rafmacalaba/datause-encoder-data huggingface dataset:
Patents offer an alternative measure of inventive activity. Patents provide a detailed record of each invention. From the bibliographic data on a patent, the researcher can learn the identity and home country of the inventor, read a description of the invention, and see references to earlier patents. Using patent data, it is possible for researchers to collect data in highly disaggregated forms. Whereas R&D data are typically available only for specific industries or general applications,<sup>5</sup> patent classifications can be used to distinguish between different types of R&D at great detail, such as air pollution control devices designed to reduce NOX emissions versus devices designed to control SO2 emissions.<sup>6</sup> In addition, economists have found that patents, sorted by their date of application, provide a good indicator of R&D activity, as patent applications are usually filed early in the research process (see, for example, Griliches 1990). As a result, patent counts not only serve as a measure of innovative output, but are indicative of the level of innovative activity itself. A recent OECD data collection effort makes data on 37 different types of environmental technologies readily available to researchers.<sup>7</sup> However, patent data also have drawbacks. While patent counts should be expected to increase as R&D activity increases, the correlation need not be exact. Variations in patent law, both across countries and across time, must be controlled for to properly interpret patent data. Furthermore, the existence of a patent does not mean that the technology has been adopted. Indeed, studies of the economic value of patents find that most patents have little commercial value, suggesting that adoption of most patented inventions is not widespread (see, for example, Lanjouw _et al_ . 1998). Moreover, firms are more likely to use patents to protect new products

> 5 For example, in the US, private R&D is available from 1972-1994 for air pollution control, but it is not broken down by pollutant.

> 6 For example, US patent classes 423/235-423/239 pertain to control of “nitrogen or (a) nitrogenous component”, and patent classes 423/242 – 423/244 and 423/569 – 423/570 pertain to control of sulfur compounds.  Using patent databases, it is possible to download all patents in these classes.

> 7 Accessible at http://stats.oecd.org/index.aspx, accessed September 21, 2011.

7
|**Table 1: Central Government Finance ‐ Impact of**|**Financial L**<br>|**iberalizatio**<br>|**n Reforms**<br>|||
|---|---|---|---|---|---|
||**2019/20**|**2020/21**|**2021/22**|**2022/23**|**2023/24**|
||**(In Mil**|**lions of Birr**|**Projection**<br>**, Unless Oth**|**erwise Indic**|**ated)**|
|EXPENDITURE|1,523.4|5,783.6|14,800.2|23,715.5|33,126.8|
|RECURRENT|1,523.4|5,783.6|14,800.2|23,715.5|33,126.8|
|o/w Additional Cost of the Reform:|1,523.4|5,783.6|14,800.2|23,715.5|33,126.8|
|Interest Expense(on ExistingT‐bills Stock)|560.1|1,344.3|2,240.4|2,240.4|2,240.4|
|Interest Expense(New Issues)|573.4|3,672.2|11,103.2|18,714.7|25,599.3|
|Fiscal Cost of Devaluation|389.8|467.1|556.6|660.3|787.1|
|Foregone Dividend on NBE Advances|0.0|300.0|900.0|2,100.0|4,500.0|
|**Overall Surplus/Deficit**|**‐1,523.4**|**‐5,783.6**|**‐14,800.2**|**‐23,715.5**|**‐33,126.8**|
|FINANCING|1,523.4|5,783.6|14,800.2|23,715.5|33,126.8|
|External net|1,454.5|3,296.0|5,603.2|8,469.3|12,004.8|
|Domestic borrowing|68.8|2,487.6|9,197.1|15,246.2|21,122.0|
|Borrowingfrom NBE|‐10,000.0|‐20,000.0|‐30,000.0|‐40,000.0|‐50,000.0|
|Borrowingfrom CBs(T‐bills)|10,000.0|20,000.0|30,000.0|40,000.0|50,000.0|
|Borrowingfrom Others|68.8|2,487.6|9,197.1|15,246.2|21,122.0|
|Memorandum Items||||||
|o/w Additional Cost of the Reform(as % of GDP):|0.1|0.2|0.4|0.5|0.7|
|Interest Expense(on ExistingT‐bills Stock)|0.0|0.0|0.1|0.1|0.0|
|Interest Expense(New Issues)|0.0|0.1|0.3|0.4|0.5|
|Fiscal Cost of Devaluation|0.0|0.0|0.0|0.0|0.0|
|Foregone Dividend on NBE Advances|0.0|0.0|0.0|0.0|0.1|
|**Overall Surplus/Deficit**|**‐0.1**|**‐0.2**|**‐0.4**|**‐0.5**|**‐0.7**|
|Financing|0.1|0.2|0.4|0.5|0.7|
|External Borrowing|0.0|0.1|0.1|0.2|0.2|
|Domestic Borrowing|0.0|0.1|0.2|0.3|0.4|
|Borrowingfrom NBE|‐0.3|‐0.6|‐0.8|‐0.9|‐1.0|
|Borrowingfrom CBs(T‐bills)|0.3|0.6|0.8|0.9|1.0|
|Borrowingfrom Others<br>Source: Authors' Calculation|0.0|0.1|0.2|0.3|0.4|

