rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3. Regional variation in urbanization and quality of transport infrastructure**

_Sizable investment is needed to accommodate urbanization and keep pace with growing economic activity._

# **A. Population living in slums**

<!-- Start of picture text --> Percent of urban population<br>100<br>EAP ECA<br>LAC MENA<br>80 SAS SSA<br>BRICS<br>60<br>40<br>China<br>South Africa<br>20 India Brazil<br>0<br>0 5 10 15<br>GDP per capita, US $ thousands<br><!-- End of picture text -->

# **C. Quality of transportation infrastructure**

# **B. Urbanization rate**

<!-- Start of picture text --> Percent of total<br>100<br>Brazil<br>80 South Africa<br>Russian<br>Federation<br>60<br>China<br>40<br>India EAP ECA<br>LAC MENA<br>20 SAS SSA<br>BRICS<br>0<br>0 5 10 15 20 25<br>GDP per capita, US $ thousands<br><!-- End of picture text -->

# **D. Quality of port infrastructure**

<!-- Start of picture text --> Score Score<br>6 7<br>Russian Panama<br>5 China South Africa Federation 6<br>India South Africa<br>5<br>4 India China<br>Brazil 4 Russian<br>3 Federation<br>3<br>2 EAP ECA Brazil EAP ECA<br>LAC MENA 2 LAC MENA<br>1 SAS SSA SAS SSA<br>BRICS 1 BRICS<br>0 0<br>0 5 10 15 20 25 0 5 10 15 20 25<br>GDP per capita, US $ thousands GDP per capita, US $ thousands<br><!-- End of picture text --> Sources: World Bank, World Economic Outlook.

Note: EAP is East Asia and Pacific, ECA is Europe and Central Asia, LAC is Latin America and the Caribbean, MENA is Middle East and North Africa, SAS is South Asia, and SSA is Sub-Saharan Africa. BRICS are Brazil, Russia, India, China and South Africa.

A. Data 2014. Population living in slums is the proportion of the urban population living in slum households, defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.

B. Data for 2015. Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.

C. Data for 2015. The score is from 1 to 7. Higher score indicates better quality. Quality of Transportation Infrastructure surveyed countries on the question of “How would you assess general infrastructure (e.g., transport, telephony, and energy) in your country?” The score is from 1 to 7. Higher value indicates better quality.

D. Data for 2015. The score is from 1 to 7. Higher score indicates better quality. Quality of Port Infrastructure surveyed countries on the question of “In your country, how would you assess the quality of seaports? (For landlocked countries: How accessible are seaport facilities?”.

6
<!-- Start of picture text --> Number of IDPs Map of the IDP distribution<br>per communes<br>1 200'000 I 100000and more<br>BH 50'0to 2 0 00000<br>HH 20000to10'000to 10000050000 JOP populTab a rretion: 4<br>Less thanto10'00020°000 Cite Soleil %<br>[] international boundary IDP population : 26,371 :<br>[~~] Commune boundary Deimas s : 5 : . yy CAN<br>na : 2 athe terrier tered<br>Port-au-Prince C_-=<br>om | :<br>¢ IOP population: 7,18<br>© 4000 ‘ <<br>=<br>neers<br><!-- End of picture text -->
the industry or the economy is the most appropriate unit of analysis for understanding capital expenditure decisions at the firm-level. In particular, panel data is most useful for distinguishing between alternative models of investment, since it controls for firm-specific and time-specific fixed effects. This in turn is a significant challenge for developing countries, which traditionally have had poor database infrastructure compared to developed countries.

The findings of the study also become interesting in the context of the emerging consensus in the economics profession on looking at economic theory as being capable of explaining economic phenomenon on a global basis, rather than being compartmentalized to address the problems of developing and developed countries separately.30 This is of course a fundamental departure from the earlier view in the profession and strikes at the very root of a discipline such as development economics. In the new perspective, what distinguishes countries are the conduct of economic policies rather than initial conditions which formed the basis for the earlier notion of different theories being necessary to understand economic phenomenon in developed and developing countries. If one were to go by the emerging view, the findings of this study with regard to the determinants of capital expenditures at the firm-level for a panel of U. S. manufacturing firms could equally be applicable to firms in developing countries.

