rafmacalaba/datause-encoder-data huggingface dataset:
- 21 -

industries, and of SNM's organized<sup>in technology intensive industries.</sup> Furthermore, newly organized SMF'e<sup>are given a 50 percent deduction from</sup> property taxes for five years and<sup>a 50 percent reduction in acquisition and</sup> registration taxes for two years.<sup>Tax incentives for companies investing</sup> in newly organized SMF's include<sup>tax free reserves for investment losses,</sup> 100 percent exemption from capital<sup>gains tax and a special 10 percent tax</sup> rate on dividend income.

# D. Incentive Effects of Tax ArranRements<sup>for Exports</sup>

Establishing the exact incentive effects of these<sup>measures and how</sup> they have changed over time is difficult.<sup>For the analysis ve make here,</sup> we draw heavily on a recent study<sup>by Kim (1988) that estimated the export</sup> subsidy effect of a range of tax<sup>and non-taxpolicies</sup> in Korea over the period 1958-83 (see Table 5). We use<sup>these estimates in our subsequent</sup> model calculations of the effects<sup>of Korean tax policies on outward</sup> orientation and growth. Kim included<sup>only those policies for which both</sup> consistent time series data were<sup>available and which were quantitatively</sup> significant. These included direct<sup>cash subsidies, exchange rate premia,</sup> interest subsidies, indirect tax<sup>exemptions, tariff exemptions and direct</sup> tax reductions (exclusive<sup>of accelerated depreciation provisions and</sup> reserve funds both for developing<sup>export markets and for covering export</sup> losses).

The export subsidy effect of direct<sup>tax exemptions was derived as</sup> the difference between tax liabilities<sup>in the absence of any such</sup> exemptions and actual direct tax<sup>payments. The incentive effect of</sup> different interest rates was determined<sup>in any analogous fashion. The</sup> interest subsidy was the difference between<sup>the interest paid at the non-</sup>
(unobservable in the data) was one of our two criteria of selecting countries for this study. The selection and endogeneity concerns remain even without this conditionality. We think it is logical to expect that the positive selection bias is low in the fixed effects specification since inherent features of firms that determine their ability to go through some application processes is likely not strongly time-variant (and are thus mostly picked up by firm fixed effects). On the other hand, we think that negative selection bias is likely present, given the discussion above. We thus consider our estimates from the panel regression to likely be lower than the true effect of the government support. For this reason, we avoid commenting on the estimated size of the effect, and we instead focus on whether or not the data is suggestive of the effect that is above zero.

To further address the selection bias, we re-estimate both methods with a sample that is restricted in a specific way. A perennial and important question in the context of assessing the effectiveness of non-random policy is as follows: why did not all the eligible firms get the support? In other words, why did only some firms that otherwise look similar to the rest receive the support and others did not? The answer to this question has a direct implication on whether the control group is appropriate and thus whether the effectiveness can be estimated without bias. In an ideal environment for estimating the effectiveness of support, one would randomize this support, so the answer to the above question would be “pure chance”. To approximate this ideal control group as much as possible with available data, we restrict the sample as follows. We exclude firms that seem to be outside of the process of government support, perhaps through their ineligibility, or lack of need, or unawareness, or some other reason. The exact variables used for this purpose are described in detail in Section 4.6, along with a brief discussion of the corresponding caveats.

# **4. Data and Main Variables**

This section describes the data sources, discusses the variables we use, and provides summary statistics for both El Salvador and Georgia.

