rafmacalaba/datause-encoder-data huggingface dataset:
# **Interpreting the lags**

If development were a one-factor process, we would expect the lag between the LDRs and the MDRs to be the same for population, life expectancy, and urbanization. Instead, we see that the lag for urbanization is longer than the lag for demographic variables.

In a very rough sense, Angus Maddison’s (2012) data give us a simple way to examine the lags in income per capita. To approximate the UN definition of the MDRs with the Maddison data, we created an aggregate consisting of Western Europe, Western offshoots, and Japan. In 1870, GDP per capita in our MDR aggregate was about $1,900 (measured in Maddison’s unit, 1990 International Geary-Khamis dollars). In the rest of the world, which corresponds roughly to the LDRs, GDP per capita did not reach $1,900 until 1970—a lag of 100 years. In 1900, GDP per capita in the MDR aggregate was $2,950. The LDR aggregate reached this level in 1995—a lag of 95 years. So, GDP per capita lags even farther behind the demographic measures than does urbanization.

One natural explanation for the lag and its variance across social indicators is differences in government capacity. Development in the LDRs today differs from development in the MDRs at comparable incomes per capita. The LDRs today can use technologies that already exist. Growth there is catch-up growth rather than growth at the technological frontier. All else equal, one would therefore expect that, at any income, LDRs would grow faster and the lags would simply disappear. However, if the LDRs have less government capacity to provide services that complement technologies from the MDRs, some gaps will persist.

In addition, government services might be strong complements to the private activities that generated rapid GDP growth through technological inflows in Shenzhen. But they might also be weak complements to the private activities that sustain rapid growth in the urban population. People can still urbanize, albeit less efficiently, in slums and _favelas_ if few government services are available. When it comes to the spread of health technologies that lengthen lives and reduce fertility, government services might be much less relevant.

At the same income per capita, the LDRs will have more access to technology but fewer government services than did the MDRs during their phase of peak urbanization. The LDRs’ GDP will lag farthest behind, their urban share will lag less, and their demographic indicators will lag the least.

# **Peak urbanization, weak capacity**

Urbanization is peaking where the capacity to govern is in short supply. Despite all the lip service to capacity building, the standard internal processes that can improve government capacity might be unable to respond in a timely fashion. A quip attributed to Gordon Brown suggests how far off the time scales might be: “In establishing the rule of law, the first five centuries are always the hardest.” 7
migration outflows.

I use the percentile ranks to identify a subset of years for each region that can be considered to be a period with large anomalous outflows (or at least larger than usual) and run the baseline parametric regression on them. Figure 5 displays the estimates of regional exposure effects for the three age segments used in the parametric specification (see equation 5).<sup>11</sup> I find estimates that are very stable until the percentile 50 and then become more volatile. However, despite the volatility, the estimates remain qualitatively similar with slopes that are non-statistically significant in early ages, a significant childhood exposure effect in the segment of primary school age, and smaller significant effect post primary school age. This suggests that the regional childhood exposure effects found in the baseline results are not driven by families choosing the timing of the move.

# **V.1.b Expected destination of moving households**

To alleviate concerns that time-varying factors may jointly drive household choice of destination and children’s educational investments in proportion to exposure to the region with higher mobility, I use past migration destinations from each origin to predict where moving household will settle with a “shift-share” design.

For each year of move _y_ and origin _o_ , I compute the share that moves to destination- _d_ as _σody_ = _D_ <u>�</u> _xy−_ =10 _Ty_ <u>0</u> _−_<sup>_movers_</sup> 10<sup>_odxD_isthetotalnumberofregionsinagivencountryand</sup> <u>�</u> _d_ =1 <u>�</u> _x_ = _T_ 0<sup>_moversodx_where</sup> _T_ 0 is the first year in which I observe a mover from this origin.

For individuals who move in year _y_ from region _o_ to region _d_ , I compute the predicted difference in mobility for permanent residents ∆<sup>ˆ</sup> _odby_ as the historic share-weighted analog, ˆ∆ =<sup>�</sup><sup>_D_Figure 6 plots the predicted difference against the actual difference</sup> _oby d_ =1<sup>∆</sup><sup>_odb×σody_.</sup> for the sample of movers.

