for men than for women (due to the much lower labor-force participation of women in Turkey) and in urban areas than in rural areas (due to much higher self-employment in rural areas) the sheepskin effects could counteract the equalizing effect of the policy on schooling disparities by gender and rural/urban residence.

In our empirical analysis, we use the 2003 and 2008 Demographic and Health Surveys data for Turkey, which are nationally representative. The key feature of our data set is that it includes information on childhood place of residence at age 12; therefore, we are able to identify urban/rural residence at the age at which individuals in our sample make their schooling decisions. In the identification of the policy effect by gender and urban/rural residence, we compare the birth-cohorts that are affected by the policy with those that are not within a regression discontinuity design, where we allow the time trends in schooling outcomes to be different before and after the discontinuity. We use various subsamples—defined by gradually taking narrower time intervals around the discontinuity—and various polynomial specifications conditional on the width of the time interval.

We find that the policy equalizes the educational attainment of urban and rural children substantially. The urban-rural gap in the completed years of schooling at age 17 falls by 0.5 years for men and by 0.7 to 0.8 years for women. However, there is no evidence of a narrowing gender gap as a result of the policy. On the contrary, we find that the gender gap in urban areas 5
of such analysis is not to interfere in internal political processes but to better understand the political environment within which economic decisions are being made.

Second, there are several obstacles associated with making PE analysis “actionable”. While many PE studies do a reasonably good job of describing the operating environment and identifying winners and losers, most of them fail to meet the “so what” test by making practical recommendations on a way forward. This is especially true when such studies focus on complex institutional and policy issues that might necessitate a long-term engagement, whereas the structure of incentives that staff (both within the Bank and the government) face is much shorter-term in nature.

Even when staff is motivated to undertake behavioral change and take into account PE factors in designing operations or in their dialogue with the government, it is sometimes difficult for them to get a broader buy-in either from the rest of their sector colleagues or from their management. The lack of support can be explained by the fact that the point of view of the team members that operate on the ground may not be shared by the management in headquarters or regional offices, either because they do not fully appreciate the reality on the ground or they do not see the payoffs from the change in approach. This can also result from poor communication between the staff working in the field and in headquarters.

When teams on the ground are expected to deliver tasks and results in a fairly short period of time, there is a tendency to focus only on “manageable” issues. Moreover, the sometimes long time lapse between the moment when a change process is initiated and the moment when results are realized can discourage teams to undertake these changes. PE studies can sometimes go against management requests or donor expectations and make it more difficult to make them actionable.

Furthermore, for Task Team Leaders focused on processing lending activities that often involve large sums of money and are fraught with implementation risks, a PE assessment of issues can prove to be a distraction as some TTLs often tend to focus narrowly on the project that they are working on, with scant attention being paid to the broader sectoral results that need to be achieved. In this case it is difficult for TTLs to apply the recommendations from the PE studies and address deeper systemic issues as their options are limited.

Lessons from the Zambia case demonstrate that while country level PE analysis is useful as background information, PE studies should probably focus more at the sector-level as the recommendations from these studies tend to me more actionable. Sector operations are at the core of lending activities and policy dialogue and in order for these interventions to deliver on an effective basis understanding the political constraints to operating in a particular sector is important. Adopting a 14
process. Note also that the other initial conditions considered had a significant impact on **_LIBERALIZATION._**

**TABLE 4. CORRELATION MATRIX**

||**_YEARPLANN_**|**_LOCATION_**|<br>**_STATE_**|
|---|---|---|---|
|**_INITFREEDO_**|**_M_**<br>**0.24**|**0.07**|**-0.36**|
|**_YEARPLANN_**||**-0.59**|**-0.54**|
|**_LOCATION_**|||0.16|

**Dividing the sample into two groups.** Finally, we have reestimated the model for the non-CIS countries alone, to evaluate whether the findings on the important role of initial freedom for the economic transition still hold<sup>23</sup> . As shown in Table 6, the coefficients of **_INITFREEDO0M_** are always positive and significant, which confirms that democracy has been a crucial precondition for the introduction and implementation of market-oriented reforms.

23 We could not estimate the model for the group of CIS countries alone, because of the limited variability of **_INITFREEDOM_** within this sub-sample.

