rafmacalaba/datause-encoder-data huggingface dataset:
- 18

-

Table 4. Performance Inicatoro for Trade Adjustmnt Loan Rlciplnt. Safore and After Trade Adjustmant Lending: Twnty-Six Pre-196 Trade Loon Recipients vs. Forty-Eight Nonrecipienta

|Indicator|Low<br>I<br>Income|Middle<br>inme|Re<br>aum|Sub-<br>H<br>Sahonrn<br>i<br>Africaco|ighly<br>ndebted<br>untries|Manufacture.<br>*xporters|
|---|---|---|---|---|---|---|
|Number of trade loanreciplents|9|17|26|11|10|7|
|Numbrof nonreciplents|21|27|40|10|4|8|
|Panel1s 190S-97coeredto 19 1|-g1||||||
|GDP growth|9(,)ee|12(*)**|21|10(.)e*|**,(.)e**|"-.)|
|Investment/OW|SO.)|14(.)|19|9(W)|GO.)|7( )|
|**Roeal oxchange rate**|***(,)e**|5<sup>(*)**</sup>|2J|**9(.)***|**8(0)**|**7()**|
|Manufacturing exports growth|**7(.)CO**|12(#)|19|1 0<sup>(.)**</sup>|1<sup>(.)e</sup>|4(-)|
|Import growth<br>"(.)C5||12<sup>(*)**</sup>|20|**9(*)e**|4(-)|S0C)|
|Rosourcebalance/CDP|2(-)|12(e)|14|SC-)|1 0 <sup>(.)e*</sup>|1(-)|
|Inflation|8(#)*|14(e)|22|10(C)**|7(-)|1(-)|
|External debt/exports|6(+)|17(.)e|28|SC.)|10(.)Oso.|)|
|ribtservice/oxports|6(.)|10t-)|16|4(-)|*t-)|7(e)|
|Shareshowingimprovementa|0.72|0.77|0.75|0.74|0.58|0.70|
|(10 intensive<br>recipient.)|(0.78)|(0.72)|(0.78)|(0.76)|(0.68)|(O.)|
|(All 40recipient.)|(0.70)|(0.64)|(0.67)|(0.68)|(0.56)|(0.64)|
|Ponol 2: Three**years**after|||||||
|compared to throe**years**before|||||||
|CDP growth|S6.)|18(e)|16|8(*)|6SC)|4(e)|
|InvoctAent/CDP|4(-)|II(C)|15|SO-)|8(O)|4(-)|
|Real exchangerat.|6(-)|16(+)|21|10(e)|9(e)|7(-)|
|Manufacturing exports growth|7(.)|14(e)|21|9(e)|**SC-)**|4(*)|
|Import growth|6(i)|14<sup>(+)*</sup>|<br>20|6(e)|5 <sup>()es</sup>|(|
|<br>Resource balance/GOP|<br>SG.)|11(.)|16|<br>8(-)|<br>6(e)|2(+)|
|Inflation|7(e)|18(.)|20|9(e)|6(e)|4(-)|
|Externaldebt/export.|SC()|<br>14(#)|19|<br>7(-)|9(e)|SC.)|
|Debt service/export|G(-)|9(W)|14|J(-)|5(-)|4(e)|
|**Share**showingimprovements|0.60|0.76|0.70|0.64|0.44|0.66|
|(10 intensive recipient.)|(0.76)|(0.69)|(0.71)|(0.64)|(0.71)|(0.78)|
|(All 40recipient.)|(0.56)|(0.70)|(0-67)|(0.62)|(0.63)|(0.54)|

Note: The nuabers in the table show for each indicator the number of trade adjustment loan reciplonts in each classification that improved In the period after the loan compared with the period before the loan relative to the change over the **some** period. for

- nonrecipient comarators. The year of receIpt of the first loon in excluded from the cosparison in panel 2. **The** plus and minus signs indicate an improvemet or a worsening of the average value of an indicator for reciplent. comred with the change In average value for nonrecipient.

- The change In means for the reciplents between the two periods relative to **the** chango for nonreciplents is significant at a 10-percent confidence Interval.

- **cc** The change In means for the recipient. between the two periods relative to the change for nonrecipionts In significant at **a** S-percent confidence Interval.

