rafmacalaba/datause-encoder-data huggingface dataset:
Can Preshipment Inspection Offset Noncompetitive Pricing of Developing Countries' Imports: The Evidence from Madagascar

# Alexander J. Yeatsl

# I. Introduction

While numerous theoretical and empirical studies have examined the positive contribution that trade makes to developing countries, industrialization and growth, several recent investigations suggest that the conditions under which some trade occurs may restrict its positive effects. For example, a World Bank analysis of European countries' prices for iron and steel goods showed former African coloriies pay 15 to 25 percent more, on average, for imports than other industrial or developing countries, and that these adverse price differentials persisted over (at least) the last three decades.<sup>2</sup> In addition, previous analyses of discrepancies in partner-country trade statistics provide evidence on the existence of illegal practices such as smuggling and false invoicing to evade tariffs or other restrictions, or to effect capitPl flight.<sup>3</sup> Cases have lPrincipal Economist, International Economics Department, The a.rld Bank, Washington 20433. The views expressed in this paper need not reflect those of the World Bank, its management, or its member countries. 2See Yeats (1990) for details. Factors which appeared to be responsible for the adverse price differences include the small size of the African countries relative to other importers, and the lack of aggressive competition by exporters. In a related study Hufbauer and O'Neill (1972) also show that small countries typically pay more for imports. Avramovic (1978) determined that market imperfections, as well as a lack of finance and countervailing power, result in developing countries generally receiving less than developed countries for exports.

3 For example, Bhagwati (1967) and Sheikh (1974) use partner-country trade data to show how Indian and Pakistani exporters inflate invoices to illegally secure export subsidies. Simkin (1970) uses the same approach to assess **the** level of smuggling and noncompliance with international commodity agreements in Afriea. **See** Ely (1961) and Morgenstern (1963) for a general discussion of fa-tors causing discrepancies in partner-country trade data.
primarily mango, pawpaw (papaya), and oranges).<sup>10</sup> Tanzanian farmers also reported the highest prevalence of trees for timber (18%) and about a quarter reported (23%) growing tree cash crops.

By contrast, in Ethiopia less than 3.5% of landowners reported having trees for timber and fuelwood on their land, but the country had the highest proportion of farms with tree cash crops (32%), mainly coffee (65% of total tree cash crops) and chat (34% of total tree cash crops), and one in six farmers reported growing fruit trees. The small share of on-farm timber trees is likely an underestimate as the presence of Eucalyptus was not properly captured in the questionnaires. Recent case studies such as Bluffstone et al. (2015) report, for example, that in the six districts they surveyed 70% of households grew eucalyptus.

Uganda, which features much less land area in plantation forests, follows a similar pattern as Ethiopia, with few trees for timber or fuelwood reported and tree cash crops the most common type of tree (27%, nearly all of which (97%) are coffee trees). Abstracting from bananas (which are not classified as tree crops), few farmers report growing fruit trees. In Malawi, fruit trees (mainly mango (56%) and pawpaw (12%)) are the most common category (23%). Information was not available on tree cash crops for Malawi, which may lead to an underestimate of the prevalence of trees on farms in that country. Tree cash crops comprised the most frequent category found in Nigeria. Information on timber and fuelwood trees was not available for Nigeria.

On average, across the study countries, about 60% of trees on farms are reported in areas with other crops present (i.e. they are intercropped). But this figure masks again substantial variation (Table 2). Farmers in Malawi and Ethiopia appear to be much less likely to mix trees on their farms with other crops than farmers in Nigeria, Tanzania, and Uganda. Only 10% of trees on farms were reported as present in the same area as other crops in Malawi, for example. At the other extreme, more than 95% of on-farm trees are reported as part of intercropped systems in Uganda. The common practice of intercropping coffee with bananas is only part of the story (as in Tanzania and Uganda) and further country-by-country case study is needed. Nonetheless, the results highlight the prevalence of agroforestry practices in the study countries.