20
_Appendix Table 13: Education and child labor program impacts of on nonbeneficiary children_

||Enrollment<br>of 6-11-<br>year-olds|Enrollment<br>of 12-14-<br>year-olds|Attendance<br>of 6-11-<br>year-olds|Attendance<br>of 12-14-<br>year-olds|Children<br>10-14 years<br>old who<br>worked in<br>the last<br>week|Children<br>10-14 years<br>old who<br>worked for<br>pay in the<br>last week|
|---|---|---|---|---|---|---|
|_Panel A: Program_<br>|_Impact_ <br>||||||
|Program village|-0.004<br>(0.010)|0.007<br>(0.018)|-0.004<br>(0.006)|-0.001<br>(0.010)|0.031<br>(0.027)|0.010<br>(0.018)|
|_Panel B: Interaction_|_with saturati_|_on_|||||
|Program village|0.006|-0.062|-0.043**|-0.015|0.006|-0.004|
||(0.028)|(0.065)|(0.019)|(0.019)|(0.109)|(0.073)|
|Saturation|0.006|-0.090|-0.034*|-0.018|-0.055|-0.184**|
||(0.037)|(0.055)|(0.020)|(0.024)|(0.118)|(0.084)|
|Program|-0.017|0.129|0.069**|0.024|0.043|0.024|
|village*saturation|(0.047)|(0.112)|(0.029)|(0.034)|(0.163)|(0.105)|
|_Panel B: Interaction_|_with above m_|_edian saturatio_|_n_||||
|Program village|-0.001|0.006|-0.010|-0.001|0.043|0.006|
||(0.013)|(0.022)|(0.008)|(0.011)|(0.041)|(0.028)|
|Above Median<br>Saturation|0.010|-0.025|-0.008|-0.005|0.016|-0.033|
||(0.010)|(0.029)|(0.006)|(0.012)|(0.038)|(0.027)|
|Program<br>village*Above|-0.007|0.002|0.015|-0.001|-0.031|0.012|
|Median Saturation|(0.018)|(0.039)|(0.011)|(0.019)|(0.049)|(0.030)|
|_Panel B: Interaction_<br>|_with top quar_<br>|_tile of saturatio_<br>|_n_ <br>||||
|Program village|-0.008<br>(0.011)|0.015<br>(0.020)|-0.009<br>(0.007)|-0.003<br>(0.011)|0.021<br>(0.031)|0.008<br>(0.021)|
|Fourth quartile of<br>saturation|-0.007|-0.001|-0.017|0.007|-0.038|-0.017|
||(0.019)|(0.030)|(0.012)|(0.013)|(0.026)|(0.021)|
|Program<br>village*fourth|0.028|-0.022|0.030**|0.011|0.065|0.011|
|quartile of<br>saturation|(0.024)|(0.049)|(0.013)|(0.013)|(0.052)|(0.025)|
|Control<br>Observations|355|181|342|163|301|301|
|Treated<br>Observation|341|185|332|176|295|297|
|Control Mean|0.986|0.950|0.985|0.985|0.103|0.033|

note: *** p<0.01, ** p<0.05, * p<0.1.

All specifications include linear controls for child age (in months) and municipality fixed effects. Standard errors are clustered by village; there are 130 villages.

Source: 2011 _Pantawid_ impact evaluation survey 62
within this framework, as Equation (1) is a _bona fide_ structural model in its own right. However, equation (A1.6) allows us to better illustrate the advantages of using the household-level data to correct for a variety of potential econometric problems associated with using the aggregated data, and to precisely highlight the role of aggregation.

# Appendix 2: Expanded Discussion of Panel Data Models

The discussion in this appendix provides further detail on the panel data analysis presented in Table 5 and discussed in Section 4.5. As noted in the body of Section 4.5, the analysis summarized in Table 5 first examines the relationship described in Equation (4) between growth and lagged inequality over short time periods, and then implements first-differenced models that eliminate the effects of fixed village unobservables. It then examines whether controlling for biases potentially introduced if growth and lagged income are both systematically related to a dynamic unobservable in the error term.

