rafmacalaba/datause-encoder-data huggingface dataset:
Prices have exhibited more volatility during this period than weather. Real prices for maize appear well-integrated across Uganda during this period, in that they move together quite strongly in all regions. Bean prices appear less well integrated. Maize and bean prices increased from 2006 to 2010. The real price of beans continued to rise in most markets in 2011, but maize prices crashed in that year.

Conflict with the Lord’s Resistance Army affected the Northern region of Uganda during the early part of this period, and also impacted some households in the northern part of the Central region. The conflict was stabilized in 2008 and the impact of this is seen clearly in the reduction of conflict related fatalities reported in ACLED from 2006 to 2010. There was an increase in the number of fatalities reported in 2010 but this fell again by 2011.

**Figure 2: Price, conflict and weather trends from 2006 to 2012**

<!-- Start of picture text --> Rainfall (Water Requirement Satisfaction Index,<br>percent of optimum)<br>100<br>80<br>Central<br>60<br>Eastern<br>40 Northern<br>20 Western<br>0<br>2005 2009 2010 2011<br><!-- End of picture text --> <!-- Start of picture text --> Maize price (log of average real price in 2011<br>Ugandan Shillings)<br>6.4<br>Arua<br>6.2<br>Kabale<br>6<br>Lira<br>5.8<br>Masindi<br>5.6<br>Mbarara<br>5.4<br>5.2 Soroti<br>5 Tororo<br>2005 2009 2010 2011<br><!-- End of picture text --> <!-- Start of picture text --> Number of fatalities within 25km of survey<br>households (average)<br>25<br>20<br>Central<br>15<br>Eastern<br>10 Northern<br>5 Western<br>0<br>2005 2009 2010 2011<br>Beans price ((log of average real price in 2011<br>Ugandan Shillings)<br>7<br>Arua<br>6.8 Kabale<br>Lira<br>6.6<br>Masindi<br>6.4 Mbarara<br>6.2 Soroti<br>Tororo<br>6<br>2005 2009 2010 2011 Kampala<br><!-- End of picture text --> Source: Rainfall: staff calculations using geoWRSI v 3.0, with global PET and RFE v2 (2001-2014) time series. Fatalities: ACLED. Prices: UBOS market price data collected for the CPI.

8
The sample we use consists of small and medium-size enterprises (SMEs) in the manufacturing sector of a large cross-section of developing and emerging countries. We follow the definition used by ES for stratification purposes whereby SMEs are all firms with fewer than 100 full-time permanent workers. The sample is cross-sectional in that each country and firm is included only once. All countries for which data are available are included in the sample and the most recent round of ES available in the country is used. Our main regression results are based on a sample of 20,502 SMEs spread across 114 countries and 22 manufacturing industries (at the 2-digit ISIC Rev. 3.1 level). In Appendix A, table A1 provides the list of countries in the sample, table A2 provides a formal definition of all the variables used in the regressions, table A3 provides the summary statistics of the variables, and table A4 provides the correlations between our main corruption measures and the baseline controls.

# 2.2 _Estimation methodology_

Our baseline results are obtained from estimating the following equation: where subscript _i_ denotes the firm, _j_ denotes the industry (at 2-digit ISIC Rev.3.1), and _k_ denotes the country where the firm operates. _Y_ is a dummy variable equal to 1 if the firm is financially constrained and 0 otherwise; _Corruption_ is a measure of the level of corruption experienced by the firm; _CFE_ is a set of dummy variables for the country where the firm operates (country fixed effects) and _IFE_ is a set of dummy variables for the firm’s industry (industry fixed effects). _Firm controls_ include various controls for firm characteristics, and _u_ is the error term. Equation (1) is estimated using logistic 8
comparative advantage in identifying investment projects. A notable example is the _Mi Comunidad (_ My community) project that was launched in 1996, by the Mexican state of Guanajuato. The idea was to use investments by migrants to start small maquiladora garment manufacturing plants in migrants’ communities of origin. The state provided technical assistance, worker training, and assistance to get the projects working. Torres and Kuznetsov (2006) looked at the program around 2000, and viewed it as highly promising, noting it had set up 21 plants and generated 500 permanent jobs in the home communities. But Iskander (2005) notes that the program was a dramatic failure, with only four maquilas surviving after five years, and those that remain were struggling. She notes several issues: the very physical isolation and lack of opportunity that was a spur for migration also isolated plants from production and supply chains, and workers, once trained, often migrated for better prospects in larger cities or abroad.