- **a.** The **share** of tho product of the number of variable. and the numer of countries showing lmprov e nt In the total.
simulation exercise. As to triggering additional duties in periods of serious domestic-price undercutting by imports, his results show that:

- in more than half of the periods of serious price undercutting, the SSM would not have triggered an additional duty, and

- more that half of the additional duties triggered would not be in periods of serious price undercutting.

A second analysis, one I conducted with Francis Ng, pays attention to the SSM (as the SSG) using unit values of imports from all sources as the basis for trigger prices. Prices often differ considerably from source-to-source, the result could be to authorize restriction when there was no surge of import volume or change of price from the previous years. We found that in a given year, unit values from specific countries are sufficiently below aggregate unit values to trigger restrictions on 59 percent of agricultural tariff lines. By comparison, the tariff modalities allow each developing Member to exempt one-third of tariff lines from full application of the tariff formula as Sensitive Products, 12 percent as Special Products. Thus typical ‘noise’ in trade data means that the SSM will bring with it a flexibility almost twice as large as the Special Products and Sensitive Products exemptions in the tariff negotiations. Unit values tend to be lower on shipments from developing countries, hence these spuriously allowed restrictions are most likely to apply exports from developing Members.

‘Filling in’ against shocks is difficult The finding above – that rules to ‘fill in’ with policy remedies in ‘exceptional’ years are likely to be inaccurate – is not an isolated one. Studies sparked by an earlier, 1970s, concern to deal with the instability likewise found that workable decision rules to identify when a buffer stock should buy or sell, or when the IMF’s Compensatory Finance Facility (CFF) should lend or collect, were difficult to devise. Simulations indicated that even when determinations of trend were based on sophisticated regressions, buffer stocks turned out to be on the wrong side of the market as often as on the correct side. Viewed in retrospect, more than half of CFF borrowings had been in years in which export receipts were above trend.

# <u>Conclusion</u>

The proposed SSM formulas would provide a poor guide for policy – would frequently prescribe action when it is not needed, frequently fail to prescribe action when it is appropriate. There are no procedural requirements that might otherwise guide good policy choice. It, like the existing SSG, would function minimally as a guide for making decisions as to when an import restriction might be appropriate, principally as an expansion beyond tariff bindings of the GATT/WTO’s limits on the restrictions a Member can impose unilaterally.

iv
# Histogram and Kernel Density Plot

<!-- Start of picture text --> iA °<br>ro) | TM<br>ii we iO<br>S } ~<br>=~ ° |<br>=<br>o<br>ra | =<br>= /<br>fi<br>Oo Fi Qo<br>:<br>So f | cS<br>/<br>A<br>° S<br>0.0 0.2 0.4 0.6 0.8 1.0<br>Propensity Score<br><!-- End of picture text -->
# INDEX

|_Values ..........................................................._<br>_2_|
|---|
|International Experience On Economies Of Scale, Farm Size And Productivity**......................2**|
|_Sources of economies of scale ..........................................................._<br>_3_|
|_Evidence on the fa_<br>_rm_<br>_size-productivity relationship .........................................................._<br>_5_|
|_Related issues: Mechanization, labor organization andfarm size .........................................................._<br>_7_|
|Structure Of South African Agriculture: Issues Related To Size**..............................................8**|
|_Farm sizes in South Africa ..........................................................._<br>_8_|
|_Evidence of economies of scale ..........................................................._<br>_9_|
|_Evidence on causes of scale efficiency .........................................................._<br>_11_|
|_Efficiency in the former homelands .........................................................._<br>_13_|
|_Synop sis .........................................................._<br>_15_|
|Evidence Of Scale Efficiency In The Homelands**..........................................................**<br>15|
|_The data .........................................................._<br>_16_|
|_Measurement ofproductive efficiency .........................................................._<br>_18_|
|_Results and interpretation ..........................................................._<br>_20_|
|_Conclusions .........................................................._<br>_23_|
|Efficiency Of Scale In Commercial Agriculture**..........................................................**<br>23|
|_Data .........................................................._<br>_24_|
|_Totalfactor productivity byfarm size category .........................................................._<br>_26_|
|_Non-parametric efficiency estimation using Data Envelope Analysis (DEA) .............................. 31_|
|_Econometric estimation offarm size-efficiency relationship ...................................................... 36_|
|Explaining The Results: Policy, Technology And Management**..............................................**39|
|Implications For Land Reform**..........................................................**<br>41|
|_References .........................................................._<br>_42_|
- 45 -

