rafmacalaba/datause-encoder-data huggingface dataset:
������ is the task content measure reported in Panel A of Table B3 (Appendix B) and �h���� is the firm share in total employment of a given occupation category.

Third, we exploit the CASEN survey, a nationwide household survey for Chile collected by the Ministry of Social Development which is representative at the regional level. We use the 2006 and 2013 rounds of the CASEN survey which cover all fifteen regions in Chile. We construct regional measures of technological development that are used as instrumental variables: the share of households in the region with a computer in use and the share of households in the region with at least one cell phone. We also construct measures that capture the degree of regional development that we use as control variables: the share of urban households in the region, the average number of years of education of members of the households in the region, and the average per capita income of households in the region.

Finally, we also exploit 2003 Chilean input-output matrix, published by the Chile Central Bank. For each 1-digit sector (11 in total), we calculate the share of ICT inputs - defined as telecommunication services - in the total value of inputs used by the sector.

# **3.2 Sample and Summary Statistics**

<mark>Our main firm-level sample, based on the ELE survey, is a balanced panel of 1,852 firms observed both in 2007 and 2013. We start by selecting firms that are present in 2007 and 2013 (a total of</mark> 1,992 firm <mark>s) and that report non-missing information on firm employment and software variables as well as firm control variables that will be included in our main specification. By focusing on a balanced panel of firms we are able to exploit changes over a six-year period in the outcomes of interest. We conjecture that this is a sufficiently long period to allow us to observe any potential expansion impacts following the adoption of complex software that could be hindered in the short run when firms have more fixed factors of production. We then</mark> exclude a set of outliers, defined as firms reporting very large changes in employment composition during the period (total of 140).<sup>10</sup> This group has likely misreported information and their inclusion could bias our estimates. We show that our main findings are not driven by this step in the definition of our estimating sample.

> 10 For example, a large manufacturing firm located in the Metropolitan region reports having managers, administrative workers, skilled production workers and no unskilled production workers in 2007 but having only unskilled production workers in 2013. Such radical changes in the structure of employment lead to extreme changes in occupation shares over time, from 0 to 1 or the other way around.

11
The dominant idea in the literature on civil wars is that poverty is the main cause of conflict. In this paper we analyze the effect of institutions on civil wars. The main innovation is to consider the role of the quality of institutions in explaining civil war. We use a broad measure of the quality and efficiency of institutions as our basic variable and we address the potential endogeneity between institutions and conflict. Using a cross-section of countries and data on civil war from 1960-2005, the results of this paper indicate that the quality of institutions is an important determinant of the likelihood of conflict. Moreover, once economic institutions are considered, per capita income does not have any significant direct or indirect effect in explaining civil wars.

19
distribution between the experimental winners and these other winners. However, we cannot say whether this larger impact reflects selection on un-observables (that the very top scoring proposals are from individuals who would have grown larger firms even without the intervention), or whether it reflects the intervention being more successful for these firms with the highest scores. We will examine this further when it comes to testing for heterogeneity in the treatment effects.

Table 5 combines our estimates of the total employment effects to calculate the overall difference in employment created between the winners and the control group. The second column shows the amount of employment reported to the program according to administrative records at the time of the second follow-up survey. This total, at 23,781, is about 10,000 workers higher than what firms report to us in the surveys. As the time of the first follow-up survey, there were a total of 9,027 workers (including the owner) employed in the winning firms. Of this, 2,495 workers is the additional impact of the program, whereas the other 6,532 would have been employed anyway. Total employment in the winning firms grew in the second year to 13,945, before falling slightly to 12,728 in the third year. The treatment effect is estimated to be 6,777 workers in the second year, and 7,027 in the third year, so that by the third year, approximately 55 percent of all employment in the firm is attributable to the program.

As noted above, we assume that the treatment group is not directly competing with the control group for customers given the firms are widely scattered geographically and operating in a large economy. As such this comparison of the treatment and control groups reflects the causal impact of the program on the difference in employment between the two groups. In order for this to reflect the overall impact on the economy, we need to make the following further assumptions:i) any wage job a YouWiN! winner leaves or doesn’t take up is filled by someone else. If not, we should use the impacts on employment rather than on business ownership for the applicants themselves, which would lower the estimated employment creation by around 100 jobs; ii) YouWiN! firms do not destroy or generate jobs in other firms outside the experimental sample. If the winners compete with other Nigerian firms and cause these firms to shut down or not expand as rapidly, the overall impact on employment is less. Conversely, if the firms provide complementary services that allow other firms to grow faster, the overall employment impact would be greater. I assume these two channels offset each other so that the first-order effect is zero here; and iii) the YouWiN!