# **3.6 Integration policies for migrants**

A number of destination countries offer public programs designed to facilitate the economic and social integration of immigrants. In some cases these are voluntary, while in others they are compulsory for certain groups of migrants such as asylum-seekers or migrants receiving welfare benefits. These programs have been used for some time in the Nordic countries, but have also been introduced recently in several other countries such as Germany (Rinne, 2013). Such programs can include language training, assistance finding jobs, and also information on the culture and norms of the country. These programs can improve development outcomes to the extent they allow migrants to access better jobs, earn higher incomes, and/or have better mental health and subjective well-being through easier assimilation.

There is a large literature which shows associations between earnings and being proficient in the language of the destination country (e.g. Chiswick and Miller, 1995), although some evidence suggests the returns to knowing the native language can be relatively low for low-skill occupations (Berman et al., 2003). Despite this, there is very little rigorous evidence that shows that stand-alone language training for migrants is successful in increasing language proficiency and raising employment outcomes. A before-after study of migrants in Canada by Weiermair (1976) found gradual improvement in immigrant wages after undertaking a language course, but it is unclear how much of these wage gains would have occurred anyway. Hayfron (2001) uses an instrumental variables approach to try to measure the impact of language training in Norway, 27
# Table V.1

# Distribution of Governme nt-Led Funds for SMEs, 1980-1990

||||||||Unit: 100|million W|on|
|---|---|---|---|---|---|---|---|---|---|
||Total<br>government<br>led funds<br>(A)|SME<br>Restructure<br>Fund|Indus'l<br>Dvlpmnt<br>Fund'|National<br>Investmt<br>Fund|Indus'l<br>Techngy<br>Improvmnt<br>Fund'|Special<br>Facility<br>Fund|Mutual<br>Aid<br>Fund|**Net**<sup>**3**</sup><br>lending<br>increase<br>to<br>SMEs<br>(B)|Ratio<br>(A/B)|
|1980|195|195|0.4|||||10,020|1.9|
|1981|185|182|3|||||14,290|1.3|
|1982|170|168|2|||||12,720|1.3|
|1983|356|347|9|||||15,440|2.3|
|1984|449|428|21||||10|14,600|3.1|
|1985|501|449|42||||87|13,980|3.6|
|1986|817|497|<br>161||72||426|14,080|5.6|
|1987|2,007|<br>919|276||386||781|14,891|13.5|
|1988|2,537|1,018|321||417||1,168|35,367|7.2|
|1989|5,320|2,835|1,143||174||620|58,404|9.1|
|1990o|6,189|2,161|206||52|3,150w|<br>6201|27,899|22.2|
|Total|20,480|9,199|2,184|[,7542|1,101|3,150|3,092|232,219|8.8|
||100%|44.9%|10.7%|8.6%|5.4%|15.4%|15.1%|||

Notc: 1) Limited to Icnding handled by SME bank.

2) Balance **at** June, 1990. Limited to lending handled by **SMB** Bank **amd** Citizen National Bank.

3) **Net** increase in lending to **SMEs** by banks.

4) Actual lending. Amount eararked is .1000 billion won.

5) Lending between January-May. 1990.

6) For the **first** six months.

Source: KIET (1992) 68
# 4.2 The Asymmetric Impact of Liberalization

In this section we will address two questions aiming at testing the predictions of our model. What is the average impact of increased import competition on productivity growth? And is this impact the same for all firms? The implicit assumption we make here is that productivity growth is an adequate proxy of innovative efforts.

It is a well documented feature of various firm-level studies that firms are heterogeneous in terms of their productivity (Tybout 2000). At the same time various firm-level studies have shown that trade liberalisation tends to influence positively the productivity of domestic firms (Pavcnik 2002, Roberts and Tybout 1996). However, based on the theoretical model developed in section 3.2, we would also expect the impact of increased foreign competition to be different across firms with different productivity levels.

In order to test this hypothesis, we first calculate the domestic production frontier defining it as the average value added labour productivity<sup>26</sup> of the top five firms in each sector<sup>27</sup> , and for every firm we measure the distance from its domestic frontier as in equation 16. More specifically, for firm i belonging to sector j this is equal to the ratio between the productive frontier πjt<sup>Fanditsownlabour</sup> productivity πit. The wider the gap between the productivity of a firm and that of the “top firms” in its sector, the larger this distance index is.<sup>28</sup> To answer the questions just outlined at the beginning of this section we estimate equation 17. Our explanatory variable is the growth of value added labour productivity of firm i between t and t + 1 , and our main explanatory variables are the distance from the frontier (DLFijt), the tariffs faced by its foreign competitors in sector j and their interaction. We also add a number of plant level covariates, as well as year dummies to absorb the effect of macroeconomic shocks. We control for size, measured by employment, capital intensity, measured by the ratio of capital stock to number of workers, and average wages as a proxy for the quality of human capital. Finally, we also control for the plant expenditures in R&D and technological transfers. All variables, except tariffs, are in logarithm<sup>29</sup> and we estimate the model using both OLS and FE estimators. Because we have a lagged dependent

> 26The value added labour productivity is calculated as the ratio between deflated value added and number of workers.