## **4.1. Data**

As a baseline pre-pandemic data source, we use the most recent Enterprise Surveys (ES) completed in El Salvador in 2016 and Georgia in 2019. The ES are firm-level surveys with a representative sample of a country’s private sector, implemented through a standard methodology with 10
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The challenge in this policy area is to provide<sup>effectivepublicsupportforS&T</sup> through a variety of channels simultaneously. First, governments must set national priorities and assure that the S&T system<sup>isdiversifiedanddecentralized,again,</sup> balancing concentration of resources in centers<sup>of excellencewithstimulatinglarge-scale</sup> and dispersed research efforts. Second,<sup>theymust play majorrolesinorganizingand</sup> sustaining education systems and sectoral<sup>ministriesthateitherproduceorrequireS&T</sup> inputs and outputs. This is done through<sup>balancingtheir rolesas producers,consumers,</sup> financers, managers, and regulators/controllers<sup>ofqualityfor researchandits</sup> accompanying human resources training.<sup>Third, it is alsonecessaryfor govenunentsto</sup> play a critical role in establishing the rules<sup>of thegameof S&T resourceallocation.A</sup> major part of this involves promoting transparency,<sup>objectivity,andpeer reviewand</sup> evaluation procedures, assuring that<sup>meritandperformancearethecriteriafor</sup> determining how to award discretionary<sup>researchfunding.Fourth,governmentsmust</sup> seek to inform their policy decisions with<sup>thenecessaryscientificdata.</sup> Finally, as monitors and evaluators of a transparent<sup>andimpartial S&T system, the governmenthasa</sup> role to ensure that women and other traditionally-excluded<sup>groupshaveaccesstothe</sup> same opportunities as men to build and advance<sup>careersinscienceandtechnology.</sup> The gender dimension of S&T in development<sup>isonerequiringparticularattentionfrom</sup> public sector agents as women are egregiously<sup>under-representedinmostscience-and</sup> technology-related professions and global poverty and its consequences fall 17
# _Data and methodology_

To carry out this analysis, we draw on micro data from 238 household surveys for 15 Latin American countries spanning the period from 1992 to 2013.<sup>4</sup> For these country-years, we use harmonized data from SEDLAC, the Socio-Economic Database for Latin America and the Caribbean (CEDLAS and the World Bank 2015), which include homogenous variables for school attendance, economic activity, and socio-demographic characteristics.<sup>5</sup> We use these data to produce internationally comparable statistics on ninis, defined as those individuals aged 15 to 24 who were neither enrolled in school nor working at the time when the relevant household survey was conducted.

Two qualifications deserve mention before we present the results. First, like the underlying variables from the original household surveys, nini status is self-reported. Second, not all countries are equally represented in the data used for this analysis, since household surveys are conducted with varying frequency across countries and the quality of harmonization varies across country-years. We exclude countries for which the number of years between survey data available is so great that interpolating would produce unreliable results. The table in Annex 1 specifies the particular surveys processed for each country.

To construct the averages, we began with the full period for which SEDLAC data are available— 1980 to 2013 for the region as a whole. However, because several countries have missing data from the beginning and end of this period, particularly for the disaggregated analysis, we confine most of our analysis to 1992-2010, which is the longest time period with reasonably complete data. This year range provides nearly two decades of evolution of the nini phenomenon across the region, while also minimizing the need to interpolate missing years and to extrapolate back for countries that lack survey data in the early years.

The regional figures presented in this note are the unweighted averages for the same 15 Latin American countries throughout. We focus on unweighted averages because they capture more accurately the experience of the average country in the region and avoid letting the largest countries 4 Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, Panama, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela. Because of data availability, this report focuses primarily on Latin America and covers only one country in the Caribbean, the Dominican Republic.

5 The SEDLAC database was developed in a collaboration between the Centro de Estudios Distributivos, Laborales y Sociales, Universidad Nacional de La Plata and the World Bank. In our analysis we use an augmented SEDLAC database; of the 238 surveys used in our analysis, 24 are original country household surveys for which we manually harmonized variables of interest to make them consistent with SEDLAC data definitions. Please refer to highlighted cells in the table in Annex 1 for the specific country-years that rely on these additional surveys.

4
**Table 2:** Share of Household Members Performing Agricultural Tasks

|**Household**|**Land Clearing**|**Plowing**|**Sow/Weed**|**Transport**|**Harvest**|**Marketing**|
|---|---|---|---|---|---|---|
|Producer|0.85|0.88|0.85|0.90|0.81|0.86|
|Spouse|0.40|0.68|0.55|0.74|0.45|0.09|
|Other men|0.52|0.71|0.65|0.69|0.59|0.25|
|Other women|0.26|0.50|0.37|0.49|0.27|0.05|
|Boys|0.34|0.50|0.40|0.51|0.38|0.05|
|Girls|0.18|0.39|0.26|0.40|0.21|0.03|

Note: Variable equal to 1 if the individual performed the agricultural task over the prior year. Sample restricted to control group.

