rafmacalaba/datause-encoder-data huggingface dataset:
- 136 -

processes that would directly contribute to both horizontal<sup>and vertical</sup> diversification in Southeast Asian agricultural economies.

# Asricultural Investment Strategy

5.24 Agricultural research is one dimension of agricultural<sup>investment,</sup> and many of the points discussed previously hold<sup>more generally. In</sup> particular, the location of crop-specific investments is a difficult issue. Should regional comparative advantage be developed<sup>through specialization,</sup> with all the attendant consequences for income distribution noted previously? Or should such investments be spread more broadly in order to diversify the geographical risks from crop specialization?

5.25 One solution to this problem is to make crop-specific investments more flexible, primarily with respect to the flexibility of irrigation investments. Irrigation projects have taken the largest investment<sup>of both</sup> domestic funds and those borrowed from donors. The Thailand<sup>Irrigation</sup> Study by the World Bank attempts to assess the costs of upgrading irrigation systems and the potential market demand for non-rice crops.4<sup>7It</sup> notes that most upland crops require better water control and drainage<sup>than</sup> paddy and this implies substantial investment costs as systems design and on-farm development of land infrastructure are often necessary to permit switching to non-paddy crops.

5.26 Whether such large investment costs are worth paying is both a market and a marketing problem. Wherever demand for high-value crops such as peppers and onions exists and the marketing system is effective<sup>in</sup> linking that demand to farmers, many of the necessary on-farm investments are made by farmers themselves as they seek to capture new profit opportunities. With sufficiernt incentives, ridges have been built up in the middle of paddy fields, and horticultural crops have been grown surrounded by irrigated rice. The emphasis on the economics of the 47/ World Bank, "Thailand Irrigation Subsector Review.' Report No. 5847-TH. (Washington, D.C., Projects Department, East Asia and Pacific Regional Office, April 25, 1986), p. 52.
**Figure 1. Limits and projected support measures in the USA, ambitious cuts**

<!-- Start of picture text --> 30<br>25<br>OTDS limit<br>20 OTDS<br>AMS limit<br>15<br>Total AMS<br>10<br>5<br>0<br>2008 2009 2010 2011 2012 2013 2014<br>Billion dollars<br><!-- End of picture text --> Source: Blandford and Josling (2008) In Europe, the most rigorous reduction formulae for OTDS considered in the Modalities seem likely to bring the overall trade distorting support below both its AMS subcomponent and the projected levels of support, as shown in Figure 2.

**Figure 2. Limits and projected support measures in the EU, ambitious cuts**

<!-- Start of picture text --> 80<br>70<br>60<br>Total AMS<br>50<br>AMS limit<br>40<br>OTDS<br>30<br>OTDS limit<br>20<br>10<br>0<br>2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14<br>Billion Euros<br><!-- End of picture text --> Source: Blandford and Josling (2008) Any projections of this type need to be treated cautiously, as world prices for agricultural commodities can change rapidly, and the current projections are very high by historical standards. While the US support measures do not appear likely to be binding under 7
We coded a new firm start, if:

- the start date of the business reported in the follow-up round was 2002 or later [the year in which the baseline survey was conducted or later].

We coded the variable _newfirmstart_ to zero, if:

- the business reported in the baseline round survived; or

- no business was reported in the follow-up round and the business of the baseline round was coded as closed; or

- the start date of the business reported in the follow-up round was a date preceding 2002 [the interview date of the baseline wave] and no business was reported in the baseline wave.

Otherwise, the variable _newfirmstart_ was coded as missing.

We coded survival and newfirmstart from round 1 to round 3 as well, based on these same criteria described above.

After this matching and coding, we inspected the characteristics of the businesses for which _survival_ and _newfirmstart_ remained unclear or inconsistent on a case-by-case basis, taking into account information on firm age and ownership reported in the three survey waves. For this, we decided to exclude firms from the sample if information on firm age was missing, since this information is important in determining _survival_ and _newfirmstart_ .