# IV. SUMMARY AND CONCLUSIONS

This paper has attempted to demonstrate three main points. First, institutional development is critical to effective, sustainable poverty reduction and growth. This statement is based on development experience which strongly suggests that economic policies alone cannot overcome the economic, political, and cultural hurdles that prevent resources from adequately reaching the poor, and development interventions that ignore institutional considerations are often unsustainable.

Second, although there is no one model for poverty-oriented ID and ID initiatives will vary considerably cross-sectorally and cross-nationally, a number of important lessons have been learned about successful institutional development initiatives which can be delineated as six broad components. They include elements that are concerned with countries' broad institutional structures--forming or strengthening local institutions and supporting institutional pluralism and components that describe specific institutional characteristics--institutional linkages, organizational structure and managerial leadership, the learning process approach, and participation and local resource mobilization. These components emphasize flexibility, adaptability, and the need for poor peoples' involvement in programming.

Third, the paper has argued that although ID initiatives have faced considerable criticisms of cost, scale, compatibility with traditional project frameworks, decentralization, and political feasibility, these objections may be unwarranted or overly skeptical of ID's viability and potential. The paper uses case studies from different productive sectors and sub-sectors to support these propositions, and to illustrate what the various components look like in practice.
of uninsured farmers, but they may have been different if these farmers were insured. This is the standard problem of moral hazard.

In this simple form, the insurance premium can be decomposed as Premium = pure premium + reserve load + administrative cost + return on equity.

We focus here on the first two components, i.e., the pure premium and the reserve load. Expressed in percentage of the liability, the pure premium is the expected LCR. The reserve load is the cost of reserves the insurer must set aside in order to pay unexpected losses, with a predefined degree of confidence (e.g., 99%).<sup>11</sup> The reserves, or minimum capital requirements, are derived from the probability distribution of insured losses. The reserve hold is thus the opportunity cost of holding these reserves. The two components of the insurance premium are illustrated using the previous example on groundnut crop insurance (see Table 1). The groundnut yield density is estimated using the nonparametric kernel procedure. The reserves are built to pay losses caused by events with a 100 year return period. The opportunity cost of capital is 5%. Table 2 shows the pure premium rate (i.e., expected LCR), the minimum capital requirements (MCR) in percent of liability and the reserve load in percent of liability, for different levels of insurance coverage.

**Table 2.  Pure premium, MCR reserve load, in percent of liability - groundnut crop insurance scheme.**

|**Insurance coverage**|**Pure premium**|**MCR**|**Reserve load**|
|---|---|---|---|
|50 percent coverage|6.01%|50.82%|2.54%|
|65 percent coverage|8.50%|62.17%|3.11%|
|75 percent coverage|10.49%|67.21%|3.36%|
|85 percent coverage|12.91%|71.07%|3.55%|

Source: Authors.

The reserve load can represent a significant fraction of the premium rate. In the above example, it represents 42% (resp. 37%, 32% and 28%) of the pure premium rate, under 50 (resp. 65, 75, 85) percent coverage. The lower the coverage, the higher is the reserve load as a percentage of the pure premium rate. In other words, under catastrophic coverage, the gross premium rate becomes more and more driven by the reserve load.

> 11 Part of these unexpected losses are usually transferred through reinsurance treaties.

