rafmacalaba/datause-encoder-data huggingface dataset:
Our results imply that, given time and opportunity, many rural dwellers may prefer to move out of isolated rural communities even if perceived income gains are minor. The evidence provided in this paper suggests that rural dwellers are attracted by the amenities and lifestyle that urban centers provide — proximity to markets, variety of goods and services, better access to schools and health care. This phenomenon may explain why countries that have seen little growth and thus little employment ‘pull’ from cities — such as many parts of Sub-Saharan Africa — have nevertheless experienced massive urbanization. The results in this paper also suggest that typical bene fi ts assessment based on monetary income and consumption will underestimate actual bene fi ts to isolation-reducing investments such as roads and communication infrastructure.

# References

- Alderman, Harold, Miriam Babita, Gabriel Demombynes, Nthabiseng Makhatha & Berk Ozler.

2002. “How Long Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa.” Journal of African Economies 11(2):169—200.

- Blanch fl ower, David G. & Andrew J. Oswald. 2004. “Well-Being over Time in Britain and the USA.” Journal of Public Economics 88(7-8).

- Dahl, Gordon B. 2002. “Mobility and the Return to Education: Testing a Roy Model with Multiple Markets.” Econometrica 70(6):2367—2420.

Deaton, Angus. 1991. “Saving and Liquidity Constraints.” Econometrica 59(5):1221—1248.

- Deaton, Angus & Christina H. Paxson. 1998. “Economies of Scale, Household Size, and the Demand for Food.” Journal of Political Economy 106(5):897—930.

34
Tablc 11: multinomial lo it estimates of household structure variables

|||**Wife**|||**Single mothe**|**r**||**Husband**||
|---|---|---|---|---|---|---|---|---|---|
|Pr(exit to<br>formal over):|self-<br>employment|informal<br>Iw|contract!<br>ae iece work|self-<br>employment|informal<br>wape|contract/<br>iece work|self-<br>employment|informal<br>wage|contract/<br>piece work|
|_Marital status_|_I_|||||||||
|married|-0.25**<br>(0.087)|-0.16<br>(0.11)|-0.64**<br>(0.16)|-|--|---|-0.12<br>(0.38)|0.058<br>(0.5)|0.44<br>(0.58)|
|separated|-0.83<br>(0.59)|-1.36<br>(0.77)|-1.47<br>(0.78)|1|||---2|---|2|
|marry|||-|1.06<br>(0.83)|0.72<br>(0.96)|2||---|---|
|_Household_||||||||||
|newborn|-0.18<br>(0.097)|-0.0096<br>(0.13)|-0.21<br>(|-0.14<br>(0.72)|-0.76<br>(0.78)|-0.047<br>(1.42)|0.36<br>(0.73)|0.64<br>(1.18)|0.37<br>(1.2)|
|#children age<br>1-5|-0.18**<br>(0.039)|-0.11I*<br>(0.052)|-0.28**<br>(0.06)|0.17<br>(0.27)|0.21<br>(0.32)|0.14<br>(0.47)|0.31<br>(0.22)|-0.2<br>(0.27)|-0.11<br>(0.3)|
|# children age<br>6-11|-0.19**<br>(0.028)|-0.19**<br>(0.037)|-0.24**<br>(0.046)|-0.2<br>(0.18)|-0.44*<br>(0.2)|-0.72**<br>(0.25)|0.17<br>(0.13)|0.44*<br>(0.21)|-0.26<br>(0.19)|
|# daughters<br>age 12-17|-0.064<br>(0.037)|-0.12*<br>(0.049)|-0.065<br>(0.061)|-0.11<br>(0.24)|-0.5*<br>(0.26)|-0.32<br>(0.36)|0.12<br>(0.14)|0.21<br>(0.21)|-0.44*<br>(0.21)|
|# sons age 12-|-0.099**|-0.15**|_-0.15*_|0.26|0.51*|-0.055|-0.057|-0.068|-0.093|
|17|(0.038)|(0.05)|(0.06)|(0.21)|(026|(0.31)|(0.13)|(0.18)|(0.23)|
|increase#<br>children|0.042<br>(0.1)|0.16<br>(0.14)|0.11<br>(0.17)|0.15<br>(0.64)|0.63<br>(0.83)|-0.49<br>(0.88)|0.41<br>(0.4)|0.21<br>(|-0.74<br>(0.54)|
|decrease#<br>children|-0.054<br>(0.21)|-0.18<br>(0.26)|0.73<br>(0.48)|0.54<br>(0.54)|-|1.25<br>(1.16)|-0.63<br>(0.67)|-1.01<br>(0.78)|-0.34<br>(1.19)|
|# females age<br>18-64|-0.055<br>(0.046)|0.073<br>(0.065)|0.058<br>(0.079)|-0.1<br>(0.22)|0.33<br>(0.26)|-0.18<br>(0.34)|0.082<br>(0.097)|-0.0088<br>(0.13)|-0.0015<br>(0.18)|
|# males age<br>18-64|-0.18**<br>(0.045)|-0.11<br>(0.063)|-0.043<br>(0.077)|0.23<br>(0.21)|0.23<br>(0.25)|1.5*<br>(0.61)|0.0098<br>(0.098)|0.18<br>(0.15)|0.22<br>(0.2)|
|# females age<br>65+|0.46**<br>(0.16)|0.22<br>(0.22)|0.083*<br>(0.34)|-0.31<br>(0.64)|0.074<br>(0.83)|-0.48<br>(0.96)|0.47<br>(0.53)|0.95<br>(0.87)|0.035<br>(0.88)|
|# males age<br>65+|-0.18<br>(0.27)|-0.058<br>(0.39)|0.24<br>(0.54)||||0.19<br>(0.96)|-1.35<br>(1.07)|1.02<br>(1.38)|
|changein#|-0.0015|0.49|-0.23|1.093|1.73|-2.02|0.81 *|0.89|0.25|
|adults|(0.19)|(0.28)|(0.31)|(1.17)|(1.22)|(1.37)|(0.37)|(0.53)|(0.63)|

