rafmacalaba/datause-encoder-data huggingface dataset:
12 decisions. In 1997 antidumping initiations declined relative to 1996 but remained high compared to the early 1990s.

Table 2: Antidumping Activity in Latin America - Initiations, 1989-97

|Country|1989|1990|1991|1992|1 1993|1994|1995|1996|1997|
|---|---|---|---|---|---|---|---|---|---|
|Argentina|||l||||6|42|18|
|Brazil|2||2|9|4|30|12|1|19|
|Chile|||||||2|4|2|
|Colombia||||||4|1|5<br>|1|
|Guatemala|||||_|||1||
|Mexico|17|9|14|25|24|23|18|3|5|
|Peru|||||=||4|**4**|**3**|
|Venezuela|||||||1|**5**||
|LAC Total|19|9|16|34|28|57|44|65|48|
|World Total|118|102|179|234|245|247|160|149|190|
|(Rest of World)|99|**93**|**160**|**200**|**217**|**190**|**116**|**84**|**142**|
|LAC %**of World**|**16%**|**8%**|**10%**|**15%**|**11%**|**23%**|**28%**|**44%**|**25%**|

**Source: Trade Policy Reviews, GATT/WTO, various years, and WTO/GATT reports of the Committee on** Antidumping Policies, various years.

Many other countries in the region took recourse to other measures to restrict imports, therefore they did not use antidumping as much as Argentina, Brazil, Mexico and Venezuela. The Andean Group of countries had introduced minimum prices for agricultural imports as had Chile. When import prices fell abroad, there was an automatic trigger mechanism in place that prevented increases in imports. In the Andean Group, some eight agricultural commodities are subject to reference prices, with their domestic price tied to five year moving average price. Moreover, the Andean Group has adopted domestic procurement agreements, which treat competitive agricultural imports as residuals. Also, some countries used the transition provisions of the Multifiber Arrangement to restrict import of textiles. Colombia was one of them. Brazil, for example, raised its agricultural tariffs as high as 100%, as it had bound these tariffs up to that level.
generalize about patient management within a given site. Because of small caseloads during the weeklong observation periods, the minimum sample size of 15 patients was not achieved at every site.

At two hospitals in Kenya (KENI and KEN2), project staff collected data on D&C patients before MVA was implemented. After MVA was introduced, data were again collected on patients treated for incomplete abortion. Since time and cost data for both procedures were cotlected at the same site, most of the site variables (such as hospital level, staff efficiency and caseload) were virtuatly the same. Thus, these sites most accurately demonstrate time and cost differentials between the two techniques.

6
solutions to some incentive problems but at the same time may cause several others. It is not easy to find instances in which policies are undertaken in a way that allows identification of the impact of each of its components, i.e. to disentangle the effect of increased school principal authority from decreased central curriculum design. The articles in Savedoff (1998), although hampered by the natural limitations in the data, provide suggestive evidence as to the importance of these agency problems that may be resolved by means other than decentralization or privatization.

Despite the high regard for Fe y Alegría and the availability of extensive data, no econometric evaluation has been done of the system's effectiveness. Navarro and De La Cruz (1998) evaluate test scores and use student-level demographic controls, but their analysis is restricted to two Fe y Alegría schools in one Venezuelan state. Our analysis also does not provide direct evidence as to the importance of decentralization, but suggests based on anecdotal evidence that this factor plays an important role in explaining the relative success of the Fe y Alegría system. This paper thus contributes to the discussion on decentralization and private schooling as ways of addressing incentive problems that arise in public administration.

# **3. Data**

In Venezuela, every graduating high school student takes the Prueba de Aptitud Académica, which is similar in spirit to the North American SAT. Extensive background data on each student is also gathered, ranging from the basics of age and gender to the profession of the father and what means of transportation the student uses to get to school.