For firms for which we had identified more than one owner, we decided to include the owner, who was reported as the owner in all of the waves in which we observed the firm. If this was the case for both owners, we included the owner,

- who had the lowest household roster number (this chose the household head as the owner if he or she was among the owners), if no other business was reported for at least one of the owners over the whole observation period; or

- who had already been identified as the owner of a different business, observed during one or more different waves over the whole observation period;

and in the remaining cases we chose the owner who was reported as the owner in most of the waves in which we observed the firm, or, if this was the case for both owners, who had the lowest household roster number.

Finally, we excluded firms for which we could not identify at least one owner among the household members who were in the MxFLS sample.

# _Nigeria General Household Survey (NGGHS)_

The Nigeria General Household Survey (NGGHS) is a cross-sectional multipurpose household survey of 22,000 households, carried out annually in Nigeria. Under the World Bank’s Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project a panel component was introduced into the multi-purpose NGGHS, which collects longitudinal data for 5,000 households. The sample is representative at the national level and provides reliable estimates of key socio-economic variables for the six zones in the country. Further details on study design and the data can be obtained in National Bureau of Statistics of the Federal Republic of Nigeria (2014).

The panel component was first introduced in 2010/2011 and has since then been implemented every two years. There are currently three waves available, of which we use the two first ones (2010/2011, and 2012/2013), given that the third wave was not yet available at the time of our 42
**Table 7: Cross-Sectional Poverty Logit Regression**

||`1993`|`1993`<br>`2000`|`2000`|
|---|---|---|---|
||`Poor (ba`|`sed on household expenditure`|`per capita)`|
|`Age`|`-0.080`|`-0.077`<br>`-0.127`|`-0.126`|
||`(1.72)`|`(1.67)`<br>`(2.90)**`|`(2.84)**`|
|`Age squared`|`0.001`|`0.001`<br>`0.001`|`0.001`|
||`(1.81)`|`(1.78)`<br>`(3.04)**`|`(2.99)**`|
|`Sex`|`0.316`|`0.318`<br>`0.042`|`-0.114`|
||`(2.99)**`|`(2.87)**`<br>`(0.44)`|`(1.13)`|
|`Working hours`|`-0.002`|`-0.002`<br>`-0.001`|`-0.001`|
|`per month`||||
||`(3.89)**`|`(3.67)**`<br>`(2.84)**`|`(3.10)**`|
|`Wage income`|`0.220`|`0.183`<br>`0.262`|`0.181`|
|`dummy`||||
||`(2.39)*`|`(1.73)`<br>`(2.92)**`|`(1.83)`|
|`Years of`|`-0.170`|`-0.169`<br>`-0.190`|`-0.180`|
|`schooling`||||
||`(12.64)**`|`(12.09)**`<br>`(17.19)**`|`(15.52)**`|
|`Household size`|`0.202`|`0.201`<br>`0.248`|`0.254`|
||`(4.68)**`|`(4.69)**`<br>`(8.40)**`|`(8.60)**`|
|`Number of`|`0.380`|`0.383`<br>`0.220`|`0.211`|
|`Children < 5`||||
||`(5.73)**`|`(5.76)**`<br>`(3.33)**`|`(3.18)**`|
|`Number of`|`0.000`|`-0.002`<br>`0.045`|`0.038`|
|`Children < 15`||||
||`(0.01)`|`(0.04)`<br>`(0.99)`|`(0.84)`|
|`Number of Men`|`-0.037`|`-0.035`<br>`0.001`|`-0.002`|
|`in hh`||||
||`(0.78)`|`(0.73)`<br>`(0.03)`|`(0.05)`|
|`Urban Dummy`|`0.289`|`0.288`<br>`0.580`|`0.596`|
||`(2.74)**`|`(2.73)**`<br>`(5.72)**`|`(5.82)**`|
|`North Sumatra`|`0.948`|`0.928`<br>`0.236`|`0.155`|
||`(3.25)**`|`(3.18)**`<br>`(0.94)`|`(0.62)`|
|`West Sumatra`|`-0.021`|`-0.047`<br>`0.768`|`0.674`|
||`(0.06)`|`(0.14)`<br>`(2.94)**`|`(2.57)*`|
|`South Sumatra`|`1.512`|`1.514`<br>`1.061`|`1.001`|
||`(5.07)**`|`(5.04)**`<br>`(4.17)**`|`(3.93)**`|
|`Lampung`|`1.736`|`1.702`<br>`1.010`|`0.954`|
||`(5.78)**`|`(5.66)**`<br>`(3.81)**`|`(3.62)**`|
|`West Java`|`0.914`|`0.866`<br>`0.883`|`0.835`|
||`(3.34)**`|`(3.17)**`<br>`(4.13)**`|`(3.91)**`|
|`Central Java`|`1.731`|`1.683`<br>`0.692`|`0.617`|
||`(6.45)**`|`(6.27)**`<br>`(3.18)**`|`(2.83)**`|
|`Di Yogyakarta`|`2.063`|`2.021`<br>`1.256`|`1.147`|
||`(719)**`|`(702)**`<br>`(531)**`|`(481)**`|
|`East Java`|`.`<br>`2.133`|`.`<br>`.`<br>`2.111`<br>`1.003`|`.`<br>`0.949`|
||`(7.94)**`|`(7.85)**`<br>`(4.61)**`|`(4.39)**`|
|`Bali`|`0.828`|`0.812`<br>`0.786`|`0.760`|
||`(2.80)**`|`(2.74)**`<br>`(3.24)**`|`(3.12)**`|
|`West`|`1.359`|`1.328`<br>`0.971`|`0.935`|
|`NusaTenggara`||||
||`(461)**`|`(450)**`<br>`(410)**`|`(396)**`|
|`South`|`.`<br>`0.553`|`.`<br>`.`<br>`0.555`<br>`0.549`|`.`<br>`0.498`|

