rafmacalaba/datause-encoder-data huggingface dataset:
To address endogeneity concerns, we take advantage of the information collected about the year of adoption of more sophisticated technologies - when adopted - and merge the data with a longitudinal dataset that includes data on export status from Brazil’s Ministry of Trade by firm and year. Moreover, to capture longitudinal information on firms’ number of employees and average wages, we combine the dataset with the census of formal workers in Brazil (RAIS). The combined dataset allows us to use a quasi-experimental design to explore the effect of entering export markets on the adoption of sophisticated technologies.

Advancing our key results, we find that entering export markets increases firms’ likelihood of adopting advanced technologies linked to Business Administration (such as Enterprise Resource Planning (ERP)), Supply Chain Management (such as SRM), and Quality Control. The paper is structured as follows. Section 2 describes the data. Section 3 provides some initial correlations between exporting and technology use. Section 4 describes the methodology used to identify the impact of exporting on technology. Section 5 shows the main results. The last section concludes.

# **2 The data**

## **2.1 The Survey**

The Firm-level Adoption of Technology (FAT) survey collects detailed information for a sample of firms about the technologies each firm adopts and uses to perform key business functions necessary to operate in its respective sector (see Cirera et al. (2020)). The survey is composed of five modules. Module A collects information on the general characteristics of the firm.<sup>3</sup> Module B focuses on technologies used for general business functions regardless of the sector where they operate, and sector-specific business functions (module C) focus on technologies that are relevant only for firms in a given sector.<sup>4</sup> Module D focuses on barriers and drivers of technology adoption, while module E gathers information about the firm’s balance sheet and employment.

> 3The survey is designed, implemented, and weighted at the establishment level. For multi-establishment firms, the survey targets the establishment randomly selected in the sample.

> 4The twelve sectors for which we have developed sector-specific modules are: agriculture and livestock; manufacturing (food processing, wearing apparel, leather and footwear, motor vehicles, and pharmaceuticals); and services (wholesale and retail, financial services, land transport services, accommodation, and health services).

3
_Table 3 - The unequal effect of shocks on the labor market. The sample includes individuals in LFS data. The binary dependent variables are estimated using Logit. Shock is measured as the year-on-year difference in log(USD to IRR rate) in each month. Control variables include being householder, gender, marital status, age, household size, job experience, and education fixed effects. Job fixed effects include occupation, job status, having insurance, and firm size dummies. Year and season fixed effects are in all regressions._

**Panel A: Regression with all LFS individuals**

||**Work**|**ing hours pe**|**r week**|**Unemp**|**loyment**|**La**|**y off**|**Unemploy**|**ment length**|
|---|---|---|---|---|---|---|---|---|---|
|||OLS||Lo|git|Lo|git|O|LS|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Afghan dummy**|1.078***|1.003***|2.588***|-0.888***|-0.839***|-0.354**|0.168|-12.123***|-3.217***|
||(0.199)|(0.190)|(0.177)|(0.038)|(0.040)|(0.163)|(0.166)|(0.850)|(0.776)|
|**Shock**|0.193***|0.334***|0.294***|0.022*|-0.000|0.153***|0.148***|-0.265|-0.508**|
||(0.073)|(0.069)|(0.064)|(0.011)|(0.013)|(0.045)|(0.045)|(0.246)|(0.223)|
|**Afghan * Shock**|2.590***|2.446***|1.465***|-0.016|-0.085|0.131|0.121|-0.251|-1.984|
||(0.425)|(0.404)|(0.375)|(0.083)|(0.086)|(0.318)|(0.323)|(1.824)|(1.654)|
|**Constant**|47.579***|37.393***|50.851***|-2.069***|-2.027***|-2.323***|-3.017***|20.355***|-11.329***|
||(0.059)|(0.112)|(1.069)|(0.009)|(0.019)|(0.038)|(0.078)|(0.191)|(0.358)|
|**Observations**|1481602|1481602|1481602|1926747|1926747|115976|115976|208156|208156|
|**Control variables**|No|Yes|Yes|No|Yes|No|Yes|No|Yes|
|**Job FE**|No|No|Yes|No|No|No|No|No|No|