25
<!-- Start of picture text --> Mean regression Median regression Average<br>8o8o 8o<br>g OPER g OPER g OPER<br>3 3 3<br>cs L cs cs<br>5ge / y 5ge / 7 5£° //<br>8s , 8s 7 8s LY<br>‘Ssto} / . —_ro / -to} 4<br>2. ort cri Se OPHe Ocrl So OPH Ocri<br>QR OOS ara QR OS06 Bra LE ° aan<br>8 OIDN 8 1DN 8 JADN<br>o i] OwReCU ” o CEAKo ° o omasu<br>-10 o 10 20 30 -10 0 10 20 30 10 0 10 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br>Avg. without outliers Including total GDP Including GDP*Informality<br>8 8 8<br>g oO o o<br>3 OPER g OPER ge OPER<br>cs 3cs 3cs<br>5ao ; J 5oo , y 5oo y<br>$s2 / Z se2 / 7 8s° / A<br>-ro} / , -Ie} e, -lo} , ,<br>Se ° ene cRI Se °Pre CRI S 8 Z cRI<br>2 “ ° BRA 2 N ° BRA 2 “ OcobsRA<br>\*) iDN oO iDN Oo<br>2 o onRecu ° o OmmESU ° o O%Ko<br>-10 o 10 20 30 -10 o 10 20 30 5 0 5 10 15 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br><!-- End of picture text -->
cleared outside of the customs office. Presumably these were low-risk shipments, and clearing them offsite offers operational benefits to the customs agency, which can focus its attention on the remaining, higher risk shipments. Moving IHC shipments away from the customs office may also offer small decongestion benefits that flow to other firms. Our estimation strategy assumes the decongestion benefits are non-existent but they may exist, and they are certainly plausible if the program were to expand. It is also possible that the program would have statistically significant impacts on firms that were less able to adopt the program. Support for this claim would require further research.

While a better understanding of the effect of the IHC program is useful, the larger purpose of this study is to introduce pooled synthetic control methods into the trade facilitation literature. Customs reforms are particularly difficult reforms to evaluate because the trade outcomes they seek to influence exhibit complex dynamics and are often heterogeneous across units. We believe the pooled synthetic control method offers an opportunity to extend rigorous evaluation methods to a body of reforms that are otherwise quite difficult to evaluate credibly.

# **References**

Abadie, A. and J., Gardeazabal (2003). “The Economic Costs of Conflict: a Case Study of the Basque Country”, _American Economic Review_ 93:113-132.

Abadie, A., Diamond, A., and J., Hainmuller (2010). “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,” _Journal of the American Statistical Association_ 105: 493-505.

Atkin, D., Khandelwal, A., and A. Osman (2014). “Exporting and Firm Performance: Evidence from a Randomized Trial,” NBER Working Paper 20690.

Billmeier, B. and T. Nancinni (2013). “Assessing Economic Liberalization Episodes: A Synthetic Control Approach,” _Review of Economics and Statistics_ 95: 983-1001.

Cadot, O., Fernandes, A., Gourdon, J. and A. Mattoo (2015). “Are Export Support Programs Effective? Evidence from Tunisia,” _Journal of International Economics_ 97: 310-324.

Carballo, J., Schaur, G. and C. Volpe Martincus (2016). “Trust No One? Security and International Trade,” University of Tennessee mimeo.

Castillo, V., Garone, L., Maffioli, A., and L. Salazar (2015). “Tourism Policy, a Big Push to Employment? Evidence from a Multiple Synthetic Control Approach,” _IDB Working Paper Series_ , 572.

45
world prices. Load shedding causes domestic production of goods and services to be more expensive, therefore domestic prices of almost all commodities would be higher than corresponding world prices. This would cause exports to decrease.

# **Figure 5: Impacts on exports**

Annual average deviation from the baseline (%) during the 2008-2015 period <!-- Start of picture text --> ‐2.8 Total<br>‐0.5 Services<br>‐4.1 Transportation & communication<br>‐5.1 Manufacturing<br>‐1.1 Food & beverage<br>‐15.9 Mining & quarrying<br>0.1 Agriculture & forestry<br><!-- End of picture text -->

# **5.3.2 Imports**

Since load shedding causes production activities to decline, inputs needed for production would also decline. Annually, the country has suffered 5% drop of imports, on average, during the 2008-2015 period. Imports of construction goods would face the highest decline as the construction sector faces the highest decline in its output. The service sector is an exception (see Figure 6). This is because some energy commodities such as cooking gas, LPG, are included in the service sector. Import of LPG has increased for cooking.