In total, there are 413,607 observations of graduating Venezuelan high school students who took the test in 2003. We include only those who are between 14 and 22 years old, are not night-school students, and actually graduated that year instead of earlier. We then drop the 4,662 students from public schools that are not included in a separate school registry that allows us to identify municipalities. Controlling for other observables, this group scores 0.15 standard deviations lower on the verbal section and statistically the same
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE return on the large dam-based schemes tend to be lower than those associated with localized water collection. The results are highly sensitive to the assumptions made about the unit costs of irrigation development. More generally, the simulation exercise found that the viability of irrigation schemes depends on crops that generate more than $2,000 per hectare, which includes mainly cash crops and horticulture.

More recent work conducted nationally suggests that under some circumstances, returns on irrigation may be even higher than suggested by these first order AICD estimates. The irrigation feasibility study for the Accra Plains found that 142,000 hectares of land could be irrigated with a rate of return between 7 and 25 percent, depending on the assumptions used.

**Table 11. Sensitivity of irrigation potential to thresholds for economic return**

||Potential (‘000s<br>hectares)|Investment needs ($ millions)|Average IRR (%)||Potential (‘000s<br>hectares)|
|---|---|---|---|---|---|
||Large dam-based|Local collection|Total|Large dam-based|Local collection|
|IRR >0%|242.2|73.0|315.2|473|377|
|IRR >6%|50.8|36.8|87.6|99|190|
|IRR >12%|0.0|14.9|14.9|-|77|
|IRR >24%|0.0|6.0|6.0|-|31|

Notes: Simulations based on assumptions that large scale dam-based irrigation can be developed at a cost of $3,000 per hectare, while schemes based on localized water collection could be developed at a cost of $2,000 per hectare. Should these costs be significantly exceeded, the number of viable hectares falls sharply. Source: You and others 2009.

# Information and communication technologies

## **Achievements**

As of 2005, when the AICD baseline survey was conducted, Ghana’s ICT sector was a strong performer by regional standards (table 12). About 60 percent of Ghana’s population lived within access of a GSM voice signal. This was above the average for the peer group, although it still fell short of exceptional performers such as Kenya, Malawi, and Uganda, which had already reached about 95 percent of their population with the GSM signal at that time (figure 17). Ghana also achieved strikingly high rates of service penetration. Some 32 percent of the population already subscribed to a mobile telephone service, twice as the level in the peer group. With respect to Internet penetration, however, Ghana did not outperform the rest of the peer group.

Since 2005, an increasingly competitive environment has resulted in further impressive growth in the ICT sector. There are now six mobile operators (two of which were licensed after 2007), and the international gateway has been liberalized. As a result, the mobile footprint expanded to 82 percent in 2009—still below the performance of Kenya, Malawi and Uganda, but impressive nonetheless. Furthermore, the mobile penetration rate more than doubled, from 32 percent in 2005 to 67 percent in 2009. International bandwidth has grown substantially, and Internet subscriptions have also doubled, albeit from a very low base.

**Table 12. Benchmarking ICT indicators**

|Unit|Low-income<br>**Ghana**|
|---|---|

Middle-income 30
**Table 2: Percent of workers in each employment category; by country, region and income group**

|**Region and Income Level**<br>**(number of countries in sample)**|**wage and**<br>**salary**<br>**employee**|**non-paid**<br>**employee**|**employer**|**own account**|
|---|---|---|---|---|
|**All Countries (98)**<br>|**55.0**|**13.2**|**2.9**|**29.0**|
|**Low and Middle Income Countries**<br>**(74)**|**49.3**|**15.4**|**2.7**|**32.7**|
|**Region (Low and Middle Income Countries)**|||||
|East Asia and Pacific (6)|43.6|17.4|3.3|35.7|
|Europe and Central Asia (15)|82.2|5.0|2.6|10.2|
|Latin America and the Caribbean (20)|67.0|4.5|4.7|23.8|
|Middle East and North Africa (5)|53.8|17.3|9.4|19.5|
|South Asia (4)|47.2|18.3|1.2|33.4|
|Sub-Saharan Africa (24)|17.0|25.1|2.3|55.6|
|**Per Capita GNI**|||||
|Low Income (18)|25.2|21.6|1.6|51.6|
|Lower Middle Income (31)|46.0|18.2|2.4|33.5|
|Upper Middle Income (25)|73.1|4.2|4.2|18.6|
|High Income(24)|85.9|1.0|3.7|9.3|

Note: Low Income less than 1,006 2010 dollars, Lower Middle Income 1,006-3,975 dollars; Upper Middle Income 3,976-12,275 dollars; High Income greater than 12,275 dollars.