**Panel B: Matching estimation using LFS data.** Matching is exact on unemployment status, and thus it is not used as a dependent variable.

|**W**|**orking hours per week**<br>OLS|**Lay off**<br>Logit|**Unemployment length**<br>OLS|
|---|---|---|---|
||(10)|(11)|(12)|
|**Afghan dummy**|1.836***|-0.514**|-4.751***|
||(0.258)|(0.211)|(0.706)|
|**Shock**|1.848***|0.480*|0.709|
||(0.367)|(0.263)|(1.071)|
|**Afghan * Shock**|0.585|-0.130|-1.624|
||(0.549)|(0.414)|(1.515)|
|**Constant**|45.515***|-1.979***|12.082***|
||(0.173)|(0.134)|(0.499)|
|**Observations**|31158|1683|2348|

24
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Total 4 Females # Males
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Mali > Road investments > Macro results**

||||Reduction in<br>other ublic|Roads (|% variation<br>Imort|)<br>Forein|Income|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|1,480,726.40|0.76|0.82|0.83|0.87|0.84|
|EV|Equivalent variation||0.76|0.81|0.83|0.90|0.37|
|s|Wage|1.00|0.76|1.29|1.45|1.80|1.49|
|yg|Government income|278,133.30|0.61|2.93|2.93|2.93|2.93|
|g|Total government expenditure|255,957.00|–1.07|1.46|1.46|1.46|1.46|
|ye|Firm income|477,113.00|0.87|0.68|0.62|0.50|0.61|
|It|Total private investment|439,511.00|1.08|0.90|0.83|0.70|0.71|
|GDP|GDP|1,819,413.40|0.85|0.85|0.85|0.84|0.85|
|e|Nominal exchange rate|1.00|–0.61|–0.64|–1.19|–2.29|–0.81|

# **Mali > Road investments > Sectoral results**

||||Reduction in<br>|R|oads<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign aid|Income<br>tax|
||Crop agriculture|379,937.10|0.92|0.86|0.84|0.81|0.79|
||Export agriculture|297,375.30|1.37|1.33|1.31|1.26|1.30|
|Va|Mining and gas|206,350.40|1.54|1.45|1.32|1.15|1.42|
|Value added or|Industries|167,281.20|0.83|0.60|0.64|0.77|0.57|
|output|Construction|116,213.20|2.25|1.49|1.67|1.95|1.78|
||Private services|516,027.70|0.50|0.32|0.37|0.33|0.32|
||Public services|136,228.50|–1.43|0.59|0.50|0.67|0.65|
||Crop agriculture|1.01|–0.12|–0.04|0.00|–0.01|–0.39|
||Export agriculture|1.02|–0.59|–0.54|–0.59|–0.83|–0.88|
||Mining and gas|1.00|–0.97|–1.11|–1.27|–1.66|–1.28|
|pq (Market<br>prices)|Industries|1.17|–0.36|–0.11|0.09|–1.41|–0.44|
||Construction|1.02|0.03|0.67|0.42|0.01|0.18|
||Private services|1.03|0.38|0.88|0.67|0.66|0.46|
||Public services|1.00|0.40|0.85|0.94|0.77|0.78|
||Crop agriculture|1.00|0.80|0.80|0.77|0.87|0.38|
||Export agriculture|1.00|0.71|0.59|0.35|–0.04|0.34|
|r<br>|Mining and gas|1.00|0.55|0.16|–0.95|–2.33|0.11|
|Rental rate of<br>capital|Industries|1.00|1.24|1.19|1.46|2.14|1.30|
||Construction|1.00|2.53|1.98|2.40|3.15|2.59|
||Private services|1.00|0.97|0.66|1.05|1.22|0.88|

33
infrastructure and equipment are being improved through strengthening the Internet connection and GIS use, and improving facilities, laboratories, weather stations and machines to examine ocean measures. Finally, within the documentation and dissemination component, CCIAM transfers information generated from the CCIAM projects and the national REDD+ initiative to interested parties. A scientific conference and workshops are held to disseminate results and policy makers, researchers, academics and NGO members participate.