# 5.4. Impact on monetary accounts

The liberalization scenario will also have a major impact on the NBE financial program. To achieve the 6 percent inflation target, the NBE would have to slightly reduce its growth of reserve money. In line with reserve money, broad money growth would also be slightly lower. To achieve a proper accumulation of foreign exchange reserves and increase the import coverage ratio to 2.7 months by the end of the period in 2023/24, the amount of domestic credit available to the economy would therefore be constrained. This would be achieved by gradually reducing the NBE advances to the government and the state‐owned banks (CBE and DBE) by up to birr 150 billion in 2023/24. The government would then need to sell T‐bills to commercial banks to compensate for the reduction in direct advances. Because of the gradual phase‐ out of the 27‐percent rule, commercial banks would have the liquidity to purchase the government T‐bills. Also, the NBE would introduce a lending/borrowing mechanism, such as certificates of deposits or other 39
# **4.2 Random assignment and treatments**

Upon being selected for the baseline survey, firms were randomly assigned by computer to either a control group (297 firms) or an information treatment group (757 firms). The first two columns of Table 2 show that these groups are similar on baseline observables, confirming that this in-thefield randomization produced a balanced assignment. The control group were just administered the baseline survey, while the information treatment group were instructed to use the computer tablet of the interviewer to access a _Business Services Marketplace_ .<sup>4</sup> Upon logging into this marketplace, the entrepreneur was asked to look through a list of providers in their city in each of three categories: HR providers, accounting firms, and marketing firms. The marketplace contained information for 35 business service providers in Lagos, and 25 in Abuja, with 7 to 15 providers in any given provider type-city category. They were then instructed to short-list and rank their top three providers in each category. This was an incentivized choice, since they would have the option to go into a draw to win funding that they could use to spend on their top choice among this list.

Firms were further randomized into one of four groups that were provided with different information on the platform when making this choice: (1) 187 firms were allocated to an _information only_ treatment group, where they were shown information about the business service providers such as their name, logo, tagline (e.g. “We are client-focused with over 10 years of experience in recruitment and selection”), and contact information. This group were not given any independent information about provider quality; (2) 187 firms were allocated to a _ratings only_ group, which supplemented the information with a star rating, based on the mystery shopper ratings<sup>5</sup> ; (3) 183 firms were allocated to a _ratings plus negative comments_ group, which provided the star ratings and any negative comments from the mystery shopper; and (4) 200 firms were allocated to a _ratings plus all comments_ group, which received star ratings and both positive and negative comments provided by the mystery shopper. Appendix B provides examples of each screen. Columns 4 onwards of Table 2 show the different treatment groups are well-balanced on most baseline observables.

> 4 Internet connectivity issues meant that only 685/757 firms (90.5%) were able to successfully connect to the marketplace and view and rate providers. We use intention-to-treat analysis, which considers the impact of being randomly assigned to see the marketplace.

> 5 We had intended to show them the customer ratings from firms that had used these firms in the GEM project, but instead the ratings from the mystery shoppers were entered. This ensures the same ratings were used as for the ratings plus comments groups.

10
without the lags, potentially showing that the effects of floods can affect next year’s budget as well. For the year after the disaster, GA and Education still show significant decreases, with spending 0.9 and 1.2 percent lower. The total effect on GA and Education on the budget for the year after the disaster is negative 1.9 and 2 percent. If one assumes that the max flood strikes a district, the changes are in excess of minus 5 percent for GA and Education and plus 5 percent for Infrastructure. Overall, there is some evidence that districts tend to redistribute costs not just for the year of the disaster, but also for the year after.

