rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Regressions for Log Welfare Ratio**

|||**Urb**|**an**<br>**Standard**||**Ru**|**ral**<br>**Standard**|
|---|---|---|---|---|---|---|
|**Explanatory variables**|**Coefficie**|**nt**|<br>**Error**|**Coeffici**|**ent**|<br>**Error**|
|Constant|-0.336|*|0.101|-0.279|*|0.107|
|**Province**|||||||
|Azuay|0.142|*|0.040|-0.001||0.085|
|Bolívar|-0.265|*|0.077|-0.238|*|0.090|
|Cañar|-0.027||0.049|0.071||0.091|
|Carchi|-0.333|*|0.047|-0.438|*|0.095|
|Cotopaxi|-0.169|*|0.055|-0.063||0.085|
|Chimborazo|-0.198|*|0.056|-0.202|*|0.089|
|El Oro|-0.186|*|0.039|-0.145||0.094|
|Esmeraldas|-0.191|*|0.044|-0.174|*|0.084|
|Guayas|-0.247|*|0.036|-0.057||0.089|
|Imbabura|-0.200|*|0.042|-0.294|*|0.100|
|Loja|-0.077|**|0.043|-0.272|*|0.094|
|Los Ríos|-0.382|*|0.039|-0.112||0.086|
|Manabí|-0.358|*|0.047|-0.189|*|0.082|
|Morona Santiago|0.020||0.083|-0.601|*|0.166|
|Napo|-0.221||0.139|-0.351|*|0.141|
|Pastaza|-0.123|*|0.052|-0.374|*|0.158|
|Tungurahua|-0.035||0.039|-0.089||0.077|
|Zamora|-0.197|*|0.084|-0.298|*|0.092|
|Sucumbíos|-0.002||0.044|-0.209|*|0.085|
|Orellana|-0.039||0.055|-0.335|**|0.175|
|**Demographics**|||||||
|Number of babies|-0.296|*|0.036|-0.376|*|0.032|
|Number of babies squared|0.035||0.021|0.076|*|0.018|
|Number of children|-0.272|*|0.014|-0.264|*|0.015|
|Number of children squared|0.024|*|0.004|0.024|*|0.004|
|<br>Number of teenagers|-0.209|*|0.022|-0.141|*|0.023|
|Number of teenagers squared|0.015||0.010|0.012|**|0.007|
|Number of adults|-0.109|*|0.016|-0.076|*|0.017|
|Number of adults squared|0.005|*|0.002|0.007|*|0.003|
|Sex of the head|-0.022||0.027|-0.091|*|0.036|
|No spouse, single|0.253|*|0.052|0.092|**|0.048|
|No spouse, separated/divorced/widowed|0.227|*|0.048|0.206|*|0.037|
|<br>No spouse, married|0.422|*|0.065|0.459|*|0.070|
|<br>Age of household head|0.028|*|0.003|0.020|*|0.003|
|Age of household head squared|-0.0002|*|0.000|-0.0002|*|0.000|
|Mixed race (mestizo) household head|0.071|**|0.040|0.214|*|0.038|
|White household head|0.167|*|0.049|0.261|*|0.054|
|Black household head|0.026||0.053|0.274|*|0.060|

6
maximum temperature. Higher NDVI measures lead to higher production in the spring and summer in India, but lower production in the fall and winter. The negative winter results were repeated in Brazil but the other seasons were not quite significant. A final measure tested was polarization, which tended to be unable to predict either land values or net revenues per hectare in the three countries tested.

The study shows that satellite data could well support analyses of agricultural performance across the landscape. Specifically, the satellite data did a very good job of capturing climate effects in the Ricardian analyses in Brazil, India, and the United States. Although it would be prudent to collect weather station data, especially of precipitation, future studies could rely solely on the satellite data in remote locations. Thus, the satellites may be particularly valuable for studying agriculture in developing countries where reliable weather station data may be sparse.

Another policy question that could be of great relevance is whether satellite technology is mature enough to replace weather station data for climate related decisionmaking. For example, satellites may be able to identify droughts, floods, and other emergency conditions in a timely and cost-effective manner. This information could be used to help relief programs anticipate problems at an early stage before a crisis is reached. The information could help insurance programs identify where and when they might have to pay large claims. The information could help develop effective predictions of crop production. Given the difficulty of maintaining an extensive network of weather stations in many developing countries, satellite data show great potential.