19
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|_Bank system explanatory variables_||||||
|---|---|---|---|---|---|
|<br>Bank regulatory capital to risk-weighted assets (%)|27,607|16.51|4.09|6.31|29.77|
|<br>Regulatory Tier 1 capital to risk-weighted assets (%)|27,676|13.58|4.22|5.75|28.68|
|<br>Bank capital to total assets (%)|27,920|10.42|3.32|6.63|20.49|
|<br>Credit to government and state-owned enterprises to|<br>31,381|11.83|7.45|0.64|23.77|
|<br>GDP (%)||||||
|<br>Bank nonperforming loans to gross loans (%)|27,607|6.90|6.12|1.34|30.47|
|<br>Bank credit to bank deposits (%)|<br>31,381|91.35|26.10|27.73|195.39|
|<br>Liquid assets to short-term liabilities (%)|<br>26,732|59.49|66.29|17.95|503.08|
|<br>Bank overhead costs to total assets (%)|<br>33,449|3.57|1.99|1.12|10.02|
|<br>Bank return on equity (%, after tax)|<br>33,449|12.87|12.11|-30.14|52.53|
|<br>Lerner index|<br>28,374|0.34|0.19|0.15|1.48|
|Five-bank asset concentration|<br>32,133|65.03|18.53|34.94|100|
|Foreign bank among total bank assets (%)|<br>28,552|24.93|28.25|0|100|
|_Country and institutional variables_||||||
|<br>GDP (current US$, billions)|33,449|628.11|866.47|1.80|2,300.00|
|<br>GDP per capita (constant 2005 US$)|<br>33,449|3,506|3,053|391|<br>14,117|
|<br>Ease of Doing Business|<br>33,449|<br>59.30|<br>8.90|43.80|<br>81.44|
|<br>Getting Credit|<br>33,449|60.43|19.85|5.00|100.00|
|<br>Enforcing Contracts|<br>33,449|52.84|12.55|29.04|73.24|
|<br>Financial Crises|33,449|0.25|0.54|0|2|

_Note:_ GDP = gross domestic credit; SME = small and medium-size enterprises.

All the institutional variables are expressed as scores. These scores capture the gap from the best performance ever attained for a given indicator since the World Bank started compiling the Ease of Doing Business (EADB) survey. Therefore, each of the institutional variables takes values from 0 to 100—with 100 being the best performance. The Ease of Doing Business represents an overall indicator of pro-business regulation and the protection of property rights. Enforcing contracts is a sub-index of EADB that measures the time and cost for resolving business disputes in local courts, as well as the overall quality of judicial processes. Getting Credit is an EADB sub-index that examines two aspects of the regulatory framework for access to external sources of financing: the strength of credit reporting systems and the effectiveness of bankruptcy and collateral laws. Tables A.3 to A.5, in the appendix, contain the correlations among the explanatory variables.

Additionally, we include the pre-treatment level of the dependent variable in all our regressions and label this variable as _Initial conditions._ In doing so, we appreciate the role of initial conditions, which have been shown to be a determining factor in firms’ outcomes in emerging markets (Ayyagari, Demirgüç-Kunt, and Maksimovic 2017).

