rafmacalaba/datause-encoder-data huggingface dataset:
control for selection bias in a balanced panel of firms and falsification tests with firms located in SEZs. To address reverse causality concerns related to the business environment and firm performance, we also perform an instrumental variable regression exploiting the revenue sharing arrangement between the central government and provinces. We find that our results hold for the overall PCI index but there might be a reverse causality concern between corruption and firm productivity.

# 7 References

Ackerberg, D., K. Caves, and G. Frazer (2006) “Structural identification of production functions”. Mimeo. UCLA.

Amiti, M. and J. Konings (2007) “Trade liberalization, intermediate inputs, and productivity: Evidence from Indonesia,” _The American Economic Review_ , 1611–1638.

Asker, John, Allan Collard-Wexler, and Jan De Loecker (2014) "Dynamic inputs and resource (mis) allocation," _Journal of Political Economy_ 122, 1013-1063.

Bai, Jie, Seema Jayachandran, Edmund Maleksy, and Benjamin Olken. (2016) “Does Economic Growth Reduce Corruption? Theory and Evidence From Vietnam.” _NBER Working Paper_ No. 19483.

Bartelsman, Eric J., John C. Haltiwanger, and Stefano Scarpetta (2009) “Cross-country differences in productivity: the role of allocation and selection,” _NBER Working Paper_ No. 15490.

Bartelsman, Eric, John Haltiwanger, and Stefano Scarpetta (2013) "Cross-country differences in productivity: The role of allocation and selection," _The American Economic Review_ 103, 305-334.

De Loecker, J. (2007) “Do exports generate higher productivity? Evidence from Slovenia,” _Journal of International Economics_ , 73, 69–98.

De Rosa, D., N. Gooroochurn, and H. Gorg (2010) “Corruption and productivity: firm-level evidence from the BEEPS survey,” _World Bank Policy Research Working Paper Series_ , No. 5348.

Fernandes, A. M. (2007) “Trade policy, trade volumes and plant-level productivity in Colombian manufacturing industries,” _Journal of international economics_ , 71, 52–71.

Fisman, R. and J. Svensson (2007) “Are corruption and taxation really harmful to growth? Firm level evidence,” _Journal of Development Economics_ , 83, 63–75.

Giordano, Raffaela, Sergi Lanau, Pietro Tommasino, and Petia Topalova. (2015) "Does Public Sector Inefficiency Constrain Firm Productivity: Evidence from Italian Provinces." _IMF Working Paper_ 168.

Greenstone, Michael, Richard Hornbeck, and Enrico Moretti (2010) "Identifying agglomeration spillovers: Evidence from winners and losers of large plant openings," _Journal of Political Economy_ 118, 536-598.

Greenstone, M., J. A. List, and C. Syverson (2012) “The effects of environmental regulation on the competitiveness of US manufacturing,” _NBER Working Paper_ , No. 18392.

Hsieh and Klenow (2009) “Misallocation and Manufacturing TFP in China and India,” _Quarterly journal of Economics_ , 124, 1403-1448.

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**Figure 10. Average energy savings rate by type of facility**

<!-- Start of picture text --> 25<br>22.6<br>21.5 21.1<br>20 19.7 19.3<br>16.7<br>15<br>10<br>5<br>0<br>Office Hospital School, Community Athletic Multiple<br>library, center, field, gym, facilities<br>institute, hotel, pool<br>museum welfare<br>facility<br>Energy savings rate (target, %)<br><!-- End of picture text --> _Source_ : Author’s survey data.

**Figure 11. Distribution of building-specific energy use in the sample**

<!-- Start of picture text --> 0 100 200 300 400 500<br>Building-specific energy use (kWh/m2)<br>.008<br>.006<br>.004<br>Density<br>.002<br>0<br><!-- End of picture text --> _Source_ : Author’s survey data.
# **Investment in Job Training: Why Are SMEs Lagging So Much Behind?**<sup>1</sup>

Rita K. Almeida The World Bank Reyes Aterido The World Bank JEL codes: J24, D24 Keywords: On-the-job training, firm size, firm level data, developing countries.