The Ad-Hoc Group on Earth Observations recently argued that “...despite the existence of numerous remote sensing and _in situ e_ arth observation capabilities, a critical need persists for improved availability, quality, and assured continuity of data in the systematic observation of the planet.” (GEO 2003) Climate has been identified on the top of the list of the possible activities that can benefit from use of satellite data. Our paper clearly demonstrates the potential of satellites for studying climate change impacts.

42
estimated the matched double-difference average treatment effects. The estimates indicate clearly that the training programs have strong effects on the adoption of standard management practices and the willingness to pay for the admission to a similar training program in future, and that there are information spillovers from the training participants to non-participants. Our experimental data also suggest that the training participants are less likely to exit the industry, even though we need longer-run observations to confirm this further.

These results of the training experiments lend strong support to our arguments about the role that management training programs can play in promoting industrial development.<sup>20</sup> However, we must emphasize that the trial training programs that we have offered are not meant to push firms remaining in the quantity-expansion phase to enter the quality-improvement phase. The content of the training programs was too rudimentary and the training period was too short to expect such a transition to take place.<sup>21</sup> Moreover, the training programs focused on management and did not include any aspect of technical training. Probably, a series of training programs are needed to impart sufficient management knowledge for successful IPM that allow trained firms to transition to the quality-improvement phase. Nonetheless, the encouraging results of our experiments suggest that further research is warranted on the impacts of training, the effective contents and design of training programs, and the way in which management and technology training is institutionalized and can be scaled up from a single cluster

> 20 These results are highly consistent with the results of other studies involving management training in developing countries (e.g., Bloom _et al._ 2011; Bruhn, Karlan, and Schoar 2010; Drexler, Fischer, and Schoar 2010; Field, Jayachandran, and Pande 2010; Karlan and Valdivia 2011)

> 21 In general we offered classroom training for three hours per day for total of 15 days.  In addition we implemented on-site visits of management consultants to participating firms twice in the cases of training programs in Vietnam (cases 6 and 14), Ethiopia (case 16), and Tanzania (case 19).

21
**Table 9. Service VS. manufacturing sample**

|Dependent Var.||Female en|trepreneur||
|---|---|---|---|---|
||Panel A: serv|ice sample|Panel B: manuf|acturing sample|
||(1)|(2)|(3)|(4)|
|**F_c**|0.699***||0.412***||
||(0.155)||(0.114)||
|**F_cj**|-0.074|0.206**|-0.087|-0.081|
||(0.144)|(0.094)|(0.081)|(0.068)|
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations|20274|21684|25322|26018|
|Adjusted R<sup>2</sup>|0.049|0.086|0.061|0.084|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

31
And as already discussed in Figure 5, a large fraction of firms report high interest rates as a chief obstacle in accessing finance.

# **_B. Adverse Selection and Moral Hazard Issues_**

In addition to transaction costs and interest rates, SMEs are also constrained by principal agent problems associated with information asymmetry (adverse selection and moral hazard) that are less salient among large firms. Adverse selection refers to difficulties of choosing good credit risks ex-ante in the absence of information on project quality of the borrowers, while moral hazard refers to the inability of the lender to enforce effectively the agreed credit contract ex-post due to costly monitoring or incomplete contracting. SMEs either do not have adequate documents and records to successfully apply for a loan or they are unregistered and have no official documentation. In such a scenario, as shown by Stiglitz and Weiss (1981) interest rates cannot be used as screening technology because the interest rate a bank charges may itself affect the riskiness of the pool of loans, either by attracting high-risk borrowers (adverse selection effect) or by adversely affecting the incentives of borrowers (moral hazard effect).