23
increase in agriculture or other factors, rank fourth.<sup>17</sup> For those rural entrepreneurs who do use electricity, a decrease in interruptions could stimulate growth. Also legal registration and lower registration costs could boost growth. Finally, reduced conflicts could potentially benefit growth.<sup>18</sup> The simulations show that the estimated effect of selected measures of the investment climate would range from less than 0.1 up to about 0.3% on annual employment growth.

**Figure 16: Improving the Rural Investment Climate: Estimated Gains on Enterprise Employment Growth**

**Figure 17: Visualization of Business Constraints’ Impact on Employment Growth over 10 Year-horizon**

<!-- Start of picture text --> 10<br>Roads: 50%<br>reduction in average Initial size = 1<br>market distance<br>9 Initial size = 5<br>Finance: 50% Initial size = 1 and 0.1% annual growth increase<br>increased access to<br>lending 8 Initial size = 5 and 0.1% annual growth increase<br>Communications:<br>50% increased 7<br>access to cell phones<br>Demand: 50% 6<br>increase of<br>agricultural wage rate<br>5<br>Electricity supply:<br>50% decrease in<br>interruptions<br>4<br>Registration: 50%<br>increase of formal<br>registration 3<br>Social cohesion: 50%<br>reduction of violent 2<br>conflict<br>Business 1<br>environment: 50% Upper and lower thin bars show<br>reduction of standard error of simulation.<br>registration time<br>0<br>0.0% 0.1% 0.2% 0.3% 0.4% 0 1 2 3 4 5 6 7 8 9 10<br>Source: 2005 Tanzania RICS 50 percent increase Firm age (years)<br>Employment (number of workers)<br><!-- End of picture text -->

> 17 We assume that higher agricultural wages due to increased demand agricultural growth may raise the income of potential customers, which may lead to higher demand for nonfarm products or services. This ought to lead to growth in the nonfarm sector.

18 The ranking of business constraints identified through the regressions is considered robust. Using spatial econometrics to assess the determinants of rural labor wage in Tanzania, Mduma and Wobst (2005) identify similar constraints.

27
in 2002, 29 persons came from formal sectors (12 from state and 17 from private sector work),<sup>40</sup> and 23 persons moved from informal sectors (8 from informal wage jobs and 15 from selfemployment). These figures may understate the number of individuals who moved from informal sector work since the VHLSS data exclude some substantial number of migrants.<sup>41</sup> Panel A in Table 8 reports the differences in log hourly wages<sup>42</sup> for 1,754 panel individuals who had wage information in both years by the ownership category in 2002 and by that in 2004. Overall, Vietnamese wage workers in the sample experienced hourly wage growth rate of 21 percent between 2002 and 2004. Those who worked in the state sector in 2004 experienced the highest hourly wage growth rate (26 percent), mainly reflecting the wage increase for those who worked for the state sector in 2002 and 2004. Those who stayed within and moved into the foreign sector experienced a 20 percent wage growth rate. Individuals who moved from the informal wage sector jobs into the foreign-owned sector experienced the largest hourly wage growth at about 40 percent. Conversely, those who moved from foreign to domestic sector jobs appear to have experienced smaller wage gains relative to the average wage growth. The pattern of wage change is broadly consistent with the study by Martins (2011) for Portugal, which reports that movers from domestic to foreign firms tend to experience pay increases whereas movers from foreign to domestic firms may experience a pay cut when they move.

Panel B in Table 8 demonstrates the differences in the log _weekly_ wages for the panel individuals which in turn reflect the changes in both hourly wages and hours worked. The average weekly wage growth of 23 percent is slightly higher than the average hourly wage

> 40 For changes in ownership categories within the formal sectors, the changes may reflect either the change in nationality of the establishment for which an individual works or individual’s move from one sector to another. 41 The VHLSS is likely to exclude migrants who hold a temporary or no registration status since the sampling of the surveys is limited to the households who had permanent registration status. Based on the _Vietnam Migration Survey_ (VMS) data in 2004, Fukase (2013b) finds that only 10 percent of migrants who work for foreign firms possess permanent registration status.

> 42 The wages are converted to January 2002 constant prices using the deflators available in the VHLSS.