14
relocation to be limited for several reasons, however. To start with, property rights have been very insecure during this period. Palestinians have tended to rely on their family and patriarchal clan ( _hammouleh_ ) to protect their property. Such protection however only applies when one lives near relatives, which helps explain low voluntary migration rates. Indeed, using census data Abrahams (2018a) finds negligible evidence of firm relocation. With regard to market access specifically, lack of relocation has an even more obvious explanation: obstacles were plainly a temporary phenomenon, so it made sense only for the firms fleetest of foot to pay the fixed costs of relocating during a time of such uncertainty and change.

In interpreting our results it is important to keep in mind that road obstacles represent only one class of mobility restrictions limiting the economic success of the West Bank. Other restrictions include international trade restrictions, Area C restrictions, and limited control of public utilities and communications infrastructure (for further discussion, see Niksic et al. (2014) or AIX Group (2013)). While mobility restrictions have gradually been eased over time, these other restrictions have remained, so our estimates of the benefits of market access may be unduly conservative.

# **3 Measuring market access in the West Bank**

## **3.1 Defining market access**

Market access as we will define it evaluates the ability of roads to connect people and firms. Being better connected to populated localities means being better connected to potential consumers as well as workers. The productivity of a locality is enhanced by cheaper access to markets and labor. Donaldson and Hornbeck (2016) derive a measure of market access that emerges as the solution to a set of implicit equations where market access for a given locality equals a weighted sum over other localities’ access to markets, with the weights being a function of the cost of interacting with these other localities and their population counts (see eq. (9) in Donaldson and Hornbeck (2016)). Donaldson and Hornbeck (2016) also derive a first-order approximation to this solution which yields a measure of market access for locality _i_ that is of the following form:<sup>7</sup>

> 7Donaldson and Hornbeck (2016) confirm that the approximation is highly correlated with the exact measure of market access, and that their empirical results are not sensitive to switching between the approximate and exact measure.

7
1.04 The World Bank has invested almost $2 billion in extension in 79 countries since the mid-1960s, with investment rising from about $1 million per year in the 1960s to almost $200 million per year in the 1980s. Extension services can be organized in different ways, and various models have emerged. Much of the Bank's investment has been in the Training and Visit (T&V) system of extension, originally used in Asia and nov in use in 30 African countries.

# Box 1. T&V: SEVEN UNDERLYING PRINCIPLES:

1. A orofessionat service with futI-time trained staff supported by resources required to perform their professionat functions.

2. A sinste tine of command with staff technicatly and administrativety responsibte to one authority.

3. Staff effort concentrated on ontv the extension function. with staff perfoming clearly defined, monitorable tasks.

4. Time-bound work and trainin oroarams, incLuding regutar farm visits. 5. Fietd and farmer orientation. with speciat reference to meeting farmers on their own fields. 6. Reoutar and continuous traininn at atl staff Levels to upgrade professionat skitts. 7. A arocedure for ensuring a ftow of information between research. extension and farmers (Senor 1987).

1.05 Any extension system must target particular categories of clients to meet their needs efficiently. One lesson to be learnt from the Asian experience of T&V is that training of extension workers should focus not only on the technical message to be transferred -- but also on learning more about "their farmers" and "their farming systems", especially about farmers' behavîor and the reasons they do things the way they do. (Cernea and others 1983). This is especially crucial in Sub-Saharan Africa, where men and women within one household can have different resources, motivations, and constraints. Furthermore, face-to-face contact between agents and villagelevel workers is particularly important when agricultural communities are heavily involved in subsistence agriculture and when large numbers of farmers are illiterate or unfamiliar with current technologies (Pickering 1983).

1.06 Unfortunately, the empirical research on T&V and other extension systems and their effects has been insufficient. Too little is known about collective as opposed to individual adoption of innovations and the role of groups and group-building through extension (Cernea and others 1983). The dynamics of intrahousehold behavior and their implications for extension are also little understood. Consequently, extension staff often lack the tools to fully diagnose the needs and constraints of farmers. This is particularly true with regard to rural vomen. Agricultural extension vas for years carried out primarily by male extension agents -- 1981 97% of extension agents in Africa were male (Swanson and Rassi 1981) -- and directed toward male heads of households. Technological packages were designed with little regard for their likely impact on women's workload, role, and responsibilities.

# 2. Women's Role in Agriculture in Sub-Saharan Africa

1.07 Almost two decades of surveys and studies have clearly demonstrated the pivotal role of women in agriculture in developing countries.