There is an extensive literature showing that countries with better information sharing systems such as credit registries and credit bureaus have greater bank loans (Jappelli and Pagano, 2002; Detragiache, Gupta, and Tressel, 2005; Djankov, McLiesh, and Shleifer, 2007). While credit bureaus are typically created by the private sector in response to market demand for reliable credit information 12
**_Figure 3: Regression results: Key Growth Drivers in Ethiopia (Real GDP per Capita)._**

<!-- Start of picture text --> 2000-2013 Early 2000s Late 2000s Early 2010s<br>persistence structural stabilization<br>external residual (model error)<br>Source: Table 2.<br>7 7 7 8<br>6 6 6<br>5 5 5 6<br>4 4 4<br>4<br>3 3 3<br>2 2 2<br>2<br>1 1 1<br>Annualized GDP p.c. growth (in %) 0 0 0 0<br>-1 -1 -1<br>-2 -2 -2 -2<br><!-- End of picture text --> Economic growth in 2000-13 was driven primarily by structural improvements, according to model results. The left panel of Figure 3 shows that almost all of the (predicted) growth performance of the 2000-2013 period can be attributed to structural factors: the model estimates these factors to have contributed 3.8 percentage points (ppts) of average annual per capita growth rate of 4.2 percent. The positive effect of the external environment was outweighed by the negative effects associated with macroeconomic imbalances (each accounting for 0.3 ppts). The remaining growth impetus was explained by persistence effects.

Public infrastructure investment and restrained government consumption were the key structural drivers of growth. These two factors were linked as an expansion in budgetary infrastructure investment was facilitated by reduced government consumption, arising partly as a result of a ‘peace dividend’ effect following the end of the 1998-2000 war with Eritrea (Figure 4.2). As previously mentioned, public investment was also financed by extension of credit through the domestic banking sector and foreign borrowing and partly executed through off-budget state owned enterprises. It also helped for growth (0.2 ppts) when Ethiopia opened more to international trade, especially in the Early 2000s, as trade of goods and services increased its share of GDP from 37.5 percent in 2002 to 48.7 percent in 2012 (World Bank, 2014a).<sup>12</sup> Although Ethiopia made substantial efforts in improving educational attainment of its population over the past decade, the growth enhancing effects hereof have been somewhat modest (0.3 ppts), as reflected in the regression results and the Solow decomposition results presented earlier (Figure 1.5), though this may be a measurement problem as discussed by Pritchett (2001). Secondary education (the explanatory variable) remains limited, even if gross enrolment improved from 13.1 to 32.8 percent over the period of analysis.<sup>13</sup>

> 12 See also Figure A1 on the trade policy reform side.

> 13 See World Bank (2012) for an analysis of the challenges related to secondary education in Ethiopia.  There is also evidence of a substantial mismatch between skills supply and demand, for instance in manufacturing (World Bank, 2014b).

13
**Table 3. The Impact of Competition, Demand, and Diversffkation Factors on Sub-Saharan African and Other Developing Countries' Exports: 1962-64 to 1991-93.**

||All N|on-Fuel|Growth R|ate (%)'||||
|---|---|---|---|---|---|---|---|
||Exports|($million)|Demand|Actual|Factor Influen|ce on Current Exp|orts (% change)|
|Country Group'|1962-64|1991-93|Growth|Exports|Demand|Competition|Diversification|
|All Sub-Saharan Africa|3,468|15,146|7.58|5.41|572.1|-41.7|0.5|
|Low Income Africa|2,757|11,433|7.55|5.21|566.8|-52.1|0.6|
|Middle Income Africa|711|3,713|7.69|6.08|596.9|-37.9|0.4|
|Low Income Asia|2,060|89,544|8.49|14.42|778.6|383.4|0.2|
|Middle Income Asia|1,635|114,976|8.33|16.40|740.1|724.9|0.1|
|Latin America & Caribbean|6,745|97,673|7.98|10.02|658.5|77.7|0.1|
|North Africa|1,064|18,490|9.53|10.53|1,058.4|-19.1||
|Middle East|289|4,712|9.34|10.48|1,020.8|36.7|0.1|
|Other Europe and Asia|2,219|45,323|10.38|11.38|1,386.4|30.7||
|High Income Non-OECD|841|105,364|14.26|18.83|3,979.6|304.7||
|All Non-OECD excluding Sub-Saharan Africa|18,954|521,231|9.68|12.57|1,131.9|115.1||
|OECD Countries|49,560|1,304,252|12.87|12.39|2,762.9|-11.6|--|

The composition of the country groups are as follows: Low Income Africa: Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Equatorial Guinea, Eritrea, Ethiopia, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Rwanda, Sao Tome and Principe, Sierra Leone, Somalia, Sudan, Tanzania, Togo, Uganda, Zaire, Zambia and Zimbabwe.