> 27Sector is defined at four digits, based on the Mexican Classification of Manufacturing Activities (CMAP94) and each sector has in average between 98 and 132 plants, depending on the years, with the largest one having 470 plants and the smallest 11.

> 28We follow in this calculation Griffith, Redding, and Simpson (2003).

> 29When performing the log transformation we add 1 to the value of the original variables to avoid that zero values are transformed into missing values.

15
countries, estimates the “common” production frontier. For consistency, the labor and capital regressions are performed using the same sample. However, results are robust to enlargement of the sample (see Section 4).

Table 1 lists the countries in the sample, classified by EU membership/Schengen area.

**Table 1. Countries in the Study Sample**

|EU Members/Schengen Area|Other ECA Countries|
|---|---|
|Austria, Belgium, Bulgaria, Croatia, Denmark,|Albania, Armenia, Azerbaijan, Belarus, Bosnia|
|Estonia, Finland, France, Germany, Greece,|and Herzegovina, Georgia, Kazakhstan,|
|Hungary, Ireland, Italy, Latvia, Lithuania,|Kosovo, Kyrgyz Republic, Moldova,|
|Luxemburg,<br>Malta,<br>Netherlands,<br>Norway,|Montenegro, North Macedonia, Russia|
|Poland, Portugal, Romania, Slovak Republic,|Federation, Serbia, Turkey, Tajikistan,|
|Slovenia, Spain, Sweden, Switzerland, United<br>Kingdom|Turkmenistan, Ukraine, Uzbekistan|

Since the number of structural indicators could be very large, variables are selected and classified according to their interpretation and in line with the WEF competitiveness approach. Since 2005, the WEF has published the annual Global Competitiveness Index (GCI), which identifies and measures the microeconomic and macroeconomic foundations of national competitiveness. The GCI has identified 12 areas (“pillars”) as sources of national competitiveness.<sup>3</sup> Table 2 lists the indicators used in the analysis, classified according to the WEF-GCI 2018 pillars.

**Table 2. Structural Reform Indicators Classified Following WEF-GCI 2018**

|**Pillar**|**Indicator**|**Description**|**Source**|
|---|---|---|---|
|Pillar 1: Institutions|Government<br>effectiveness|Captures perception of the quality of public services, the civil service<br>and its independence from political pressures, the quality of policy<br>formulation and implementation, and the credibility of the<br>government’s commitment to its policies.|WGI (2016)|
|Pillar 2:<br>Infrastructure<sup>a</sup>|Per capita energy<br>use|Energy use, kg of oil equivalent.|WDI|
|Pillar 3: ICT<br>adoption<sup>b</sup>|Individuals using<br>Internet (%)|Individuals who have used the Internet, from any location, in the last<br>three months|WDI|
|Pillar 4:<br>Macroeconomic<br>stability|Output gap<br>_or_<br>Change in terms of<br>trade|Output gap<br>Change in terms of trade (log).|WEO<br>WEO|
|Pillar 5: Health<br>Pillar 6: Skills|Human capital<sup>c</sup>|Index of human capital per person, based on years of schooling (Barro<br>and Lee 2013) and returns to education (Psacharopoulos 1994)|Penn World Table|
|Pillar 7: Product<br>market|Taxes<sup>d</sup>|Top marginal income and payroll tax rate, percent|Fraser (2016)|
||Regulatory quality<sup>e</sup>|Measures perceptions of the ability of the government to formulate<br>and implement sound policies and regulations that permit and<br>promote private sector development.|WGI (2016)|

3The tenth pillar in the WEF GCI classification is market size (proxied by either GDP or volume of imports), a variable that either coincides or is highly correlated with the dependent variable.