31
laned, dual-carriageway highways with grade separators and access roads. This group increased from 4% of India’s highways in 2002 to 12% by the end of 2006 due to GQ upgrades alone.

The GQ network, totaling a length of 5,846 km, connects the four major cities of Delhi, Mumbai, Chennai, and Kolkata. Figure 1 provides a map of the GQ network, along with some depictions of output levels in young firms before and after the upgrades. Beyond the four major cities that the GQ network connects, the highway touches many smaller cities like Dhanbad in Bihar, Chittaurgarh in Rajasthan, and Guntur in Andhra Pradesh. The GQ upgrades began in 2001, with a target completion date of 2004. In total, 23% of the work was completed by the end of 2002, 80% by the end of 2004, 95% by the end of 2006, and 98% by the end of 2010. Differences in completion points were due to initial delays in awarding contracts (128 different contracts were employed), land acquisition and zoning challenges, funding delays, and related contractual problems. Some have also observed that India’s construction sector was not fully prepared for a project of this scope. As of August 2011, the cost of the GQ upgrades was about US$6 billion (1999 prices), about half of the initial estimates. By contrast, only about 10% of the other parts of NHDP were completed by 2006.

# _Distance to the GQ Network and Nodal Cities_

Our next step is to measure the distance of districts to the GQ highway network (the closest point on the highway) and to the nodal cities. We calculate these distances using official highway maps and ArcMap GIS software. Our reported results use the shortest straight-line distance of a district to the GQ system. We find very similar results when using the distance to the GQ system measured from the district centroid. The empirical appendix of Ghani et al. (2012a) provides additional details on our data sources and preparation, with the most attention given to how we map GQ traits that we ascertain at the project level to district-level conditions for pairing with ASI and NSS data.