> 1 We thank the research assistance of Marta Faria at an early stage. The authors benefited from comments provided by Andreas Bloom, T. Gindling, Yoon Cho, David Newhouse, David Margolis and Pedro Carneiro. We also thank participants at a World Bank HDNSP seminar and at the 5<sup>th</sup> IZA-WB conference on Employment and Development in South Africa. This project would not have been possible without the financial support of a World Bank Development Economics Research Support Grant. Contact author: Rita K. Almeida, World Bank (ralmeida@worldbank.org).
**_Table 3: Response rates by study groups_**

|**Study**<br>**Group**|**Consent**<br>**granted**<br>**(%)**|**N**|**Refused to**<br>**participate**<br>**(%)**|**N**|**Temporarily**<br>**out of the**<br>**community**<br>**(%)**|**N**|**Out of**<br>**reach/not**<br>**found**<br>**(%)**|**N**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|Group||||||||||
|One|97.8%|400|1.00%|4|0.5%|2|0.07|3|409(100%)|
|Group||||||||||
|Two|96.4%|400|3.4%|14|0.0%|0|0.02|1|415(100%)|
|Group||||||||||
|Three|97.8%|400|1.2%|5|0.0%|0|0.01|4|409(100%)|
|Group||||||||||
|Four|97.8%|400|2.0%|8|2.0%|1|0|0|409(100%)|
|Control||||||||||
|Group|100.0%|400|0.0%|0|0.0%|0|0|0|400(100%)|
|Total|97.9%|2000|1.5%|31|0.1%|3|0.4%|8|2042(100%)|

# B. **<u>Data collection</u>**

Facility administrative records from the e-health database collected at the end of the study period serve as the primary data source on whether the study participants came for hypertension and diabetes screening. In addition, a baseline survey for the impact evaluation was administered face-to-face to study participants during the visits by the study fieldworkers. Its main objective was to collect socio-demographic variables not available in the e-health database to assess the balance across study groups at baseline and to serve as control variables in the estimation models. For intervention groups 1-4, the baseline survey took place between July 22, 2019, and September 2, 2019. It consisted of administering a short socio-demographic survey to the sample of 1,600 study participants. Fieldworkers administrated the baseline survey in the same visit that they distributed screening invitations (and vouchers for group 3).

To avoid contamination of the control group (n=400) from asking specific questions about diabetes and hypertension screening, the baseline survey was not administered to the control group between July and September 2019 but instead was administered in January 2020, at the end of the intervention period. This short time difference in administering a survey consisting of questions linked to fairly stable socio-demographic variables was deemed preferable to the risk of changing the behavior of the control group by asking them health specific questions and in particular, asking them why they had not screened in the last 12 months.

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Table 6. Projected Impact of Cotton Export Expansion on SSA Cotton Market, to _2000/01._

|**SSA**Productiour'00 o:4ns)|
|---|
|**Change04602**<br>191528<br>%435223|
|<br> <br><br>**_SSA_**Exports**('000 tons)**<br>|
|Shock77839097<br>00<br>1,3<br>221321,2107|
|**Change**<br>`6.0<br>61219<br>1928<br>3<br>2<br>3|
|<br> <br> <br> <br> <br> <br><br>**SSA**Real**Vau of**NCEpi<br>mlin<br>f19U$|
|%Chang.<br>0.00~.,KK.:.<5.68.~<br>12.23<br>1343<br>24.51<br>3097<br>3772<br>44.73<br>**35207**<br>**_2515_**|
|**SSA**C<br>mjt1on('000<br>**$6i).**<br> <br> <br> <br>|
|**Shock**<br>410<br>404545<br>4641<br>1<br>2<br>2<br>1|

**Source IECIT, Worl Bank.**

13
<!-- Start of picture text --> GE<br>| o —0<br>COze Generation g<br>ne<br>Intermediates_ 0.01 Other Inputs<br>A} ine_ A,}9 KFyeLt — 0.01<br>lame 0.5<br><!-- End of picture text -->