The base OLS specification is presented in column (1) and suggests that when short-term growth rates are regressed on lagged inequality, the results are statistically insignificant whether we use the Mean Log Deviation or the 90-10 ratio as our inequality measure. Column (2) shows straight first-differenced (FD) models that sweep out unobserved fixed effects, employing differences of increasing duration from short (one-period) to long (four-period) differences. In these first-differenced models, there is no significant effect of growth on inequality, and if anything, there is a positive (though insignificant) effect as one expects with a Kuznets process.

Dynamic issues arise with the concern that change in growth and lag growth (or lag change in levels) will be may be systematically related through a common error component. Remember that change in growth (the dependent variable) is calculated as (ln _yit_ − ln _yit_ −1)<sup>−</sup> (<sup>ln</sup><sup>_y_</sup> _it_ −1 − ln _yit_ −2 )<sup>, and the change in</sup> lagged levels (a regressor) is (ln _yit_ −1 − ln _yit_ −2 )<sup>. Common error in periods</sup><sup>_t_−1or</sup><sup>_t_−2may lead to a</sup> mechanical negative correlation between the change in growth and the change in the lagged levels. More importantly, the lag change in the inequality measure (Mean Log Difference) has components of the same error term as the mean log level of income. We thus estimate instrumented versions of the FD models (FD-IV) models, in which we employ as instruments period _t_ − 3 values of the levels of lagged income and lagged inequality. At least mechanically in this model, period _t_ − 3 measures of these variables are not part of either the dependent variable, or the lagged changes of our main regressors. As long as the _t_ − 3 _levels_ of these variables significantly predict the regressors (which are _changes_ ), and are in turn uncorrelated with the error term, then these lagged values will be eligible instruments. We implement this procedure in the third column of each panel, for the first-difference specifications only. Note that while Inequality and Growth in Rural China, Appendices: Page 3
_<u>Table 6 Computer and Software Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|86.07|104.93|74.47|80.07|107.98|119.52|100.00|
|**Main Sectors**||||||||
|Manufacturing|87.79|95.28|82.74|85.19|109.94|118.88|99.84|
|Services|84.29|108.12|71.39|78.40|107.44|119.80|100.15|
|**Size**||||||||
|Small|74.56|98.00|70.49|81.13|103.21|100.71|83.12|
|Medium|100.44|131.10|84.28|71.86|104.30|140.55|117.25|
|Large|145.55|179.08|116.21|101.83|190.28|160.90|157.23|

Source: Author’s own elaboration from enterprise surveys

|_Table 7 Firms with Internet Connect_<br>Categories|_ion by Co_<br>Ghana|_untry (P_<br>DRC|_ercentage i_<br>Tanzania|_n terms of_<br>Uganda|_Firms Usi_<br>Zambia|_ng Comp_<br>Kenya|_uter)_<br>Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|57.7|27.6|21.9|23.0|49.5|72.7|42.1|
|**Main Sectors**||||||||
|Manufacturing|56.1|33.3|21.9|17.8|64.5|73.5|44.5|
|Services|59.5|25.7|21.9|24.7|45.2|72.4|41.6|
|**Sectors**||||||||
|Food|60.7|65.3|38.2|22.7|71.6|59.6|53.0|
|Textiles|35.4|0.0|13.2|32.9|65.9|80.3|37.9|
|Garments|37.0|5.6|6.8|1.6|22.1|-|14.6|
|Wood|18.7|23.2|31.6|14.2|40.9|81.2|35.0|
|Publishing, printing, and Recorded<br>media|77.9|79.3|74.6|94.1|100.0|67.6|82.3|
|Chemicals|100.0|63.5|38.7|100.0|83.5|98.5|80.7|
|Plastics & rubber|80.9|100.0|69.7|0.0|68.7|100.0|69.9|
|Non metallic mineral products|50.8|36.3|22.5|78.3|49.6|100.0|56.2|
|Fabricated metal products|38.0|17.4|16.6|6.4|71.6|100.0|41.7|
|Machinery and equipment|-|9.7|0.0|27.0|78.9|97.1|42.5|
|Furniture|27.2|9.5|12.8|0.8|30.1|100.0|30.1|
|Other Manufacturing|74.0|69.6|9.6|34.9|83.2|91.1|60.4|
|Construction|82.6|87.8|26.4|6.5|86.6|100.0|65.0|
|Services of motor vehicles|45.8|26.1|24.4|28.5|59.8|77.3|43.7|
|Wholesale|72.9|55.3|29.5|24.6|29.6|62.1|45.7|
|Retail|43.7|16.6|24.0|24.9|43.6|74.8|37.9|
|Hotel and restaurants|52.9|16.4|16.4|28.5|35.7|60.2|35.0|
|Transport Section|90.0|52.9|46.2|25.4|59.4|93.4|61.2|
|IT|91.1|78.6|0.0|3.0|96.9|79.1|58.1|