Our empirical specifications use a non-parametric approach with respect to distance to estimate treatment effects from the highway upgrades. We define indicator variables that take a value of one if the shortest distance of a district to the indicated highway network is within the specified range; a value of zero is assigned otherwise. We report most of our results using four distance bands: nodal districts, districts located 0-10 km from a highway, districts located 10-50 9
diet quality and inadequate nutrient intake.<sup>47</sup> And micronutrient deficiencies have been linked to multiple negative outcomes for children, as well as adults; these include impaired cognitive development, physical and mental disabilities, child and maternal deaths, and lower productivity (UNICEF, 2004). Additionally, young children, lactating and pregnant women and the chronically ill are particularly susceptible to decreases in nutrient intakes due to their high nutrient requirements.<sup>48</sup> The quality-quantity trade-off observed at the national level is also observed at the sub-national level, when we look at differences across rural and urban areas. Rural households experience small declines in calorie intake, whereas urban households are able to buffer the shock to calories by reducing the diversity of their diets. We also explore the possibility that access to agricultural land and producing wheat (since net sellers of wheat can benefit from higher prices) can help to buffer the shock of higher prices. We find that these two groups of households are in fact better able to buffer the shock of increasing food prices with respect to their food consumption and dietary diversity; though, with respect to calorie intake, they are unable to buffer as well as their counterparts.

Finally, we find evidence that among certain subpopulations, the demand for wheat products is upward sloping (i.e., Giffen good); specifically, among urban households, large decreases in purchasing power brought about by the spike in staple food prices caused per capita consumption of wheat products to increase as the income effect of the price change dominated the substitution effect.

These new survey data provide a unique opportunity to calculate indicators of socioeconomic development and to analyze changes in these outcomes over time.<sup>49</sup> Such analysis is particularly crucial in conflict areas, which may be most susceptible to shocks, but which usually have little quantitative data. Our findings inform current policy discussions within Afghanistan and, more generally, within the international development community. They

> 47 See Arimond and Ruel (2004) for evidence from 11 Demographic and Health Surveys worldwide.

> 48 World Food Program and UNICEF (2008); See also Grantham-McGregor _et al._ (2001) for a review of studies on iron deficiency and childhood cognitive development.

> 49 The Islamic Republic of  Afghanistan (2010) has published initial findings in the _National Risk and Vulnerability Assessment 2007/08: a profile of Afghanistan_ . The report is being used to better inform policy makers on many dimensions of wellbeing, including  progress towards the Millennium Development Goals (MDG). The reduction of hunger – part of MDG1 – is a priority for the Government of Afghanistan and is a target under the Economic and Social Development Pillar of the Afghanistan National Development Strategy. (IMF, 2008)

23
- Nayar, R., P. Gottret, P. Mitra, G. Betcherman, Y. M. Lee, I. Santos, M. Dahal, and M. Shrestha (2012) “More and Better Jobs in South Asia,” The world Bank.

- Njoh, A. (2009). The Development Theory of Transportation Infrastructure Examined in the Context of Central and West Africa. The Review of Black Political Economy, 36(3), 227‐243.

- Organization for Economic Cooperation and Development. 2002. “Handbook of Biodiversity Valuation: A Guide for Policy Makers.” Paris, France.

- Ridgley, M.A. (1991), Selection of Water‐Supply Projects under Drought, **_Journal of Environmental Systems_** , Volume 21, Number 3, pp.

- Saaty, T.L. (2008), Decision Making with the Analytic Hierarchy Process, **_International Journal of Services Sciences_** , Vol. 1, No. 1, pp. 83‐98.

- Sanchez‐Triana, E., Afzal, J., Biller, D., and Malik, S. (2013). “Greening Growth in Pakistan through Transport Sector Reforms: A Strategic Environmental, Poverty, and Social Assessment.” World Bank, Washington, D.C.

- Seetanah, B., S. Ramessur, and S. Rojid (2009), “Does Infrastructure Alleviate Poverty in Developing Countries?” **_International Journal of Applied Econometrics and Quantitative Studies_** , Vol. 6, No. 2, pp. 31‐36

- Sinha, K. C. and S. Labi (2007), “Transportation Decision Making; Principles of Project Evaluation and Programming.” New Jersey: John Wiley & Sons, Inc.

