rafmacalaba/datause-encoder-data huggingface dataset:
Frey and Osborne (2013) distinguish between occupations at high, medium and low risk of being automated, and argue that close to 47% of the total work of the United States can be placed within the high-risk category. For its part, the World Bank (2016) estimates that an average of 50% of the current work in Latin America might not continue being performed by people in the future.

However, not all jobs are susceptible to being automated. The analysis of this phenomenon requires jobs to be differentiated, not by their levels of qualifications or skills, as may be suggested, but by the combination of tasks which are performed. This analysis framework, known as “task content”, is the proposal of Autor et al. (2003) and Acemoglu and Autor (2011), among others. According to these authors, the tasks are not strictly the skills with which the worker is equipped, but those which are closely related with them.

Specifically, a task is defined as an activity which enables the creation of a product (Acemoglu and Autor, 2011). However, the workers need a series of skills to be able to carry out these tasks. As an example, an architect needs great numerical and mathematical skills to perform cognitive tasks which are generally non-routine, such as the design and development of plans. The skills may be seen as the ability of the workers to perform particular tasks.

Tasks can be classified in two large categories: routine or non-routine. A task is routine if its performance implies a clear and repetitive set of invariable actions. Many tasks, such as the temperature control of a steel production line or the transfer of a car part to its place in an assembly line, among others, have this characteristic. Since these tasks require the methodical repetition of a constant procedure, they can be clearly specified in a computer program and performed by a machine.

On the other hand, a non-routine task is that whose performance requires various actions to vary in time, and requires the ability to adapt to the context, using language, visual recognition and social interaction, among others. Following Polanyi (1966), this skill that says that a driver cannot be completely replaced; the knowledge that a person has about their own body differs completely from their knowledge of physiology; and the rules of rhyme and prose do not explain in themselves what a poem conveys. In this sense, the passage of a car through the traffic of a city, or the writing of a poem, fall into the category of non-routine tasks. The reason being that those tasks require the abilities of visual, socio-emotional and motor processing that cannot be described in terms of a set of programmable rules.

- 4 -
The information provided in these tables goes beyond a simple aggregation of country-level input-output tables. Besides the harmonization of product categories, input flows by parent and source countries are estimated by means of additional surveys and thus deviate from the proportionality assumption<sup>29</sup> which, according to Puzello (2012), is rejected in these data. This constitutes an important advantage of using the IDE-JETRO input-output compared to previous attempts at constructing input-output tables based on the proportionality assumption (as in Johnson and Noguera 2012, for example).

# **5.2 Measuring the position along the chain: indexes** _N_ **,** _N_<sup>_∗_</sup> **,** _D_ **&** _D_<sup>_∗_</sup>

To better understand the degree of fragmentation in vertical production chains we adopt four indexes that generalize the two indexes proposed in Fally (2012) and applied there to US data. The indexes are designed to describe industries’ position in vertical production chains by exploiting information about relationships in the input-output table. The ‘D’ index measures an industry’s weighted average distance to final demand, where distance is a measured by the apparent number of plants visited by the industry’s output before reaching consumers.<sup>30</sup> The ‘N’ index calculates, for each industry, the number of stages that are embodied in each industry’s production. These two calculations are distinct for each industry, and in the US data there is only a weak correlation between them.

**Distance to final demand or “upstreamness”:** We turn to a formal representation of the two indices. Consider a variable _Dik_ , which is intended to measure the distance of a product _k_ from final demand. Some part of product _k_ ’s sales will be intermediate trade purchased by downstream industries, so the industry in question’s distance measure will depend upon which industries buy its output, and in turn how far those downstream industries are from final demand. Because an industry’s sales go to several industries, which will vary in their respective measures of D, the industry measure must be weighted, and it must also be defined recursively. Let _Dik_ indicate the distance measure in region _i_ for product _k_ . We define _Dik_ as:

where _φikjl_ denotes the amount of output from product _k_ in country _i_ that is used to produce

> 29The proportionality assumption is made to construct input purchase by source country and parent industry when only partial information is provided. For instance, traditional country-level input-output tables describe how much steel is used by the auto industry in each country. Using trade flow data (which describe how much steel is imported from a particular country), previous international input-output tables have been constructed by allocating the use of input across source countries on a proportional basis.

> 30The measure is equivalent to the ‘upstreamness’ measure in Antras, et al. (2012).