The second pillar of the program relates to the national policy that is in charge of the vice president’s office and the Ministry of Natural Resources (MRT). As part of the national policy, the REDD+ task force was formed. It includes both of these offices as well as local governments, the Ministry of Agriculture, the Ministry of Gender and the Ministry of Finance. The goal is to coordinate national level efforts on the development of the national REDD+ initiative and on actions addressing the drivers of deforestation at the national level.

The third pillar is the implementation of nine pilot REDD+ projects in a period of three years. The project sites have been geographically identified and some of them are already working. The pilot projects include different ecosystems and different socioeconomic profiles. Non-governmental organizations (NGOs) implement and supervise the projects on-site while academic stakeholders perform research within the pilot projects. The primary activities of the projects involve exploring alternative economic activities and other sustainable activities in the forest, improving means of energy (the use and production of charcoal), sustainable agriculture, and addressing fire management. We explain the major challenges and lessons learned from the PFM implementation and REDD+ initiatives in the following section.

7
# **References**

- Acemoglu, D. and Robinson, J. (2005) Economic Origins of Dictatorship and Democracy, _Cambridge University Press_

- --- (2008) Persistence of Power, Elites, and Institutions, _American Economic Review_ , 98(1), pp. 267-293

- --- (2011) Why Nations Fail: Origins of Power, Poverty and Prosperity, _Crown Business_

Arezki, R. and Gylfason, T (2011) Resource Rents, Democracy and Corruption: Evidence from Sub-Saharan Africa, _CESifo Working Paper #3575_

- --- and Bruckner, M (2012) Commodity Windfalls, Democracy and External Debt, _Economic Journal_ , 122 (June), pp. 848-866

- Banerjee, A. and Duflo, E. (2000) Reputation Effects and the Limits of Contracting: A Study of the Indian Software Industry, _Quarterly Journal of Economics_ , 111, pp. 989-1018

Bajari, P. and Tadelis, S. (2001) Incentives versus Transaction Costs: A Theory of Procurement Contracts, _RAND Journal of Economics_ , 32(3), pp. 387-407

- Besley, T. and Ghatak, M. (2010) Property Rights and Economic Development, in Chapter 68, _Handbook of Development Economics_ , vol. 5

Besley, T. and Persson, T. (2009) The Origins of State Capacity: Property Rights, Taxation, and Politics, _American Economic Review_ , vol. 99, no. 4, pp. 1218-1244

- --- (2010) State Capacity, Conflict, and Development, _Econometrica_ , 78(1), pp. 1-34

- --- (2011) Pillar of Prosperity: The Political Economics of Development Clusters, _Princeton University Press_

- --- (2013) Taxation and Development, in Chapter 2, _Handbook of Public Economics_ , vol. 5

- Blackman, A. and Wu, X. (1999) Foreign Direct Investment in China’s Power Sector: Trends, Benefits and Barriers, _Energy Policy_ , 27, pp. 695-711

- Bourguignon, F. (2012) Commodity Price Volatility, Poverty, and Growth Inclusiveness in Sub-Saharan African Countries, in Chapter 11 of ‘Commodity Price Volatility and Inclusive Growth in Low-Income Countries”, IMF

- Bruckner, M. and Ciccone, A. (2011) Rain and the Democratic Window of Opportunity, _Econometrica_ , 79, pp. 923-947

- Dell, M., Jones, B., and Olken, B. (2012) Temperature Shocks and Economic Growth: Evidence from the Last Half Century, _American Economic Journal: Macroeconomics_ , 4(3), pp. 66-95

- Cai, H. and Treisman, D. (2005) Does Competition for Capital Discipline Governments? Decentralization, Globalization, and Public Policy. _American Economic Review_ , 95(3), pp. 817‐30.

- Eden, M., Kraay, A., and Qian, R. (2012) Sovereign Defaults and Expropriations: Empirical Regularities, _World Bank Policy Research Working Paper_ , #6218

22
Ethiopian households that receive international remittances typically do not sell their productive assets such as household assets (in case of urban households) or livestock (in case of rural households) to cope with shocks related to food shortages (table 4). These households typically rely on own cash and other means, presumably from remittances, for coping with shocks. However, while these findings suggest a positive role of remittances during shocks related to food shortages in Ethiopia, they should not be treated as causal since the differences between the three sets of households could result from differences in their initial wealth and other characteristics.