- Smith, J.T. and S. L. Tighe (2006), “Analytic Hierarchy Process as a Tool for Infrastructure Management,” **_Journal of the Transportation Research Board_** , No. 1974, pp. 3‐9.

- Straub, S. (2008) “Infrastructure and Growth in Developing Countries: Recent Advances and Research Challenges,” Policy Research Working Paper 4460. Washington, DC: World Bank.

- Thomas, A.V, S. N. Kalidindi and L. S. Ganesh (2006), “Modeling and Assessment of Critical Risks in BOT Road Projects,” **_Construction Management and Economics_** , Vol. 24, pp. 407‐424.

- Yoshino, Y. (2008) “Domestic constraints, firm characteristics, and geographical diversification of firm‐ level manufacturing exports in Africa,” **_Policy Research Working Paper_** No.4575, World Bank, Washington, DC.

- Ziara, M., K. Nigim, A. Enshassi, B. M. Ayyub and F. Ascf (2002), “Strategic Implementation of Infrastructure Priority Projects: Case Study in Palestine,” **_Journal of Infrastructure System_** , Vol.  8, No. 2, pp.1‐ 10.

- United Nations (2014), “World Urbanization Prospects: The 2014 Revision, Highlights” United Nations, Department of Economic and Social Affairs, Population Division (ST/ESA/SER.A/352).

17
on the validation. Fifth, the custodian agency sends the validated data to the UN Statistics Department, which finally published the data. This process is fairly standard.<sup>30</sup> Given the magnitude of their role, international organizations not only curate and provide quality assurance for SDG-related data, but they are also uniquely positioned to make a significant contribution to improving their quality, as well as generating new research on the SDGs. As an example, the international financial market has become more developed and is now accessible to most, if not all, poorer countries. This may result in a more diminished role for the lending operations of international financial organizations like the World Bank. As such, there have been stronger calls for the World Bank to move more vigorously to becoming a knowledge bank that generates new data and trend-setting research (see, e.g., Clemens and Kremer (2016) and Ravallion (2016)). This would, in fact, build on its recognized strengths in data and analytics.<sup>31</sup> In this regard, international organizations may be expected to offer their vision for the development landscape in the decades to come. They may also be expected to spearhead new data initiatives to meet evolving global data needs beyond the SDGs, such as statistics on displaced population groups like refugees, who are usually not captured well in traditional surveys and censuses. Another example would be the increasingly common use of subjective well-being data, such as life satisfaction, to supplement the traditional money-metric data used to measure welfare

> 30 For an example, the World Bank often estimates a country’s poverty rate based on a joint consultation process between Bank staff and government officials (often from the national statistical office) after intensive analysis of household surveys.

> 31 Indeed, Birdsall (2015) proposes that the World Bank can invest more resources in supporting researchers, particularly in poorer countries. Clemens and Kremer (2016) even suggest that the World Bank has had more influence on policies in poorer countries through its policy advocacy than its lending portfolios; see also Gavin and Rodrik (1995) for a similar viewpoint. Ravallion (2016) further argues that in its activities related to data and research, the World Bank has not fully reached its potential as a “knowledge bank”. It is not clear to what extent that similar arguments can apply to other international organizations like the UNDP that are also well known for their technical assistance.