<!-- Start of picture text --> Energy K?<br>| a=¢ |<br>Fossil fuels AG Ames<br>| r=] |<br>Coal Lae Petroleum<br>| o=0 | | e=0 |<br>Acest COn6 Apetroleum COse<br><!-- End of picture text -->
To test these hypotheses, we split the sample into four productivity quartiles within each industry‐year combination and run the main specifications in these subsamples (Table 6).<sup>25</sup> We find some evidence of heterogeneity, as productivity and wage effects are the strongest for firms with a medium level productivity, with significant differences between the middle quartiles on the one hand and the lower and upper quartile on the other. Firms with very low productivity levels may lack the capabilities to serve HGFs effectively, while very productive firms may already have easy access to domestic and international markets and therefore their performance is less dependent on the local HGFs. This difference is robust to taking deciles instead of quartiles and to using the other two TFP measurements, and to employing the OECD definition of HGF instead of the one developed in the first section.

**Table 6 HGF presence for firms with different productivity quartiles**

|**Dependent:**|**L**|**abor productivit**|**y (3‐year change)**||
|---|---|---|---|---|
|**LP Quartile:**|**1**|**2**|**3**|**4**|
|HGF Downstream share|0.014**|0.051***|0.044***|0.021***|
||(2.169)|(8.017)|(6.822)|(3.275)|
|HGF Upstream share|0.002|0.012*|0.011*|0.007|
||(0.299)|(1.956)|(1.855)|(1.307)|
|HGF intra‐industry share|‐0.001|‐0.005|0.004|0.005|
||(‐0.136)|(‐0.851)|(0.702)|(0.930)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|28,633|33,084|36,946|41,931|
|R‐squared|0.269|0.258|0.269|0.228|
|**Dependent:**||**ACF TFP (3‐y**|**ear change)**||
|**Quartile**|**1**|**2**|**3**|**4**|
|HGF Downstream share|0.025***|0.060***|0.053***|0.021***|
||(3.341)|(7.656)|(7.452)|(3.160)|
|HGF Upstream share|0.002|0.005|0.009|‐0.003|
||(0.288)|(0.702)|(1.407)|(‐0.576)|
|HGF intra‐industry share|0.008|0.016**|0.014**|0.012**|
||(1.174)|(2.463)|(2.354)|(2.029)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|28,633|33,084|36,946|41,931|
|R‐squared|0.236|0.189|0.195|0.181|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch

> 25 The quartiles are created based on labor productivity. For each firm we calculate the quartile for each period, and take the modal quartile to ensure that firms do not move across subsamples.

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32. Literature concerning osusus and related financial mechanisms in West Africa appear in Rural Africana no. 2, Fall 1978.

33. Tuck reports that in her sample of 54 Senegalese households in 11 rural villages, 342 of the households had members in rotating savings and<sup>credit</sup> associations. She finds that "only women belong". (1983:68) 34. Tuck reports that in her Senegalese sample, 701 used osusu funds for ceremonies, 20X for buying clothes, and 1OZ for buying "general merchandise" (1983:69).

35. The optimum size, or the range of acceptable sizes, may vary with the task, and with the characters of the members or leaders. It might also vary by gender, by ethnic group, and by age: little is yet known on these questions.

36. In 1988, lower ranking Gambian workers in both the Ministry of Finance and Trade and the USAID mission in Banjul had unsupervised osusu groups. Why salaried workers with access to banks use osusu groups too (or instead) is a useful question for further research. It should also be asked the other way around. Convenience, diversification to avoid risks, and secrecy about one's total wealth are likely reasons.

37. That mixed-sex kafo and osusu financial groups are more common in the cities and large towns appears to result partly from the fact that salaries put some men and women, particularly single ones, into comparable financial circumstances.

38. For some case studies see Mann et al. 1989. Notable institutional experiments in group-based lending have included "solidarity groups" of Accion International in Latin America and the equally famous Grameen Bank in Bangladesh.

39. In 1986, 48Z of the money supply of The Gambia was reported to be outside of the banking system (Ramamurthy 1986: 25).

40. The postal savings system is marginally more accessible but still hard to withdraw from, and useless as a source of credit.