# 5. Conclusion

This paper has presented an analysis of how migrant remittances respond in the aftermath of natural disasters, and whether these flows contribute to preparedness for natural disasters such as earthquakes, droughts and floods.

Based on the analysis using the macroeconomic data and micro-data from household surveys, the paper has the following conclusions. Remittances increase in response to natural disasters in countries that have a larger emigrant stock as a share of the home country population. In the period after a flood in Bangladesh in 1998, per capita household consumption was higher for households that receive remittances, even after controlling for the possibility that these households may be self-selected. International remittance-receiving households in Burkina Faso and Ghana, especially those that receive remittances from high-income OECD countries, have housing built of concrete rather than mud and have greater access to communications, which can help in coping during natural disasters. Ethiopian households that receive international remittances tend to rely more on cash reserves during shocks to food security, and less on selling productive assets such as household assets or livestock.

The macro and micro-evidence indicate a positive role of remittances in preparing for and in coping with the consequences of natural disasters. The finding from household surveys suggest that international remittances from high-income countries tend to be more important in enhancing ex-ante preparedness for disasters compared to those from other developing countries or domestic remittances. This is likely to be the case since international remittances are usually much larger in magnitude compared to intra-regional remittances and domestic remittances (see Mohapatra and Ratha 2011 for evidence from Africa).

The findings also provide a role for policy. Disaster response measures could include leveraging official assistance for tapping into the diaspora after natural disasters, 19
# **Table 9. Guatemala: Effect of Schooling on Growth by Level of Education Considering Quality of Capital and Trade Openness, 1971-2002**

||j = pr<br>|Depe<br>imary<br>|ndent variable: Perce<br>j = sec<br>|nt change of GDP/w<br>ondary<br>|orker<br>j = te<br>|rtiary<br>|
|---|---|---|---|---|---|---|
||OLS<sup>c/</sup>|OLS<sup>c/ d/</sup>|OLS<sup>c/</sup>|OLS<sup>c/ d/</sup>|OLS<sup>c/</sup>|OLS<sup>c/ e/</sup>|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Constant|-0.048<sup>+</sup>|-0.065**|0.042**|0.031**|0.125**|0.112**|
||(-1.93)|(-3.19)|(4.07)|(3.33)|(3.19)|(4.08)|
|Percent change of quality-adjusted capital/|0.833**|0.862**|0.791**|0.838**|0.832**|0.868**|
|worker|(26.5)|(31.9)|(25.9)|(29.2)|(25.8)|(36.4)|
|Percent change of quality-adjusted capital/|0.158**|0.182**|0.153**|0.162**|0.106*|0.050|
|worker [-1]|(3.20)|(4.50)|(3.11)|(3.97)|(2.63)|(1.64)|
|log GDP/worker [-1]<sup>a/</sup>|-0.342**|<br>-0.266**|-0.322**|-0.332**|-0.694*|-0.571**|
||(-5.32)|(-7.48)|(-5.37)|(-6.56)|(-4.89)|(-5.64)|
|log quality-adjusted capital/worker [-1]|0.181**|0.106**|0.119**|0.141**|0.352**|0.283**|
||(3.19)|(3.81)|(2.95)|(3.91)|(4.08)|(4.58)|
|log participation of education levelj|<br>0.094<sup>+</sup>|0.163**|0.075**|0.063**|0.071**|0.056**|
|<br>in labor force [-1]|<br>(1.72)|(4.28)|(3.90)|(3.84)|(3.03)|(3.39)|
|Step dummy 1977|...|...|-0.034**<br>(-2.88)|-0.024*<br>(-2.38)|-0.049**<br>(-3.92)|-0.051**<br>(-5.62)|
|Step dummy 1984|...|...|...|...|0.032**<br>(4.11)|0.030**<br>(5.44)|
|Step dummy 1986|0.026*<br>(2.37)|...|...|...|...|...|
|Percent change of trade volume/GDP|...|0.141**|...|0.117**|...|0.017|
|||(4.20)||(3.20)||(0.68)|
|Long-run elasticity of capital|0.529|0.401|0.370|0.424|0.507|0.496|
|Long-run elasticity of schooling in education<br>levelj|0.274|0.614|0.233|0.188|0.103|0.098|
|Adjusted R<sup>2</sup>|0.972|0.982|0.970|0.980|0.976|0.988|
|F-statistic|132.7|180.0|124.3|148.1|150.9|232.1|
|Durbin Watson<sup>b/</sup>|2.023|2.007|2.028|1.787|1.961|1.979|
|S.E. of regression|0.011|0.009|0.011|0.009|0.010|0.007|
|N|31|31|31|31|31|31|