For earthquakes, most of the sectors are significant the year after the disaster. As mentioned, earthquakes are more likely to be declared national disasters and/or recovery costs exceeding what local governments are capable of covering, which might limit the local costs during the year of the disaster, while one might see a stronger effect the year after, once the true reconstruction phase starts. For the year after the disaster, Education is strongly negatively affected. In terms of Health and GA there is a strong positive effect, and Health is 1 percent significant. For two sectors that one might expect a large change in, Infrastructure is just 10 percent significant and Housing not significant at all. It is not clear why this would be the case, but potentially it is easier to get central government funding for infrastructure and housing reconstruction. This is supported by the findings in a follow-up mission to the The Global Facility for Disaster Reduction and Recovery (2011) report, which found that local governments have incentives to inflate loss estimates since they are not required to contribute toward reconstruction costs.<sup>15</sup> One sees a strong significant increase in most other sectors, with Agriculture, Economy, Environment, Public Law and Order, Social Protection

> 15Findings presented in an Aide Memoire in Jakarta 13-17 April 2015. The mission was a joint effort between the World Bank and the Government of Indonesia represented by BNPB and the Ministry of Finance.

24
**Figure 13** : Closeness of CA’s total intra-regional exports value to the Unconstrained Expectations Frontier

<!-- Start of picture text --> Expectations Frontier<br>Costa Rica<br>Honduras<br><!-- End of picture text --> Source: World Bank calculations based on trade flows data from WITS, LCSSD Economics Unit (2010) In terms of the closeness to the frontier of expected exports by separate export types, Grains and Steel products are particularly strong examples of the two countries being at very different levels of “adjacency performance” (Figure 14). In line with the results shown in Figure 10, Costa Rica’s is close to the level of the region’s best performer, while Honduras is much more distant from the benchmark.

**Figure 14** : Intra-regional Observed Exports vs. Projected Exports, assuming that Costa Rica and Honduras’ Trade Behavior follows Central America’s own “Best Adjacency Performance"

<!-- Start of picture text --> Expectations Frontier<br>1.0<br>0.9<br>Costa Rica<br>0.8<br>0.7<br>0.6<br>0.5<br>Honduras<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Total Exports Grains (Volume) Steel (Volume) Proc. Food (Volume)<br>(Value)<br><!-- End of picture text --> Source: World Bank LCSSD Economics Unit calculations based on WITS data, 2010 In turn, when projecting the potential increase in total absolute intra-regional exports under three different scenarios—each of which represents a type of improvement in the region’s “trade performance”—estimations show that intra-regional trade could be 33-percent higher if the effect of adjacency between each pair of the six Central American countries were to improve to the level of the region’s “best performer.” If, however, the improvement in adjacency performance 18
were covered in the government basic urban health-care insurance scheme, which does not cover migrant workers (Hu et al., 2008). Furthermore, migrant workers tend to underuse health services in their destination cities, as almost two-thirds of migrant workers who report illness do not visit a doctor (Gong et al., 2012). Another type of public spending—social protection spending—provides similar evidence.<sup>28</sup> In the 15 cities of our estimation sample, the correlation between social protection spending and public education spending shocks is almost 0 (i.e., -0.06).<sup>29</sup> Nevertheless, we employ two different strategies to provide additional layers of robustness checks on the exclusion restriction. First, we employ different model specifications that control for a number of variables in estimating equations (7) and (8). These include the migrant worker’s (household head’s) demographics, employment, and dummy variables indicating his work industry, province of origin, and whether he migrates within the same province. If somehow there is a reason to believe that the city-level shocks to public education spending can have differential effects on different occupations and migrant workers coming from different locations, these variables can help net out such effects. Furthermore, we explicitly control for social protection spending in a robustness check. If the estimated coefficient on school fees does not lose its statistical significance (or change significantly) when social protection spending is included, this would provide supportive evidence for the validity of the exclusion restriction. Second, we apply a bounding method recently developed by Chernozhukov, Lee, and Rosen (2013) that does not require the exclusion restriction. This second strategy, in fact, generally allows for the violation of the exclusion restriction to occur

> 28 Social protection spending is defined in the China City Statistical Yearbooks as being composed of social security benefits, employment subsidies, and unemployment grants. 29 This result is consistent with the fact that migrant workers generally have no access to other social welfare benefits—which are closely tied with residence status—such as social security, housing, transportation, and medical benefits (Wang and Zuo, 1999; Wong et al., 2007; Song et al., 2008; World Bank and DRC, 2014).