> 10 Bananas are especially prevalent in Tanzania and Uganda, but they are classified under the category of plant/herb/grass/roots based on their biological characteristics (Parr et al. 2014).

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Policy Research Working Paper 10405

# **Abstract**

Modern inputs and mechanization are promoted across Africa to raise smallholder labor productivity and broker the structural transformation. Yet, adoption has remained low and the implications for returns to labor and labor allocation remain poorly understood. This paper explores the effects of different intensification packages on farm performance, market orientation, and food security using data from lowland rice farmers in Côte d’Ivoire. Employing a multinomial treatment effect model, the findings reveal that intensification increases land and labor productivity, especially when agro-chemicals and mechanized land preparation are combined. Returns to labor double to triple, inducing specialization and greater market orientation as well as greater food security, while productively releasing agricultural labor for other activities. Labor in agriculture becomes more waged. The gender balance remains the same. Child labor input does not decrease. The findings call for greater attention to labor productivity and confirm that agricultural intensification can pay and enhance rural transformation.

This paper is a product of the Social Protection and Jobs Global Practice and the Jobs Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lchristiaensen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(Parameter values common for all countries are in parentheses.)

# **Table 2.** Notation and Parameter Values

_ηMI_ : (0.60) Elasticity of demand of vehicle-miles traveled with respect to disposable income.

_ηMF_ : (0.22) Elasticity of demand of vehicle-miles traveled with respect to gasoline price.

_ηF F_ : (0.55) Own-price elasticity of demand for gasoline.

_εLL_ : (0.20) Elasticity of labor supply.

_ε_<sup>_c_(0.35)Compensatedelasticityoflaborsupply.</sup> _LL_<sup>:</sup> _β_ : (0.40) Fraction of motor fuel demand elasticity due to reduced vehicle-miles traveled; _β_ = _ηMF /ηMM_ .

Fuel efficiency, measured as miles/gal is given by a constant-elasticity formula: The effective (average) tax rate on labor is defined as _tL_ = _αG −_ ( _tF /qF_ ) _αF_ , where _αG_ and _αF_ are the shares of government spending and gasoline expenditures in national output.

Table 2 and section 2.2 explain the remaining notation (all notation here is identical to Parry and Small (2005); ‘0’ superscripts indicate initial values). Parameter values that are common for all countries are in parentheses in Table 2. When the values are countryspecific, section 2.2 explains the sources.

# _2.2. Data Sources for Country-Specific Parameter Values_

- ( _M_<sup>0</sup> _/F_<sup>0</sup> ) : **Initial motor fuel efficiency:** P-S use values of 20 miles/gal for U.S. and 30 miles/gal for U.K. From IEA (2007) we obtain values for OECD countries and from IEA (2008) we obtain motor fuel efficiency values for India, Brazil, China and transition countries, the latter we assume is equal to Russia’s. A Korean government report<sup>4</sup> stated passenger cars motor fuel efficiency was 10.69 kilometers per liter in 2006.<sup>5</sup> For Mexico we obtain values of 28 miles/gal.<sup>6</sup> For the rest of the countries (Portugal, Spain, Switzerland, Austria, Belgium, Luxembourg, Hungary, Iceland, Poland, Turkey and Czech Republic) we used the median value for European countries, 31 miles/gal, and the sample median for South Africa (27 miles/gal).

- _E_<sup>_C_</sup> : **External congestion costs:** Congestion costs are highly variable across times and locations; thus P-S use central values of marginal congestion cost of 3.5 cents/mile

> 4 See http://www.highbeam.com/doc/1G1-143417929.html; article appeared in YON - Yonhap News Agency of Korea , March 19, 2006.