Source: Author’s own elaboration from enterprise surveys 16
commodities important to the poor. Also it may affect the incentives to production of commodities mostly produced by the poor vis-à-vis those produced by the non-poor. These policies also indirectly affect the rural poor if they have an impact on the performance of the agricultural sector. Finally, the rural poor may also be affected by the non-rural sectors of the economy through migration and remittances, and by affecting real wages in the rural areas.

We estimate variants of the following poverty equation, (10) _pov it_ = _β_ 1 _L_ [ _g it_ ] + _β_ 2 _L_ [ _Yit_ ] + _β_ 3 _L_ [ _Eit_ ] + _β_ 4 _L_ [ _S it_ ] + _β_ 5 _L_ [ _Tit_ ] + _ξ i_ + _τ it_ , where<sup>_ξ_</sup> _i_<sup>is a random country effect and</sup> _τ it_ is a random disturbance. We expect the effects of g, E, and Y on poverty to be non-positive while the impact of T to be ambiguous and, according to the discussion in the previous section, the effect of S to be positive. As in the case of the GDP equation, we tried various lag structures to capture for cumulative effects of the explanatory variable E over time as well as other instruments. The most popular measure of poverty is the head count ratio. However, head count measures may be quite heterogeneous as the definition of poverty lines tends to differ quite considerably across countries and over time. We use per capita income of the poorest two quintiles as a proxy for the variable _pov it_ .

# **IV. The Data**

In this section we examine a new data set for rural public expenditures for ten countries in Latin America recently assembled by the FAO regional office for Latin America and the Caribbean with the support of the World Bank and others. The annual data for the ten countries cover the period 1985–2000.<sup>18</sup> An advantage of this data is that it is quite disaggregated while at the same time it has been measured using a reasonably consistent methodology across countries and over time. In addition the public expenditure data as provided is comprehensive, covering the vast majority of the items in which the public sector spends money in the rural areas. The level of detail of the data allows us to obtain a fairly accurate measure of the evolution of the level and composition of government expenditures in rural areas over the period.

The data for public expenditures in the rural sector cover all rural expenditures, including expenditures for agriculture as well as rural infrastructure, animal and plant sanitary protection, environmental expenditures, and social services to rural areas. The latter group covers

> 18 The countries for which the data covers the full period are Costa Rica, Dominican Republic, Ecuador, Honduras, Jamaica, Panama, Paraguay, Peru, Uruguay, and Venezuela. For a detailed description of the data see Kerrigan (2001).

14
of crops. Figure 1 depicts a set of such crop specific net income functions with respect to temperature. At cool temperatures, farmers would choose to grow wheat. As temperatures rise, farmers would switch from wheat to corn. As temperatures grow warmer, they might shift from corn to millet which is more heat tolerant. The Ricardian function, (2), captures the locus of maximum profits for each temperature. It reveals the net effect of changing an exogenous variable, in this case, temperature. The assumption across space is that farmers find the outputs and inputs that maximize net income given the climate they face. Cross sectional data can therefore be used to trace out the Ricardian function.

We assume that farmers will continue to maximize profits if climate changed and so remain on the Ricardian function over time as well. The change in welfare, ΔV, resulting from a climate change from C0 to C1 can then be measured as follows.

Whether farmers remain precisely on the Ricardian function each year is not likely as there is a great deal of variance in weather as well as uncertainty about the path of climate change. There could be substantial transition costs over time (Kelly et al. 2005). We argue that the Ricardian model measures long-term impacts after adjustments have been resolved.

Note also that this measure of welfare assumes that prices are constant. With an inelastic demand for food, changes in the world supply of food would change food prices.

7