14
- 24 -

**<u>Table 7. IV regression under different endogeneity assumptions</u>**

|Endogenous var.:|_RAIL_and|_Z_||_RAIL_|||
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|RAIL|-4.686|(2.822)|*|-5.168|(2.658)|**|
|lnZ|0.021|(0.183)||0.078|(0.048)|*|
|lnWT- lnWM|-0.011|(0.058)||-0.014|(0.059)||
|lnWL- lnWM|0.115|(0.045)|**|0.109|(0.038)|***|
|lnWE- lnWM|-0.134|(0.073)|*|-0.127|(0.067)|*|
|lnWF- lnWM|0.288|(0.046)|***|0.287|(0.047)|***|
|lnWW- lnWM|0.395|(0.079)|***|0.397|(0.080)|***|
|lnWC- lnWM|0.214|(0.072)|***|0.222|(0.069)|***|
|lnY|0.447|(0.077)|***|0.458|(0.070)|***|
|lnAGE|0.471|(0.187)|**|0.493|(0.186)|***|
|EDU|0.248|(0.075)|***|0.258|(0.070)|***|
|GLBL|0.493|(0.343)||0.511|(0.355)||
|FORN|-0.003|(0.004)||-0.003|(0.004)||
|Constant|11.570|(1.220)|***|11.457|(1.207)|***|
|Obs.|343|||343|||
|R squared|0.6753|||0.6597|||
|Wald chi|1139.03|||1130.06|||
|No. of dummy varia|bles:||||||
|Industry|4|||4|||
|Region|16|||16|||
|Exogeneity test:|||||||
|Chi2 stat.|3.557|||3.537|*||
|Overidentifying rest|riction||||||
|test:|||||||
|Chi2stat.|1.526|||1.529|||

The dependent variable is ln _C_ -ln _WM_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

**<u>Table 8. Implied Own Price Elasticity of Demand for Input: OLS and IV</u>**

||OLS<br>|||IV<br>|||
|---|---|---|---|---|---|---|
||Elasticity|Std.Err.||Elasticity|Std.Err.||
|Labor|-0.862|(0.039)|***|-0.891|(0.038)|***|
|Electricity|-1.074|(0.058)|***|-1.127|(0.067)|***|
|Fuel|-0.718|(0.044)|***|-0.713|(0.047)|***|
|Water|-0.678|(0.065)|***|-0.603|(0.080)|***|
|Transport|-0.970|(0.045)|***|-1.014|(0.059)|***|
|Communications|-0.820|(0.069)|***|-0.778|(0.069)|***|

Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.
statistical significance. For instance, the 5% confidence interval for the estimate of α of 0.0891 for 1998‐2003 is [0.040, 0.139] which includes the point estimate of 0.0494 for the period 2004‐ 2015. On the basis of this comparison alone we could not conclude that there is a _statistically significant_ decline. However, the changes in all four of the yellow‐shaded periods are in the same direction, and the changes using breakpoints from 2007 onwards suggest – not prove – that there is a real effect in the earlier period but none in the later. Putting all of this together it appears that at some point between 2003 and 2006 there may have been change in the relationship between policy actions and the quality of government policy as measured by Clusters A & B of the CPIA. However, we will have to await more or better data sets before we can be sure.

To explore further the possibility of regime change, we deploy our second dependent variable, the Economic Freedom Index of the Heritage Foundation. As described above in the section about the data set, the content of the Economic Freedom Index largely overlaps with the content of the CPIA. We split the samples in the same way as we did for Table 15. The results are presented in Table 16.

**Table 16.  Estimates of α and γ in model (8) (with Economic Freedom Index as dependent variable), for different sub‐periods**

||**Coeffic**|**ients**|||**Coeffici**|**ents**|||
|---|---|---|---|---|---|---|---|---|
|**Sub‐**<br>**SMPL**|**α**|**γ**|**N**|**Sub‐**<br>**SMPL**|**α**|**γ**|**N**|**Change**|
|98‐99|0.786|0.0|253|00‐15|0.849***|0.0|1953||
|98‐00|0.463**|0.46|385|01‐15|0.774***|0.08|1821|‐9.3%|
|98‐01|0.442**|0.42|512|02‐15|0.697***|0.16|1694|3.8%|
|98‐02|0.624***|0.28|639|03‐15|0.654***|0.26|1567|1.5%|
|98‐03|0.598***|0.30|767|04‐15|0.606***|0.34|1439|8.1%|
|98‐04|0.676***|0.24|895|05‐15|0.585***|0.34|1311|1.6%|
|98‐05|0.376**|0.34|1002|06‐15|0.583***|0.30|1204|45.4%|
|98‐06|0.300*|0.36|1111|07‐15|0.618***|0.20|1095||
|98‐07|0.336*|0.28|1220|08‐15|0.717***|0.04|986||
|98‐08|0.444*|0.16|1329|09‐15|0.757***|0.0|877||
|98‐09|0.636**|0.02|1458|10‐15|0.712***|0.0|748|8.4%|
|98‐10|0.767**|0.0|1586|11‐15|0.653***|0.0|620|‐14.9%|
|98‐11|0.822***|0.02|1711|12‐15|0.695***|0.0|495|‐18.1%|
|98‐12|0.824***|0.04|1833|13‐15|0.639**|0.0|373|‐27.3%|
|98‐13|0.833***|0.04|1959|14‐15|0.0956|0.62|247||

