rafmacalaba/datause-encoder-data huggingface dataset:
Managerial skill in the Vaalharts area was measured explicitly according to the **method proposed** by Burger (1971), who developed and validated a scale of 'managerial aptitude' of farmers. This scale is based on six different factors: vision, planning, record keeping, labor manageinent, budgeting and maintenance tasks. This scale was found to be positively associated with size related variables, including farm size, and rettim to assets (Groenewald, 1991; Van Schalkwyk, _et. al,_ 1993; Sartorius von Bach and van Zyl, 1992).

Scale efficiency of the individual farms in the Vaalharts area was determined using the DEA methodology described earlier.23 Figures 5 and 6 present the results of the analysis by plotting the inverse of the scale efficiency index (I/SE) against quality adjusted farm size and managerial ability. This inverse (1/SE) can be interpreted in a way similar to an average cost function: (I/SE) is a decreasing function of outputs under increasing returns to scale, and an increasing function under decreasing returns to scale.

Figure 5 shows that a whole range of farm sizes is efficient, from the smalles **t** o the largest. However, there is evidence of econornies of scale for very snall farms, with the mnajority of inefficient farms being on the small side. This result differs from the previous findings where no economies of scale were found However, farn sizes in Vaalharts are much smaller, making supervision of labor easier and less expensive. On the other hand, mnachinery is the slack variable for many of the small farms that are scale inefficient. Indivisibility **seems** to play some role here, particularly with respect to tractors. To perform the necessary tasks on even the smallest farms in the sample, at least two tractors are required, as some of the tasks has to be performed simultaneously. While combine harvesters and other large rnachinery can be contracted or hired, this is not possible for Iractors within the present farm support structure. Two small tractors can work areas of up to 50-60 ha easily **if** managed properly; therefore, the lumpy input argument seems to apply for small farm sizes. But, this argument applies only to some farms: roughly one third to one half of the small farmers are scale efficient. Upon further investigation it seems that these farm managers found a way around the lumpy machinery input **problem** by entering into sharing arrangements and/or diversification of their operation to minimize the need for simultaneous use of machinery. Management seems to be important in this respect.

> 23 The procedure is similar to that used by Chavas and Aliber (1993) who analysed efficiency of grain and dairy farmers in Wisconsin.

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6 the actual height variations along local vertical structures. Since VE can still include vertical vegetation structures such as trees or hedges, all VE-candidate pixels dominated by vegetation are masked out. For this purpose, an ensemble of support vector regression models is trained separately on the NDVI ~~m~~ ax layer for all K ̈oppen-Geiger climate zones to estimate the percent impervious surface (IMP) within each WSF2019 pixel. Based on the resulting WSF-IMP, all VE candidate pixels showing a value _<_ 10 (meaning that more than 90 % of the pixel area is covered by vegetation) are masked out. To produce the final BH product, all local height differences observed for the remaining VE pixels are then spatially aggregated from their 12 m resolution to a 90 m cell by calculating the mean of all VE heights observed within this cell.

As reported in Esch et al. (2022), the BH layer of the WSF3Dv1 shows certain inaccuracies due to the systematic underestimation for the heights of high-rise ( _>_ 50 m) buildings. In dense, high-rise areas, complex and compact morphological structuring leads to multiple scattering, layover, and shadow effects causing ambiguities in the SAR signal, which ultimately results in errors in the absolute heights provided by TDX-DEM.

Thus, information from Emporis (EM) for the year 2019 is now integrated to create an enhanced version of World Settlement Footprint 3D (WSF3Dv2). EM is the most complete collection of global skyscraper and high-rise building data (see Appendix Section B for details). They rely on information provided by the industry for “thousands of cities worldwide. Emporis (2022) collects information about the full life-cycle of each building. The database (last accessed 09-07-2022) contains information for 781,136 completed tall buildings.

