rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: IV estimates of the effect of ICT use on the Number of workers: Services and Commerce

||Ser|vices|Com|merce|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Dependent variable:** ∆**ln(Number of w**|**hite-collar **|**workers)**|||
|∆Share of labor that uses computers|0.0068***|0.0075***|0.0013***|0.0012***|
||(0.0025)|(0.0026)|(0.0003)|(0.0003)|
|F- frst stage|82.53|74.85|1,067.39|1,038.03|
|Sargan p-value|0.7409|0.8255|0.6321|0.7069|
|∆Share of labor that uses Internet|0.0063***<br>(0.0024)|0.0070***<br>(0.0025)|0.0012***<br>(0.0003)|0.0012***<br>(0.0003)|
|F- frst stage|98.06|89.27|1429.109|1455.34|
|Sargan p-value|0.4656|0.5104|0.6929|0.7764|
|∆ln(stock of computer equipment/worker)|0.1575**|0.2297**|0.2404***|0.4014***|
||(0.0694)|(0.0968)|(0.0711)|(0.1406)|
|F- frst stage|88.88|52.73|30.77|12.45|
|Sargan p-value|0.9345|0.9580|0.9070|0.5607|
|**Dependent variable:** ∆**ln(Number of b**|**lue-collar **|**workers)**|||
|∆Share of labor that uses computers|0.0091***|0.0111***|0.0005|0.0006*|
||(0.0023)|(0.0025)|(0.0003)|(0.0003)|
|F- frst stage|82.53|74.85|1,067.39|1,038.03|
|Sargan p-value|0.4257|0.2925|0.2142|0.1426|
|∆Share of labor that uses Internet|0.0088***|0.0107***|0.0003|0.0006**|
||(0.0022)|(0.0023)|(0.0003)|(0.0003)|
|F- frst stage|98.06|89.27|1429.109|1455.34|
|Sargan p-value|0.7562|0.6342|0.1540|0.2451|
|∆ln(stock of computer equipment/worker)|0.2088***|0.3306***|0.1150*|0.2667**|
||(0.0550)|(0.0762)|(0.0659)|(0.1285)|
|F- frst stage|88.88|52.73|30.77|12.45|
|Sargan p-value|0.2081|0.2207|0.9097|0.4628|
|**Controls**|||||
|ln(capital/worker)|No|Yes|No|Yes|
|Observations|6,241|6,241|8,333|8,333|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

35
If we keep the assumption that the rate of growth of the traditional sector's export share is zero then, obviously, a positive value of Px implies unbalanced growth, i.e. a change in the export specialization pattern. Balanced growth between import shares implies p1 = **_PH_** = 0 and from (17.1) this implies **_PT_** _= ._ Considering (1 6) and rearranging terms we then obtain XT(1+IP8P) 6 )=XTI3 9 _la 6_ **_+XAFf7_** (21) Under balanced growth the depressing effect of foreign knowledge over domestic knowledge (l.h.s.) must exactly match the sum of the enhancing effect of foreign knowledge over domestic knowledge and the effect of domestic R&D on domestic knowledge (r.h.s.). All this implies that the knowledge diffusion effect of imports is not relevant under balanced growth.

The final step is to compute the steady state growth rate of output. which is the following: _PY=XW+XPXXAPM+PX-PM (22)_ Domestic output will grow in excess of world demand, and hence we will witness a "caching-up effect", if _-PX_ **_XPM_** _+_ Px _PM > °_ The expression above suggests a distinction between a "terms of trade effect" (the sum of the first two terms) and a "market share effect". If we assume constant terms of trade,%PM _=XPX,_ there is no excess growth if aggregate imports and exports shares grow at the same rate. If **_px#PM_** then, given the assumption that the trade share of traditional goods does not change, we must have unbalanced growth from (20) above. Note that this result is independent from the fact that world demand for different goods grows at different rates. In other words, if technology accumulation influences trade performance differently across sectors, catching up implies a change in the specialization structure.

Finally, we can consider the relationship between "technological catching up" and "output catching up"<sup>29</sup> .