These findings regarding the relative importance of various factors that underpin the alternative theories of investment are also related to the larger issue of the relationship between financial and real factors in the economy. The evidence presented in this paper regarding the superiority of the cash flow theories of investment in cross-section and fixed effects regressions suggest that financial decisions are indeed closely linked to real decisions in the economy. This 30 See Agenor and Montiel (1996) for an interesting exposition of this view.

30
Teacher Incentives in the Kyrgyz Republic awardees, by level of award. Teachers who received lower performance ratings held less favorable opinions about the motivational aspects of the incentive program. Despite this, lower-rated teachers were more likely to report using what they learned to evaluate their own teaching, as compared with more highly rated teachers, and were more likely to take professional development courses in the years following the program’s implementation.

In other respects, teachers’ performance ratings were uncorrelated with their: (a) recall of using new teaching methods, such as monitoring student learning and using formative assessments, (b) report of having more positive attitudes regarding their career prospects, or (c) report of being motivated to continue teaching. These results are generally consistent with findings from recent studies of teacher incentives in the United States (Yuan et al. 2013, Springer et al. 2012).

Since the Kyrgyz Republic’s teacher incentive program is best described as a standards-based performance incentive program rather than as a competitive incentive program, the overall results are also consistent with the previous studies in India and the US, with respect to overall favorability of teachers’ opinions. However, the somewhat more negative attitudes of lower rated teachers – with respect to the motivational aspects of the program -- could reflect the within-school competitive aspect of the process. The overall budget for incentives in the program was insufficient for all teachers to receive Master Teacher salary bonuses, even if they had qualified. Although lower-rated teachers received a bonus, their bonus was not as large as that received by the higher-rated teachers, which may have affected their favorability ratings.

Other things may have accounted for the lack of heterogeneity in program effects, as reported by teachers. First, the teachers were asked to reflect on the program approximately four years after their participation, which may have diminished their recall and eliminated the likelihood of finding differences among teacher-awardees. Second, the teacher incentive program was followed by a “whole school” incentive program, whose effects may have diluted teachers’ recall about the individual teacher incentive program that had preceded it. Third, the salary bonuses were relatively large for all groups, which could account for the overall positive opinions for all teacher awardees, even those whose bonuses were on the 15
29 climate mitigation activities. Such intervention can take many forms, including: retraining workers from industries that will shed labor, assisting companies with R&D budgets to accelerate the development of alternative products (hybrid cars from automakers), and contributing to the costs of new abatement infrastructure (pipelines and demonstration plants for CCS). Money may also be spent to encourage the retirement of polluting assets. Although such payments can be justified in various ways, in political terms they are a means of diffusing opposition. An example of such an approach in the energy sector (though not related directly to climate change) was the German government program to phase out nuclear power, which offered generous incentives to nuclear operators to bring forward the closing of plants.

_Establishing new centers of economic power_ . Government intervenes to strengthen economic actors whose activities point in the direction of desired social ends. The best example here is encouragement of the green business sector (renewable energy, organic farming, climate change consultancies, and so on). _Not only do such interventions secure direct environmental and economic gains (more green energy, growing export markets), they also have the indirect effect of bolstering constituencies advocating further change ‐‐ for example, the removal of subsidies to fossil fuels._ Current economic interests (with established facilities, technical know‐ how, jobs, export earnings, and so on) are like large masses in a gravitational system – they exert political ‘pull’ in proportion to their size. To counter‐balance their influence it is necessary to build rival enterprises that can offer jobs, tax revenues, exports, along a line of advance congruent with a low carbon economy.

_Creating new institutional actors_ . Governments lend support (financial, organizational, moral) to encourage the creation of autonomous actors who can promote change. This can include transferring functions from the core of government to bodies that work at arms length. This can provide many advantages including reducing day to day political interference, freeing the bodies from bureaucratic routines and mind‐set, increasing public confidence, and serving as a hedge against changed political priorities. New actors can be created by hiving‐off existing structures, fusing discrete units, or starting from scratch. And the resultant bodies can be granted different degrees of autonomy. Alternatively, governments can assist other parties to organize themselves – to promote particular causes or regulate their own affairs. The encouragement of sector self‐organization to engage with climate change is a case in point. The creation of semi‐independent bodies such as the Carbon Trust in the UK illustrate this approach.