Technically, the improvement of the BH layer is based on a correction factor (fxy) calculated using the relative differences between spatially overlapping BH values derived from WSF3Dv1 (BHv1), and EM building height values (BHEM) provided for the exact geographical coordinates of all buildings as defined in eq. (1) and (2): The application of a 90% adjustment factor f _xy_ (instead of directly using the EM-provided height at the xy location) is dictated by the need to prevent the original, license-protected EM information from being reconstructed from the resulting BHv2 layer.

In a second processing step, EM is also used for BH outlier detection and removal. As EM is supposed to be globally complete for all buildings _>_ 100 m (see Appx. Section B), BHv1 pixels showing a BH _>_ 100 m and not having any EM point in a 3x3 neighborhood (BHv1xy _∈/_ EM), are considered as outliers. However, as the WSF2019 settlement mask indicates the presence of buildings at this location, the actual BH value is not completely removed, but drastically reduced by applying an adaptation factor of 0.01 to the current BH value as shown in eq. (3):
bank lending to local governments are displayed graphically in Figure A2.<sup>33</sup> Prior to the Law, relative lending to local governments and firms followed a relatively similar trend across banks. However, in the quarters following the implementation of the FD Law, banks ex ante more exposed to local public debt begin contracting relatively more their lending to local governments.

# **4.3. Impact of the FD Law on Firm-Level Outcomes**

Finally, we examine the impact of public debt limits on real outcomes of firms. To do this, we apply our identification strategy on firm-year level data obtained from Orbis. With this data, we compare changes in the real activity of firms across states of varying ex ante indebtedness before and after the FD Law. Since Orbis identifies the municipality where the firm is headquartered and given that bank lending tends to be local, we additionally examine heterogeneity in firms’ real activity across municipalities of varying exposure to the FD Law. We define the exposure of a municipality to the FD Law by averaging the exposure to local government debt of the banks that operate in the municipality. Our intuition is as follows. If changes in credit supply are concentrated in banks that were ex ante more exposed to local public debt, then firms headquartered in municipalities where these banks operate should be relatively more impacted by the FD Law. Our specification is outlined in equation 6.

The outcomes of interest _yf,y_ in equation 5 correspond to the total liabilities, assets, fixed assets, and sales (all in logs) of firm _f_ in year _y_ . The variable _DebtBankMunim,Mar16_ is our measure of exposure of a municipality to the FD Law and is calculated as the weighted average of _BankExposureGovs,b,Mar16_ of the banks operating in municipality _m_ in March of 2016. All other variables are as defined above. We include year fixed effects to control for yearly shocks, as well

> 33 The test of non-linear trends is based on regressions akin to equation 3, where we relate the loan-level outcome to a set of interactions of the ex-ante exposure to local government debt of banks with monthly dummy variables, and further include bank and time fixed effects. Figure A1 plots the coefficients and respective confidence intervals of these interactions. In results not shown, we also confirm that our loan-level outcomes follow parallel linear trends across states of varying ex-ante indebtedness and banks of varying exposure during the pre-reform period.

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Krugman (1979), Flood and Garber (1984) and Sargent and Wallace (1981). This eventually led to a much bigger public external debt burden when events came to a head on August 17, 1998.