* the estimates a e from Model 3, the Model that had the best fit. The coefficient values for Models I and 11 differ slightly, bu the sign and magnitude of the coefficient estimates is very similar among the three models.

2 dropped because the variable predicted failures perfectly.

44
salvage value of capital. The intuition for this assumption is that in conditions of underdevelopment, the value of capital depends on the informal knowledge and institutional networks associated to a particular activity. In this case, capital cannot be easily transformed to serve as production factor elsewhere. As countries develop, however, the value of capital becomes more related to broadly applicable human capital (e.g., engineers that can work with a large variety of machines) and formal institutions (e.g., contracts that are enforced by the judicial system irrespective of economic activity, production sector, or geographic location). With higher development, capital can be retooled and reconverted, thus facilitating the process of technological adoption.

According to this theory, differences in income levels are accounted for by differences in the speed of technological adoption (which determines the rate of growth acceleration). The process is exacerbated when world knowledge expands continuously –economies that suffer from obstacles to innovation lag further and further behind the leading-edge technology, and thus, the leaders’ income per capita. Using this framework, we calibrate the model economy to the U.S. and 107 developing countries around the world. The empirical counterpart of the chosen distortions is taken from the World Bank’s _Doing Business_ database.<sup>4</sup> Then, we conduct simulation exercises to analyze the independent impact of developmental and regulatory barriers and the complementarity between them. Consistent with the data, the model implies a slow adoption of new technologies by developing economies and a complementarity between the developmental barrier and the policy distortion. That is, the effect of regulatory freedom on technological innovation – and on aggregate productivity and per capita GDP– is larger the higher the level of economic development, and _vice versa_ . Our model accounts for over 50% of the income gap between the median LDC and the U.S, 60% of this gap is explained by the barriers individually and 40% by their complementarity.

From these results, a clear policy implication emerges: reforms must be implemented jointly. Economic reforms have been undertaken in developing countries during the last two

> 4 Some recent papers use the _Doing Business_ database to simulate the effect of entry or exit costs on aggregate productivity across countries in industry-dynamics models. These papers, however, do not stress innovation as a transmission mechanism, neither the complementarity between distortions. See Barseghyan and DiCecio (2010), Moscoso and Mukoyama (2010), and Poschke (2009).