> 5 http://www.highbeam.com/doc/1G1-143417929.html

> 6 Results presented at a _Workshop on Sustainable Transport in Latin America_ in Washington DC in 2005 http://www.autoproject.org.cn/english/new ~~a~~ dvance ~~e~~ n/Mex%20TRB%20presentation.pdf

6
<!-- Start of picture text --> Benchmark<br>South Upper | South Africa's<br>Africa Middle SBASB Worcel performance vs.<br>Income Benchmark<br>Deviation of +10% or more from Benchmark<br>Electricity: Average End-User Prices (US cents/k Wh) - . . _, ve<br>(Residential/Non-Energy: Residential} " a a eet<br>Energy: Electric Power Transmission and Distribution Losses<br>(% of total output) $.0 14.0 19.0 14.0 Outperform<br>ICT: Cost of Phone Call to the US 5 5 OF 335<br>(US cents/3 minutes) 8 30 4 Outperform<br>ICT: Cost of Celhilar Local Call 25 5 2 o<br>(US cents/3 off-peak minutes) a . 7 pease<br>Deviation of between -10% and +10%o from Benclunmark<br>Enerty: Energy Use Per PPP GDP * -<br>(kg of: oil equivalent/1000_ PPP dollars, Constant 7000) 257 240 364 275 AA ver aage<br>Water:(%o7 ofer population)oeAccesstoo ImprovedImproved WaterWater SourcesSource: 87 03 64 s0 Average<br>Water: Households Using Piped or Well Water as Main Source of<br>Drinking Water (0 of households)= 85 80 78 72 AA ver aage<br>ICT: Cellular Teledensity - -<br>(cellular subscribers/1000a people) 364 381 73 296 Averawerage<br>Deviation of -10% or more from Benchmark<br>Energy: eT) to Electricity Network 66 $7 15 60 Underperform<br>(%o of Population)<br>Energy: Households Reporting Access to Electricity 65 74 23 45 Underperform<br>(% of households)<br>a Acces ES to Improved Sanitation 67 86 a7 G4 Underperform<br>(%o of population)<br>ICT: Mainlines Teledensity<br>(Qmainlnes subscribers/LOOD. people) 107 261 31 213 Underperform1<br>I “T ost of Local Phone Call 15 9 10 9 Underperform<br>(US cents/$ minutes)<br>ICT: Phone Faults 48 18 57 37 Underperform<br>(reported faults/100 mainlines) T<br>Transport: Road Density in Terms of Population 61 02 44 6.7 Underperform<br>(voad-km‘1000 people)<br>Transp Pe Road Density in Terms of Land 127 1076 156 841 Underperform<br>(road-kiw'1000 sq kim)<br>Transport: Rail Lines Density in Terms of Land<br>(vail-km’1000 sq km) . 17 31 4 23 Underperform¥<br>Peas Travel Time u ork in Main Cities 45 20 a4 a] Underperform<br>(Qinutes/one-way work trip)<br>Transport: Paved Roads 21 57 25 50 Underperform<br>(%o of total roads}<br><!-- End of picture text -->

Note: Data used isfrou the latest observation available between 1997 and 2045, Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
least broadly neutrally, so that there was a substantial associated reduction in poverty. There is evidence that providing all-weather roads also reduces poverty in south-east Asia. Between 1997-98 and 2002-03, the headcount ratio in rural Laos fell by 9.5 percentage points: Warr (2010) estimates that 13 percent of this decline is attributable to the conversion of dry- into all-weather roads.

In a quite different tradition, there is the so-called ‘capitalization’ approach to estimating benefits (net of any costs entailed by ownership, such as land taxes), which is well-established in the regional and urban economics literature. Although there are drawbacks when applying it to poor rural regions, one careful study of Madagascar (Jacoby and Minten, 2009) arrives at the conclusion that this approach yields a much larger estimated benefit than that based on the savings of time.

