rafmacalaba/datause-encoder-data huggingface dataset:
begin by taking weighted averages across modes of supply _m_  _M_ , whereby the set of modal ( _j_ ) weights _wm_ is specific to sector _j_ . The sectors differ in the relative importance of alternative modes for delivering a specific service. For instance, in a ‘consumer service’ such as life insurance, a higher modal weight is attached to commercial presence than in the reinsurance sector in which cross-border provision amongst firms is the dominant mode of supply. Formally, the sectoral scores are given by Sectoral scores are then aggregated across all sectors _j_  _J_ using weights _w j_ that reflect the relative importance of constituent services sectors in domestic value added. Sector weights _w j_ are based on services sectors’ standardized share in total services output for an ‘average’ industrialized country.<sup>16</sup> Overall country-level scores are obtained as The complete weighting schemes used to aggregate modes, subsectors and sectors, respectively, can be found in Annex Table A.7, including further details regarding the sectoral weights. All scores at any level of aggregation are available from the ‘STRI’ section of the database; in particular, the full set of baseline values _s jmc_ is accessible so that users are free to devise alternative aggregation schemes if they so wish.

We recognize the subjectivity of this approach but given data constraints and the wide range of sectors covered, there is no obviously superior method of quantification. A demonstration that the STRI assessments are broadly corroborated by alternative methods of quantification can be found in the working paper version of this paper (Borchert, Gootiiz and Mattoo, 2012b). The subjectivity of the STRI is somewhat mitigated by the extensive consultations we have conducted with the private sector and regulators in making the assignments of weights to specific categories and developing a scoring rule sheet, which sets out how measure specific restriction is scored. We

> 16 A sense of how sectors are over-/underweighted in low-income countries can be gleaned from the fact that the share of financial and business/professional services tends to rise with income whereas the share of retail distribution and, to some extent, telecommunications services tends to decline with income.  However, for the STRI to be comparable across countries, we need to use one uniform set of weights for all countries (see Annex 1 for further details).

5
recent papers, including Carr, Markusen, and Maskus (2001); Blonigen, Davies, and Head (2003); and Blonigen and Davies (2004).

As noted by Blonigen and Head, one problem with FDI data is that it is highly skewed. Table A.1 in the appendix shows that the standard deviation in our dependent variable is about five times its mean. We therefore follow their specification throughout the paper, and estimate the model in logs<sup>5</sup> . Our benchmark regression is then: _FCSijt_ = _f_ ( _SUMGDPijt_ , _GDPDIFSQij_ , _t_ , _SKDIFFijt_ , _SKDIFF_ NEGijt_ , _F_ COSTjt_ , _T_ COSTit_ , _T_ COSTjt_ , _DISTijt_ , _FCSiChinat_ , _FCS_ LACiChinat_ . _FCSiIndiat_ , _FCS_ LACiIndiat_ ) The first independent variable, SUMGDP, refers to the sum of source-country and host-country GDPs and is a measure of total market size. According to the KCM, the coefficient on this variable should be positive, since larger markets should attract multinational enterprises. This variable is thus expected to capture the horizontal motivation for FDI. GDPDIFSQ is the square of the difference of the two countries’ GDP. The KCM predicts that, controlling for GDPSUM, differences in country size discourage horizontal FDI. The intuition is that when one of the countries is small, multinational firms would open production facilities mostly in the larger economy, and thus a negative sign on the coefficient of GDPDIFSQ captures this effect (when also controlling for SUMGDP) **.** The next two terms in the equation capture the vertical motives of FDI. SKDIFF refers to the skill difference between the source and the host country. Theory predicts a positive coefficient on this variable, since differences in skill should proxy for differences in wages, which encourage MNCs to vertically integrate their production with economies

> 5 There is nothing wrong with estimating the model in logs, since the theory does not provide an equation for the KCM.

7
measures of social public spending, and objective indicators of poverty and inequality. Section seven concludes.

# **2. Data**

We use a novel data set of presidential speeches, first used in a companion paper (CalvoGonzález et al., 2017). The data set is comprised of 888 speeches delivered during the period 1819-2016 in ten Spanish-speaking Latin American countries: Argentina, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Paraguay, Peru, and the República Bolivariana de Venezuela. Figure 1 shows the cumulative distribution of speeches by country and by year, while Appendix Table A1 details the precise number of observations on each decade by country.

**Figure 1. Accumulated number of presidential speeches by country over time**

<!-- Start of picture text --> Argentina 900<br>Chile<br>Colombia<br>Costa Rica<br>600<br>Dominican Rep.<br>Ecuador<br>Mexico<br>Paraguay 300<br>Peru<br>Venezuela<br>0<br>Number of speeches<br>1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br><!-- End of picture text --> Source: Authors’ calculations.