5
||Mean|SD|Min|Max|N|
|---|---|---|---|---|---|
|Main outcome variables||||||
|rCsl|11.02|1060|0|56|2947|
|Any economic activity|0.61|0.49|0|1|8418|
|Attends school|0.74|0.44|0|1|5445|
|Economic activityamongworkers (age > 14)||||||
|Number of activites|2.05|1.10|1|5|5029|
|Hours worked perweek|34.83|19.96|0|84|4547|
|Months worked peryear|9.15|3.25|1|12|A971|
|Sector ofmain activity is agriculture|0.24|0.43|0|1|5055|
|Schoolattendance (age 6-14)||||||
|Abilityto read|0.55|0.50|fe)|1|5438|
|Ability to write|0.54|0.50|0|1|5435|
|Years of education|407|2.23|0|13|3908|
|Attends school|0.74|0.44|0|1|5445|
|Reason fornot attendingschool||||||
|Lack of financial resources|0.66|0.47|0|1|1417|
|Tooyoung|0.14|0.35|0|1|1417|
|Demographics, displacement and local violence||||||
|Age|18.92|16.73|0|85|17951|
|Female|0.49|0.50|0|1|17945|
|IDP|0.47|0.50|0|1|17951|
|Camp IDP|0.18|0.39|0|1|17951|
|Years displaced|3.69|1.70|0|20|8517|
|Number of time household moved|1.34|0.65|1|8|8424|
|Boko Haram as main reason for displacement|0.92|0.27|0|1|8517|
|Events of local violence (last 12 months)|6.87|1437|0O|44|17951|
|Exposedtoanylocalviolence(last12months)|0.39|0.49|0|1|17951|
development in Indonesia. Note, however, that there has been no evidence for this spatial divergence since 2001.

Third, the effect of the sectoral composition of a district may matter but, perhaps because different factors appear to matter at different points in time, it is difficult to identify any systematic sectoral bias to growth. To identify this better would require data on terms-of-trade changes (other than those in agriculture), but unfortunately these data are not available. Moreover, there is no systematic evidence that a more or less concentrated distribution of sectors gives rise to faster or slower growth, although there is some evidence that reliance on the oil and gas sector weakened district growth prior to the crisis.

Fourth, having a larger labor force is positively associated with growth. Also, having a better educated labor force appears to be strongly associated with growth, but only since 2001.

Despite compiling a comprehensive dataset of potential factors associated with district level growth, the models above still struggle to explain most of the variation in growth rates that we observe across the country. In part, this is likely to be because GRDP data at the district level are extremely noisy. Further work will need to identify in more detail the variations in the manner in which the district GRDP data are compiled and to reconcile the inconsistencies with provincial and national level data.

However, it may also be that idiosyncratic institutional factors play an important role in determining growth. For example, having a politically strong and visionary district head/major may have a large impact, since they can pursue policies that stimulate investment and growth. These political and institutional policy factors have not been well measured in the past, but efforts are currently underway to collect more accurate data on these variables. Moreover, data are available on the political composition of local parliaments and the votes received by directly elected district heads/mayors. Such data have not been used in this analysis because local parliaments were only elected in 2004, whilst direct elections for local leaders only occurred in 2005 and 2006. Thus, the potential exists for future analysis using these political data to obtain a clearer picture of the determinants of district growth.

26
Table 11: The determinants of disaster fatalities - ZINB

||Sto|rms|Flo|ods|Earth|quakes|
|---|---|---|---|---|---|---|
||11.1|11.2|11.3|11.4|11.5|11.6|
|Ln(_AIDj,t-1_)|0.252*<br>(0.133)||-0.263**<br>(0.116)||-0.344<br>(0.263)||
|Ln(_3yr._ _AIDj,t-1)_||0.229*||-0.319**||-0.338|
|||(0.125)||(0.136)||(0.312)|
|Ln(_GDPj,t-1_)|-0.690***|-0.729***|-0.542***|-0.605***|-0.406|-0.528|
||(0.260)|(0.887)|(0.215)|(0.203)|(0.745)|(0.863)|
|_GINIjt_|-0.010|-0.015|-0.033*|-0.032*|-0.002|-0.001|
||(0.022)|(0.023)|(0.017)|(0.018)|(0.033)|(0.036)|
|Ln(_POP jt)_|<br>0.703***|<br>0.746***|<br>0.196|<br>0.215|<br>0.341|<br>0.420|
||(0.134)|(0.138)|(0.138)|(0.145)|(0.333)|(0.335)|
|Ln_(POPDENSjt)_|-0.049|-0.075|-0.076|-0.104|-0.855**|-0.883**|
||(0.146)|(0.147)|(0.131)|(0.149)|(0.373)|(0.374)|
|_OPENjt_|<br>0.004|<br>0.006|<br>-0.003|<br>-0.002|<br>-0.002|<br>-0.001|
||(0.005)|(0.005)|(0.004)|(0.004)|(0.012)|(0.014)|
|_DISASTERSjt _|<br>0.097**|<br>0.090**|<br>0.384***|<br>0.336***|<br>0.028|<br>0.023|
||(0.0437)|(0.0407)|(0.128)|(0.127)|(0.219)|(0.239)|
|_NATHAZ – RISKj _|<br>0.934***|<br>0.982***|<br>0.352*|<br>0.345*|<br>2.093**|<br>2.126**|
||(0.207)|(0.215)|(0.186)|(0.202)|(0.875)|(0.943)|
|**1**<sup>**st**</sup>**stage Probit model**|||||||
|<br>Ln(_AIDj,t-1_)|-0.165||-0.376**||-0.483**||
||(0.133)||(0.174)||(0.219)||
|Ln(_3yr._ _AIDj,t-1)_||-0.070||-0.293||-0.925**|
|||(0.165)||(0.184)||(0.417)|
|Ln(_GDPj,t-1_)|-0.107|<br>-0.066|-0.473**|<br>-0.308|0.249|<br>-0.528|
||(0.321)|(0.887)|(0.215)|(0.233)|(0.353)|(1.051)|
|Ln(_POP jt)_|-0.446***|-0.385*|-0.598*|-0.557**|-0.063|-0.872**|
||(0.138)|(0.232)|(0.178)|(0.210)|(0.337)|(0.425)|
|_DISASTERSjt _|<br>-8.916***|<br>-10.150|<br>-8.363***|<br>-8.163***|<br>-12.330***|<br>-17.410***|
||(1.115)|(8.761)|(0.702)|(0.661)|(2.826)|(5.295)|
|_NATHAZ – RISKj _|<br>0.0530|<br>0.288|<br>-0.163|<br>-0.159|<br>1.470|<br>1.446**|
||(0.207)|(0.215)|(0.186)|(0.202)|(0.875)|(0.943)|
|Obs.|2186|1978|2174|1966|2186|1978|
|Nonzero Obs.|430|403|605|560|197|177|
|Log pseudolikelihood|-2468.351|-2282.336|-3614.626|-3307.053|-1200.485|-1080.443|
|<br>Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest(z-value) <sup>a</sup>|14.21***|14.05***|16.55***|16.36***|7.22***|6.97***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).