35
|Tsunami|-0.675_∗∗∗_<br>(0.172)|-0.027<br>(0.310)|0.001<br>(0.077)|0.721_∗∗∗_<br>(0.186)|||7,540||
|---|---|---|---|---|---|---|---|---|
|Lag<br>lcanic Eruptions_a_|-0.794_∗∗∗_<br>(0.253)|-0.105<br>(0.182)|0.000<br>(0.187)|0.902_∗∗∗_<br>(0.153)|-0.539_∗_<br>(0.284)|0.338<br>(0.209)<br>-0.148<br>(0.206)<br>0.332_∗_<br>(0.172)|7,540||
|With <br>Vo|||||||||
|Earthquake|0.910<br>(0.974)|-0.006<br>(0.733)|-0.426<br>(0.837)|-1.122<br>(0.702)|3.262_∗∗_<br>(1.430)|0.660<br>(0.924)<br>1.169<br>(1.290)<br>-4.158_∗∗∗_<br>(1.257)|7,540||
|Flood_a_|0.241<br>(0.194)|-0.345_∗∗∗_<br>(0.102)|0.125_∗∗_<br>(0.062)|-0.183<br>(0.135)|0.375<br>(0.284)|-0.145<br>(0.142)<br>0.085<br>(0.102)<br>-0.353_∗_<br>(0.201)|7,540||
|Tsunami|-0.846_∗∗∗_<br>(0.157)|-0.003<br>(0.326)|-0.010<br>(0.101)|0.856_∗∗∗_<br>(0.195)|||7,540|1,000.|
|Without Lag<br>Earthquake<br>Volcanic Eruptions_a_|0.416<br>-0.696_∗∗∗_<br>(1.086)<br>(0.266)|-0.110<br>-0.186<br>(0.682)<br>(0.184)|-0.490<br>0.024<br>(0.880)<br>(0.196)|-0.666<br>0.842_∗∗∗_<br>(0.706)<br>(0.149)|||7,540<br>7,540|nt at the 1 percent level.<br>nt at the 5 percent level.<br>t at the 10 percent level.<br>nts and standard errors multiplied by|
|Flood_a_|0.191<br>(0.172)|-0.323_∗∗∗_<br>(0.097)|0.119_∗_<br>(0.068)|-0.144<br>(0.134)|||7,540|_∗∗∗_Signifca<br>_∗∗_Signifca<br>_∗_Signifcan<br>_a_ Coefcie|
|ory|l Expenditure|and Services|Expenditures|nel Expenditures|l Expenditure Lag|and Services Lag<br> Expenditures Lag<br>nel Expenditures Lag|vations||
|Categ|Capita|Goods|Other|Person|Capita|Goods<br>Other <br>Person|Obser|_Notes:_|

28
37 Table 16. Types and number of vehicles in use in selected countries

||Cars|Buses|Vans<br>2|-Wheelers|Goods Vehicles|Tractors &<br>Trailers|
|---|---|---|---|---|---|---|
|OECD**Countrie**|||||||
|U.S.A (1987)a|135,323,632|602.055|..|4.885,772|41,118,762||
|Iapan (1988)|30,776,277|238,021|..|11,450,266|21,440,494|75,034|
|U.K. (1988)|18,432,000|132,000|648,000|912,000|1,951,000||
|Portugal(1987)'|1,285,000|7,860|443,479|106,925|78,600|171,285|
|Turkey (1988)|1,406,387|63,853|124,357|411,126|590,809|683,577|
|Yugoslavia (1986)b|<br>2.957,116|29,009..|102,223|203,011|208,082||
|Asia|||||||
|China (1987)'|761,000|354,000|..|..|2,812,000||
|Hong Kong (19S8)|201,775|13,853|..|17,323|108,479|14,671|
|India (1 98 5)d|1,178,228|213,410|..|4,960,311|783,393|186,507|
|Indonesia (1986)b|1,059,851|256,576|..|5,115,925|876,684||
|Pakistan (1988)|278,506|39,219|34,303|635,888|80,301|232,584|
|Singapore(1 9 8 5)d|225.034|8,717|..|127,564|66.694||
|Korea,Rep.(1988)|1,117,999|259,600|22,404|1,066,841|635,445|20,757|
|<br>Thailand (1988)|816,693|428,366|..|3,894,824|1,422,773||
|Africa|||||||
|Egypt (1988)|791,245|31,590|14,358|263,389|318,478|39,942|
|Kenya (1984)'|122,300|7,001|64,805|17,944|24,769|29,791|
|Nigcria (1987)'|8,981|2,250|14,790|8,907|5,687|2,036|
|S. Africa (1988)|3,147,697|25,950|145,967|317,254|1,106,953|649,774|
|Tunisia (1988)|292,673|8,986|6,066|12,290|151,826|33,699|
|LatinAmerica|||||||
|Argentina (1986)b|3,898,000|59,700|..|76,000|1,375,000||
|<br>Brazil (1988)|14,995,837|193,683|..|1,666,407|1,416,081||
|Mexico (1987)f|5,282,200|..|..|..|2,193,300||
|Chile (1987)'|660,000|22,400|..|36,000|256,000||
|Colombia (1986)b|840,776|245,206|..|385,954|146,227||
|Peru (19 8<br>'<br>7<sup>)</sup>|388,788|..|..|..|214,863||

