rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Cutting Robots and Export Quality**

**Table 7: Handing Robots and Export Quality**

|Robot Measure:<br>Outcome:|(1)<br>(2)<br>**Cutting Robots**<br>Quality|Robot Measure:<br>Outcome:|(1)<br>**Handlin**<br>Qu|(2)<br>**g Robots**<br>ality|
|---|---|---|---|---|
|Mechanical cutting robot|0.082**<br>(0.038)|Pallet handling robot|0.145***<br>(0.052)||
|Laser cutting robot|0.085***<br>(0.032)|Handling & inspection robot|-0.023<br>(0.041)||
|Mechanical cutting robot - Developing|0.246***<br>(0.071)|Pallet handling robot - Developing||0.300***<br>(0.099)|
|Mechanical cutting robot - Developed|-0.012<br>(0.022)|Pallet handling robot - Developed||0.049*<br>(0.028)|
|Laser cutting robot - Developing|-0.001<br>(0.041)|Handling & inspection - Developing||-0.062<br>(0.073)|
|Laser cutting robot - Developed|0.084**<br>(0.035)|Handling & inspection - Developed||-0.032<br>(0.031)|
|Observations|864,039<br>864,039|Observations|864,039|864,039|

_Note: Robots reflects log robot stock. We approximate robot applications at the country-industry-level by interacting the (country-level) share of a given robot application with the (country-industry-level) of robot stock. Quality refers log quality, estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also include exporter*10-digit product and exporter*year fixed effects. IV results are presented in Table A8 and A9. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ 34
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31
# **Table 14 Momentum trading**

The _L_ 0 _M_ is the contemporaneous correlation between trades and stock returns. The _L_ 1 _M_ measure momentum investing based based on lag-daily stock returns.

|Sorted by Active Share|Group 1|Group 2<br>|Group 3|Group 4|G1 - G4|
|---|---|---|---|---|---|
|Pa<br>|nel A. Lag-1|Momentum|(L1M)|||
|Foreign Investors<br>||||||
|Average (3)|0.03|0.03|0.08|0.07|-0.03|
|(t-statistics: _H_0 = 0)|(1.99)|(1.23)|(2.86)|(2.51)|(1.05)|
|Percentage positive|62.8|60.2|64.3|57.3||
|Number of Funds|207|206|207|206||
|Domestic Institutions||||||
|Average (4)<br>|-0.05<br>|-0.17<br>|-0.20<br>|-0.12<br>|0.07<br>|
|(t-statistics: _H_0 = 0)|(3.25)|(12.32)|(8.69)|(4.42)|(1.98)|
|Percentage positive|38.9|32.3|31.8|39.8||
|Number of Funds|247|818|600|394||
|Average (3) - (4)|0.08|0.20|0.28|0.19||
|(t-statistics: _H_0 = 0)|(3.66)|(6.65)|(6.59)|(4.42)||
|Pa|nel B. Lag-0|Momentum|(L0M)|||
|Foreign Investors||||||
|Average (1)|0.18|0.03|0.02|0.03|0.15|
|(t-statistics: _H_0 = 0)|(7.01)|(0.97)|(0.59)|(0.89)|(3.81)|
|Percentage positive|77.8|56.3|65.7|58.3||
|Number of Funds|207|206|207|206||
|Domestic Institutions||||||
|Average (2)|-0.12|-0.36|-0.46|-0.35|0.23|
|(t-statistics: _H_0 = 0)|(6.04)|(16.82)|(14.6)|(8.35)|(4.13)|
|Percentage positive|36.8|27.3|23.8|34||
|Number of Funds|247|818|600|394||
|Average (1) - (2)|0.30|0.40|0.48|0.37||
|(t-statistics: _H_0 = 0)|(9.37)|(8.54)|(8.24)|(6.08)||

39
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Employer wages are significantly higher than those of self-employed, and informal employees but in line with wages of formal sector employees (Table 2.3). Figure 2.3 shows the hourly wage distribution for the formal and informal sector workers for 2003, a picture that remained unchanged during 1995-2003. Formal sector workers are defined as workers that make contributions to social security, etc. and therefore are protected. The graph shows clearly that median wages are higher in the formal sector and this picture did not change during 1995-2003. Not only do protected formal sector workers receive benefits and other social services, but also the average hourly wage is larger than for unprotected informal workers.