Thus motivated, this paper presents the main findings of a specially designed resurvey of 30 villages in upland Orissa, which had been the subject of an earlier study of how households respond to, and cope with, fluctuations in the state of nature in this drought-prone region (van Dillen, 2008). The original survey covered three seasons, starting in _kharif_ 2000,<sup>1</sup> when only two of the villages surveyed had an all-weather road. As time passed, PMGSY began to make itself felt; it turned out that, starting in 2007, a further 9 villages had been provided with such a connection by the end of 2009. This development offered an opportunity to investigate the effects of PMGSY on the lives and doings of the villagers so served. In particular, a re-survey of the original households promised to provide certain empirical underpinnings for an economic analysis of PMGSY in connection with a proposed World Bank loan.

The paper is organized in keeping with the three domains identified above. Following a brief description of the area studied and the sample frame in Section 2, the effects of the new roads on trade, education and health are described and analyzed in Sections 3, 4 and 5, respectively. The emphasis is on positive rather than normative measurement, whereby the former supports and serves the latter. The only attempt at normative measurement is made in Section 6, which reports and discusses the respondents’ rankings of the benefits they derived in each of the three domains. Those readers who hanker after a normative analysis of PMGSY may consult Bell (2011a, 2011b), which draw on some of the material presented here. The main conclusions are drawn together in Section 7.

> 1The agricultural year runs from 1 July to 30 June, the first six months of which define the _kharif_ season, the second the _rabi_ season.

3
_Appendix Table 3: Characteristic balance at baseline: nonbeneficiary households in above_ _<u>median saturated areas</u>_

|Baseline survey variables|Control<br>Mean|Difference<br>(Treated-<br>Control)|Standardized<br>Mean<br>Difference|p-value|
|---|---|---|---|---|
|LN Proxy Means Test Score|9.816|0.003|-0.070|0.910|
|Household composition:|||||
|Household size|4.088|-0.429**|0.269|0.016|
|Children 5 years old and below|0.491|-0.047|0.099|0.344|
|Children between 6 and 14 years old|0.894|-0.078|0.127|0.397|
|Primary occupation: Farming and livestock|0.372|-0.036|0.143|0.566|
|Educational attainment of the household head:|||||
|No grade completed|0.031|0.002|-0.080|0.931|
|Some elementary school|0.231|0.059|-0.110|0.236|
|Completed elementary school|0.200|-0.043|0.135|0.264|
|Some high school|0.124|-0.013|-0.003|0.653|
|High school graduate|0.213|-0.032|0.104|0.412|
|Some college|0.107|0.008|-0.030|0.794|
|College graduate|0.093|0.019|-0.060|0.499|
|School Attendance:|||||
|Children between 6 to 11 years|0.931|0.022|-0.073|0.498|
|Children between 12 to 14 years|0.851|-0.037|0.057|0.579|
|Housing Amenities:|||||
|Strong roof materials|0.611|-0.086|0.182|0.135|
|Strong wall materials|0.482|-0.079|0.164|0.149|
|Light roof materials|0.221|0.095*|-0.148|0.087|
|Light wall materials|0.212|0.093**|-0.139|0.032|
|Owns a house and lot|0.389|0.031|-0.061|0.573|
|House has no toilet|0.208|0.009|0.024|0.827|
|Shares a water source|0.195|0.032|-0.087|0.612|
|Household Assets:|||||
|Electricity in house|0.735|0.036|-0.049|0.301|
|Owns a television|0.562|-0.051|0.110|0.334|
|Owns a video recorder|0.389|-0.099*|0.241|0.071|
|Owns a Stereo/CD player|0.367|-0.068|0.154|0.231|
|Owns a refrigerator|0.398|-0.091**|0.148|0.028|
|Has a telephone/cellphone|0.279|-0.109**|0.225|0.037|
|Owns amotorcycle|0.204|-0.051|0.147|0.235|

*** p<0.01, ** p<0.05, * p<0.1.

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 65 villages. Source: 2008 Household Assessment Form 52
exceeded $1.3 billion, around 7 percent of the country’s GDP.<sup>4</sup> The tsunami’s impact was concentrated in a narrow strip along the coast, and in the fishing and tourism sectors. Aggregate Sri Lankan GDP fell only by between 0.5 and 1.0 percent (Jayasuriya et al, 2005). But two-thirds of the island’s fishing fleet was destroyed (Asian Development Bank et al, 2005), and—in spite of the fact that the arrival of aid workers dampened the blow—hotel bookings fell by 60 percent on Sri Lanka’s southern coast in 2005.