_Adjusting legal rights and responsibilities_<sup>_3_</sup> _._ Government intervenes to adjust the legal obligations of established actors, making some avenues of development easier and others more difficult (costly, contentious, and risky) to pursue. An example is provided by disclosure 3 The following four paragraphs draw directly from Meadowcroft 2007a.

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ratio between housing units and the number of households in cities, as shown in Figure 8. Note the correlation between periods of severe financial repression and a fall in the supply ratio (compare with Table 1).

53. As slready seen, tenants are required to provide large lumpsum payments which are about equal to one year of their salary. Most young renters without accumulated savings (or affluent parents) cannot afford to rent a full unit but must sublet part of a unit from others. Monthly rents are associated with low-income status until now. Korean surveys show that multiple renters are more frequent when income is lower because of limited chonsei savings. Crowding at the low-income end of the market is very severe. In the 1980 census, only 30 percent of urban households occupied an entire unit, while 30 percent shared it with one other family, 18 percent with two and 22 percent with three or more households in a single unit (see 1980 Census, Table 7).

54. Analyses of the censuses over time show that the low value of the "supply ratio" in cities graphed in Figure 8 is also linked to two other factors: the removal of a large number of small units or their enlargement. The enlargement of small units is again linked to the scarcity of financing which leaves room only for an incremental approach to housing investment. Apartment units do not lend themselves to such enlargement; this is another reason why the lack of mortgage finance increasingly distorts the supply of new housing in Korea.

# E. Rapidly Rising Real Rents

55. The lack of mortgage financing for new housing construction is reflected also in continuing rise of real rents with rental charges rising by 5.3 percent in 1986; this incryg7e is twice the CPI rise of - only 2.6 percent during the same period. Long-term series on real rent increases remain to be developed.

# VII. CONCLUSION

56. The findings for Korea confirm the hypothesis that the nature of economic planning and financial policies not only play significant roles in explaining the performance of the housing sector, but is likely to have a greater impact than other variables such as the level of per capita income or the degree of urbanization, (see Renaud and Buckley, 1987). For a long period of time, Korea has shown the features of nonperforming housing finance systems under policies of strong directed credit and alternating phases of mild and strong financial repression.

16/ KHB press release March 6, 1987.
# **Abstract**

This paper analyzes the labor markets in rural and semi-urban Mexico. The empirical analyses show that nonfarm income shares increase with overall consumption levels and, also, with time. Rural-dwellers in lower quintiles of the consumption distribution tend to earn a larger share of their nonagricultural incomes from wage labor activities. For the poorest, low-productivity wage labor activities are important. The quantile wage regression analysis for rural Mexico shows a rather heterogeneous impact pattern of individual characteristics across the wage distribution on monthly wages. The findings reveal that education is key to earning higher wages and that workers in more dispersed rural areas earn less than their peers in semi-urban rural areas (localities with less than 15,000 inhabitants). The rural nonfarm sector is heterogeneous and includes a great variety of activities and productivity levels across nonfarm jobs. Moreover it can reduce poverty in a couple of distinct but qualitatively important ways in rural Mexico. The analysis of nonfarm employment in rural Mexico, suggests that the two key determinants of access to employment and productivity in nonfarm activities are education and location.

ii
|R<sup>2</sup>|0.13|0.20|0.18|0.17|0.20|0.20|0.27|0.23|0.28|
|---|---|---|---|---|---|---|---|---|---|
|# obs.|18751|18751|18751|11992|11992|11992|6284|6284|6284|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. KCPE = Kenya Certificate of Primary Education.

