rafmacalaba/datause-encoder-data huggingface dataset:
# 2. Theory

We derive our estimating equation from a new economic geography model, based on Krugman and Venables (1995) and Amiti (2005)<sup>6</sup> . Firms are assumed to compete in a monopolistically competitive environment, with each fi rm producing a differentiated variety. All varieties of fi nal goods enter symmetrically into the consumer’s utility function and all varieties of intermediate inputs enter symmetrically in the fi rm’s cost function. Pro fi ts of a single representative fi rm in industry i in province p are given by The total cost function comprises a fi xed cost, F , a constant cost, b<sup>i</sup> , and factor prices, where wp is the wage in province p, rp is the price of capital in province p or any other factor of production, and Pp<sup>uistheintermediateinputpriceindex.Itisdefinedas</sup> The transport cost, t<sup>i</sup> lp<sup>, of shipping a good from province lto p is modelled as Samuelsonian</sup> iceberg costs, with t ≥ 1. This means that a proportion of imported inputs, 1 −<sup><u>1</u></sup> t<sup>,meltsin</sup> transit. Hence, to deliver one unit of any good from one province to another t units must be shipped as only a fraction<sup><u>1</u></sup> t<sup>arrives.Ift = 1thereisfreetradeandift = ∞thereisno</sup> trade.

The fob producer price is given by pro fi t maximization, which gives the usual marginal revenue equals marginal cost condition, with prices proportional to marginal cost:

> 6Amiti (2005) extends Krugman and Venables (1995) from a one-factor model to a two-factor model, thus allowing for different production stages to vary in factor intensities.

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hypotheses. Section 6 presents the empirical models. The results are presented in Section 7, and the paper concludes in Section 8 with suggested policy implications.

# **2. Climate, Hydrology, and Flow Regimes in Rivers**

The hydrology of river basins is affected by changes in climatic conditions. Anthropogenicinduced climate change is expected to influence water resource cycles significantly. However, the stochastic nature of the changes in the water cycle is uncertain. A useful explanation of the interaction between climate change and the hydrological cycle can be found in Miller and Yates (2005). They suggest that global climate change is expected to alter the hydrologic cycle by affecting the amount, intensity, and temporal distribution of precipitation. Warmer temperatures will affect the amount of winter precipitation in the form of rain or snow, the amount stored as snow and ice, and its melting dynamics. Long-term climatic trends could trigger vegetation changes that would alter a region’s water balance. In forest areas, the combination of warmer temperatures and drying soils caused by snow melting earlier than usual or longer droughts can lead to more frequent and extensive wildfires. When this occurs, land cover and watershed runoff characteristics may change quickly and dramatically as wildfires reduce forest cover and thereby affect the runoff response. Less dramatic, but equally important, changes in runoff can affect transpiration of plants, altered by changes in soil moisture availability, as well as plant responses to elevated CO2 concentrations. In addition, changes in the quantity and quality of water percolating to groundwater will result in changes in aquifer levels and quality, in base flows entering surface streams, and in seepage losses from surface water bodies to the groundwater system (Miller and Yates 2005:37).

A comprehensive assessment of available water hydrology-climate studies from around the world is provided in IPCC (1996a, b) and IPCC (2001). The findings in IPCC (2001:Section 4.3.6.1) suggest that: _“In general, the patterns found are consistent with those identified for precipitation: Runoff tends to increase where precipitation has increased and decrease where it has fallen over the past few years. Flows have increased in recent years in many parts of the United States, for example, with the greatest increases in low flows ...[]. Variations in flow from year to year have been found_ 5
the sample, we can assume that our sample gives a reasonable snapshot of global inequality around 1820.

The social tables from the period 1750-1880 include 650 million people. According to Maddison, total world population moved from almost 1 billion in 1820 to 1.1 billion in 1850 to 1.2 billion in 1870. Therefore an average population at any point between 1750 and 1880 can be estimated at around a billion. Our time-window therefore comprises around 2/3 of the world population living at any point between 1750 and 1880.