Middle Income Africa: Angola, Cameroon, Cape Verde, Congo, Cote d'lvoire, Namibia, Senegal, Swaziland, Botswana, Mauritius, Mayotte, Reunion, Seychelles.

For the country composition of all other groups see World Bank (1995).

"The demand growth column shows the compound annual growth rate for global exports of the products exported in the base period. The actual exports column shows the actual rate of growth of the countries' exports.

Source: Computed from UN Comtrade statistics.
## **6.2 Market Expansion**

Another channel underlying the economic impact of Internet connectivity is the expansion in market access for both firms and consumers (Hjort and Tian, 2021). In addition to traditional "brick-and-mortar" markets, Internet access offers an additional market channel via "online markets", thus allowing firms to sell products outside the boundaries of traditional markets, at relatively low cost. The expansion in market access via online platforms (in addition to offline/traditional platforms) will result in increased revenue for firms with positive implications on profitability. The potential for high revenue is particularly important for investors as it increases the expected return on investment.

To test this channel, we use firm-level data from the WBES to explore the relationship between high-speed Internet connectivity and firm sales by estimating the following equation: _Ymict = φ ·_ 1 ( _Submar inect ×Connectedi_ ) _+ βXmt +_ Γ _i + ηct + μmict_ (4) where _Ymict_ is the outcome of firm _m_ in city _i_ , country _c_ at time _t_ ; _Xmt_ is a vector of firm controls (ownership type, firm size, and whether it is foreign or domestic), while Γ _i_ represent city fixed effects. All else remains as previously defined. Standard errors are clustered at city level.

In Table 7, we find a positive effect of Internet connectivity and the two measures of firm sales: log of total sales and sales per worker. These results provide suggestive evidence of the economic benefits of Internet connectivity on revenue, potentially signaling a high return on investment.

# **7 Robustness Checks**

Before concluding the paper, we address two potential challenges to our baseline analysis, namely: standard error clustering and extending the sample to include countries with prior connection to the first-generation SMCs.

## **7.1 Standard Errors**

We begin by showing the robustness of our analysis to alternative clustering. Given that one dimension of our treatment indicator–SMC arrival–varies at the country level, we conduct additional analysis by clustering our standard errors at the country level to account for serial correlation within countries. The results are shown in Tables A1 and A2 in the Appendix. The statistical significance remain robust as in the baseline results (Tables 1 and 3) where we cluster the standard errors at the subnational level.

19
tasks and routine-cognitive tasks carried out by skilled production workers (although with different intensities, see Table B2). This impact stands in contrast with the argument posed in the literature studying the automation of routine tasks performed by middle-educated workers through the adoption of a type of technology (computer or internet) that complements skilled employment. For this reason, all else constant, we expect the use of complex software to potentially _substitute_ for skilled (production) employment. Indeed, Brambilla (2017) proposes a theoretical model with heterogeneous firms on digital technology adoption and jobs that is flexible enough to allow for substitution between skilled employment and technology. On the other hand, skilled workers have the ability to interpret/draw upon the results produced by the complex software. Hence, we expect the degree of substitution of skilled production workers to be bounded by this consideration. Importantly, the use of complex software may have positive impacts on firm efficiency and output, which would increase the demand for any type of worker. Depending on which effect dominates (substitution effect or output expansion effect) there may be a reduction or an increase in the share of skilled production workers in firm total employment.

**Unskilled production workers:** If the adoption of complex software has an output expansion effect, then we should observe an increase in the demand for unskilled production workers and a possible increase in their share in total employment (depending on the relative increase for other occupations). Keeping the firm’s output level fixed, we believe that an increase in the use of services workers related to software support and IT services is possible. As the ELE survey includes workers providing software support and IT services in the unskilled production workers category, we therefore would also expect an increase in the demand for unskilled production workers even if the output level is kept fixed.