<!-- Start of picture text --> Figure 2.4: Rural Average Hourly Sectorial Wages<br>(2002 Pesos), Mexico, 1995-2003<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0 Agriculture<br>Industry<br>2.0<br>Services<br>0.0<br>1995 1996 1998 1999 2000 2001 2002 2003<br>Source: ENE 1995-2003, 2nd quarter.<br><!-- End of picture text --> The average hourly wages in the three sectors agriculture, industry, and services followed the same downward trend as unemployment from 1995-99 (see Figure 2.4) indicating a small trade-off between unemployment and wages in the rural labor market. In this period, agriculture, industry, and service wages fell 27.7, 19.1, and 22.9 percent, respectively. Although agricultural wages fell the most, they did not pick up as fast as the other sectors during the general average hourly wage upswing in 1999-2003. In this period, agriculture, industry, and service wages increased 26.6, 39.4, and 35.0 percent, respectively, leaving agriculture hourly wages 19.1 percent below their 1995 level. Considering the whole distribution of hourly wages for the three sectors, median wages are higher in the nonagricultural sector, but the distribution shows a thicker right tail indicating that more people are being paid higher wages in agriculture than in other sectors. This picture did not change significantly during 1995-2003 (see Figure 2.5).

12
# **B.2 Survey period in context**

Figure A1: Survey period in context.

<!-- Start of picture text --> AFG ALB<br>100<br>0 80<br>0<br>-20 70 80<br>-20<br>-40 60 60<br>-40<br>-60 50 40<br>-60<br>-80 40 -80 20<br>30<br>-100 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>BGD BGR<br>100 80<br>0 0<br>-20 80 -20 60<br>-40 60 -40<br>40<br>-60 -60<br>40<br>20<br>-80 -80<br>20<br>-100 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>BRA DZA<br>0 80 100<br>0<br>-20 80<br>60<br>-20<br>-40 60<br>40 -40<br>-60 40<br>-60<br>20<br>-80 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>KHM TCD<br>0 80 100<br>0<br>-20 80<br>60<br>-20<br>-40 60<br>40 -40<br>-60 40<br>-60<br>20<br>-80 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

35
digital investments, which could be rather lumpy.

Figure 17: Probability of digital or innovation response and severity of initial shock <!-- Start of picture text --> (a) Increase use of digital tech. (b) Investment in digital solutions<br>80 50<br>60 40 36<br>53<br>48 29<br>30<br>24<br>40 36<br>27 20<br>15<br>20<br>10<br>0 0<br>Below median Above median Below median Above median<br>Drop in mobility in wave 1 Drop in mobility in wave 1<br>Wave 1 Wave 2 Wave 1 Wave 2<br>(c) Probability of introducing new products<br>30<br>20<br>20<br>15<br>10<br>0<br>Below median Above median<br>Drop in mobility in wave 1<br>Probability of investing in digital<br>Probability of starting/increasing use of digital<br>Probability<br><!-- End of picture text --> _Third_ , firms’ adjustment responses (e.g. in terms of digital upgrading) are associated with increased market concentration and may widen the gaps in performance (and productivity) between smaller and mid-sized or larger firms. The concentration of online sales in a few firms, especially in the retail industry is striking although not surprising given that the option to sell online is usually adopted by retailer chains. Based on the BPS data in Vietnam, we find that the top 5% largest firms accounted for a share of online sales that increased from 65% in the first wave to 77% in the second wave in barely a six-month period. This is especially worrying in the light of recent evidence of increasing market concentration at the global level (De Loecker and Eeckhout, 2018).