I use the predicted difference in upward mobility between origin and destination to in-

> 11Table A5 in the Appendix reports these estimates.

20
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS**1**64 Power, Distortions,<br>**Revolt,**<br>and<br>Reform in Agricultural<br>Land Relation|Hans P. Binswanger<br>s Klaus Deininger<br>Gershon Feder|July 1993|H. Binswanger<br>31871|
|WPS**1**65 Social Costs of the Transition<br>to<br>Capitalism:<br>Poland, 1990-91|Branko Milanovic|August 1993|R. Martin<br>39026|
|WPS**1**66 The Behavior of Russian Firms in<br>1992: Evidence<br>from**a**Survey|Simon Commander<br>Leonid Liberman<br>Cecilia Ugaz<br>Ruslan Yemtsov|August 1993|**0.**del Cid<br>35195|
|WPS**1**67 Unemployment<br>and Labor Market<br>Dynamics in Russia|Simon Comrnander<br>Leonid Liberman<br>Ruslan Yemtsov|August 1993|**0.**del Cid<br>35195|
|WPS**1**68 How Macroeconomic<br>Projections<br>in Policy Framework<br>Papers for the<br>Africa Region Compare<br>with Outcome|Rashid Faruqee<br>s|August 1993|N.Tannan<br>34581|
-10-

# 2. **Specification**

The estimation of the switching regression model described above involves specifying, first, the optimal level of productive investment (1*) and, second, the supply of subsidized loans (X). To estimate the desired level of investment in productive assets, we rely on the following standard model of corporate invesmennt behavior.

It = **)Yo** + Yy12Qt -Kt-1) + 73Ft + vt , (11) where yo, y1, y2, y3 are paramneters to be estimated, Qt is a finn's level of output as measured by sales plus the change ii inventories of final goods, Kt_I is its capital stock of productive assets lagged one year, Ft is a financial variable altematively measured by either the finn's previous year profits or by the first difference in the firm's value as measured by the market capitalization of its equity. Finally, vt is a disturbance term. All variables are scaled by the finr's begining of year book value of total assets.

Equation (11) combines the conventional accelerator model with the usual interemporal adjustment specification. It also contains a measure of profitability and stock maket performance designed to capture the firn's present and future investment oppo10nties.

If the supply of subsidized loans to each firm is observable, it is possible to detrmine the probability, Pit, that firm i at time t belongs to regime 1. Using equation (11) we obtain: Pit =Prllit* < _X,i1_ <!-- Start of picture text --> =Prvt <Xit -yo - yl(y2Qit - Kit_) - y3 Fitd<br>xit - yo - yl(y2Qit - Kit.,) I,3i (12)<br>L a av<br>'V<br><!-- End of picture text --> where **4(s)** represents the unit normal distribution function and o, is the standard deviation of v.

°Pizm profits are included in the micro investment study of Tybout (1985) for Columbia and Nabi (1989) for Pakistan. The relevance of stock market performance to corporate investment behavior in Korea is discussed in detail in Dailami (1990).
take a different approach based on a well-established model of bilateral trade (Chor, 2010). The model incorporates trade costs, technology, and comparative advantage including institutions. The model fits the data very well, as in the previous literature. We use it to construct indices of exporter tradability, importer tradability, and sectoral tradability. Each index ranges between zero (non-tradable) and unity (highly tradable). In our approach, tradability is considered to be the average probability of observing trade for a particular country or sector: a higher score on an index indicates a higher probability of observing trade, and thus a higher level of tradability. Because our approach is based on trade data, rather than production data, we are able to produce tradability indices covering up to 99 countries in all regions of the world, and at a range of development levels. Although our findings are not comparable to those of previous work due to the different methodology adopted, they represent, we believe, a useful addition to knowledge in this area, and one that can potentially be of use to those working on services policy in the context of international development.