# 1.1 Global Context

In the _Emerging Market Crises Hall of Fame_ --if there ever were such a thing--Russia 1998 would surely be assigned a place of distinction. It was preceded by the East Asian crisis of 1997-98, which began with the collapse of the Thai baht in July 1997, and then spread to envelope many of the prominent economies of East Asia. It was followed by Brazil in January 1999 and then Turkey and Argentina in 2000-01. Contagion from Russia 1998 led to a substantial rise in spreads on sovereign bonds in emerging markets and on long-term corporate bonds in industrial countries as well as a big increase in the volatility of these spreads. A Bank of International Settlements poll of market participants described the joint event of the August 1998 Russian meltdown plus the bailout of the hedge fund Long-Term Capital Management (LTCM) the following month as the “worst crisis” in recent times.<sup>3</sup> A review of the events suggests that LTCM’s near-collapse was precipitated in part by the Russian meltdown. Dungey et al. (2006, p. 3) note that bond spreads, especially in emerging markets, spiked after the Russian devaluation and default as global credit risks were re-priced; LTCM had taken big bets on the expectation of falling spreads. Jorion’s (2000) post-mortem indicates that LTCM took a substantial hit soon after Russia’s August 17, 1998 meltdown. He reports that credit spreads rose sharply following the Russian “bombshell” while stock markets plunged. “LTCM lost $550 million on August 21<sup>st</sup> alone” and 52 percent of its December 31 1997 value had been erased by the end of August 1998. Had LTCM been allowed to fail, the risks to the US and global financial system were judged to be catastrophic because of both its onbalance sheet ($125 billion) and off-balance sheet ($1.25 trillion in various derivatives) transactions. The forced deleveraging to meet margin calls would have resulted in a downward 3
Table 3 presents summary statistics and correlations for our main variables from the formal manufacturing Census. Table 3 shows that after winsorizing, Establishment Size ranges from 1 to 1,616 employees with a mean of 121 employees. The mean Establishment Size Ratio is 1.38, suggesting that the average firm in our sample is 1.38 times the size of its birth cohort. 62% of the firm-year observations are classified as small firm industries, 23% are classified as labor intensive and 42% are classified as being in industries that are highly dependent on external finance. Sixty-nine % of the establishments are in states with flexible labor regulations and 73% are in financially developed states.

Panel B presents the correlation matrix between these variables. The sample correlations with age dummies show that older firms are larger and growing faster. When we look at the industry classifications we find that none of the correlations between the industry categories are larger than 0.19, suggesting that the three measures – Small Firm Industry, Labor Intensive, and EFD – are capturing three different aspects of industry technology. Overall we find that small firm industries are positively correlated with labor intensive industry and negatively correlated with external finance dependence. The correlations with Establishment Size and Establishment Size Ratio show that firms are larger and grow faster in large firm dominated industries, in capital intensive industries and in industries that are not highly dependent on external finance. The raw correlations also show that firms in financially developed states are larger and grow faster. On the other hand, while firms in states with flexible labor regulations are smaller, they are growing faster.

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Table F.2. Impacts of PEP on Rights and Services

||Rights and<br>Services<br>(Index)|SISBEN<br>Access|Subsidized<br>Healthcare|Financial<br>Products|Transfers from<br>Government|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|1[PEP_i_ = 1]|3.968***|0.481***|0.238***|0.435***|0.129**|
||(0.574)|(0.099)|(0.081)|(0.086)|(0.082)|
|q-values|[0.001]|[0.001]|[0.002]|[0.001]|[0.024]|
|Obs. left|527|573|453|537|534|
|Obs. right|1067|1,261|936|1,210|1,200|
|Observations|3,385|3,742|3,338|3,756|3,760|
|Mean values (Non-RAMV refugees)|0.000|0.023|0.015|0.030|0.065|
|Outcome Level|Individual|Household|Individual|Household|Household|

_Notes:_ Dependent variables: (i) Rights and Services (Index) is constructed using the outcome variables of columns (ii)–(vi) using the methodology of Kling et al. (2007); (ii) SISBEN Access is a dummy [=1] if the respondent is enrolled in the vulnerability assessment system; (iii) Subsidized Healthcare is a dummy [=1] if the respondent benefits from public healthcare; (iv) Financial Products is a dummy [=1] if the respondent has a savings account or other financial or banking products; (iv) Transfers from Government is a dummy [=1] if the respondent receives transfers from any official social assistance program. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

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# 7. References

- Brandt, Loren, Gueorgui Kambourov, and Kjetil Storesletten. 2020. “Barriers to Entry and Regional Economic Growth in China,” CEPR Press Discussion Paper No. 14965. <u>https://cepr.org/publications/dp14965</u>

- Beuselinck, Christof, Lihong Cao, Marc Deloof, and Xinping Xia. 2017. “The Value of Government Ownership During the Global Financial Crisis,” _Journal of Corporate Finance_ 42(February):481493.