This tendency towards heterogeneous responses across firms with different sizes is in line with differences in investment in digitalization and product innovation. Micro and small firms appear to invest significantly less in digital technologies and product innovation strategies (Apedo-Amah et al., 2020). Although the gaps between micro and small, and medium and large firms with respect to the use of digital technologies narrowed by wave 2 (Figure 18, panel a), the divide regarding 23
3 run growth over the next 10 to 30 years. Furthermore, higher levels of financial development are strongly associated with future rates of capital accumulation and future improvements in the efficiency with which economies employ capital. These results emerge from simple procedures that link the growth indicators with lagged values of the financial indicators and from a range of instrumental variables procedures that link the growth indicators with the predictable components of the financial development indicators. Thus, finance does not only follow economic activity, and the strong relationship between the level of financial development and the rate of economic growth does not simply reflect a positive association between contemporaneous shocks to both financial and economic development.

These results suggest an important link between financial development and long-run growth as suggested by Schumpeter 80 years ago. Furthermore, the significant, robust relationship between the level of financial development and both the current and future rate of economic growth contrasts sharply with the weak, fragile partial correlations between growth and a large variety of other economic indicators as shown by Levine and Renelt (19921.

# II. Financial Development. Growth. and the Sources of Growth: Contemporaneous Associations

We begin our analysis by studying the contemporaneous associations between financial development, growth, and the sources of growth. First, we examine the strength of the empirical relationship between long-run real per capita GDP growth and four indicators of the level of financial sector development. The design of our study is the tradition of recent cross-country empirical studies of growth (e.g., Kormendi and Meguire (19851, Barro (19911, Mankiw, Romer, and Weil (1992), and Levine and Renelt (19923). In particular, after controlling for initial conditions and other economic indicators, we find a positive, significant, and robust partial correlation between the average annual rate of real per capita GDP growth and the average level of financial sector development o-r the 1960-1989 period. We term this a study
- 5 -

# **I. INTRODUCTION**

Transport infrastructure is among the most important driving forces for economic growth. Sustained access to infrastructure is essential to improve living standards. In particular in rural areas, the lack of access to reliable transport infrastructure remains a significant constraint for people’s daily life and local businesses in developing countries. About 900 million rural dwellers are estimated to have no access to all-weather road within two kilometers over the world.

In Nepal, rural road accessibility is one of the most crucial challenges. The vast majority of rural residents had to spend more than 30 minutes to access to paved roads. About one-third do not have paved road within more than 3 hours. The lack of access to all-season roads significantly limits their economic opportunities in remote areas. About 20 percent of rural residents have to spend more than 3 hours to go to the nearest marketplace or agriculture center. The road deficiency also has adverse effects on social welfare. Most urban people may find a health post within less than 30 minutes. However, for rural people, it often takes more than 1 hour (Nepal CBS, 2004).

In Nepal, road density remains low particularly in mountainous areas. On a per capita basis, road density is also low in northern and northeastern districts (Figure 1). In these regions, many rural roads were in operational condition only during the dry season. Road density looks relatively high around the capital, Kathmandu. However, with the significant concentration of population on the capital area taken into account, there may not be sufficient road network even in the capital area. In Terai, the road density per population is relatively high.
# **Figure 11: Labor force participation controlling for race composition in the PUMA. Males.**

<!-- Start of picture text --> 0<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>-0.06<br>-0.07<br>-0.08<br>-0.09<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text -->

**Figure 12: Labor Force Participation controlling for race composition in the PUMA. Females.**

<!-- Start of picture text --> 0.08<br>0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (in for natives, out for foreign born) and controlling for the race composition of the PUMA in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table A5.