a) 1987 data [IRF 89] b) 1986 data [IRF 89] c) 1987 data from the Asian Development Bank Regional Seminar on Transport Policy, Manila, Feb. 1989 and [MVMA, 1989] d) 1985 data [IRF 89] e) 1984 data [IRF _891_ f) 1987 data [MVMA, 19891; goods vehicles include trucks and buses
12 at 8.9 percent), in part because of attempts to stabilize the dollar, US inflation was also up (from -3 percent in 1986 and +2.6 percent in 1987 to 4.1 percent in 1988, wholesale price index) so that real international interest rates were not much changed at the end of 1988 (and were lower than in 1986).

International prices of commodities and especially oil were the principal area in which the world economic environment caused serious difficulty during the Baker plan period. As figure 1 shows, the dollar price of oil fell by half in 1986, and after a modest recovery in 1987, in 1988 was not much higher than the weak level of 1986. Several debtors among the 15 countries identified in the indicative Baker Plan depend heavily on oil, especially Mexico, Venezuela, Nigeria, and Ecuador. Because oil is a smaller share of imports for such oil-importing debtors as Brazil than it is of exports for the oil-exporting countries, it is fair to say that the collapse of oil prices during the Baker Plan period was the most severe blow to the strategy from the international economy.

Other commodity prices were weak but less dramatically so. Figure 1 shows an index of nominal dollar prices for six commodities weighted by their shares in Latin America's exports.<sup>5</sup> The prices of these raw materials had shown promising recovery from 5 Coffee (32.6 percent), soybeans (25.8 percent), copper (22.8 percent), corn (9.1 percent), sugar (5.9 percent), and beef (3. 9 percent). Price series are from International Financial Statistics; trade shares calculated from Inter-American Development Bank, Economic and Social Progress in Latin America: 1987 Regort (Washington: IDB, 1987), pp. 474-5.
unobservables drives a selection of districts into the reform.

# 4 Results

## 4.1 Unconditional in overall means

Table 3 presents a comparison of the means of the total fertility rate (TFR), constructed from the age-specific fertility rates (ASFRs) in 5-year intervals per 1000 women in their childbearing years, between the reform and non-reform districts and over time. Mean fertility rates from the 1994 and 2007 census data disaggregated by the 1997 land reform status at the district level are reported in columns (1) and (2), respectively. The simple differences between the two means over time show the trend between census waves for reform and nonreform districts. They are given in column (3). The mean double-difference and the relative percentage change in TFR are reported in columns (4) and (5), respectively. The latter is calculated comparing the observed differencein-differences with a counterfactual case assuming that the non-reform districts had the same trend as the reform districts in the absence of the land reform.

The TFR substantially declined from 4.89 in 1994 to 3.96 in 2007 (a decrease by about 19 percent) in the districts covered by the 1997 land reform while it slightly increased from 4.40 in 1994 to 4.45 in 2007 (an increase by about 1 percent) in the non-reform areas. The difference in these differences, -0.989, is significantly different from zero at conventional levels, suggesting that women from the reform districts reduced their lifetime fertility by roughly one child between the two census waves as compared to those from the non-reform districts. In relative terms, this is equivalent to a decrease in total fertility by about 20 percent.

### [Insert Table 3 about here]

This observed reduction in the TFR can most likely be ascribed to the 1997 land reform which was implemented in the southern half of the Amhara regional state. Although this is consistent with the natural experiment provided by the staggered military campaign of the EPRDF forces, it is prudent to be cautious as there might be other variables related to fertility rates which could systematically vary over time and administrative units. Therefore, we rechecked these findings in a multivariate regressions framework with additional controls for observable characteristics to improve the credibility of a causal inference.