**Table 3:** Treatment compliance

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|Household has cattle|2113|0.88|0.02<br>(0.01)|
|Number of household cattle<sup>_⋄_</sup>|2113|4.87|0.22<br>(0.28)|
|Household received traction oxen|2113|0.46|0.09<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 09/17|2113|0.24|0.19<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 04/18|2113|0.38|0.16<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 08/18|2113|0.47|0.10<sup>_∗∗∗_</sup><br>(0.02)|
|Number of traction oxen received<sup>_⋄_</sup>|1811|0.93|0.23<sup>_∗∗∗_</sup><br>(0.04)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions include controls for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

19
# **FURTHER READING FOR PART I**

# **AUSTRALIA**

Department of Finance and Deregulation, _Commonwealth Property Disposals Policy,_ Feb. 25, 2009 [Updates strategy for land disposition, including priority for housing supply] Commonwealth Government, _Property Ownership Framework,_ July 1, 2005. [Modifies framework to conform to Labour’s policy platform] Department of Finance and Deregulation, Guidelines for Land Audit, _Estimates Memorandum 2005/31 and 2007/28_ [provide guidance on how the business case for land retention should be calculated] Department of Finance and Deregulation, _Commonwealth Property Management Guidelines,_ Oct. 2009 [requires all agencies to have a property management plan and provide data on all property that they own or lease.] Department of Finance and Deregulation, _Better Practice Guide on the Strategic and Operational Management of Assets by Public Sector Entities,_ 2010 [guidance on the own vs. divest decision for land assets and other asset management issues] Commonwealth Government, _Financial Management and Accountability Act,_ 1997. [establishes the basic requirement that agencies have “strategic planning, policies and procedures in place to achieve value for money in property management.” Department of Finance and Deregulation, _Register of Surplus Commonwealth Land Potentially Suitable for Housing and Community Outcomes,_ Sept. 2011 [most recent register of lands available for housing etc.; 90% consists of Defense properties] Department of Finance and Deregulation, _Commonwealth Land Audit_ , Jan. 2011 [most recent agencies’ Land Audit]

# **CANADA**

Canada Lands Company, www.clc.com [current information and past publications] Canada Lands Company, _Annual Report, 2010-2011._ Published Jan. 2012 [available at above website] Canada Lands Company, _Corporate Plan Summary, 2010-2011 to 2015-2016._ Published 2011 [available at above website] 24
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Moenius, J. (2000). Three Essays on Trade Barriers and Trade Volumes. PhD Dissertation. University of California, San Diego.

MOFCOM (Ministry of Commerce of China) (2005). Challenges for Agricultural Trade. February 27, http://english.mofcom.gov.cn Niedercorn, J., and B. Bechdolt (1969). An Economic Derivation of the ‘Gravity Law’ of Spatial Interaction. _Journal of Regional Science_ 9 (2): 273–282.

Otsuki, T., J.S. Wilson, and M. Sewadeh (2001). Saving Two in a Billion: Quantifying the Trade Effect of European Food Safety Standards on African Exports. _Food Policy_ (26): 495–514.

Parks, R. (1967). Efficient Estimation of a System of Regression Equation When Disturbances Are Both Serially and Contemporaneously Correlated. _Journal of the American Statistical Association_ (62): 500–509.

Rose, A., and E. van Wincoop (2001). National Money as a Barrier to Trade: The Real Case for Currency Union. _American Economic Review, Papers and Proceedings_ 91 (2): 386–390.

Sun D., J. Zhou and X. Yang (2005). Study on the Effect of Japan’s Technical Barriers on China’s Agricultural Exports. Journal of Agricultural Technical Economics 5: 6-12.

Tinbergen, J. (1962). _Shaping the World Economy—Suggestions for an International Economic Policy_ . New York: Twentieth Century Fund.

United Nations Conference on Trade and Development (UNCTAD). Trade Analysis Information System (TRAINS) within the World Integrated Trade Solution (WITS).

United Nations Statistics Division, Commodity Trade Statistics Database, COMTRADE.