6
|Nepal|1991|3|-0.12|1||C|0.40||
|---|---|---|---|---|---|---|---|---|
|Netherlands|2010|2|-0.03|||D|||
|New|||||||||
|Zealand|2008|4|-0.10|2|4|C|0.15|1.11|
|Norway|2008|4|-0.12|3|2|A|0.11|15.53|
|Pakistan|1998|3|-0.13|5|7|C|0.13|1.00|
|Panama|1998|4|-0.06|4|0|A|0.11|2.31|
|Papua New|||||||||
|Guinea|1999|2|-0.10|0|2|C|0.21|25.96|
|Philippines|1997|3|-0.14|4|2|A|0.33|1.77|
|Poland|2008|4|-0.14|3|3|B|0.10|0.72|
|Portugal|1993|2|-0.03|2|1|A|0.12|1.28|
|Russian|||||||||
|Federation|1998|3|-0.38|5|1|A|0.27|2.11|
|Singapore|1998|3|-0.05|3|2|A|0.13|16.49|
|Slovenia|2009|2|-0.16|2||C|0.11||
|South Africa|2001|4|-0.22|2|7|C|0.22|3.24|
|Spain|1993|2|-0.08|0|4|C|0.15|0.59|
|Sri Lanka|1994|2|-0.07|0|6|C|0.16|4.19|
|Sweden|1992|4|-0.15|4|5|C|0.13|3.04|
|Thailand|1997|3|-0.24|7|2|A|0.08|0.89|
|Turkey|2001|1|-0.27|1|0|A|0.10|2.62|
|United|||||||||
|Kingdom|2008|4|-0.15|2|2|B|0.06|0.83|
|United|||||||||
|States|2009|2|-0.07|2|0|A|0.11|7.58|
|Uruguay|2002|3|-0.29|3|2|A|0.31|1.26|
|Vanuatu|1995|2|-0.10|1||C|0.24||
|Venezuela|||||||||
|Rep. Bol.|1994|2|-0.25|0|6|C|0.28|0.62|

Source: Author’s calculations based on data from the IMF. See text for details.

*Notes: A = The increase in FDI occurred first or there was an increase in FDI but not in Exports: 43.33% B = The increase in FDI and the increase in Exports occurred in the same quarter: 6.67% C = The increase in Exports occurred first or there was an increase in Exports but not in FDI: 43.33% D = There was not an increase in FDI or in Exports: 6.67%

13
measure of services liberalization is multiplied by the proportion αjk of inputs sourced by the manufacturing sector _j_ from the services sector _k_ , as with the services index in equation 1. In this way we create two instruments: (i) pertaining to China's commitments and (ii) pertaining to Indonesia's commitments. Each instrument varies by time, manufacturing industry and services sector. An alternative specification, using instead the commitments of all WTO members yielded similar results (available upon request).

The results from IV regressions are reported in Table 7. As expected, the first stage results indicate that Indian services reform responded to services liberalization in China and Indonesia. The F-statistics suggest that our instruments perform well. The Sargan test does not cast doubt on their validity with the exception of the specification focusing on the transport sector. The second stage confirms our earlier finding that services reforms have improved manufacturing performance. This gives us confidence that reverse causation is not driving our results.

# **_Alternative Measure of Service Reform_**

While the construction of our services reform index was undertaken with great care and confirmed by extensive consultations with sector experts in India, a composite index is by its very nature always prone to measurement imperfections. We therefore wish to check the robustness of our findings to more parsimonious approaches to measuring services reform. Although a “true” measure of policy reform does not exist, it may be possible to identify the key structural break points in policy regimes with greater objectivity than is involved in the construction of a composite index that reflects a judgment of the relative importance of specific reforms. Hence we check the previous findings by using a simpler measure of structural breaks for each services sector.<sup>24</sup> This is done by identifying the year in which a service sector experienced the most transformative policy reform and generating a simple indicator variable that divides years into “before” and “after” this structural break. These policy cornerstones in services sectors are then weighted by the input-output coefficients linking services and manufacturing sectors, in the same way as with the policy index: _Breakjt = ajkIkt_ (5)

> 24 Note that it is not possible to do this for the aggregate measure as the timing of structural breaks varies from sector to sector.

25
11 effects of public capital on economic growth have identified a positive empirical link in this context. These have mostly relied on data from the Unites States and other developed countries, however, which limits their suitability for estimating the impact of changes in Mexico's public capital stock on output growth. The analysis below, therefore, estimates a growth equation based on data for 46 developing countries with similar levels of output per capita as Mexico.