33
|Cohort|Coefcient|Standard error|Treated|N<br>Control mean|
|---|---|---|---|---|
|**A. Aver**|**age treatme**|**nt efect on the **|**treated **|**by cohort**|
|2003|-0.369|(0.126)|604|3.003|
|2008|-1.100|(0.072)|87|3.003|
|2010|0.474|(0.139)|1,850|3.003|
|2015|0.546|(0.071)|839|3.003|
|2017|-0.691|(0.581)|123|3.003|
|2019|-0.377|(0.081)|54|3.003|
|2020|0.170|(0.079)|1,502|3.003|
|2021|0.785|(0.057)|1,234|3.003|
|**B. Aver**|**age treatme**|**nt efect on the **|**treated **|**(all cohorts)**|
|All|0.318|(0.072)|7,574|3.003|

**Table S7** : **Average effects of AP management on bird abundances.** This table displays average treatment effects on the treated (ATTs) derived from the regression described in SI A.2. The dependent variable in this regression is the log(bird count) per birding trip. Panel A displays all ATTs returned from summarizing the regression object by the calendar year in which protected areas were transferred to AP (“cohort”). The command in R, summary(regression object, agg = “cohort”), returns ATTs for 8 of 12 possible cohorts, perhaps due to insufficient data or variation in the data for the remaining 4 cohorts. For comparison, Panel B displays the ATT across all protected areas managed by AP. The Coefficient displays a given ATT and the Standard error column displays the ATT’s standard error. The Treated N column is the number of observations in the given AP cohort (Panel A) or across all protected areas managed by AP (Panel B). The total number of observations in the regression is 145,200. The Control mean column is the mean log(bird count) across all control protected areas.

41
4. Repeat steps 1 to 3 a large number of times _M_ . Let _τc_<sup>_∗_(</sup><sup>_m_)</sup> be indicator obtained in mth replicate, for _m_ = 1 _, . . . , M_ . The extended Census EB estimate is just the average of the _M_ indicators, In contrast with the traditional ELL and CB-EB approaches, in the above MC procedure, the model parameter estimates _β_<sup>ˆ</sup> 0, _σ_ ˆ _η_<sup>2</sup> 0<sup>and ˆ</sup><sup>_σ_</sup> _e_<sup>2</sup> 0<sup>, as well as</sup><sup>_η_ˆ</sup><sup>_c_0and</sup> var [ˆ<sup>�</sup> _ηc_ 0], are kept fixed across the whole procedure; that is, there is a single DGP.

As a measure of noise of the extended CEB estimators, here the actual MSE under the nested error model is considered. To estimate this MSE, we propose to apply a parametric bootstrap procedure similar to the one in MR (2010), but adapted to the case where the survey is not necessarily a subset of the census. This procedure is also described in Molina (2019) for the CEB predictor, but here we apply it to the extended CEB predictor that includes heteroscedasticity and sampling weights:

1. Fit model (1) to the survey data. This yields the set of parameter estimates:

In the implemented version, the model is fit either via FGLS and decomposing residuals as in the traditional ELL fitting approach, or by H3.

2. Using the estimates obtained in step 1 as true values of the model parameters, simulate a vector of census welfare _yc_<sup>_∗_= (</sup><sup>_yc_1</sup><sup>_, . . . , ycN_</sup> _c_<sup>)</sup><sup>_T_asfollows:</sup> ln( _ych_<sup>_∗_) =</sup><sup>_xchβ_ˆ0+</sup><sup>_η_</sup> _c_<sup>_∗_+</sup><sup>_e∗_</sup> _ch_<sup>_,h_= 1</sup><sup>_, . . . , Nc,_</sup> where the location effect is generated as In the homoscedastic case, household-specific errors are generated as and, in the case of heteroscedasticity, as 3. With the simulated census of welfare _yc_<sup>_∗_=(</sup><sup>_yc_1</sup><sup>_, . . . , ycN_</sup> _c_<sup>)</sup><sup>_T_foreacharea</sup><sup>_c_=1</sup><sup>_, . . . , C_,produce</sup> indicators of interest: _τc_<sup>_∗_=</sup><sup>_f_(</sup><sup>_y_</sup> _c_<sup>_∗_)</sup><sup>_._</sup> where _f_ ( _yc_<sup>_∗_)canbeanyindicatorfunction,suchasoneoftheFGTpovertyindicators.</sup> 4. Use the model parameter estimates obtained in step 1 to obtain new sample welfares, _ych,s_<sup>_∗_,for</sup> every location _c_ as follows: ln( _ych,s_<sup>_∗_) =</sup><sup>_xch,sβ_ˆ0+</sup><sup>_η_</sup> _c_<sup>_∗_+</sup><sup>_e∗_</sup> _ch,s_<sup>_._</sup>

- (a) The estimates _β_<sup>ˆ</sup> 0 come from step 1.