- Cerdeiro, Diego A. and Cian Ruane. 2022. “China’s Declining Business Dynamism,” IMF Working Paper WP/22/32, International Monetary Fund.

- Dall’Olio, Andrea Mario, Tanja K. Goodwin, Martha Martinez Licetti, Ana Cristina Alonso Soria, Maciej Adam Drozd, Jan Alexander Kazimierz Orlowski, Fausto Andres Patino Pena, Dennis Sanchez Navarro. 2022(a). “Are All State-Owned Enterprises Equal? A Taxonomy of Economic Activities to Assess SOE Presence in the Economy.” Policy Research working paper No. WPS 10262, Washington, D.C.: World Bank

- Dall’Olio, Andrea; Goodwin, Tanja; Martinez Licetti, Martha; Orlowski, Jan; Patiño Peña, Fausto; Ratsimbazafy, Francis; Sanchez-Navarro, Dennis. 2022(b). “Using ORBIS to Build a Global Database of Firms with State Participation.” Policy Research Working Papers;10261. World Bank: Washington, DC.

- EBRD (European Bank for Reconstruction and Development). 2020. “Life in Transition Survey, Transition - -

- Report 2020-2021: The State Strike Back”, <u>https://www.ebrd.com/publications/transition report 202021.</u>

- EC (European Commission). 2015. “Macroeconomic imbalances Country Report – Romania 2015,” Occasional Papers 223. Directorate-General for Economic and Financial Affairs,

- EC (European Commission).  (2020), European Semester Country Report Romania 2020, European Commission.

- Harrison, Ann, Marshall Meyer, Peichun Wang, Linda Zhao, and Minyuan Zhao. 2019. “Can a Tiger Change Its Stripes? Reform of Chinese State-Owned Enterprises in the Penumbra of the State” NBER Working Paper 25475, National Bureau of Economic Research, http://www.nber.org/papers/w25475.

- IMF (International Monetary Fund). 2019. “Reassessing the role of state-owned enterprises in Central, Eastern, and Southeastern Europe”, IMF Departmental Paper, Vol. No. 19/11.

- IMF (International Monetary Fund). 2021. “State-Owned Enterprises in Middle East, North Africa, and Central Asia: Size, Role, Performance, and Challenges”, IMF Departmental paper DP/2021/019, Washington, DC: International Monetary Fund.

- Iootty, Mariana, and Seidu Dauda. 2017. “Assessing Firm Markup in China: First Insights into the Manufacturing Industry,” unpublished.

- Iootty, Mariana, Georgiana Pop, and Jorge Pena. 2020. “Corporate Market Power in Romania: Assessing Recent Trends, Drivers, and Implications for Competition.” Policy Research Working Paper No. 9487. Washington, DC.: World Bank.

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**Figure 4. Government revenues (composition and percentage of GDP)**

Domestic revenue (composition by taxes) <!-- Start of picture text --> Taxes as % of GDP<br><!-- End of picture text --> <!-- Start of picture text --> 100 12<br>10<br>80<br>8<br>60<br>6<br>40<br>4<br>20<br>2<br>0 0<br>2009-2011 2012-2013 2009-2011 2012-2013<br>Other domestic revenue Fishing licences Taxes on cashew Other domestic revenue Fishing licences Taxes on cashew<br><!-- End of picture text --> Source: IMF and World Bank.

**<u>Cost break-down:</u>** Table C1 in Appendix C shows the theoretical cost break down of raw cashews presented by ANCA for 2014. Table 3 extracts and summarizes the relevant components, which are used later on in the simulations adapted to data from 2013. First, exporter costs must be at least 101.28 USD/ton, while trader costs must be at least 21.28 USD/ton (they may be higher if additional labor costs, financial costs, etc., are taken into account). Second, the FOB price is set at 800 USD/ton and the farm-gate price is estimated to be 351 USD/ton (the average for 2013). Third, taxes are 199 USD/ton (11 percent of the reference FOB price plus FUNPI). These three conditions put bounds on costs and margins of market players, and also on the trade price.