Technological catching up may be defined as **_PT >A,_** while output catching up implies _PX_ **_>_** _PM_ from (22) above. Let us now consider the interaction of these two effects. To keep the analysis as simple as possible let us assume that at least one sectoral export share grows faster than the

> 29 This distinction is analyzed in Verspagen (1993).

17
## **1. Introduction**

**Russia experienced a well-documented productivity surge over the period 19992005, following the 1998 crisis** . This contributed to a dynamic growth and poverty reduction. Estimated annual total factor productivity (TFP) growth of 5.8 percent was the driving force behind the observed average annual real GDP growth of 6.5 percent over this period (Alam et al. 2008). Part of the productivity surge is explained by better utilization of excess capacity, especially in the manufacturing sector, a key driver of Russia’s growth, but also within-firm factors and inter-sectoral allocation of labor. Productivity in manufacturing itself––an important engine of Russia’s growth––grew at a healthy rate of over 4 percent in this period. At the same time, infrastructure constraints began to limit firm activities, as evidenced by several firm surveys. Extending the Alam et al. study, the questions arise whether Russia was able to sustain such productivity growth in a more recent period and how firm-level characteristics and infrastructure gaps affected productivity growth.

**This note, with a limited scope, aims to analyze the impact of firm characteristics and infrastructure deficiencies in Russia for the more recent period 2003-08 using the Amadeus database; next, we use the 2004 and 2008 BEEPs data for Russia to explore the firm-level links between infrastructure and productivity.** More specifically, we seek to answer the following questions: Did Russia’s manufacturing productivity growth slow in the latter part of the past decade, as conjectured by some analysts who attributed Russia’s pre-global crisis growth to only high oil prices? What are the firm-specific characteristics that correlate with manufacturing productivity growth? Who are the productive firms in Russia? Which factors contributed most to the observed performance? And, finally, what role infrastructure gaps played in productivity dynamics.

## **2. The Empirical Approach**

# **(i) Estimating TFP**

**We estimate total factor productivity (TFP) for Russian manufacturing firms using the Generalized Method of Moments (GMM) model developed in Levinsohn and Petrin (2003) that accounts for endogeneity in productivity functions.** This approach recognizes that profit-maximizing firms respond to positive productivity shocks by expanding output via mobilization of additional inputs. By contrast, firms experiencing negative productivity shocks reduce output by reducing the use of inputs. The GMM model is superior in this regard to the alternative OLS functions which yield biased estimates of productivity under these circumstances.

**Levinsohn and Petrin (2003) assume a Cobb-Douglas production function of the form** :

2
zones based on the long‐term (30 years) annual mean of monthly rainfall (157mm) and mean temperature (25.4°C) across the communes in the data set (Figure A2) in order to define _Dry‐cold_ , _Dry‐hot_ , _Wet‐cold_ and _Wet‐hot_ climate zones. Long‐term rainfall and temperature conditions as well as their inter‐annual variation greatly varies by zone (Figure A.3). Different climate zones mostly coincide with the different regions (Table A.3). In what follows current weather variation is described by five sets of variables measuring annual, seasonal, abnormal and extreme weather conditions and weather events.

# **4.1 Annual weather conditions**

Variables measuring annual rainfall and temperature levels control for the effect of average annual conditions and variation between years. The use of annual values is common in the literature exploring linkages between weather and economic outcomes (Burke et al., 2011; Dell et al., 2009; Feng et al., 2010; Schlenker and Lobell, 2010).

Based on the CRU data, annual values are calculated as the mean of the monthly rainfall and mean temperature values in the 12 months prior to the survey. Accordingly, 2010 values are not limited to the 2010 calendar year, but measure conditions in the 2009/10 season.