33
# **Table A.3b: Full Estimates for Panel B of Table 2 in the Manuscript** **<u>(Margins Based on 2 Week Lagged World Price of Crude Palm Oil)</u>**

|||Wh|olesale-Worl<br>|d Price Margi|ns<br>||
|---|---|---|---|---|---|---|
||Full<br>Sample||Restricted<br>Sample||Matched<br>Sample||
|**Effect of Announcement**|||||||
|Announcement Dummy|-4.304***|-3.576***|-5.004***|-3.340***|-3.730***|-3.495***|
||(-6.871)|(-4.615)|(-9.312)|(-6.389)|(-5.006)|(-4.543)|
|**Announcement*Palm**|**38.14****|**31.32***|**41.91****|**33.50***|**33.28****|**30.63***|
||(2.348)|(1.896)|(2.440)|(1.928)|(2.106)|(1.674)|
|**Announcement*Palm world**<br>**Price**|**-0.355***|**-0.300**|**-0.376***|**-0.321**|**-0.320**|**-0.285**|
||(-1.744)|(-1.456)|(-1.752)|(-1.487)|(-1.617)|(-1.244)|
|**Effect of Implementation**|||||||
|Policy Implement. Dummy|-1.149*|-0.631|-2.925***|-0.437|1.267|0.295|
||(-1.708)|(-0.780)|(-4.320)|(-0.624)|(1.641)|(0.330)|
|**Policy Implement.*Palm**|**23.08*****|**15.85*****|**22.92*****|**16.96*****|**24.80*****|**22.91*****|
||(6.983)|(4.123)|(7.103)|(4.326)|(7.198)|(4.521)|
|**Policy Implement.*Palm world**<br>**price**|**-0.210*****|**-0.146*****|**-0.182*****|**-0.157*****|**-0.257*****|**-0.230*****|
||(-4.870)|(-3.076)|(-4.421)|(-3.296)|(-5.638)|(-3.499)|
|World price*palm|0.119***|-0.209***|0.103*|-0.0252|0.156***|0.0153|
||(2.822)|(-3.522)|(1.899)|(-0.326)|(3.486)|(0.188)|
|World Price|-0.421***|-0.137**|-0.389***|-0.311***|-0.490***|-0.386***|
||(-8.416)|(-2.323)|(-6.299)|(-3.550)|(-9.311)|(-5.253)|
|Palm|18.11***|30.60***|15.31***|25.17***|21.48***|26.64***|
||(14.21)|(12.73)|(13.50)|(11.42)|(12.71)|(6.568)|
|Intercept|18.07***|17.75***|14.07***|27.08***|19.15***|16.44***|
||(16.47)|(5.843)|(8.747)|(3.714)|(16.56)|(4.999)|
|Observations|1,912|1,912|1,374|1,374|1,292|1,292|
|Year and Quarter dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Ramadan Dummies|No|Yes|No|Yes|No|Yes|
|Relative Prices|No|Yes|No|Yes|No|Yes|
|Fuel Price|No|Yes|No|Yes|No|Yes|
|Dummyif World Price rising|No|Yes|No|Yes|No|Yes|
**Figure 2. FSAP Recommendations on Supervision**

<!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>Supervisor based<br>4.0 Institution based<br>Market based<br>3.0<br>2.0<br>1.0<br>0.0<br>1999-2001 2002-2004 2005-2007<br><!-- End of picture text -->

# **B. Income effects**

Tables 6 and 7 provide an overall breakdown by income level. Recommendations are classified as “advanced” if their intensity peaks at higher income levels, “intermediate” if they peak at mid-income, and “basic” if they peak at low income.

Broad trends in Table 6 are very clear:

- FSAPs become more stability-oriented and less development-oriented as income rises (in high income countries, prudential oversight accounts for over two-thirds of total recommendations versus less than half in the low income countries).

- The drop in FSAPs’ developmental component as country income rises reflects a declining emphasis on both overall infrastructure and financial development policy.

- Also not surprisingly, the (absolute) share of general enabling public policies declines sharply as income rises, reflecting the fact that these are basic building blocks that need to be well in place for a financial system to develop.

Within each of these broad categories there are interesting contrasts, however:

- Within infrastructure, creditor rights, the judiciary, accounting, and registries are all basic; auditing, and trading are intermediate; rating, disclosure, and corporate governance, are advanced. This agrees well with what one would expect about the relative complexity of each of these items and their evolving role as financial systems develop.

14