22
|Require government<br>agencies to collect<br>data on hate crimes<br>committed against<br>LGBTI individuals or<br>those perceived as<br>such?|Yes|Judiciary System Act<br>(2007), Art. 80 Para. 1,<br>Art. 86 Para. 1, Art.<br>382 Para. 3|<br>N/A|N/A|N/A|N/A|N/A|
|---|---|---|---|---|---|---|---|
|<br>Does the law mandate<br>|trainin|gs of the following profess|ionals on recognizing <br>|and identifyinghate crimes?<br>||||
|Paramedics?|No||N/A|N/A|N/A|N/A|N/A|
|Police officers?|No||N/A|N/A|N/A|N/A|N/A|
|Prosecutors?|No||N/A|N/A|N/A|N/A|N/A|
|Judges?|No||N/A|N/A|N/A|N/A|N/A|
|Social workers?|No||N/A|N/A|N/A|N/A|N/A|
|Other?|No||N/A|N/A|N/A|N/A|N/A|
|Services to Victims of<br>Does the law mandate|Hate C<br>thepr|rimes<br>ovision of anyof the follow|ingservices to victim|s of hate crimes?||||
|Legal assistance (in-<br>cluding asylum applica-<br>tions)?|Yes|Legal Aid Act (2005),<br>Arts 21, 22<br>Asylum and Refugees<br>Act(2002), Art. 20|<br>N/A|N/A|N/A|N/A|N/A|
|Shelter/Housing?|No||N/A|N/A|N/A|N/A|N/A|
|Forensic or medical<br>examinations?|Yes|Law on Support and<br>Financial Compensa-<br>tion to Crime Victims<br>(2006), Art. 8|N/A|N/A|N/A|N/A|N/A|

Anti-Discrimination Law and Shared Prosperity 78
<!-- Start of picture text --> outstandingTotal$on formal creditSME creditFormal$bn gap &ME ‘Gapcartstanding% o3 percentageSME creditof current<br>East Asia | 2000-2,600 290-310 11-14%<br>Latin America | 180- 230 125-155 OR-75%<br>Easter E | BU0-700, 105 130 1 20<br>‘South Asia | B-118 40 ee<br>Middle East and | e100 11140 125% 150%<br>North Africa<br>High-income OECD | 11,000 -13,600 600-700 5%-6%<br>Total | ff ae 1400-1600, B10<br>Totalhighed excludingeaed | ffi es 700 +. 860 21%-26%.<br><!-- End of picture text -->
# **Table 8 – Regulators: Adverse reform spillovers to fund longer-term development needs**

_Please select one or more reform areas where you have a reasonable indication that their adoption by other jurisdictions has already or is expected to have the following significant adverse cross-border spillovers in your jurisdiction on financial conditions to fund critical infrastructural and other longer-term development needs (e.g. long-term finance, syndicated lending, project bonds from private sources domestic/foreign)._

|**N = 7**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing with**<br>**OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't know**|
|---|---|---|---|---|---|---|---|---|
|Higherprices|1|0|0|1|0|0|5|0|
|Lower volumes|0|0|1|1|0|0|5|0|
|Shorter maturities|0|0|0|0|0|0|5|0|
|Higher compliance costs|1|1|1|0|1|0|3|1|
|Higher collateral requirements|0|0|0|0|0|0|6|0|

27
# **4. Productivity Analysis**

This section decomposes labor productivity growth into within-sector and between-sector productivity changes. The methodology utilized also enables an assessment of the contribution of demographic change and employment rates, thus providing a richer perspective on the ongoing changes in the Ethiopian labor market.

## **4.1. Decomposition at the Aggregate Level**

Our starting point for decomposing output per capita is the following equation: where _Y_ is total output, _N_ the total population, _A_ the working-age population, and _E_ is total employment. These three components represent the relative size of the working-age population ( _A_ / _N_ ), the employment rate ( _E_ / _A_ ) and output per worker ( _Y_ / _E_ ). In order to calculate the contribution of each of these components to GVA per capita growth, we employ an analytical tool that uses Shapley decompositions.<sup>15</sup> This complements the employment elasticities presented previously.

Table 8 presents estimates for the full period (1996-2011) as well as its three sub-periods. The results for the entire period suggest that the majority of the change in GVA per capita can be explained by changes in output per worker – i.e. labor productivity growth. In fact, out of the 103 percent increase in GVA per capita, over 90 percent of this was due to higher labor productivity. Changes in the employment rate also played an important (though smaller) role, with a contribution of nearly 10 percent. Perhaps surprisingly, the impact of demographic change was negligible (Figure 10).