Vogt, D. (1994). Sanitary and Phytosanitary Safety Standards for Foods in the GATT Uruguay Round Accords. CRS Report for Congress, 94512 SPR. Available from Penny Hill Press, http://pennyhill.com Wei, S. (1996). Intra-National versus Inter-National Trade: How Stubborn Are Nations in Global Integration? NBER Working Paper 5531. National Bureau of Economic Research, Cambridge, Mass. 106 Wei, S., and J. Frankel (1994). A ‘Greater China’ Trade Bloc? _China Economic Review_ 5 (2): 179–190.

Wilson, J., T. Otsuki, and B. Majumdsar (2003). Balancing Food Safety and Risk: Do Drug Residue Limits Affect International Trade in Beef? _Journal of International Trade and Economic Development_ 12 (4): 377–402.

18
funding to be secured. SBIRI allows companies that do not have DSIR recognition to apply, as long as they pursue such recognition while the application is being reviewed. Delays in securing such recognition have created a time lag in securing final approval. There are also other factors for time lags due to some of the conditions stipulated by SBIRI as a result of the review process not being fulfilled by the applicant before the announcement of the next call. Though SBIRI processes each of the calls and concludes the review process prior to the next call, there is some carryover of proposals that remain in the pipeline for approval for up to 2 years or more due to compliances being pursued by the applicants.

SBIRI has funded a heterogeneous mixed of ventures from very early-stage to well established companies. Of the 86 SBIRI-funded projects, 13 enterprises had annual revenues of US$ 25 million and above either on their own or combined with their group companies in 2010. Only 5 companies had annual group turnover of over $100 million.<sup>18</sup> The rest of recipients are smaller ventures, though all recipients have been in existence for three years or more prior to receiving approval. One of the reasons for SBIRI not to fund de novo start-up ventures is the need to secure DSIR recognition. Since DSIR does not grant this recognition to start-ups, the new ventures must take some time until their research facility is inspected and granted recognition by DSIR.

**Table 2: SBIRI approvals relative to applications by area**

|Category|% of applications received to<br>total applications|% of approvals secured to total<br>approvals|
|---|---|---|
|**Healthcare**|49|51|
|**Agriculture**|21|25|
|**Industrial Processes**|15|15|
|**Instrumentation and**<br>**Devices**|6|6|
|**Environment**|4|1|
|**Bioinformatics, food**<br>**biotechnology and others**|5|2|
|Total|100|100|

Source: DBT SBIRI database.

The composition of approved relative to proposed SBIRI projects across biotechnology application areas is provided in Table 2. Of the approved 86 projects, 51% have been in healthcare. Agriculture projects are second, with 25% of approved projects. Applications of biotechnology to instrumentation and devices cut across traditional disciplines, so they have

> 18 To put these sales figures into perspective, again based on the pharma subset of all biotech industries, the top 10 Indian pharmaceutical companies by world-wide sales in 2009 ranged from $1.52 billion (Dr. Reddy‘s) to $498 million (Glenmark Pharma). See Bruche (2012), Table 1. According to the 2011 BioSpectrum–ABLE biotech industry survey, the size of the Indian biotech industry by revenues stood at roughly $3.5 billion (with bio-pharma accounting including diagnostics and devices accounting for 62%, bio-agriculture 14%, bio-industry 3.6%, bio-informatics 1.4%, and bio-services 19%). See http://biotechnews.co.in/April2011/toTheReaders.pdf.