19
- 18 -

**<u>Table 6. Output elasticities with respect to transport variables, infrastructure quality and agglomeration</u>**

||SUR<br>Elasticity|Std. Err.|SFA<br>Elasticity|Std. Err.|
|---|---|---|---|---|
|Transport connectivity:|||||
|Local access|0.137|<br>(0.074) *|0.081|<br>(0.101)|
|MAI|-0.068|<br>(0.294)|-0.314|<br>(0.417)|
|Cost to access port|-0.094|<br>(0.051) *|-0.095|<br>(0.055) *|
|Infrastructure quality:|||||
|Power outage|-0.049|<br>(0.059)|-0.010|<br>(0.059)|
|Water interruption|0.063|<br>(0.077)|0.073|<br>(0.067)|
|Inventory|-0.161|<br>(0.050) ***|-0.175|<br>(0.059) ***|
|Agglomeration economies:|||||
|Number of firms in theprovince|0.103|<br>(0.162)|0.240|<br>(0.204)|

30. Another unexpected result may be that the firm production does not exhibit agglomeration economies. The estimated elasticity is positive but statistically insignificant. There are several possible reasons for this. First, the variation in firm location is very limited in the BEEP data set, leading to very little variation in the number of firms in the same province as a firm is located. Second, our agglomeration variable may be too broad. Each province is very large in Mozambique. The potential intangible impacts of neighboring firms may not be so clear at the provincial level. Unfortunately, however, more granular data are not available.

31. To examine a more dynamic aspect of improved transport infrastructure, the firms’ locational choice is estimated by the conditional logit model. The estimated results show that firms prefer to locate themselves where (i) other firms exist (i.e., agglomeration economies), (ii) local connectivity, measured by road density, is higher, (iii) transport costs to port are low, and (iv) the local market size is large ( **Table 7** ).

32. The policy implications are straightforward: First, the existing clusters and cities will continue to grow because of agglomeration economies. This calls for efforts toward improving efficiency and sustainability in urban transportation and mitigating congestion. Second, port connectivity is essential to keep competitiveness of firms. The share of Mozambican firms involved in international trade to purchase inputs or sell outputs is still
# TABLE OF CONTENTS

|Summ|ary||
|---|---|---|
|I.|Introduction|1|
|II.|The Debt Crisis as a Fiscal Phenomenon|1|
|III.|Origin of the Debt Crisis in Insolvency<sup>of the Public</sup><br>Sector<br>a. The Solvency Condition of the Public<sup>Sector</sup><br>b. Why a Debt Crisis?|3<br>4<br>6|
|IV.|The Nature of the Fiscal Response|9|
|V.|The Brady Plan and Fiscal Policy|12|
|VI.|Summary and Conclusions|23|
|Appe|ndix: Sources for Fiscal Data|25|
|Refe|rences||
O.A.M.D.G.

register for the lowest income stratum (the so-called SISBEN register, last updated with the 1993 census). When confirming the addresses of the around 20,000 households in the SISBEN database, the surveyors found that nearly 40% had left their municipality. It is not likely that these 40% left at random. We need to test the assumption that the leavers censored the sample we ultimately use. In other words, we observe a sample of ‘stayers’ in municipalities that experienced (potentially systematic) displacement already. The reduced sample may be different from the original sample censored by outmigration, and display a stratification that is not representative. Therefore, our regression results may be different than with a complete sample. Likewise, peaceful locations will have experienced immigration of people marked by violence elsewhere.<sup>23</sup> Therefore, controlling for violence at the municipality level, rather than the personal history level may also bias our results.

There are various ways to remedy the bias in the empirical specification, depending on the specification used. If the specification is linear, and the selection process from the original sample is known, the standard remedy is Heckman’s (1979) two-step procedure. This involves calculating the non-selection hazard (inverse Mills ratios) in a first step, and including the ratio in the principal regression in a second step. The basic Heckman model involving sample selectivity can be summarized as

* * * where _yt_ = _yt_ if _zt_ > ;0 _yt_ = 0 otherwise;<sup>_z_</sup> _t_ = 1 if _zt_ > ;0 _zt_ = 0 otherwise.