# **References**

Barro, R., and J. Lee. (Forthcoming) “A New Dataset of Educational Attainment in the World, 1950-2010”, _Journal of Development Economics_ .

Borchert, I., B. Gootiiz and A. Mattoo. (2012a) “Guide to the Services Trade Restrictions Database”, _Policy Research Working Paper_ , No. 6108, World Bank.

Borchert, I., B. Gootiiz and A. Mattoo. (2012b) “Policy Barriers to International Trade in Services: Evidence from a New Database”, _Policy Research Working Paper_ , No. 6109, World Bank.

Borchsenius, V., N. Malchow-Moller, J. Roland Munch, and J. Rose Skaksen. (2010) “International Trade in Services: Evidence from Danish Micro-Data”, _Nationalokonomisk Tidsskrift_ , 148(1), 86-107.

Chor, D. (2010) “Unpacking Sources of Comparative Advantage: A Quantitative Approach”, _Journal of International Economics_ , 82(2): 152-167.

Costinot, A. (2009) “On the Origins of Comparative Advantage”, _Journal of International Economics_ , 77(2), 255-264.

Eaton, J., and S. Kortum. (2002) “Technology, Geography, and Trade”, _Econometrica_ , 70(5), 1741-1779.

Francois, J., and O. Pindyuk. (2013) “Consolidated Data on International Trade in Services v8.9”, _Discussion Paper_ , No. 20130101, IIDE.

Gervais, A., and B. Jensen. (2013) “Are Services Tradable? Evidence from US Micro-Data”, _Working Paper_ .

31
Table 4. Impact of diaspora on selection (ratio high-skill/low-skill) level and change: OLS and Heckman

||(1)<br>Skill ratio|(2)<br>Skill ratio|(3)<br>Skill ratio|(4)<br>Skill ratio|(5)<br>�SR|(6)<br>�SR|
|---|---|---|---|---|---|---|
|Lagged diasp|-0.171<br>(16.19)***|-0.088<br>(8.47)***|-0.194<br>(20.62)***|-0.132<br>(11.83)***|-0.143<br>(17.62)***|-0.108<br>(11.47)***|
|Col. links|-0.042|-0.439|-0.022|-0.410|0.101|0.096|
||(0.62)|(6.08)***|(0.32)|(5.21)***|(1.67)*|(1.46)|
|linguage|0.466<br>(9.38)***|0.703<br>(11.03)***|0.460<br>(9.37)***|0.721<br>(11.68)***|0.176<br>(4.17)***|0.257<br>(4.95)***|
|Log(dist)|0.096<br>(3.35)***|0.273<br>(10.17)***|0.090<br>(3.40)***|0.263<br>(9.96)***|0.086<br>(3.78)***|0.116<br>(5.25)***|
|Schengen|0.502|0.305|0.519|0.303|0.390|0.117|
||(5.65)***|(3.14)***|(6.26)***|(2.97)***|(5.48)***|(1.37)|
|Immig pol||-0.014<br>(4.98)***||-0.015<br>(5.52)***||0.001<br>(0.30)|
|Soc exp||-1.206<br>(16.11)***||-1.253<br>(20.12)***||-0.756<br>(14.42)***|
|Pop. at dest||0.061<br>(3.45)***||0.082<br>(4.58)***||0.056<br>(3.75)***|
|Wage at dest||0.044<br>(9.86)***||0.045<br>(10.47)***||0.035<br>(9.78)***|
|Constant|-1.109|0.002|-0.734|0.257|-1.250|-0.563|
||(1.16)|(0.00)|(1.32)|(0.34)|(2.54)**|(0.87)|
|Dest dum|Yes|No|Yes|No|Yes|No|
|Orig dum|Yes|Yes|Yes|Yes|Yes|Yes|
|Method|OLS|OLS|Heckman|Heckman|Heckman|Heckman|
|Mills|||-0.380|-0.446|-0.10|-0.99|
||||(6.86)***|(7.37)***|(0.22)|(1.88)*|
|Obs|3604|3084|5760|4992|5760|4992|
|R-squared|0.60|0.45|||||