Note: The column “Change” is the percentage change in Δ _Qt_ = α _At_<sup>_γ_</sup> between the earlier and the later period, after substituting the estimated values for α and γ, and 5 policy actions for _At_ .

Again, we contrast the earlier with the later periods, e.g. in the second line, we compare 1998‐2000 with 2001‐2015. Yellow shading indicates periods _both_ of whose coefficients are significant at 5% or better, and where comparisons are best made. In order to make direct comparisons we cannot now compare the estimated α and γ directly, because they work together 36
- 16 -

_<u>ρ</u>_ <u>5.331 5.317 (0.100) 5.319 (0.075)</u> Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

|**Table 5. Spa**|**tial autor**<br>|**egressive**<br>|**model**|**with the**<br>|**Battese’s s**<br>|**pecific**|**ation**<br>|||
|---|---|---|---|---|---|---|---|---|---|
||Spatial r|egression||Spatial r|egression||Spatial r|egression||
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_A_|0.030|(0.010)|***|0.048|(0.010)|***|0.045|(0.010)|***|
|ln_L_|0.040|(0.018)|**|0.048|(0.019)|**|0.044|(0.019)|**|
|ln_R_|0.167|(0.020)|***|0.157|(0.020)|***|0.161|(0.020)|***|
|ln_I_|0.316|(0.024)|***|0.311|(0.024)|***|0.313|(0.024)|***|
|ln_F_|0.527|(0.022)|***|0.525|(0.023)|***|0.523|(0.023)|***|
|ln_KM_|-0.053|(0.010)|***|-0.062|(0.010)|***|-0.061|(0.010)|***|
|ln_MAI_|0.310|(0.024)|***|||||||
|ln_AGAI_||||0.166|(0.024)|***||||
|ln_AGAI_i_|||||||0.224|(0.046)|***|
|ln_AGAI_o_|||||||-0.041|(0.048)||
|d_R_|0.268|(0.085)|***|0.260|(0.088)|***|0.266|(0.087)|***|
|d_I_|1.056|(0.092)|***|1.022|(0.093)|***|1.031|(0.093)|***|
|d_F_|0.073|(0.114)||0.117|(0.116)||0.103|(0.116)||
|constant|-6.719|(0.362)|***|-8.243|(0.387)|***|-8.168|(0.390)|***|
|Obs|3,198|||3,198|||3,198|||
|Wald||||||||||
|chi2|9426.3|||9189.5|||9230.7|||
|Spatial para<br>|meters:<br>|||||||||
|_λ_|1.332|(0.051)||1.497|(0.056)||1.503|(0.056)||
|_ρ_|5.963|(0.087)||5.404|(0.122)||5.540|(0.046)||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

# **IV. CONCLUSION**

In the literature, there are a number of empirical studies showing a wide range of impacts of improved transport connectivity on agricultural growth. Still, the assumed nexus between transport infrastructure and agricultural growth remains somewhat mysterious, particularly in the African context. One of the missing important elements is agrobusiness. Because many rural farmers do not have their own transport means, someone has to deliver advanced inputs, and go and pick up their produce. Agrobusinesses, such as input suppliers, collectors, processors and exporters, have a crucial role to play in this regard.
trade facilitation reforms in Serbia would mainly lead to a reduction in costs for freight forwarders only or whether that would be passed on to the firms hiring them. That is, it will be important to study in the future the pricing behavior and degree of market power of freight forwarders as the costs they face change.