33
were arbitrary fees levied on such items as construction permits and various “authorizations’ for domestic and international business operations. Nonetheless, they provided a source of unrestricted local income that often was channeled into infrastructure investment. The growth of fees used for infrastructure finance up to the 1994 intergovernmental fiscal reform and the subsequent sharp fall-off is shown in Figure 6.

Figure 6: Share of Urban Infrastructure Investment financed by Fees and Charges <!-- Start of picture text --> 600 50<br>500<br>40<br>400<br>30<br>300<br>20<br>200<br>10<br>100<br>0 0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Fees resulting from policies proportion<br><!-- End of picture text --> Source : Authors calculations

# Debt Financing

The financing gap created by the decline in direct budgetary spending on urban infrastructure has been filled primarily by borrowing. In 1986, domestic bank lending for urban infrastructure totaled only RMB 0.32 billion accounting for 2.4% of total urban infrastructure investment. As Figure 7 makes clear, the share of investment financed by borrowing declined up to the 1994 fiscal reform, then began 38
century, their prevalence begins to increase visibly after the 1970s. The evolution of the keywords related to inequality also supports our previous results. Despite a rather erratic evolution of the terms “equitable” and “rich,” mentions of “equality of opportunities” increase abruptly in usage after the 1970s.

**Figure 5. Prevalence of keywords related with poverty and inequality**

_(% of speeches that contain each keyword by decade)_

- (a) Keywords related with poverty (b) Keywords related with inequality

<!-- Start of picture text --> 100 100<br>75 75<br>Equality of<br>Middle Class opportunities<br>50 50<br>Vulnerable Rich<br>Equitable<br>25 25<br>Indigence<br>0 0<br>% of speeches % of speeches<br>1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br><!-- End of picture text --> Note: This Figure shows the percentage of presidential speeches on each decade that contained at least once the words “poor,” “vulnerable,” “middle class,” “equity,” “equality” and “inequity,” and were calculated pooling the presidential speeches of 10 Latin American countries. See Appendix A for more details about the data.

While in this section we assessed the robustness of the political enlightenment to different thresholds in the prevalence of the keywords, alternative measures of the variable of interest, and prevalence of related keywords, in the following section we address a set of potential concerns about our findings. First, we assess whether the observed changes in the prevalence of the keywords are statistically significant. Second, we examine if the evolution was not due increased prevalence, but rather reflects a different way of talking about the same issues. Finally, we assess whether the enlightenment is spurious, by comparing the evolution of other non-related keywords.