26
<!-- Start of picture text --> Number of reforms<br>80<br>60<br>40<br>20<br>0 oe) + te) Ke) N co fo)<br>cu o + % © x &<br>g g g g g <4 q<br><!-- End of picture text --> <!-- Start of picture text --> US$/bbl . US$/bbl<br>250 miFiscal breakeven 500<br>200 ~-Brent average (May 2020) 400<br>150 300<br>100 200<br>50 ph LLLitLe. 100<br>0 0<br>GB2S=SESSEYSERETS<br>5 S58 FVESE<br>E << <°O88e%<br>£ i] N 5<br>e 52 8 &<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>8<br>6<br>4<br>2<br>0<br>2013 2018 2013 2018<br>Energy exporters Energy importers<br><!-- End of picture text --> <!-- Start of picture text --> US$/bbl<br>120 m External breakeven<br>100 --Brent average (May 2020)<br>80<br>60<br>4020 sp PPP.<br>0<br>SHESESFEREEECc bt = Cc Cc<br>@3>§e5s5-§ PES<br>5 << OZ gf<br>E ke) N<br>£= 53 g<br><!-- End of picture text -->
The advantages of the network are made even clearer by examining the determinants of access to supplier credit in the two groups of firms separately. The analysis in models III and IV shows that, for SMEs in networks, the only determinant of access to supplier credit is firm size, and the magnitude of its importance is much smaller than for indigenous-African firms. This suggests two things. First, networked SMEs do not have to rely on establishing long-term relationships with suppliers to get credit, as their reputation in the network provides enough information to lenders. Second, even new firms in the network get access to supplier credit.

**Table 8: Probability of Receiving Supplier Credit**

||**Pr**<br>|**obit regression results**<br>|||
|---|---|---|---|---|
||Model I|Model II|Model III<br>(Network)|Model IV<br>(Africans)|
|**Constant**|-2.1***|-2.3***|-1.07***|-2.9***|
||(0.23)|(0.23)|(0.36)|(0.38)|
|**Log(empt)**|0.41***|0.40***|0.25***|0.56***|
||(0.06)|(0.05)|(0.07)|(0.09)|
|**Log(yrssrel)**||0.13**|0.07|0.17*|
|||(0.06)|(0.08)|(0.10)|
|**Asian network**|0.44***|0.41**|||
||(0.17)|(0.16)|||
|**European network**|0.54***|0.49***|||
||(0.21)|(0.21)|||
|**Food**|-0.59***|-0.59***|-0.84***|-0.41|
||(0.19)|(0.19)|(0.24)|(0.36)|
|**Wood**|-0.28|-0.26|-0.43**|-0.10|
||(0.18)|(0.18)|(0.24)|(0.30)|
|**Metal**|-0.07|-0.05|-0.21|0.07|
||(0.20)|(0.20)|(0.26)|(0.32)|
|**Kenya**|0.28*|0.27*|0.20|0.57**|
||(0.16)|(0.16)|(0.20)|(0.28)|
|**Zimbabwe**|1.28***|1.25***|1.68***|1.00***|
||(0.18)|(0.18)|(0.25)|(0.29)|
|**LLr**|-244.7|-244.6|-145.3|-87.3|
|**N**|555|555|304|251|

*** Significant at 99% confidence level; ** Significant at 95% confidence level; *Significant at 90%.

29
depicted in Figure 5. As can be seen, there is clearly a positive, albeit imperfect, correlation between the two variables. To quantify this relationship, we estimated the following: where subscript _r_ denotes a quarter-year time unit, subscript _i_ denotes nightlight pixel _i_ of all pixels _i=1,....N_ within the Dominican Republic, and _e_ is an error term. We estimated (5) using OLS with robust standard errors. The coefficient β was estimated to be 0.107 with a standard error of 0.003, and thus suggests a positive and highly significant relationship between quarterly logged aggregate nightlights and GDP; see Table 4. To further demonstrate the link between quarterly GDP and nightlights we depict the actual and predicted quarterly series, where the latter is generated using the product of logged nightlights and the estimated coefficient for each quarter. As can be seen, while there are some obvious outliers, overall the fitted series seems to capture the actual series reasonably well.

# _V.B: Monetary Impact of Hurricanes_

We depict the implied losses in GDP for each storm in the last column of Table 2. Overall, the storms over our sample period produced on average a USD 1.1 billion fall in economic activity. Relative to the annual GDP at the time of each storm this translated into losses of around 3.62 percent. As can be seen, losses were largest from Hurricane Georges, standing at over USD 14 billion and at least ten times as large as any other storm over our sample period. This storm also had the largest impact on the GDP at the time, estimated to have reduced it by around 50 percent.

# **Section VI: Conclusion**

15