# **References**

Arvis, J.-F., M. Mustra, L. Ojala, B. Shepherd, and D. Saslavsky. 2010. _Connecting to Compete 2010: Trade Logistics in the Global Economy_ . Washington, DC: The World Bank.

Behar, A., Manners, P., and B. Nelson (2013). “Exports and International Logistics,” _Oxford Bulletin of Economics and Statistics_ 75 (6): 855-886.

Cebeci, T., A. Fernandes, C. Freund, and M. Pierola (2012). “Exporter Dynamics Database,” Policy Research Working Paper No. 6229, The World Bank.

Dennis, A., and B. Shepherd (2011). “Trade Facilitation and Export Diversification,” _World Economy_ 34 (1): 101–122.

Djankov, S., C.S. Pham, and C. Freund (2010). “Trading on Time,” _Review of Economics and Statistics_ 92 (1): 166–173.

Freund, C. and M. Pierola (2015). “Export Superstars,” _Review of Economics and Statistics_ forthcoming.

Hummels, David L., and Georg Schaur (2013). “Time as a Trade Barrier,” _American Economic Review_ 103(7): 2935-59.

Marteau, J., Raballand, G. and J.-F. Arvis (2007). “The Cost Of Being Landlocked: Logistics Costs And Supply Chain Reliability,” Policy Research Working Paper 4258.

Persson, M. (2013). “Trade Facilitation and the Extensive Margin,” _Journal of International Trade and Economic Development_ 22(5): 658-693.

28
# **Annex 2  Commercializing IP:  The US Experience**

Several countries have developed different models ranging from transfer of ownership of all government-funded IP to the private sector (US) to a system in which the state retains some ownership rights and actively promotes commercialization of government funded S&T (UK, France, Germany, Japan). Despite their differences, these systems all work reasonably well. Thus, the real question is not “who owns” government funded IP but rather how government-funded IP can be introduced into the economic turnover. This Annex will describe the US approach, in part because the US model is generally recognized as an example of international best practice and also because this is the approach which many in GOR say that they wish to emulate.

The US approach to ownership and commercialization of government-funded IP is codified in two major pieces of legislation – the Bayh-Dole Act (P.L. 96-517) and the Stevenson-Wydler Act (P.L. 96-418), both approved in 1980. Both laws are designed to encourage the commercialization of R&D that was funded by, or developed by the government. Bayh-Dole pertains to the ownership of patents resulting from governmentfunded R&D that was performed in non-government facilities – e.g., universities, nonprofit research laboratories, etc. Stevenson-Wydler pertains to the ownership of patents resulting from cooperative research efforts between government research laboratories and outside partners where there is no direct federal funding to the outside partner.<sup>62</sup> Both laws were based on the premise that simply funding more basic research would not solve the US technology commercialization problem. On the contrary, technology commercialization is not a linear process in which more basic research inputs automatically generate complementary applied research, development, commercialization, and diffusion of the results into the economy. The problem with the US in the 1980s was that despite its overall strength in basic research, other countries were commercializing the results. A second related premise was that the US government had not been an effective owner of the IP which it had already created and funded. At the time both laws were passed, the USG owned approximately 28,000 patents. But fewer than 5% of these inventions were licensed for commercial use.<sup>63</sup> The remainder lay idle.

> 62 For details see Wendy Schacht, “Patent Ownership and Federal Research and Development (R&D): A Discussion of the Bayh-Dole Act and the Stevenson-Wydler Act, (Congressional Research Service: The Library of Congress, December 11, 2000).  Note:  CRS reports are not generally available to the public although they can occasionally be found on the internet or obtained via Congressional offices.  CRS reports cited in this note were obtained directly from the CRS.

- 63 U.S. Government Accounting Office (GAO) Report to Congressional Committees entitled “Technology Transfer: Administration of the Bayh-Dole Act by Research Universities, May 7, 1998.

74