The international response to the disaster was rapid and strong. Governments, international NGOs and the international financial institutions committed over $2 billion in relief and recovery funds, of which $1.1 billion had been dispersed and $0.6 billion expended 18 months after the disaster (Government of Sri Lanka, 2006). Of the total aid pledged, $184 million was categorized as “relief” aid. The remainder was for recovery of the housing ($370 million), transportation ($245 million committed), and water ($190 million) infrastructure and for livelihood restoration ($219 million).

Though the prevalence of insurance and aid differs in high- and low-income countries, businesses face a similar set of shocks following a natural disaster regardless of their location. We discuss three factors that typically affect the recovery process and give us reason to believe the findings here would apply more broadly to recovery from other large disasters. First, labor and capital are destroyed. In Sri Lanka, it appears the impact on the capital stock was proportionately larger than the impact on the labor force, almost certainly the usual pattern. Second, demand shifts. In aggregate, the shift will generally be inward. But for some sectors (e.g., construction materials), demand may shift outward. Third, many trading relationships are destroyed, either temporarily or permanently. The time required to re-form relationships depends on the nature of the relationships in a given sector. This effect is analogous to the “disorganization” effects discussed in the

> 4 This is the figure reported in the EM-DAT database. An assessment by the Asian Development Bank, World Bank and Japanese Bank for International Cooperation estimated losses of roughly $1.0 billion and replacement costs of $1.5 billion. The Sri Lankan government estimated recovery costs to be $1.8 billion.

5
|_Provinces’ dummies_<br>Butare|||||
|---|---|---|---|---|
|<br>Byumba|4.41*|-4.10**|4.15**|0.30|
||(0.03)|(0.01)|(0.02)|(0.02)|
|Cyangugu|6.91**|0.53|5.14**|1.67|
||(0.03)|(0.02)|(0.02)|(0.02)|
|Gikongoro|10.02**|-0.04|7.45**|2.61*|
||(0.03)|(0.02)|(0.03)|(0.02)|
|Gisenyi|-3.93**|-6.13**|-2.75**|-0.90|
||(0.02)|(0.01)|(0.01)|(0.01)|
|Gitarama|6.80**|-1.32|3.63**|3.41**|
||(0.03)|(0.02)|(0.02)|(0.02)|
|Kibungo|5.10**|-3.99**|3.49|1.88|
||(0.03)|(0.01)|(0.02)|(0.02)|
|Kibuye|4.29|-3.88**|-0.04|3.93**|
||(0.03)|(0.01)|(0.02)|(0.03)|
|Kigali Ngali|5.69**|1.27|5.00**|0.86|
||(0.03)|(0.02)|(0.02)|(0.01)|
|Ruhengeri|<br>6.97**|<br>-0.85|<br>2.69*|<br>4.23**|
||(0.03)|(0.02)|(0.02)|(0.02)|
|Umutara|-0.96|-2.37|0.35|-1.17|
||(0.02)|(0.01)|(0.02)|(0.01)|
|_Community’s characteristics_|||||
|Daily market|||||
|Weekly market|-3.30||-1.15|-1.79|
||(0.02)||(0.02)|(0.01)|
|No market|-2.53||-0.48|-1.83|
||(0.03)||(0.02)|(0.02)|
|No road|||||
|Road sometimes inaccessible|1.58|2.05|0.66|0.64|
||(0.02)|(0.02)|(0.01)|(0.01)|
|Road always accessible|<br>2.40|<br>4.09**|<br>1.46|<br>0.64|
||(0.02)|(0.02)|(0.01)|(0.01)|
|Primary school in cell|||||
|Primary school at 1 km|-2.35**||-1.48**|-0.64|
||(0.01)||(0.01)|(0.01)|
|Primary school at 2 kms|-0.95||-0.80|0.04|
||(0.01)||(0.01)|(0.01)|
|Primary school at 3+ kms|<br>-2.29**||<br>-1.89**|<br>-0.23|
||(0.01)||(0.01)|(0.01)|
|More arrivals in community|||||
|More departures||1.41|||
|||(0.01)|||
|An equivalent number||3.85|||
|||(0.03)|||
|No arrivals, no departures||<br>4.58**|||
|||(0.02)|||
|Distance to health clinic||<br>-0.15|||
|||(0.001)|||
|Distance to extension services||<br>-0.09**|||
|||(0.0005)|||
|Observed probability %|9.57|<br>9.32|5.99|3.73|
|Predicted probability %|7.49|6.97|4.25|2.92|
|Observations|5256|5247|5224||