**Table 3. Simplified price structure of raw cashews. Adaptation to prices 2013**

||**Estimated ca**|**mpaign 2014**|**Adapted to**|**2013prices**|
|---|---|---|---|---|
|**Summary**|**XOF/ kg**|**USD/ ton**|**XOF/ kg**|**USD/ ton**|
|Producerprices|250.00|531.91|165.00|351.06|
|Intermediary (identified)costs|10.00|21.28|10.00|21.28|
|Intermediarymargin|10.00|21.28|||
|Intermediate Bissauprices|270.00|574.47|||
|Exporter(identified)costs|47.60|101.28|47.60|101.28|
|Exporter margin|30.00|63.83|||
|Export taxes|93.95|199.88|93.95|199.88|
|**EXPORT PRICE - FOB PORT BISSAU**|**441.55**|**939.46***|**376.00**|**800.00**|
|FOB Reference price: 850 USD/ton.<br>* Recentlyupdated exportprices fo|Exchange rate:<br>r 2014 season r|470 XOF /USD<br>ound 1050-11|.<br>00 USD/ton.||

Source: own elaboration based on ANCA (2014) and World Bank. See details in Appendix C.

**<u>Summary of the cashew value chain:</u>** Table 4 summarizes the data on the total volume produced by farmers, which is traded through formal channels (138,000 tons are exported and 2,000 tons are sold for processing, totaling 140,000 tons). Production valued at farmer prices gives a total

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welfare losses from the FUNPI contribution could also be compensated by a 100 percent subsidy. While this seems unfeasible, the exercise at least quantifies the scenario and can be perhaps complemented with other interventions (maybe in other links of the supply chain) and assistance programs to the farmers so that, even if they are individually difficult to implement, they can be jointly beneficial.

# **6. Conclusions and recomendations**

This paper explored the impact of changes in cashew prices received by exporters on farm-gate prices and welfare in Guinea-Bissau. We built a theoretical model of supply chains in export agriculture to capture interaction between exporters, traders, and farmers, and adapted it to the GNB case. We used the model and the Guinea-Bissau household survey data to address several issues.

First, we analyzed the effect of a standard shock to export prices and the introduction of a new tax on cashew exports on margins along the value chain, and within farmers along the income distribution. Given the current competitive conditions in the cashew sector in GNB, the introduction of the FUNPI contribution has strong effects on farm-gate prices, as farmers absorb about 80 percent of the tax (while exporters take up 13 percent and traders absorb the remaining 7 percent). Symmetrically, farmers benefit the most from increases in international prices and also are mostly exposed to world uncertainties (and also taxes). But this effect is uneven across households: poor rural households are more exposed than rich rural and poor urban households, while the least exposed are richer urban households.

Second, we simulated the combined effect of the FUNPI contribution and improvements in competition in cashew trading. Complementary policies aimed to reduce costs at the export and trader links of the supply chain do help in softening the farm-price reduction, and hence welfare of farmers, caused by the FUNPI contribution. Therefore, they are all preferred to the baseline scenario of the FUNPI contribution because farm-gate prices are higher in the equilibrium with complementary policies. Farm-gate price full recovery is possible if policies reduce intermediation costs, which pass through to farm-gate prices throughout (albeit imperfect) competition. We provided two examples, one that reduces costs at both segments of the supply chain and another that levels the playing field in a specific segment (exporters). On the other hand, cost-reducing policies are more effective than entry-inducing ones, as pushing the sector to perfect competition (for existing cost structures) is insufficient to recover pre-tax farm-gate prices and farmers’ welfare.

Third, we simulated an interaction between the FUNPI contribution and an increase in valueadded processing capacity. Fostering cashew processing creates added value through a displacement of volume from exporters to processors. A significant displacement (in size) reflects on farm-gate prices and farmers’ welfare, although not enough to revert them.

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