41. A few, including Mauritanian shopkeepers, said that they did not bank because to do so would be to charge interest, an act discouraged in Islam. However, since some of these merchants do charge interest in informal lending, and since Mauritanians in Senegal and The Gambia have recently been victim to persecutions requiring temporary flight, the stated reason is probably not to be taken at face value. 42. In the 1987 survey, when farmers were asked whether they like the idea<sup>of</sup> mobile banks, 60Z replied yes, 112 replied no, and 292 were uncommitted, some of these lacking familiarity with the idea. The Agricultural Development Bank had tried mobile banks in the early 1980s, but it was unable to sustain them before it failed.

43. The inflation and current interest rate figures are from<sup>the Ministry of</sup> Finance and Trade.
# **Table 1.  Types of fishing gear used by artisanal fishermen in Tanzania**

|_Type of Gear_|_Associated Gears_|_Comments_|
|---|---|---|
|Gillnets<br>_(Jarife)_|Drift gillnet, demersal<br>gillnet, shark nets|Used in tidal, sub-tidal, and reef areas to capture fish moving with the tides. Target species are tuna,<br>billfish, kingfish, and sharks._Have high incidental capture of turtles and dolphins._|
|Seine nets<br>_(Nyavu)_|Purse seine, ring net,<br>beach/boat seine|Use light to attract pelagic fish on moonless nights._They are usually dragged onto the beach. Beach_<br>_seine is illegal in Zanzibar and only permitted in uninhabited islets._|
|Drag nets<br>_(Kavogo Kigumi)_|Any seine net can be used as<br>a drag net|_This method is illegal._Nets may be assisted by divers to untangle, chase, and retain fish. Nets are set<br>from a boat or from the beach. May use two vessels, and run nets on the bottom.|
|Cast nets<br>_(Kimia)_||Used in sheltered areas. A circular net that is thrown over a shoal of fish or allowed to sink to the<br>bottom. Closed when retrieved. Caches small fish such as eels, catfish, mullet, silver biddy, and shrimps.|
|Scoop nets<br>_(Senga)_||Used in catching small pelagic fish in the light attracting fishery. Mean catch rate is 60 kg per boat per<br>day.|
|Basket traps|_Dema_(large basket),_towe_<br>(small basket)|Hexagonal in shape with a single opening. Traps are usually set in seagrass beds, among wild or cultured<br>seaweed, or coral reefs. The_dema_mean catch rate is 1-2 kg per trap.|
|Fixed traps|_Weir, fence, uzio, wando_|Usually made of mangrove stick or palm frond midrib. They are semi-permanent stake tropes that are set<br>in intertidal areas especially in sheltered areas. Catch rate varies from 12-100 kg per trap.|
|Handline<br>_(Mshipi)_||Probably found on every fishing vessel. Can be used from a vessel or from shore. Currently important as<br>a sport fishery in the tourist industry at several fishing sites.|
|Longlines<br>_(Dhulumati)_|Surface long line, bottom<br>long line|Mostly for catching tuna especially by industrial vessels in offshore waters. Used by artisanal fishermen<br>for catching demersal fish.|
|Troll lines<br>_(Kurambaza)_||Lines are usually tailed by a vessel near the surface or at certain depth and use natural or artificial lures<br>to target specific species, such as tuna and mackarel.|
|Spear, stick<br>_(Mkuki, kijiti)_|Used for octopus fishing|A spear is made up of a stick with a sharp end or a piece of iron is inserted at the tip._Kijiti_is a stick.<br>Collection is done by diving or on exposed reefs. Catch rate of 4-7 kg/person/day.|
|Harpoons<br>_(Bunduki, mshare)_|Spear gun, used by skin or<br>scuba divers|_Illegal_ _fishing technique_which is banned in Tanzania, but still in use. Used for catching lobster and reef<br>fish.|
|Poison fishing,<br>dynamite fishing||_These practices are illegal_and have been banned because they not only kill target fish, but also other<br>fish, shellfish, and coral in the area. These methods are particularly harmful to larvae and juvenile<br>organisms.|

Source: Adapted from Jiddawi and Ohman (2002) 7
**Figure 6: Cluster buffer, sub-basins, and deforestation since 2000**

As precipitation and temperature have been known to be significant predictors of DD (Halpenny et al., 2012; Escamilla et al., 2011), we also construct climate controls for each cluster within its buffer area. In particular, we construct average annual temperature in Celsius and total precipitation in meters for each cluster, using the ERA5-Land product (Muñoz-Sabater et al. 2021). This data is provided as monthly aggregates at 11,132m resolution. To match the temporal resolution of the deforestation data, we further aggregate this data to create yearly means for temperature and total monthly precipitation.