Due to variability in data availability not every country nor every decade is represented equally in the data set. Mexico, Costa Rica, and the República Bolivariana de Venezuela are the most highly represented countries in the data set, each with 16% of total speeches. Argentina, Chile, Ecuador, Paraguay, and Peru have a moderate representation, between 6% and 12% of total speeches. Colombia and the Dominican Republic are equally underrepresented in the data set, with only 2% of total speeches each. Representation in terms of time periods also varies. The majority of speeches (70%) correspond to the period between 1920 and 2016, with the remaining 30% dating back to the 1819-1919 period.<sup>2</sup>

> 2 Excluding stop-words, the 888 speeches contain a total of 5,049,173 words, and 114,434 unique terms. The speeches from the 19<sup>th</sup> century (1819-1899) contain only 10% of the total words (and account for 23% of total

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finally, identifying implications for investment strategies intended to develop institutionalized monitoring and evaluation capacity.

# A Conceptual Framework for Institutional Development

Arturo Israel's recent analysis of institutional development in developing countries provides a useful framework for such an analysis (1987). The main components of the framework can be summarized as follows.

Institutional development depends fundamentally on incentives for performance of the individuals in the institution. The degree to which incentives are inherently present in turn depends on the nature of the activities carried out by the organization. Some activities are intrinsically stronger with respect to performance incentives than others, and consequently, more likely to lead to institutional development.

The strength of inherent incentives depends on two factors in the work of the institution. The first is the degree of specificity of the activities in which the institution is engaged. Specificity is high when objectives, means and ways of controlling performance can be clearly identified; and when the effects of these activities are intense, immediate, identifiable and focused. Under such circumstances the performance of individuals, and hence the potential utility of incentives in motivating that performance, is relatively high. In contrast, specificity is low when objectives, means and controls can be less clearly determined, and the effects are weaker, delayed, less identifiable and diffuse. Under such conditions intrinsic incentives are much weaker.

The second is competition. Defined more broadly than economic competition, the concept includes "surrogates" for market competition: external pressures from clients, beneficiaries and suppliers; external pressures from political or regulatory bodies; and internal competition among individuals or units within the institution. Again, intrinsic incentives are higher under conditions of high competition.

Israel's review of World Bank experience indicates that activities in high technology, finance and industry are generally high in both specificity and competition. Activities with low technology, and those relating primarily to human behavior ("people-oriented" activities), are low in both. Institutional development has been more successful where specificity and competition are high. In his analysis, education is an exemplar of a low technology, people-oriented activity where institutional development has been relatively unsuccessful.

Israel suggests that priority for institutional development work should be given to low specificity, low competition institutions. Strategies for institutional development under such conditions operate at both national and institutional levels.

At the national level, increasing awareness of the need for improved institutional performance, simplifying objectives and programs to match institutional capacity, reducing the distortions in wages and salaries that
_Table 3._ Back-casting comparison of poverty rates ($1.90 per day poverty line) from survey-tosurvey methods and actual estimates

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Actual**|
|---|---|---|---|---|---|
|**Panel A: 2004/05**||||||
|National|29.88|28.64|30.38|28.4|38.90|
||[27.16,32.61]|[25.68,31.60]|[27.53,33.24]|[25.62,31.18]|[38.1,39.7]|
|Rural|32.57|31.52|33.03|32.22|43.40|
||[28.97,36.18]|[27.83,35.21]|[29.38,36.67]|[28.69,35.75]|[42.6,44.2]|
|Urban|21.97|20.16|22.6|17.15|25.40|
||[18.88,25.05]|[16.83,23.49]|[19.31,25.90]|[14.75,19.54]|[24.2,26.6]|
|**Panel B: 2009/10**||||||
|National|17.49|17.65|17.51|17.23|31.70|
||[15.37,19.61]|[15.4,19.89]|[15.58,19.44]|[15.25,19.22]|[30.9,32.5]|
|Rural|18.38|18.39|18.39|18.79|36.10|
||[15.53,21.22]|[15.49,21.29]|[15.83,20.95]|[16.12,21.45]|[35.1,37.1]|
|Urban|15.09|15.65|15.14|13.04|19.80|
||[12.76,17.41]|[12.88,18.41]|[12.87,17.41]|[11.09,14.99]|[18.8,20.8]|
|**Panel C: 2014/15**||||||
|National|19.49|17.04|20.93|16.8|14.61|
||[16.74,22.23]|[13.84,20.23]|[18.05,23.81]|[13.99,19.61]|[13.04,16.78]|
|Rural|22.18|19.52|21.95|20.17|16.81|
||[18.56,25.79]|[15.38,23.66]|[18.25,25.66]|[16.36,23.97]|[15.24,18.38]|
|Urban|13.13|11.16|18.52|8.85|10.01|
||[9.98,16.27]|[8.10,14.22]|[15.09,21.95]|[6.82,10.88]|[8.44,11.58]|

_Note_ : Actual estimates in 2014/15 are estimates from Newhouse and Vyas (2019). Actuals in 2004/05 and 2009/10 are authors' estimates using data from NSS0. 95 percent confidence intervals in square brackets.