## 4.2 Graphing means by age group

We descriptively showed that there was a substantial reduction in the TFR in the reform districts as compared to that of non-reform districts. In Figure 2, we project the difference-in-differences approach into a graphical representation, comparing the ASFR for the reform and non-reform districts. The upper and 13
Our analysis includes 1,158 cities and towns, in 42 countries (all of mainland Sub-Saharan Africa except Somalia, plus Madagascar). We define cities as contiguous lit areas in the DMSP data set for which a population estimate is available from a comprehensive census database.<sup>23</sup> More specifically, we overlay lit areas for all years and find the outer envelope of lights as pictured in Figure 7. The city’s total amount of light for each year is the sum of the digital number (light intensity) over all grid cells that fall within this outer envelope (maximum extent) of the city light footprint. Rainfall measures are from the Africa Rainfall Climatology Version 2 (Novella and Thiaw 2012), which combines weather station data with satellite information, resulting in a shorter time series but finer spatial resolution (0.1 degree) than Wilmott and Matsuura (2012). We use rainfall rather than moisture in this section because we are unaware of any temperature measures at such fine resolution that do not heavily rely on interpolation of sparse data. Each city’s hinterland annual average rainfall is calculated as an average of grid-cell values within 30 km of the lit area. Summary statistics are in Annex 2.

Our specification is where is light DN summed over all pixels in city i , country c , in year t ; is average rainfall in millimeters per day within 30 km of city i , current or lagged; _Xi_ are country-level indicators for moisture level and agricultural share, as well as city-level indicators; are city and time fixed effects;  _c_<sup>_t_</sup> _c_ is a city-specific linear time trend;  _ict_ is an error term, clustered at the city level to capture city-specific serial correlation.

Equation (14) is an annual panel specification for cities. To identify rainfall effects on lights, we control for time-invariant city conditions, time effects (to account for annual differences in sensor settings across and within satellites), and city-specific linear growth trends. The idea is that each city is on a growth path and rainfall fluctuations in the local area cause it to deviate from that growth path. If climate changes are more permanent then the growth path is shifted up or down.

The empirical context is different from the urbanization analysis of Section 5 in two important respects. First, we are looking at year-to-year fluctuations rather than 10-15 year changes. This suggests

> 23 http://www.citypopulation.de

21
economies of South-East Asia less than in other parts of the world. (3) We try to identify the individual policy measures that account for results obtained using the aggregate index. Restrictions on the nationality of employees and lack of transparency in the licensing process are among the measures deterring investment.

This paper is connected to the literature on the determinants of cross-border M&A. Early empirical studies of aggregate cross-border mergers and acquisitions include Rossi and Volpin (2004) and Di Giovanni (2005). Head and Ries (2008) present a model of bilateral flows of M&A based on a theory of optimal corporate control, and test it on the same data used by Di Giovanni. Hijzen et al (2008) analyze cross-border M&A among OECD countries and focus on the number of deals and the distinction between horizontal and vertical M&A. Coeurdiacer et al (2009) analyze the impact of European integration on bilateral crossborder M&A and the impact of product market deregulation in services within the EU. Hyun and Kim (2010) extend the analysis of determinants of M&A to a large sample of countries using aggregate data. Ahern et al. (2012) analyze the effects of cultural values on M&As. Boudier and Lochard (2013) explore the impact of deregulation in services on cross-border M&A focusing on the OECD economies. Ahern and Harford (2014) analyze the importance of inter-sectoral linkages on waves of M&As within the U.S. We extend this literature by offering a perspective, through the lenses of an economic model, on the importance of policy barriers on cross-border M&A in a variety of service sectors and for a large sample of developed and developing countries.

This paper is also connected to the literature on the measurement of policy restrictiveness in services. We use a novel policy database described in Borchert, Gootiiz and Mattoo (2014). Borchert et al (2012) use this data to explore the implication of policy restrictiveness for landlocked economies in the telecommunication and air transport sectors.<sup>1</sup> Our findings also speak to the literature on inter-sectoral linkages in FDI patterns. Devereux and Griffith (1998) show that previous FDI in manufacturing attracts additional manufacturing FDI for the U.S. case. Head et al. (1995, 1999) analyze the case of Japan,

> 1Van der Marel and Shepherd (2013) use the data to explore the implication of policy restrictiveness for cross-border trade in services.