Robust t statistics in parentheses * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% Results in Table 4 show that the selection of migrants is also in‡uenced by a large set of variables. Bilateral varaibles such as linguistic proximity, distance, the Schengen agreement and wage di¤erentials favor the selection of high-skilled migrants. On the contrary, non-selective immigration policies and generous social expenditures lower the educational mix of the migrants which is in line with the results in Cohen and Razin (2008). More importantly, large diasporas exert a strong negative impact on the skill ratio of migration ‡ows and attract low-skill migrants. Once again, this e¤ect is robust to alternative speci...cations (presence or absence of destination dummies), 27
**Table 1. The size of significant private cities in developing countries**

|||Land area<br>|Population<br>|(thousands)<br>|Populati<br>|on (percent of<br>|city with)<br>|
|---|---|---|---|---|---|---|---|
|Country|City|(sq. km)|Current|Planned|Rank 1|Rank 10|Rank 20|
|Bangladesh|Bashundhara Residential Area|7|274||1.4|89.2|148.0|
|Bolivia|El Alto|370|975||71.5|1095.5|3482.1|
|China|Gu’An|26||1,000|4.5|14.0|23.5|
|Egypt|Festival City|173||52|0.6|10.9|21.2|
|Honduras|San Pedro Sula|840|876||80.6|1536.8|5475.0|
|India|Gurgaon|406|1,308||6.6|42.9|77.6|
||Jamshedpur|244|1,373||6.9|45.1|81.5|
|Indonesia|Batam Island|1,600|1,283||13.4|99.6|201.5|
||Kota Jababeka Cikarang|56|172||1.8|13.4|27.0|
|Nigeria|Eko Atlantic|11||250|1.7|18.9|38.8|
|Pakistan|Bahria Town – Karachi|134||1,000|6.7|99.9|256.1|
||Sialkot|32|928||6.2|92.7|237.7|
|Senegal|Damniadio|23||300|9.8|380.7|1139.5|
|South Africa|Waterfall City|3||100|2.9|20.9|32.7|
|Sri Lanka|Port City|3||80|10.6|80.0|123.1|
|Vietnam|Phu My Hung|36|278||3.1|132.0|182.8|
|All|Median|34|5|88|6.4|84.6|135.5|
|US|Reston|41|60||0.7|5.8|9.0|

_Source:_ Authors, based on background studies for Li and Rama (2021), master plans, cities’ websites, LandScan<sup>TM</sup> 2018 and World Population Review.

5
|ined Instrument|Husband<br>Wants More<br>Children|(7)<br>-0.069<br>(0.078)<br>[0.374]<br>5.86|12,761|frst-stage estimate<br>that equal 1 if true.<br>�<br>ears of Schooling_izy_<br>988. All regressions<br>ustered at the zone|
|---|---|---|---|---|
|omen Only - Comb|ation<br>Agriculture<br>/ Unskilled<br>Manual|(6)<br>-0.129<br>(0.043)<br>[0.003]<br>6.97|19,814|he six columns. The <br>e indicator variables <br>n groups included.<br>Y<br>cohorts from 1970 to 1<br>tandard errors are cl|
|Married W|and’s Occup<br>Service<br>/ Sales|(5)<br>0.121<br>(0.047)<br>[0.010]<br>6.97|19,814|2016).<br>op of each of t<br>olumns 4–7 ar<br>the occupatio<br>omen in birth<br>e regression. S|
|’s Characteristics,|Husb<br>Skilled /<br>Professional|(4)<br>-0.039<br>(0.044)<br>[0.375]<br>6.97|19,814|ears 2005, 2011, and <br> is described at the t<br>endent variables in c<br>gories exactly describe<br>ples include married w<br>mate is from a uniqu|
|ms on Husband|Husband’s<br>Years of<br>Schooling|(3)<br>0.869<br>(0.475)<br>[0.067]<br>6.94|17,998|ealth Survey (in y<br>ependent variable <br>olumn 1. The dep<br>ps; the other cate<br>re, ∆_Izy_. All sam<br> for age. Each esti|
|xposure to Refor|Husband’s<br>Age|(2)<br>-0.017<br>(0.976)<br>[0.986]<br>5.70|18,174|Demographic and H<br> instruction. The d<br>women is shown in c<br>led manual job grou<br>ned intensity measu<br>rends, and a cubic <br>brackets.|
|f Efect of Wife’s E|Wife’s Years<br>of Schooling<br>[ First Stage<br> Married Only ]|(1)<br>0.097<br>(0.041)<br>[0.020]<br>5.70|18,174|a from the Ethiopian <br> MTI = mother tongue <br> schooling for married <br>sional, clerical, and skil<br>mented with the combi<br>ts, zone-specifc linear t<br>es are shown in square|
|Table C.20: National Estimates o|-|Add’l Years of FPE or MTI<br>w/out Script Change<br>(∆_Izy_)<br>�<br>Years of Schoolingizy<br>First Stage F-Statistic|N|_Source_: Author’s analysis based on dat<br>_Note_: FPE = free primary education; <br>of the efect of the reforms on years of <br>Skilled/Professional jobs include profes<br>is the predicted level of schooling, instru<br>include birth year and zone fxed efec<br>level and shown in parentheses; _p_-valu|