<u>Table 8: Aggregate Decomposition of GVA per capita</u>

||<br>GVA per capita|<br>% <br>|<br>Contribution of<br>||
|---|---|---|---|---|
|Period|<br>(total change, %)|Demographic<br>Change|Employment<br>Rate|Output per<br>worker|
||∆(Y/N)|∆(A/N)|∆(E/A)|∆(Y/E)|
|1996-2011|103.11|<br>-0.38|9.72|90.67|
|1996-1999|1.06|<br>-231.49|987.55|-656.06|
|1999-2005|29.85|<br>-7.51|39.61|67.90|
|2005-2011|54.77|<br>9.71|-33.27|123.56|

Note: The values for 1999-2005 differ slightly from Martins (2012), especially for the demographic change component, due to the use of different data sources.

A breakdown of these results into sub-periods provides additional insights. The values for the initial three years (1996-1999) are quite striking. However, this is likely to be due to the 1998-2000 Ethiopia-Eritrea war, when output fell and productivity levels declined. GVA per capita growth averaged only 0.3 percent a year, and therefore the high contribution shares are likely to be a consequence of that. The period from 1999-2005 observed a rebound in GVA per capita growth – nearly 30 percent over 6 years, or 4.4 percent per year – partly due to increased productivity growth and a rise in the employment rate. In the most recent period (i.e. 2005-2011), the economy

> 15 See World Bank (2012) for further details on the methodology.

19
a country and sector characterized by a high STRI score (75-100) as compared to an ̳open‘ policy regime.<sup>36</sup> These adverse effects are sizable relative to average inbound M&A flows.

**Table 5: Global M&A activity in services and policy restrictiveness**

||(1)<br>|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Log(GDP) 2007|0.6271***|||0.6466***|||
|Log(GDP per capita) 2007|0.5958***|||0.4448***|||
|Population Density (people/sqkm)|0.0007|||0.0009*|||
|Dummy STRI (25-50]|-0.3347*<br>|-1.2122***|0.0266||||
|Dummy STRI (50-75]|-1.0844*<br>|-2.0094**|0.1350||||
|Dummy STRI (75-100]|-2.5569***|-5.1664***|-1.6993**||||
|No Investment (0%)||||-1.2156***|-3.0110***|0.1062|
|Minority Ownership||||-1.2573***|-1.1678**|-1.0676**|
|Limit number of licenses||||0.2131|0.4931|0.5100**|
|Discrim licensing criteria||||-0.6286***|-0.8852***|-0.5587**|
|Restriction repatr earnings||||-0.6951**|0.4245|-0.7666*|
|No recourse/appeal||||-0.6813**|-1.5015***|-1.0085**|
|Observations|744.0000<br>|744.0000|744.0000|478.0000|478.0000|478.0000|
|Pseudo R^2|0.7901<br>|0.6434|0.8792|0.7795|0.7215|0.8818|
|LogL|-4.280e+05|-7.272e+05|-2.462e+05|-3.613e+05|-4.563e+05|-1.936e+05|
|Sector fixed effects|yes|no|yes|yes|no|yes|
|Countryfixed effects|no|yes|yes|no|yes|yes|

Dependent variable is total value of sectoral M&A inflow in million USD; Poisson estimation with robust standard errors based on 8 services sectors.

Columns 4-6 demonstrate that individual policies, too, have a highly significant effect across all specifications. The prominent role of equity restrictions is not surprising given that the value of acquisitions is the dependent variable. Restricting ownership to a minority stake, or not permitting foreign ownership at all, strongly deters mergers and acquisitions. Other significant variables turn out to be discriminatory licensing criteria, restrictions on the repatriation of earnings and whether investors can appeal licensing decisions. The coefficient sign pertaining to limits on the number of licenses is unexpected, though.<sup>37</sup>

> 35  As indicated in Table 5, levels of restrictiveness in this and all subsequent analyses are defined over the following intervals: open = [0, 25], first level = (25, 50],  second level = (50, 75], third level = (75, 100]. 36  The coefficients in column 2, which controls for any unobserved country characteristics, correspond to marginal effects on the expected value of investment of $1.6bn, $2.6bn and $6.8bn, respectively; this is roughly equivalent to an annual reduction of expected investment between $230-$980m.

37 This may be due to the cross-sectoral nature of the exercise. In a sector-specific study, Borchert et al. (2012) show that license limits exert a significant negative effect in fixed-line telecommunications, a sector in which licenses are known to be a key policy instrument.

39
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