> 23 In this context, the findings of Glaeser and Shapiro (2001) are interesting, which suggest that (terrorist) violence shapes the choice of settlement form, e.g. a dense urban setting allows more mutual protection and short transport times. In this sense the fairly urban structure of our sample (50% urban settlers) is likely to have been shaped by years of violence.

21
# **Agricultural Distortions in Sub-Saharan Africa: Trade and Welfare Indicators, 1961 to 2004***

Johanna Croser University of Adelaide <u>johlou@gmail.com</u> and Kym Anderson University of Adelaide <u>kym.anderson@adelaide.edu.au</u>

**Keywords:** distorted incentives; agricultural price and trade policies; trade restrictiveness index

**JEL classifications:** F13, F14, F15, N57, Q17, Q18

**Author contact:** Kym Anderson School of Economics University of Adelaide Adelaide SA 5005 Australia Phone +61 8303 4712 Fax +61 8223 1460 <u>kym.anderson@adelaide.edu.au</u>

*This is a product of a World Bank research project on Distortions to Agricultural Incentives (see www.worldbank.org/agdistortions). The authors are grateful for the distortion estimates provided by the authors of the various African country case studies, for funding from World Bank Trust Funds provided by the governments of the Netherlands (BNPP), the United Kingdom (DfID) and Ireland, as well as from the Australian Research Council. The views expressed are the authors’ alone and not necessarily those of the World Bank and its Executive Directors, nor the countries they represent, nor of the institutions providing the project research funds.
26 Vogel, Ronald J. 1988. Cost Recovery in<sup>the HealtCae</sup> Sector: SelSeced Country Studies in West Africa. Technical<sup>Paper Number 82. Washington,</sup> D.C.: World Bank.

Vogel, Ronald J. 1989a. Trends in Health Expenditures and Revenue Sources in Sub-Saharan Africa. Paper prepared in<sup>conjunLtion with World Bank</sup> Africa Health Policy Paper. PHRHN. Washington,<sup>D.C.: World Bank. March.</sup> Vogel, Ronald J. 1989b. "Cost Recovery in the<sup>Health-Care Sector in Sub-</sup> Saharan Africa". University of Arizona Working<sup>Paper. Tucson, AZ.</sup> November.

World Bank. 1988. Kenya: Review of Expenditure Issues<sup>and Options in Health</sup> Finalng. Washington, D.C.: World Bank. June.

World Bank. 1989. Zimbabwe: Issues in the<sup>Financing of Health Care.</sup> Washington, D.C.: World Bank. August.
part reflects that the change in investment readiness score is not large enough to generate sizeable impacts on subsequent firm outcomes, and also that more of these firms are able to obtain financing without the program than was originally anticipated.

We believe these results offer lessons for governments deciding whether and how to use such policies. Starting with firms that express interest in outside funding, but that require many steps to take place before being in a position to receive funding may end up including many firms for which investment readiness is not the main constraint to receiving outside funding and to firm growth. As a result, investment readiness programs that start from the demand side of outside financing may have stronger impacts on getting firms to take steps towards investment readiness, than on investment outcomes, where other constraints also play a role. A possible alternative to test would be to start from the supply side, starting with investors and asking them for a list of firms that are at the margin of being investible, but for which assistance on specific aspects of investment readiness is needed.

# **References**

Bernstein, Shai, Arthur Korteweg and Kevin Laws (2017) “Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment”, _Journal of Finance_ 72(2): 509-538.

Catalini, Cristian, Jorge Guzman and Scott Stern (2017) “Hidden in plain sight: venture growth with or without venture capital”, Mimeo. MIT.

- Clark, Colin (2008) “The impact of entrepreneurs' oral ‘pitch’ presentation skills on business angels' initial screening investment decisions”, _Venture Capital_ , 10:3, 257-279

Clingingsmith, David and Scott Shane (2017) “Training Aspiring Entrepreneurs to Pitch Experienced Investors: Evidence from a Field Experiment”, _Management Science_ , advance online access. Holm, Sture (1979) “A simple sequentially rejective multiple test procedure”, _Scandinavian Journal of Statistics_ 6: 65-70.

Gompers, Paul, Will Gornall, Steven Kaplan and Ilya Strebulaev (2016) “How do Venture Capitalists Make Decisions?”, Mimeo. Harvard Business School.

29