a/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). b/ A Breusch-Godfrey test finds no evidence for the presence of first, second and third order correlation in the residuals. c/ Includes impulse dummies for 1976 and 1982 significant at 5%. d/ Includes 1986 impulse dummy significant at 1%. e/ Includes impulse dummies for 1975 and 1996 significant at 5%.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%,<sup>+</sup> significant at 10%.

_Source:_ Author’s calculations.

-46-
static distortion is introduced into the sectoral capital allocation, as captured by the expression for _κ_ ( _τ TK, τ NK_ ). Furthermore, note that taxing non-tradable capital and subsidizing tradable capital (or vice versa) in a revenue-neutral fashion does not have a first-order effect on the interest rate, since capital is unspecific in our model: the aggregate return to capital cannot be increased by taking from capital owners and giving back to them; such a policy only introduces a second-order distortion into the economy.

More generally, any bundle of sector-specific factor taxes can equivalently be represented as a pair of taxes on capital and labor ( _τ K, τ L_ ) together with a revenue-neutral pair of taxes on intermediate goods ( _τ T , τ N_ ). The effects of these two sets of policy measures are discussed in sections 4 and 6.1 respectively.

Our analysis of sector-specific factor taxation suggests that in countries in which the non-tradable sector is predominantly informal (e.g. small shops and street vendors) and therefore difficult to subject to taxes or subsidies, subsidizing (formal) tradable capital and raising the revenue by taxing (formal) tradable labor would constitute another second-best policy option: the policy would achieve a first-order increase in the private return to capital (in both sectors, since capital is unspecific) at the cost of second-order distortions to the capital ratio _κ_ (as excess capital flows into the formal tradable sector to take advantage of the subsidy) and the labor ratio _λ_ (as labor flees into the informal non-tradable sector to avoid taxation).

# **7 Conclusions**

This paper has established conditions under which policy measures to undervalue a country’s real exchange rate generate dynamic gains in terms of increased economic growth by internalizing learning-by-investing externalities. Our findings depend on two critical properties, (i) that technological progress in the economy is subject to learning-by-investing externalities and (ii) that the tradable sector in the economy is more capital-intensive and therefore generates a disproportionate amount of these externalities.

If first-best policy measures are are not feasible because of targeting problems, or because multilateral agreements constrain tax-cum-subsidy measures, reserve accumulation may be desirable if certain deep parameter restrictions are met regarding the magnitude of growth externalities, the relative capital-intensity and size of the tradable sector, the willingness of agents to intertemporally smooth and their rate of time preference. The conditions under which it is desirable for a country to accumulate reserves are identical to those under which countries suffer from a foreign aid curse or resource curse when they experience an exogenous inflow of tradable resources.

Our paper analyzes these issues from the perspective of a small open economy that does not affect equilibrium in world capital markets. An interesting next step on our research agenda is to evaluate the welfare effects of real exchange rate undervaluation 34
demonstrated at the beginning of this section.

Let us look at these three factors in<sup>a bit more detail.</sup>

# 3.4.1. Exposure ratios and the incentive<sup>to accept debt reduction</sup>

The money-center banks are more heavily<sup>exposed than the</sup> smaller banks in the U.S. At the end of<sup>1987, the money-center</sup> bank LDC exposure to the 42 troubled debtor<sup>countries was $56.1</sup> billion, or 109 percent of capital.31 For the rest of the U.S. banks, the exposure was $27.3 billion, or<sup>35 percent of capital.</sup> High exposure Rer se can be an important barrier to<sup>acceptance of</sup> debt reduction.