Figure 5 _Annual weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _a. Annual rain_

## _b. Annual temperature_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>400 30<br>300<br>25<br>200<br>20<br>monthly rainfall in mm<br>100 monthly temperature in C<br>0 15<br><!-- End of picture text --> Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of monthly rainfall levels and mean temperature in the last 12 months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

Annual weather conditions vary between the zones and years. 2010 was the driest and warmest year with considerable variation between and within zones (Figure 5). Although on average 2012 is the wettest year in all zones, 2014 has a greater number of communes in the _Wet‐cold_ and _Wet‐hot_ zones which have rainfall levels far above the zone’s average. This skewed distribution reflects the heavy rainfalls that occurred in the Central Provinces at the end of 2013. These rainfalls resulted in severe flooding and livelihood damages (UN, 2013).

12
# **Table 7. Production Function for Guatemala: Mincerian Human Capital Specification, 1951-2002**

||Dependent variable: Perc<br>|ent change of GDP/worker<br> <sup>/</sup>|
|---|---|---|
||OLS|IV<sup>a</sup>|
|Explanatory variables|(1)|(2)|
|Constant|-0.068**<br>(-4.28)|<br>-0.072**<br>(-3.78)|
|Percent change of capital/worker|0.865**<br>(28.7)|<br>0.752**<br>(6.05)|
|Percent change of capital/worker [-1]|0.104**<br>(2.77)|0.163*<br>(2.56)|
|log GDP/worker [-1]<sup>b/</sup>|-0.200**|-0.240**|
||(-5.35)|(-4.94)|
|log capital/worker [-1]|0.069*|0.058|
||(2.56)|(1.45)|
|Average years of schooling [-1]|0.029**|0.034**|
||(4.56)|(4.28)|
|Step dummy 1977|-0.035**<br>(-3.97)|-0.035**<br>(-3.40)|
|Impulse dummy 1963|0.058**<br>(4.63)|0.058**<br>(4.11)|
|Impulse dummy 1982|-0.070**|-0.080**|
||(-4.24)|(-3.85)|
|Long-run elasticity of capital|0.343|0.240|
|Effect of 1 additional year of average schooling|0.219|0.184|
|Adjusted R<sup>2</sup>|0.962|0.953|
|F-statistic|159.2|41.08|
|Durbin Watson<sup>c/</sup>|1.858|2.133|
|S.E. of regression|0.012|0.014|
|N|51|50|

a/ Lags of the independent variables are used as instruments. b/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). c/ A Breusch-Godfrey test finds no evidence for the presence of first, second and third order correlation in the residuals.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%.

_Source:_ Author’s calculations.

-39-
_Do migrants really foster trade?_ The answer based on the results in this paper would be a tentative _Yes_ . Abstracting from the question of _how_ migrants foster trade, the key question asked in this paper is whether migrants foster trade _whatsoever_ . Implementing a panel which comprises the majority of world trade and migration in each period, the core results show that a failure to account for unobserved pairwise heterogeneity, which in turn is positively correlated with migrant networks, lead to a substantial overestimate of the impact of migration on trade. The estimates imply that in the longer run, migration has a negligible or indeed negative impact on trade. Dividing the world into the relatively affluent North and poorer South, the results show that migrants from either region _only_ affect Northern exports to the South. Further, the country level results suggest that migrants may both create and divert international trade. Taken as a whole, the results demonstrate the large biases inherent in cross-sectional studies investigating the trade-migration nexus and highlight the need to be cautious when interpreting previous findings. The surprise therefore is not that few positive effects are found, but rather that such unambiguously positive and robust results have featured so prominently in the literature to date. While an international examination of the trade-migration nexus at the product level is absent from the existing literature, the results from this paper strongly suggest that this should be a research priority should the complexities of the trade-migration nexus be truly understood.