# **3.2 STATISTICAL SIGNIFICANCE OF THE ENLIGHTENMENT**

The first potential concern we address is that our results may not be statistically significant. To empirically assess the evolution of the keywords’ frequency over time, we run ordinary least squares (OLS) regressions. In the baseline specification, we assume that keyword prevalence can be characterized according to the following equation: 9
Figure A.6: Distribution of infrared detections by M10 sensor <!-- Start of picture text --> Radiance of M10 infrared detection<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-2 -1.5 -1 -0.5 0 0.5 1 1.5<br>logarithm (10) of radiance<br>density<br><!-- End of picture text --> 24
Changes in the priority that policy makers gave to education in government budgets also generally contributed little to the increases in public education spending. For example, upper-middle-income countries had the largest increases in public spending on education overall but devoted a smaller share of their government spending to education in 2015 than they did in 2000. The exception was low-income countries, where making education a higher priority in government budgets did lead to greater public education spending (Figure 6), although this represented a relatively small proportion of the overall increase. Over this period, the share of the budget devoted to the education sector increased from an average of 15 to 16 percent in those countries (see Table 1).

**Figure 6: Economic Growth Has Been the Main Driver of Increased Public Spending on Education** _Decomposition of public education spending changes (%), 1998_ **–** _2017_

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>High-income Lower-middle-income Low-income Upper-middle-income<br>Contribution of GDP growth to growth in education spending<br>Contribution of share of education in total government budget to growth in educaiton spending<br>Contribution of total government spending growth to growth in education spending<br>Total change in education spending - 2000–2015<br>%ge change between 2000 and 2015<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF data.

_Note:_ Average percentage growth in all variables calculated as the percentage change from the four-year **– –** average in 1998 2001 to the four-year average in 2014 17, for all countries with data available in at least one year across both four-year periods.

Regions with the fastest growth in public education spending were those that experienced rapid economic growth and managed relatively large increases in government spending as a share of GDP. For example, about one-third of increased public education spending in the East Asia, Latin America, and South Asia regions was due to an increase in the size of the government sector (Table 3). Between 1998 **–** 2001 and 2014 **–** 17, government spending as a percentage of GDP increased from 20 to 26 percent in East Asia and the Pacific and from 20 to 24 percent in Latin America and the Caribbean.<sup>7</sup> In contrast, the impact of economic growth on increased public education spending in the Middle East and North Africa was reduced because both government spending as a share of GDP and the share of government spending allocated to education declined.

> 7 Annex Table A2 presents a summary of patterns and trends in government spending as a share of GDP.

17
# Figure 3.4: TANZANIA, Kernel density estimates of PF variables in Tanzania

(without M.V in PF variables and with M.V in any PF variable) <!-- Start of picture text --> A. Sales B. Materials<br>5 10 15 20 5 10 15 20<br>Log-sales Log-materials<br>Reported sales and the rest of PF vars Reported materials and the rest of PF vars<br>Reported sales with m.v in at least one PF var Reported materials with m.v in at least one PF var<br>C. Labor D. Capital<br>8 10 12 14 16 5 10 15<br>Log-employment (hours per year) Log-capital stock<br>Reported employment and the rest of PF vars Reported capital and the rest of PF vars<br>Reported empl. with m.v in at least one PF var Reported capital with m.v in at least one PF var<br>.2<br>.15<br>.15<br>.1<br>.1<br>Density Density<br>.05<br>.05<br>0 0<br>.4 .15<br>.3<br>.1<br>.2<br>Density Density<br>.05<br>.1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> C. Labor D. Capital<br>8 10 12 14 16 5 10 15 20<br>Log-employment (hours per year) Log-capital stock<br>Reported employment and the rest of PF vars Reported capital and the rest of PF vars<br>Reported empl. with m.v in at least one PF var Reported capital with m.v in at least one PF var<br>.4 .15<br>.3<br>.1<br>.2<br>Density Density<br>.05<br>.1<br>0 0<br><!-- End of picture text --> Notes: Reported X and the rest of PF variables is the distribution of those establishments reporting all PF variables Reported X with m.v in at least one of the rest of P.F is the distribution of those establishments reporting the corresponding PF variable and also reporting at least one missing value in the remaining PF variables Epanechnikov kernel. Each point estimated within a range of 300 values. Source: Authors’ estimations with ICSs data.

Figures 3.1 to 3.4 support the story of weaker firms reporting more missing values. However, the story is not yet conclusive. Firms with low sales (and materials, capital and employment) do not usually need proper accountability also tend to operate in more corrupt environments and are less innovative and dynamic. In addition, as most of the firms are accumulated around low values, it is easy to infer that the probability of picking a firm with any missing value in the PF variables will be higher within this range of values as well. From these figures we cannot conclude that low sales do not imply weakness or low productivity, and therefore higher probability of having missing values.

44