The key point is that regulatory oversight of the<sup>banks is</sup> based on the book values, not market values, of<sup>the banks' assets</sup> and liabilities. This means that heavily exposed<sup>banks may</sup> sometimes have the incentive to avoid book<sup>losses on their</sup> portfolio even if they represent market gains.<sup>A bank with a</sup> large exposure of LDC debt relative to bank capital<sup>might satisfy</sup> capital-adequacy requirements when measured at<sup>book values, but</sup> fail to satisfy them when measured<sup>at market value. In this case,</sup> the bank might turn down participation in a debt reduction scheme, even if it raises the market value of<sup>the LDC exposure, if at the</sup> same time it causes a book loss<sup>on the bank's claims that pushes</sup> the bank out of compliance with<sup>the regulatory guidelines on</sup> 31Calculations based on "Country Exposure Lending Survey: December 1987", of the Federal Financial<sup>Institutions</sup> Examination Council, April 22, 1988. By the<sup>end of 1988. the capital ratios</sup> have come down to well below 100<sup>percent of bank capital.</sup> 38
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_Low-Income Countries’ Access to Private Debt Markets_

**Table 1 Share of private debt and equity flows to developing countries, 2003-7**

|_Percent_<br>||||||
|---|---|---|---|---|---|
|_Bank loans _<sup>_~~1~~_</sup>||_Bond issuance _<sup>_~~1~~_</sup>||_Short-term debt_||
|Turkey|12.1|Russian Federation|18.9|China|26.0|
|Poland|11.0|Brazil|13.5|Russian Federation|7.3|
|Mexico|8.2|Mexico|9.1|Turkey|6.3|
|Russian Federation|7.8|Turkey|6.2|Argentina|5.5|
|China|7.7|India|6.0|Indonesia|5.2|
|Brazil|7.3|Poland|5.5|Brazil|4.8|
|Kazakhstan|6.8|Kazakhstan|4.6|Poland|4.3|
|India|6.6|Philippines|3.2|India|3.3|
|Thailand|3.8|Lebanon|3.1|Thailand|2.8|
|Ukraine|3.7|China|3.1|Ukraine|2.5|
|Top 5|46.8||53.7||50.3|
|Top 10|74.9||73.2||67.9|
|BRICs<sup>2</sup>|29.4||41.5||41.3|
|Low income|0.5||1.0||3.8|
|Lower middle income|29.8||22.8||49.4|
|Upper middle income|69.7||76.2||47.9|
|_Private debt flows _<sup>_1_</sup>||_Private equity flows_||_Nominal GNI_||
|Russian Federation|19.7|China|26.0|China|23.4|
|Mexico|10.7|Russian Federation|7.7|Brazil|8.7|
|Brazil|10.1|Brazil|7.4|Mexico|8.5|
|Turkey|7.3|India|6.9|India|8.4|
|China|6.0|Mexico|5.5|Russian Federation|8.0|
|India|4.7|Turkey|3.8|Turkey|4.7|
|Poland|3.7|Poland|3.7|Indonesia|3.0|
|South Africa|3.5|Thailand|2.9|Poland|3.0|
|Kazakhstan|3.4|South Africa|2.7|South Africa|2.3|
|Malaysia|2.7|Chile|2.2|Iran, Islamic Rep.|2<br>.0|
|To<br>p 5|53.7||53.6||57.0|
|Top 10|71.9||68.8||72.0|
|BRICs<sup>2</sup>|40.5||48.0||48.5|
|Low income|0.7||5.3||6.0|
|Lower middle income|27.9||49.8||49.1|
|Upper middle income|71.4||45.5||45.2|

_Source:_ World Bank Debtors Reporting System.

_Notes:_ 1. Disbursements of private debt with original maturity of over one year. 2. Brazil, Russian Federation, India and China.

4