**<u>Bibliography</u>**

30
Where _InvitedtoTraining_ is instrumented with being above the scoring threshold. Since we are only looking within a very narrow window of the score around the threshold, I estimate equation (A1) with and without a linear control in the initial application mark. The results are shown in appendix table 4 below. The first column shows having a score above the threshold is a strong and significant predictor of being invited to the training course. However, the remaining columns show no significant impacts of the training on the likelihood of operating a firm or of employment. The one exception is with the round 3 data for existing firms, where adding a linear control in the application mark does result in a significant point estimate, although the coefficient is close to zero and not statistically significant when no linear control is added. Even in this case we see no significant impact on employment.

|**Appendix 4: Regression Di**|**scontinuity Esti**|**mates of th**|**e Impact o**|**f the 4‐day**|**Business**|**Training C**|**ourse**|
|---|---|---|---|---|---|---|---|
||Invited to|O|perate a Fi|rm|E|mployme|nt|
||Training Course|<br>Round 1|Round 2|Round 3|Round 1|Round 2|Round 3|
|**Panel A: New Firms**||||||||
|Score above threshold|0.881***<br>(0.019)|||||||
|Invited to training||0.064|‐0.013|0.002|0.450|0.626|0.342|
|||(0.054)|(0.049)|(0.050)|(0.434)|(0.455)|(0.544)|
|Score above threshold|0.742***|||||||
|with linear control|(0.038)|||||||
|Invited to training||0.089|0.039|0.040|0.412|0.947|1.135|
|with linear control||(0.095)|(0.086)|(0.095)|(0.758)|(0.861)|(0.906)|
|Sample Size|772|509|641|628|493|634|594|
|Control Mean|0.000|0.488|0.565|0.505|2.224|2.503|2.901|
|**Panel B: Existing Firms**||||||||
|Score above threshold|0.902***<br>(0.020)|||||||
|Invited to training||‐0.004|0.016|0.014|‐1.085|‐1.655|‐0.908|
|||(0.039)|(0.046)|(0.051)|(1.053)|(1.457)|(1.464)|
|Score above threshold|0.772***|||||||
|with linear control|(0.059)|||||||
|Invited to training||‐0.007|0.114|0.240**|‐2.791|‐1.723|‐5.043|
|with linear control||(0.100)|(0.101)|(0.112)|(2.591)|(3.266)|(3.554)|
|Sample Size|433|305|358|369|293|353|334|
|Control Mean<br>Notes: robust standard er<br>levels respectively.<br>RD estimation is estimate<br>for region fixed effects.|0.000<br>rors in parenthe<br>d within a wind|0.893<br>ses, *, **, *<br>ow of +/‐ 5 p|0.801<br>** indicate<br>oints on e|0.748<br>significan<br>ither side|6.933<br>ce at the 1<br>of the thre|7.121<br>0, 5, and 1<br>shold, and|7.376<br>percent<br>controls|

57
The 1985-1994 Global Real Estate Cycle. Page 4 Figure 2. Japan: Balance of Payments and Net Capital Outflows, 1970-1993 <!-- Start of picture text --> 20 00<br>---------------------- f --------------- ---<br>e~~~~~~~~~~~~~~~~~ - -, - - - - - - - - - - - - - - - - -- -<br>I -0+CurrentAccount a -- etCapital OutfowY<br><!-- End of picture text --> Source: IMEF, International Finance Statistics, 1994 Yearbook This rnassive surge of Japanese net capital exports raises four basic questions: what triggered this tidal wave, or more appropriately this _tsunami?_ What domestic institutional arrangements made the generation of this capital possible? How was it exported? Why did the flow stop so abruptly?