4
**Table 1: Summary Statistics**

||Full S|ample|||Mean|s by Coun|try|||
|---|---|---|---|---|---|---|---|---|---|
||Mean|SD|Bangladesh|Chile|Ghana|Kenya|Mexico|Nigeria|Sri Lanka|
|Male|0.23|0.42|0.99|0.09|0.78|0.00|0.00|0.85|0.47|
|Owner's Age|41.0|12.6|41.9|36.6|39.3|35.7|45.3|30.9|37.3|
|Years of Educ|9.7|4.1|9.7|10.0|14.0|9.0|8.6|14.8|10.8|
|Digitspan recall|4.5|2.1|5.4|n.a.|6.5|5.0|3.3|7.5|6.4|
|Raven test score|5.0|2.9|n.a.|n.a.|n.a.|6.9|4.9|4.4|3.2|
|Father Owned a Business|0.35|0.48|n.a.|n.a.|0.36|0.30|n.a.|n.a.|0.42|
|Mother Owned a Business|0.28|0.45|n.a.|n.a.|0.53|0.41|n.a.|n.a.|0.07|
|Trade|0.55|0.50|0.51|0.73|0.44|0.76|0.62|0.04|0.34|
|Manufacturing|0.11|0.32|0.28|0.37|0.24|0.00|0.05|0.24|0.31|
|Firm age (years)|8.7|10.5|12.4|3.5|10.7|6.4|9.2|4.5|10.0|
|Number of Paid Workers|2.3|25.8|12.5|0.2|4.2|0.2|0.2|6.7|3.6|
|Monthly Sales (USD)|2751|25258|13999|293|3571|279|1285|3835|2818|
|Monthly Profits (USD)|647|5523|1461|195|2067|53|512|1996|280|
|Capital Stock (USD)|25229|945813|42757|n.a.|9809|163|1463|229730|6740|
|Registered for Taxes|0.34|0.47|0.46|n.a.|0.61|n.a.|0.31|0.38|0.33|
|Closest competitor within 1km|0.81|0.39|0.96|n.a.|0.63|n.a.|n.a.|n.a.|0.70|
|Customers can replace in <1 day|0.44|0.50|0.59|n.a.|0.71|n.a.|n.a.|0.24|n.a.|
|Business Practice Score|0.39|0.25|0.40|0.38|0.44|0.52|0.30|0.76|0.32|
|Marketing Score|0.36|0.31|0.27|0.37|0.41|0.62|0.25|0.66|0.30|
|Buying and Stock Score|0.57|0.30|0.55|0.57|0.42|0.81|0.47|0.78|0.57|
|Record‐Keeping Score|0.46|0.34|0.66|0.44|0.62|0.42|0.38|0.85|0.42|
|Financial Planning Score|0.28|0.32|0.20|0.26|0.31|0.35|0.23|0.73|0.14|
|_Medians_||||||||||
|Median Sales (USD)|500||2676|128|1135|132|667|1290|231|
|Median Profits (USD)|167||294|96|355|47|267|645|93|
|Median Capital Stock (USD)|506||1176|.|2128|39|545|45161|926|
|Median number paid workers|0||4|0|3|0|0|4|0|
|Number of Observations|20400||1724|158|335|3532|10265|1725|2661|

Notes: n.a. denotes not asked in this dataset 31
seasonal gap (8 to 9 percent lower than those found using the dummy variable model, as illustrated through Monte Carlo simulations). Caution is warranted in using dummy variable models in future empirical work to estimate the seasonal gap, especially when less than 15 years of (monthly) price data are available and the peak and trough months are not a priori known.

Second, turning to the findings, food price seasonality in Africa remains substantial (despite the somewhat lower estimates than those reported in the literature). It is also quite diverse across crops, regions and market places. Looking across commodities and countries, the average seasonal gap is 28.3 percent. It is highest for fruits and vegetables (60.8 percent for tomatoes) which are highly perishable and whose production is seasonal, and lowest for eggs and cassava, which are harvested throughout the year and which can be stored in the ground (cassava). Among the cereals, seasonality is highest for maize (33.1 percent), about half this for rice (16.6 percent), which is more irrigated and traded internationally, and around 20‐24 percent on average for millet, sorghum and teff (which store better than maize). These averages hide substantial differences across markets within countries, cautioning against generalizations from non‐representative samples, and highlighting the need for targeted interventions (e.g. when providing better storage facilities).

Third, African seasonal price variability appears substantially higher than what is observed internationally. Looking at both maize and rice, for which there are well‐defined international reference prices, the seasonal price gap is two and a half to three times higher than on the international reference markets. This suggests substantial scope for reduction.

Fourth, price seasonality explains on average about 17 percent of domestic staple crop volatility, rising to 25 percent for maize. Clearly, domestically, there is substantial regularity in price volatility, especially for maize. Internationally, the share is only 6 percent.

Finally, seasonal gaps vary more according to the identity of the crop than the location at which the price is measured. Looking at each food crop separately, there is only evidence of statistically significant country‐specific variation in seasonal gaps for maize and, to a lesser extent, plantain. Specifically, Malawi stands out as having the highest maize seasonal gaps both 26