- (b) _ηc_<sup>_∗_isthesameonegeneratedinstep2.Specifically,alllocationspresentinthesurveyare</sup> matched to the census locations and the same value of _ηc_<sup>_∗_that was simulated for that location</sup> in step 2 is applied to the survey households within the same location.

15
26

# References

Centro Latinoamericano de Demografia (Celade). 1987. "Population projections." Computer printouts. Santiago, Chile.

Coale, Ansley J., and Paul Demeny (with Barbara Vaughn). 1983. Regional Model Life Tables and Stable Populations. 2nd ed. New York: Academic Press.

- Eurostat. 1987. Demographic Statistics. Luxembourg: Statistical Office of the European Communities.

- Monnier, Alain. 1987. 'La conjoncture demographique: L'Europe et les pays developpes d'outre-mer," Population 42(4-5):719-736.

- Unesco. 1977. Statistics of Educational Attainment and Literacy. 1945121_4. Statistical Reports and Studies, no. 22. Paris: Unesco.

- United Nations. Department of International Economic and Social Affairs. 1984. Population and Vital Statistics ReRort: 1984 SRecial SupRlement. New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. 1986. World Population ProsRects: Estimates and Proiections as Assessed in 1984. New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Forthcoming. World PoRulation Prospects: 1988. New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. Population and Vital Statistics ReRort. New York: United Nations.

- United States, Bureau of the Census. 1985. World PoRulation: Recent Demographic Estimates for the Countries and Regions of the World. Washington, D.C.: U. S. Government Printing Office.

- World Bank. 1988. World Development Report. New York: Oxford University Press.

- Zachariah, K. C., and My T. Vu. 1988. World Population Projections: 1987-88 Edition. Baltimore and London: The Johns Hopkins University Press.
productivity difference in productivity that can be attributed to sectoral differences in technology. For each measure of technology considered in Table 12 and Table 13 we calculate the gap between Brazil and Senegal for agricultural and non-agricultural firms. Then, we use the estimated coefficients to compute the predicted average productivity gap between Brazil and Senegal in the average agricultural and non-agricultural firms. Finally, we compute the ratio of the predicted productivity gaps in agriculture vs. non-agriculture, and compare them to the actual relative cross-country productivity gap in our sample (i.e. 4.0). For example, using the specification from column 1 in Table 12, we find that the predicted productivity gap in agriculture between the average firm in Brazil and Senegal is 1.61, while in non-agriculture it is 0.99. Therefore, the ratio of the Brazil-Senegal productivity gaps in agriculture vs. non-agriculture is 1.6. This implies that differences in technology across sectors and countries account for a differential in the Brazil-Senegal productivity gap between agriculture and non-agriculture of 61% (i.e. (1.61-1)*100) which represents 21% of the actual differential in the cross-country productivity gap in our sample which is 296% (i.e. (3.96-1)*100). We conduct equivalent calculations for all the specifications of regression (7) and report them in the Appendix (Table F.13). They result in similar magnitudes with a share of the sectoral difference in Brazil-Senegal productivity gap that can be accounted for by differences in technology which ranges from 21% to 22% (see Appendix Table F.13). We conclude that about a quarter of sectoral differences in cross-country productivity can be accounted for by sectoral differences in cross-country technology.

# **8 Conclusions**

In this paper, we have introduced the FAT dataset, a representative firm-level data set that covers Senegal, Vietnam and the Brazilian state of Cear ́a, and that contains comprehensive information about the technologies used in each of the key business functions of companies. Exploiting the FAT data, we have constructed measures of the sophistication of the technologies employed at the business-function and firm levels. We have then used these measures to explore the patterns of technology adoption (i) across firms, (ii) across business functions, within firms, and (iii) the relationship between technology and productivity across firms and countries.

Our exploration of the FAT dataset has revealed large cross-firm differences in the sophistication of technologies used in production. A variance-decomposition shows that the within-country component of the average firm-level sophistication of technology represents between 51% and 95% of the cross-firm variance in the average sophistication of technologies used. The cross-firm variance in technology sophistication differs across countries/regions, 41