**Managers and administrative workers** : The impact of complex software adoption on firm employment of managers is likely to be negligible, while the impact on firm employment administrative workers is ambiguous. We do not expect the adoption of complex software to directly affect the demand for managers; furthermore, any output expansion effect may not translate into growth in that because managers are not directly involved in the production process. A negative impact on employment of administrative workers is possible as some tasks covered by complex software may enter in the domain of administrative workers, especially those covered by client management software. At the same time, an expansion in firm output resulting from complex software adoption may increase the demand for administrative workers.

14
# 1 Introduction

For years, governments have been actively attracting foreign direct investment (FDI) in the belief that it creates jobs, boosts productivity, and increases wages (World Bank, 2020). However, the existing literature documents both positive and negative effects of FDI on productivity (Aitken & Harrison, 1999; Lu et al., 2017). The ambiguity of the FDI effects might not be surprising since previous research mostly focused only on single-country cases, and the heterogeneity across countries and sectors has been ignored. In this paper, we propose a key source of heterogeneity that can reconcile the mixed empirical results. This paper defines FDI spillovers as the effect of FDI on labor productivity. We find that country variation in global value chain (GVC) participation affects the sign and the size of FDI spillovers and gives rise to the seemingly contradicting results in different countries.

Specifically, this paper sheds light on the following questions. First, if a production process is a series of country/sector ‘nodes’ joined together in a value chain, how does FDI improve sector and firm productivity through spillovers? What is the role of the GVC participation in shaping FDI spillovers? Does the FDI only affect the recipient sector? Can FDI spillovers occur through the suppliers or the buyers (forward and backward linkages)? Moreover, if there is FDI in a country/sector, can it benefit foreign suppliers and buyers?<sup>1</sup> To answer these questions, we construct a database with detailed country-sector coverage from 2011 to 2017. Our data is constructed by merging three panel data sets: (i) the fDi Markets data collected by the Financial Times; (ii) the Multi-Regional Input-Output tables (MRIOT) compiled by the Asian Development Bank which includes 61 countries and 35 sectors from 2011 to 2017; and (iii) the World Bank Enterprise survey (WBES) for over 100 countries consisting of detailed data on more than 8,000 firms. The fDi Markets data consist of rich information of the greenfield FDI projects including the source country, the parenting firm, the destination country and the sector, number of projects and the commitment amounts of FDI. The data at the firm level (WBES) and sector (MRIOT) level also allow us to check whether the relationships are robust and hold at the firm and sector

> 1 For example, if Nepal is building a hotel, will it help the tourism/real estate sector as well as construction suppliers?  Will it also be a great boost to international architecture and engineering firms as well as potential tourists?
# Table 6: Employment Profile of Loss-makers: 1992

||-----<br>Size of Loss------<br>**>1 bn HUF 0.1-1 bn HUF**<br>**Combined**|
|---|---|
|**Majority State**<br>**Firms**<br>|<br> <br>|
|Number<br>|25<br>281<br>306<br> <br> <br>|
|Employment (000s)<br>|149<br>172<br>321<br> <br> <br>|
|Av. Employment|_5960_<br>612<br>1049|
|Other<br>|<br> <br>|
|Number<br>|16<br>281<br>297<br> <br> <br>|
|Employment (000s)<br>|21<br>91<br>112<br> <br> <br>|
|Av. Employment|I313<br>324<br>377|
|**Total**<br>|<br> <br>|
|Number<br>|41<br>_562_<br>603<br> <br> <br>|
|Employment (000s)<br>|170<br>263<br>433<br> <br> <br>|
|Av. Employment|4146<br>468<br>718|

Source: Ministry of Finance and World Bank Table 7: Types of Loss-makers, Employment **and** Bankruptcy/Liquidation

||Total<br>|Total<br> <br>|No.**in**<br>||
|---|---|---|---|---|
|Category<br>|Number<br>|Employment<br> <br>|Bankrp/Liqdt<br>|Employment<br>|
|(i)<br>|113<br>|16,400<br> <br>|60<br>|10,100<br>|
|(ii)<br>|242|127,800<br>|140|62,900|
|(iii)<br>|189|.107,500<br>|83|51,200|
|(iv)|58|81,000<br>|9|11,500|

[Note: (iv) includes firms that were profitable before deducting interest or depreciation] Source: Ministry of Finance and World Bank 16