Note: Marginal effects in percentage points, they are the change in the probability for an infinitesimal change in each independent, continuous variable and, the discrete change in the probability for dummy variables. These changes are calculated at the mean predicted probability. For example, increasing men’s education from none to primary (keeping all other variables at their actual value) will increase the probability that they become non-farm self-employed from the mean of 7.79 percent to 9.43 percent. An infinitesimal increase in agricultural land holding will increase the probability that men become non-farm self-employed from the mean of 7.79 percent to 8.79 percent. The latter change is not exactly equal to the change in predicted probability corresponding to a one-unit increase in agricultural land holding, but it is very close to it.

Robust standard errors in parentheses, corrected for clustering; ** significant at 5% level; * significant at 10% level. Dependents ratio: Ratio of dependents to total number of household members.

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- Pankratz, Nora M. C. and Christoph M. Schiller (2022). “Climate Change and Adaptation in Global Supply-Chain Networks”.

- Pécastaing, Nicolas, Jorge Dávalos, and Andy Inga (2018). “The effect of Peru’s CDM investments on households’ welfare: An econometric approach”. _Energy Policy_ 123, 198– 207.

- Pelli, Martino and Jeanne Tschopp (2017). “Comparative advantage, capital destruction, and hurricanes”. _Journal of International Economics_ 108, 315–337.

- Pelli, Martino, Jeanne Tschopp, et al. (2023). “In the eye of the storm: Firms and capital destruction in India”. _Journal of Urban Economics_ 134, 103529.

- Pérez-González, Francisco and Hayong Yun (2013). “Risk Management and Firm Value: Evidence from Weather Derivatives”. _The Journal of Finance_ 68, 2143–2176.

- Pons, Marc et al. (2013). “Exploring the impact of energy efficiency technologies on manufacturing firm performance”. _Journal of Cleaner Production_ 52, 134–144.

- Potoski, Matthew and Aseem Prakash (2004). “Regulatory Convergence in Nongovernmental Regimes? Cross-National Adoption of ISO 14001 Certifications”. _The Journal of Politics_ 66. Publisher: The University of Chicago Press, 885–905.

- Przychodzen, Wojciech, Fernando Gómez-Bezares, and Justyna Przychodzen (2018). “Green information technologies practices and financial performance - The empirical evidence from German publicly traded companies”. _Journal of Cleaner Production_ 201, 570–579.

- Rentschler, Jun et al. (2021). _Floods and Their Impacts on Firms: Evidence from Tanzania_ . Policy Research Working Papers. The World Bank.

- Rietbergen, Martijn G., Jacco C. M. Farla, and Kornelis Blok (2002). “Do agreements enhance energy efficiency improvement?: Analysing the actual outcome of long-term agreements on industrial energy efficiency improvement in The Netherlands”. _Journal of Cleaner Production_ 10, 153–163.

- Roth Tran, Brigitte (2022). “Sellin’ in the Rain: Weather, Climate, and Retail Sales”.

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