# **2.4. Data for Peru Case Study**

Peru was chosen as a case study for our analysis, given that it is one of the top ten forest-rich countries in the world, which has experienced both rapid changes in land use and health outcomes over the last two decades. Compared to other countries in Latin America, only Brazil has larger forest cover, with Peruvian forests representing 54 percent of the country’s territory (FAO and SERFOR, 2017). However, the country has experienced rapid tree cover loss of about 3.15 million hectares between 2004 and 2020, equivalent to a 4 percent decrease in tree cover since 2000, resulting in 2.03 Gt of uncompensated CO2e emissions.<sup>8</sup> Five Amazon regions - Loreto, Ucayali, San Martin, Madre de Dios, and Amazonas - were responsible for 78 percent of all tree cover loss,<sup>9</sup> which was driven by the growing demand for fuel wood, exports of hardwoods, and expansion into the Peruvian Amazon for cultivation, livestock, and the extraction of minerals (Swenson et al., 2011; Piu and Menton, 2014).

Despite tree cover loss, child health indicators have improved in the last two decades. Particularly, the incidence of diarrhea and stunted growth decreased significantly more in children in rural areas than in urban areas due to various government- and donor-supported interventions and social programs. Specifically, the Peruvian government began a handwashing initiative in 2003 with technical assistance from the World Bank through public education and media messaging to increase handwashing among mothers and young children,<sup>10</sup> which

> 8 Global Forest Watch. “Tree cover loss in [Peru/Madre de Dios]” from www.globalforestwatch.org.

> 9 Global Forest Watch. “Location of tree cover loss in Peru”. From www.globalforestwatch.org.

> 10 The Global Public-Private Partnership for Handwashing (2003-2010) led by the World Bank was implemented by Peru, Senegal, Tanzania, and Vietnam.

12
# **1. Introduction and motivation**

It is well established in the literature that exporters are larger and more productive than nonexporting firms. Recent literature has also shown that exporters achieve better labor market outcomes than non-exporters. Firms that have a larger share of exports in total sales are found to have higher levels of employment and pay higher wages (Bambrilla, Lederman and Porto 2012). Cebeci, Lederman and Rojas (2013) attempt to identify a causal effect of changes in the structure of sales on labor market outcomes using exogenous fluctuations in exchange rates combined with firms’ initial exposure to various markets as instrumental variables. Under this approach, increases in exports as a share of total sales in Turkish firms are associated with increases in employment levels within firms, but a higher export share generates no observed impact on wages.

In this analysis, we explore whether changes in firms’ sales structure – exporting versus selling to the domestic market, as well as by export destination – matters for labor outcomes. We use firmlevel data for Georgia to estimate the quasi-elasticity of employment and wages with respect to the share of exports in total sales. Following the methodology of Cebeci, Lederman and Rojas (2013) allows us to test for a causal effect of firms’ growth in exports relative to domestic sales (measured by the change in a firm’s share of exports in total sales<sup>2</sup> ) on their labor market outcomes, namely employment level (total employment, as well as female employment) and average wages (average for total firm employment, and for female employment).<sup>3</sup> In addition, we assess whether exporting to different destination markets such as the European Union or high-income countries has different impacts on labor-market outcomes.

The remainder of the paper proceeds as follows. Section 2 presents the methodology and Section 3 describes the firm-level data source used. Section 4 discusses stylized facts about exporters in Georgia. Section 5 concludes with the results of the analysis.

# **2. Methodology**

> 2 The share, rather than the level, of exports is used to capture the structure of firms’ sales (as only using levels would capture size effects).

> 3 Whereas an OLS regression can identify statistically significant correlations between changes in the structure of firms’ sales and employment and wage growth, we employ an instrumental variables approach to identify causal relationships. We use exogenous fluctuations in exchange rates combined with firms’ initial exposure to various markets as instrumental variables (see Cebeci, Lederman and Rojas 2013).

2