A.39
|**Table A.1, Panel B: 2004**|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA2004)**|**0.277*****|**0.712*****|**0.382*****|**0.879**|
||**(0.07)**|**(0.17)**|**(0.09)**|**(2.35)**|
|ln(Slope)|-0.063**|-0.120|-0.097*|-0.656|
||(0.03)|(0.10)|(0.05)|(1.12)|
|Elevation|-0.170|0.054|0.272|3.612|
||(0.15)|(0.41)|(0.24)|(5.28)|
|Crop Suitability|-0.003**|-0.008**|-0.001|0.000|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.086|0.186|0.034|-1.114|
||(0.06)|(0.17)|(0.10)|(2.14)|
|ln(Temperature)|-0.491|-0.147|0.526|1.586|
||(0.44)|(1.08)|(0.66)|(14.85)|
|ln(Rain CV)|0.143|0.451|0.445*|10.237*|
||(0.15)|(0.42)|(0.24)|(5.65)|
|ln(Temp seasonality)|0.014|-0.036|-0.074|-4.464|
|.|(0.08)|(0.22)|(0.11)|(3.25)|
|Pop. density, 1961|0.017***|0.026|-0.006|-0.088|
||(0.01)|(0.02)|(0.01)|(0.31)|
|Constant|-2.983|-14.393**|-11.56***|-16.911|
||(2.65)|(6.53)|(3.77)|(90.32)|
|First Stage|||||
|ln(dist. to GQ) x ln(Slope)|-0.132***|-0.127|-0.127|-0.127|
||(0.04)|(0.04)|(0.04)|(0.04)|
|ln(dist. to GQ) x Elevation|0.162|0.111|0.111|0.111|
||(0.17)|(0.17)|(0.17)|(0.17)|
|ln(km of railroad) x Crop<br>|0.001***|0.001***|0.001***|0.001***|
|Suitability|(0.00)|(0.00)|(0.00)|(0.00)|
|Weak Identification|7.68|9.93|9.93|9.93|
||0.0001|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.181|0.331|0.524|0.151|