# _2.2 The 1985 Plaza Accord as Trigger of the Japanese Asset Boom_

The massive Japanese international capital flows were directly linked to the "land bubbie" and the interactions between very rapidly appreciating land values, ap-oreciating equity prices and easy credit by the banking sector. 'Me trigger that lead to the accelcration in land valucs has been the G-5 Plaza Accord of Septernber 1985. That accord led to the major currency realignment across G-5 countries, particularly the Dollar-Yen exchange rates. This currency realigmnment was accornipanied by negotiations on a coordinated stimulus package and the stabilization of exchange rates (Louvre Agreement) which included in the case of Japan a lowering of the cenftral bank discoimt r-ate first to 3.00 and soon to a then historical low of 2.5%.4 Banlc loans as well as bank loans from non-bank financial insfitutions to real estate companies. From a stable shar of about 7% of total bank loans for two decades, such real estate 4 Such a stimulus package was at first stongly resisted by Japanese financial authorities because the national goverrnment deficit had reached almost 22 percent of the budget by 1985, and total outstanding public debt was equivalent to 42 percent of GNP and growing. Earlier in 1984, work by the Regan-Takeshita committee lad the basis for the rapid emergence of the Euroyen market. (For details on internaadonal fuinance coordination during the 1980's, see Volker and Gyohten 119921 pp. 248-280, especially pp. 274-75). At the beginning of the boom, in 1986, the Bank of Japan discount rate had fallcn to a first historical low point of 2.5 percent In 1993, following three consecutive years of brutal real estate and eqtuities price deflation, Japan experienced a new historical low in the discount rate at 1.75%. The Bank of Japan's new discount rate of 1.75% aimed to remedy the rnassive credit crunch caused by the severe deterioration of the balance-sheets of Japan's banking sector.
paper, the Harvest Choice data now allow for comparison across countries and continents. The World Bank has started to study the business environment for agriculture and agro-industry across a number of countries around the world that includes comparisons of credit provision, so that these factors could also be taken into account. In addition comparable data sets on macro-economic environment and corruption are now also available.

But the different intensification outcomes across continents could also have behavioral sources such as poor response to price and investment incentives. The supply and investment analysis requires panel data over more than two periods. Such data will become available for the African LSMS-ISA countries, and are already available for Asia, so that similar analysis can be performed across the different countries and continents and response elasticities compared.

The final issue is an assessment of public and private research expenditures and available technologies across countries. The former can be done based on the ASTI data, although it will be challenging to isolate expenditures for upland crops from expenditures for irrigated crops. Measuring differences in available technology across countries and environments remains a major challenge, however.

It is clear that the research agenda is vast, and that data from LSMS, IASA and FAO, Harvest Choice, ASTI and other sources will provide much opportunity for this research. By developing new measures of agro-climatic potential and urban gravity that are comparable across environments and countries, we hope to have made a small contribution to the research methods needed to tackle this large research agenda.

19
In Nicaragua, Maluccio (2005) investigates the effects of a sudden reduction in the price of coffee between 2000 and 2002. The data he uses were collected for an analysis of the impact of the Nicaraguan pilot conditional cash transfer (CCT) program, known as the _Red de Protección Social_ . These data are not nationally representative—rather, they cover households in select rural areas. Maluccio first uses household survey data to show that, between 2000 and 2002, per capita consumption for households in the control group—those that were randomly assigned not to receive transfers from the _Red_ —fell by an average of 10 percent. The reduction in consumption in coffee-growing areas was larger, approximately 27 percent. Concurrent with these reductions in consumption levels, school enrollment of children aged 7-12 increased, and increased particularly in coffee-growing areas. For example, among boys, school enrollment increased by 15 percentage points, which suggests that the opportunity cost of going to school fell sharply.

In sum, in five countries in Latin America for which careful analysis has been done, there is little evidence that school enrollment systematically declined during aggregate economic contractions. Enrollment rose markedly during recessions in Mexico and Nicaragua, and marginally in Peru and Brazil. Only in Costa Rica did school enrollment behave procyclically. Nor does it appear that school attainment suffered: in Peru, children who were exposed to the crisis of the late 1980s had completed more years of schooling for their age than comparable children who were not exposed, while even in Costa Rica school attainment appears to have been unaffected by the economic contraction earlier in the decade, despite the decline in enrollment at that time.

Much less is known about the effects of aggregate income shocks on the _quality_ of education and about how, if at all, any possible changes in quality affected the labor market performance of cohorts who received their education during crisis years. In Peru, public recurrent expenditures on education dropped sharply between 1987 and 1990, by approximately 50 percent (Schady, 2004). Recurrent expenditures are largely made up of teacher salaries, which were severely eroded by the hyperinflation of the late 1980s. The quality of instruction may have deteriorated during the crisis—for example, if there was a higher incidence of teacher absenteeism (although outside options for teachers would also have diminished in the economic and completing fewer years of schooling, and others completing more, as appears to have been the case in Peru (Schady, 2004).

14