23
|(g/cm^3)|(0.874)|(0.873)|(0.909)|(0.908)|(1.069)|(1.068)|(1.104)|(1.103)|
|---|---|---|---|---|---|---|---|---|
|Density^2|-3.333*|-3.024*|-5.002***|-4.665***|1.797|2.166|0.408|0.763|
||(1.714)|(1.712)|(1.780)|(1.777)|(2.091)|(2.090)|(2.158)|(2.156)|
|Total nitrogen (g/kg)|0.325***|0.338***|0.427***|0.433***|0.592***|0.602***|0.673***|0.679***|
||(0.098)|(0.098)|(0.102)|(0.102)|(0.121)|(0.121)|(0.126)|(0.126)|
|Nitrogen^2|-0.175***|-0.173***|-0.210***|-0.205***|-0.270***|-0.268***|-0.301***|-0.298***|
||(0.032)|(0.032)|(0.033)|(0.033)|(0.040)|(0.040)|(0.041)|(0.041)|
|Soil pH (pH)|-10.174***|-8.827***|-10.577***|-9.235***|-5.053|-4.921|-7.414**|-7.348**|
||(2.776)|(2.774)|(2.884)|(2.882)|(3.443)|(3.443)|(3.579)|(3.578)|
|pH^2|2.969***|2.566***|3.050***|2.644***|1.622*|1.555*|2.248**|2.198**|
||(0.740)|(0.740)|(0.769)|(0.768)|(0.918)|(0.918)|(0.954)|(0.953)|
|Soil organic carbon|-2.128***|-1.979***|-2.002***|-1.846***|-0.443|-0.423|-0.369|-0.342|
|stock (g/kg)|(0.348)|(0.348)|(0.361)|(0.361)|(0.442)|(0.441)|(0.456)|(0.456)|
|Organic carbon^2|0.655***|0.596***|0.620***|0.559***|0.183|0.167|0.163|0.146|
||(0.102)|(0.102)|(0.106)|(0.106)|(0.129)|(0.129)|(0.133)|(0.133)|
|year=2022|-0.138***|-0.136***|-0.139***|-0.137***|-0.160***|-0.157***|-0.162***|-0.160***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.007)|(0.007)|(0.007)|(0.007)|
|year=2023|-0.192***|-0.189***|-0.194***|-0.191***|-0.240***|-0.236***|-0.240***|-0.236***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|(0.006)|
|year=2024|-0.221***|-0.216***|-0.209***|-0.204***|-0.258***|-0.248***|-0.269***|-0.259***|
||(0.018)|(0.018)|(0.018)|(0.018)|(0.008)|(0.008)|(0.008)|(0.008)|
|Constant|13.135***|11.833***|13.067***|11.760***|7.127**|7.011**|8.536**|8.472**|
||(2.604)|(2.603)|(2.705)|(2.703)|(3.233)|(3.233)|(3.360)|(3.359)|
|No. of obs (farms)|236,644|236,644|219,904|219,904|151,474|151,474|141,921|141,921|
|Adj R-squared|0.346|0.347|0.354|0.356|0.379|0.379|0.388|0.389|

_Note:_ OTG fixed effects are included. Standard errors in parentheses (* p<0.10, ** p<0.05, *** p<0.010).

38
34 exported were found to be about-0.6. The possibilitythat export agriculture may crowd out food production was examined. It appears that growth in agriculture exports is not at the expense offood production. Food production is positively correlated with export crop prices and real exchange rates, suggesting complementarity.

AGGREGATE **SuPPLY ELASTICrnES.** Even when individual crop-supply response is strong, it does not eliminate the ambiguity of the aggregate agriculture supply response. If the aggregate elasticity is low, then export crops may increase at the expense of food crops. Econometric evidence confirms that the elasticity of aggregate agricultural response is positive, so cash crops are not obtained at the expense of food crops. However, there is little reliable or robust data estimating aggregate agricultural supply response for SSA countries. This limited evidence is presented in Table A3 and is drawn from Bond (1983), and Berthelemy and Morrisson (1989). In Table 6 Chhibber (1988) provides data on available estimates for long-run elasticities for developing countries derived from at least four types of models. These models include cross-country, cross-section (farm households), inter-sectoral general equilibrium, and time-series. In general, the first two models produced relatively high estimates of elasticities. (Cavallo and Mundlak (1982), and Peterson (1988)). Cross-section models give lower values, yet they suffer from the unreliability of farm-level price data. Time-series models rely directly or indirectly on the Nerlovian (1958) dynamics-of-supply model.

As noted by Chhibber (1991), elasticities of aggregate supply response m agriculture in SSA countries seems to be comparable to those in other parts of the developing world. One important paper written by Pritchett (1992) must be taken into account when considering the effects of a tradeables supply response. Pritchett shows that in an economy subject to foreign exchange rationing, like many economies in SSA, a real depreciation will affect exports in the "normal" sense, but imports may exhibit a perverse response. If the flow of total imports is constrained by the availability of foreign exchange, a real depreciation will have two conflicting effects: on the one hand, it will increase the price of imported goods, and on the other hand, it will increase the export receipts and, through this channel, induce higher demand for imports. Empirical evidence provided by Moran (1988) and Faini et al. (1992) indicates that the effect on imports can be quite strong and therefore account for the overall low responsiveness of the trade