Note: All columns are estimated by Lasso and include state fixed effects. Weak identification and R-squared are from 2SLS using the IVs chosen by Lasso. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
7 agreements. For example, some products - like wood shingles exported from Canada to the United States - are excluded from the Canadian-United States FTA. Similarly, some developing country arrangements (like ASEAN or LAIA) ailow for preferential tariffs below MFN rates instead of duty free trade. Also, the degree of product coverage varies significantly among different agreements -- e.g., only 40 percent of intra-regional trade among LAIA members was conducted under preferential terms by 1988.10 Data required for measuring the imuportance of the existing arrangements were drawn from three different sources. UNCTAD (1990, Appendix Table Al) estimated total world exports for 1988 and other years (as well as trade in broad classes of goods like manufactures or energy products) and these figures formed the base for our analysis. This source also provided detailed data on major trade flows such as the intra-trade of EFTA and EEC countries, trade between Europe and Japan, Japan and North America, etc. which greatly assisted our tabulations of 'affected' and non-affected trade. Second, Inotai (1991) compiled data on intra-trade among developing countries' existing regional arrangements. In situations where required data were not available from either of these two sources (such as trade under the United States-Israel FTA) it was compiled directly by the authors from United Nations Series D Trade Tapes." III. The Relative Importance of Minilateral Arrangements Employing these three statistical sources, Table 1 tabulates information on the relative importance of exports that occur under existing minilateral arrangements. The table shows the actual total value of 1988 "affected" trade that occurs within the framework of these arrangements and each specific flow's share of world trade. Similar statistics are given for (i) all non-energy goods (i.e., excluding SITC 3) and (ii) manufactures. To '° It is worth emphasizing, however, that if MERCOSUR and other Flanned FTA initiatives - such as the Chile-Mexico FTA - evolve as planned, they will significantly increase the proportion of intra-regional trade in Latin America which is exchanged under preferential terms.

" There are some inconsistencies between the data sources employed in these tabulations, but it is anticipated that their overall effects are relatively small. Inotai employed IMF (DOT) and OECD statistics for his tabulations and these data may differ from United Nations trade statistics. See Rozanski and Yeats (1992) for a detailed analysis of the importance of these differences. Data published in UNCTAD (1990) are based on United Nations trade data.
## 6% by non-technical measures.

Table 8: Number (share) of products affected by at least one active NTM in 2017

||Technical|Non-technical|
|---|---|---|
|ARG|4,479 (81)|5,205 (95)|
|BOL|1,930 (35)|945 (17)|
|BRA|3,960 (72)|1,221 (22)|
|CHL|3,309 (60)|107 (2)|
|COL|2,963 (54)|3,254 (59)|
|CRI|1,777 (32)|322 (6)|
|CUB|5,363 (98)|5,362 (97)|
|ECU|2,937 (53)|2,198 (40)|
|GTM|1,233 (22)|54 (1)|
|HND|1,633 (30)|588 (11)|
|MEX|1,848 (34)|837 (15)|
|NIC|1,898 (35)|752 (14)|
|PAN|1,717 (31)|111 (2)|
|PER|2,045 (37)|84 (2)|
|PRY|1,527 (28)|393 (7)|
|SLV|1,937 (35)|88 (2)|
|URY|2,555 (46)|367 (7)|
|VEN|4,891 (89)|4,803 (87)|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: The total number of products at the HS 6-digit is 5,400 (HS Combined).

# **3.4 Colombian Firms and Non-Tariff Measures in LAC**

We now study the extent to which Colombian exporters face NTMs in their regional product-destination sales markets. The focus is on technical trade regulations since they are the most frequently used type of NTM, as discussed above. To make sense of the high-dimensional data, we define three groups of exporting firms. Fully exposed firms (green bar chart) are defined as those facing at least one NTM in each product-destination market. Partially exposed firms (blue bar chart) enjoy at least one productdestination market without trade regulation. The last group is made up of firms whose exports do not face any trade regulations in productdestination markets.

15
# **Digital Technology Uses among Microenterprises: Why Is Productive Use So Low across Sub-Saharan Africa?**

Izak Atiyas and Mark A. Dutz<sup>1</sup> Keywords: internet, smartphone, digital technologies, productivity, jobs, inclusion. JEL codes: D22, J24, L25, L86, O14, O33, O55.

> 1 Affiliations: Atiyas (Economic Research Forum and TUSIAD-Sabanci University Competitiveness Forum), Dutz (Consultant, IFC). Corresponding author: mdutz@ifc.org. This research has received funding from the joint Africa Chief Economist-Digital Development Research Program on Digital Transformation for Africa (project ID: P170151) and from IFC. The authors are grateful to Alison Gillwald and Onkokame Mothobi for making available the RIA (Research ICT Africa) ICT Access business data for 2017-18 and for their support in the use of these data.