## _Theory_

The growth equation below is derived from the (Robert) Solow-inspired analytical framework elaborated by Mankiw, Romer and Weil (1992) and Hulten (1996). That framework departs from a simple neoclassical production function, written here for simplicity in a Cobb-Douglas specification with labor-augmenting technical progress: (1) Y = F(K, EK, H, L) = (K" )aP(EKG)aG(H)aI(AL)(- ap- **aG- aH)** where, Y denotes aggregate output, K" and KG denote the private and public capital stocks, H stands for human capital, L is the stock of labor, A denotes the level of total factor productivity and E is an index of the efficiency of use of public capital. Dividing both sides of the equation by AL and taking natural logs yields, (2) y = f(k", k, e, h) = a, + ap k + aGk + aE e + aHh, where all variables except 'e' are now expressed in terms of "effective" units of labor.

The evolution of the capital stocks is assumed to obey the following sirnple formulation: (3) d(KZ/AL)/dt = szY/AL - _(KZI/AL)_ for z = P, K, H.

where sz represents the savings (and investment) rate associated with the capital stock z, and X represents the effective rate of depreciation, equal to the sum of the rates of population growth (n), technological progress (g) and of the physical depreciation of capital (s). The economy modeled by this framework has the characteristic of converging to a steady state solution, given by the point where, d(KZ/AL)/dt = 0 for all z. From (3), this yields that the steady state value of capital is KZ/Y = sz/I2, which, when expressed in natural logs, yields kZ* = ln(sz/k), where the asterisks denote steady state values.

Along the transition path from an arbitrarily given starting point, y(O), the level of output is governed by the following equation:

# (4)

- y(t) = (1 e)y* + el"y(O),

public investment and private sector performance, rather than the impact on overall growth and productivity
|DVA-in-Services|(10)|-0.13**<br>(0.060)<br>0.13<br>(0.097)<br>0.18<br>(0.19)<br>-0.24*<br>(0.14)<br>0.32<br>(0.24)|8,187<br>0.997|: _ti_<br>_xjt_.<br>Log DVA<br> country (_j_). The<br>tiguity data from<br>rter-exporter pair.|
|---|---|---|---|---|
|ity Proxies:|(9)|-0.16***<br>(0.059)<br>0.12<br>(0.094)<br>0.17<br>(0.19)|8,187<br>0.997|at time _t_<br> exporting <br>e, and con<br>d by impo|
|with Grav|(8)|-0.21***<br>(0.053)<br>0.045<br>(0.16)|8,187<br>0.997|exporter _j_<br>ry _x_ in the<br>y, languag<br>re clustere|
|Linear IV|(7)|-0.16***<br>(0.058)<br>0.090<br>(0.082)|8,187<br>0.997|y _x_ against <br>on in indust<br>tance, colon<br>rentheses) a|
|xies|(6)|-0.099<br>(0.065)<br>0.16<br>(0.14)<br>0.19<br>(0.31)<br>-0.24<br>(0.23)<br>0.33<br>(0.38)|8,187<br>0.997|in industr<br>l producti<br>ilateral dis<br>rors (in pa|
|ravity Pro|(5)|-0.11*<br>(0.064)<br>0.15<br>(0.14)<br>0.18<br>(0.30)|8,187<br>0.997|ountry _i_ <br>d in fna<br> force. B<br>andard er|
|S with G|(4)|-0.17**<br>(0.068)<br>0.021<br>(0.25)|8,187<br>0.997|arif of c<br> embodie<br>ment in <br>e EU. St|
|OL|(3)|-0.12*<br>(0.063)<br>0.12<br>(0.12)|8,187<br>0.997|bilateral t<br> country(_i_)<br>trade agree<br>de with th|
|LS|(2)|-0.15**<br>(0.073)<br>-0.091**<br>(0.045)|8,104<br>0.997|e applied <br> importing <br>eciprocal <br>hts for tra<br>_< ._01.|
|O|(1)|-0.17**<br>(0.068)<br>ln(_IM i_<br>_xjt_)|8,187<br>0.997|n all columns is th<br>lue added from the <br>y pairs without a r<br> country-GDP weig<br>, ** _p < ._05, *** _p_|
|||DVA: ln(_DV Aj_<br>_xit_)<br> Bilateral FG Imports: <br> Bilateral Distance<br>ny<br>mon Language<br>tiguity|ervations<br>quared|Dependent variable i<br>_V Aj_<br>_ijt_)) is domestic va<br>e includes only countr<br>I and aggregated using<br>cance levels: * _p < ._1|
|||Log <br>Log <br>Log <br>Colo<br>Com<br>Con|Obs<br>R-S|Note: <br>(ln(_D_<br>sampl<br>CEPI<br>Signif|

62