_Table 4._ Estimated pass-through rates for MOB sub-samples

|**Survey Type**|**Comparability Pass-through**|**95% CI**|**_N_**|
|---|---|---|---|
|Income|1<br>1.003|[.890, 1.12]|841|
|Consumption|1<br>0.661|[.576,.746]|457|

_Note_ : This table reports the pass-through estimates (estimate of coefficient β in equation 1) and their 95 percent confidence intervals for the sub-samples in Figure A.1. The sample size ( _N_ ) is the number of comparable survey spells that can be included in the MOB estimation.

22
**Figure 15: Administrative burdens**

a. Sole proprietors <!-- Start of picture text --> 4<br>3.3<br>3.0 3.0<br>3<br>2.3 2.3<br>2 1.8 1.8 1.9 2.0<br>1.0<br>1<br>0.2<br>0<br>b. Corporations<br>5<br>4.3<br>4<br>3.0<br>3<br>2.3<br>2 1.8 1.9 2.0 2.0<br>1.4<br>1.3<br>1.0<br>1 0.8<br>0<br>c. Sector-specific<br>5<br>4.1<br>4<br>3 2.6<br>2.2<br>2 1.5 1.6 1.6 1.9 1.9 2.0<br>1.1<br>1 0.8<br>0<br>Romania Bulgaria Albania 2007 EU15 OECD Czech Rep. Slovak Republic Croatia 2007 Hungary MICs Poland<br>Romania Albania 2007 Croatia 2007 Bulgaria EU15 OECD Slovak Republic MICs Hungary Czech Rep. Poland<br>Romania Croatia 2007 EU15 Albania 2007 OECD Bulgaria Slovak Republic Hungary Czech Rep. MICs Poland<br><!-- End of picture text -->

# b. Corporations

# c. Sector-specific

_Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._ 16
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# <u>Figure 2:  Measurement Model</u>

<!-- Start of picture text --> Good Government<br> VA PS RQ RL GE CC<br> eva eps e rq erl ege ecc<br><!-- End of picture text -->

# **<u>Results:</u>**

# <u>Preliminaries:  The correlations</u>

First, it is important to report that the 6 WGI variables, based on data for up to 6 years for 216 countries **,** show vary high bivariate correlations. (See Table 1.) The smallest correlation (r=.64) is between political stability (PS) and voice and accountability (VA), and the largest (r=.91) is between rule of law (RL) and control of corruption (CC). While these correlations seem high, it is important to point out that, in terms of shared variance, there is actually not much overlap between PS and VA, since r<sup>2</sup> = .41. In fact, of the 15 pairs of correlations, only the 6 pairs involving rule quality (RQ), government effectiveness (GE), rule of law (RL) and control of corruption (CC) share more than half their variance.

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Behaviour of Firms was of this category. Moreover, the share of unskilled labour remains fairly low -ad primarily characteristic of the smaller firms. Second, we note the high proportion of female workers in the labour force. Third, part time work remains a fairly limited phenomenon across all firn size classes.

At an aggregate level, the main conclusions that emerge from Russian data regarding the path of employment in 1992 can be summarized as follows. Employment losses have been relatively small across the state sector as whole and for industry in particular. Indeed, over 1992 industrial employment declined by under 1% relative to 1991 levels. The losses in the state sector as a whole - under 2% - have been larger and more regionally concentrated. Preliminary information suggests that a sizable share of job losses in the state sector, primarily in the non-material sector - have been concentrated in the Central region and most particularly in the area around Moscow. Therefore, a starting assumption would be that our survey might inipart a upward bias to estimates of job losses, given the overall characteristics of the region.

This is not fully supported by the firm evidence. There has been an undoubted acceleration in the rate of job destruction but the process has not been as one-sided as one might intuite from a casual understanding of the size of the shocks to output. Reallocation shocks resulting from the change in relative prices would hypothetically result in differential paths of job destruction and creation. But we observe little that accords with this view. Rather, we continue to observe the coincidence of relatively high rates of job destruction and creation that do not apparently match to relative price shifts. Of course, one may be failing to capture intra-branch shifts but the dominant impression is of persistence in job hires with low net inflows to unemployment as a consequence. Indeed, aggregate unemployment fell below 2% of the labour force at the end of 1992 in both the Moscow region and in Russia as a whole.

Figure 10 provides the basic infomiation at the Russian level on inflows to and outflows from unemployment. Of course, on the inflows side it includes new entrants but the fact of relatively constant monthly inflow numbers alongside a significant flows out of unemployment, including to jobs, provides a first approximation at the underlying process we appear to be observing. That process seems characterized by a high degree of churning in the labour market that does not simply accord with the prognosis that only job destruction should be occurring. While job destruction obviously dominates new starts, net destruction rates are fairly low. This gives some insight into the trpe of churning we should expect to see.

The response of firms to a perceived permanent negative shock might be expected to show up in reductions in labour capacity, through short time and involuntary holidays or an increase in part time working. It would also be expected to show up in increased net separations. With regard to the first, we
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