_How are average social group incomes converted into equivalent 1990 PPPs?_ The 13 social tables that we use have in total 591 social groups or classes. That means that we have average incomes, expressed in national currencies, for 591 social groups. (One such social table for England and Wales 1801-03 is given in the Annex.) These national currency income per social group are converted into 1990 PPP dollars using (i) Maddison GDP per capita data and, (ii) for the countries for which Maddison’s data are unavailable, the ratio between average income and the subsistence minimum with the latter priced at $PPP 300 per year. According to the first method, the social class income estimates are made by linking overall national currency mean income (calculated across all social groups) to GDP per capita in 1990 international prices from Maddison. To explain: from the social table for (say) Brazil in 1872, we calculate that the average per capita income is 311 _milreis_ . From Maddison’s (2004) GDP per capita data, we know that the estimated GDP per capita for Brazil in 1872 is $PPP 721. By linking the two, we obtain the conversion ratio of 1872 _milreis_ into 1990 international dollars (1 _milreis_ is worth $PPP 2.3 of year 1990). This then enables us to directly convert average _milreis_ income of every social class into its 1990 $PPP equivalents. For some data prior to 1820, the procedure is different. When we do not have Maddison’s GDP per capita data, we need to get an estimate of the subsistence minimum in local currency (of the time). Thus, for example for the Kingdom of Naples 1811, the estimated subsistence minimum is 31 _ducats_ per capita annually (from Melanima 2000). Since the subsistence minimum is by assumption priced at $300 PPP dollars at 1990 prices, we again directly get the conversion factor (1 _ducat_ from year 1811 = $PPP 9.67 in year 1990). Using this 6
**Table 3: Potential marginal lands available for non-grain sugar & starch energy crops**

||Unit|Cassava|Sweet sorghum|Sweet potato|
|---|---|---|---|---|
|Current planting area|Million ha.|0.387<sup>a</sup>|0.0033-0.0067<sup>b</sup>|4.76<sup>c</sup>|
|Current production|Million tonne|7.94<sup>a</sup>|n.a.<sup>d</sup>|102.2<sup>c</sup>|
|Current utilization||70% for starch &<br>alcohol, etc.<br>30% for feed<sup>e</sup>|n.a.<sup>d</sup>|50% for feed<br>15% for industry<br>processed<br>14% for food<br>6% for seed<sup>f</sup>|
|Unutilized land available|Million ha.|0.124<sup>b</sup>|5.31<sup>g</sup>|1.30<sup>g</sup>|
|Low-productivity<br>arable<br>land available|Million ha.|7<sup>h</sup>|7<sup>h</sup>|3<sup>h</sup>|
|Marginal land available|Million ha.|7.12|12.31|4.3|
|Land<br>productivity<br>of<br>feedstock|Tonne /ha.|20.55<sup>a</sup>|45-75<sup>b</sup>(60)|21.47<sup>c</sup>|
|Starch or sugar content|%|25|15|20|
|Assumed<br>conversion<br>efficiency|Tonne<br>(ethanol)/tonne<br>(feedstock)|0.14|0.625|0.112|
|Land<br>productivity<br>of<br>ethanol|Tonne /ha.|2.88|2.81-4.69 (3.75)|2.4|
|Ethanol yield potential on<br>unutilized land|Million tonne|0.36|14.93-24.91(19.93)|3.13|
|Ethanol yield potential on<br>low-productivity arable land|Million tonne|20.14|19.69-32.81(26.25)|7.21|
|Ethanol yield potential on<br>marginal land|Million tonne|20.5|34.63-57.72(46.18)|10.35|

Notes: a. the 2008 value; data from Fang, et al (2010); b. Data from EB (2009); c. the 2008 value; data from DRST, et al. (2009) ; d. the value is not available; e. data from Tian et al.(2010); f. the 2008 value with other 15% of sweet potato was rotting; data from Jin et al. (2011); g. data from EB (2009), but because North China is suitable for both sweet sorghum and sweet potato, unused land here is assumed to be divided evenly between both crops; h. data from Shi (2011);

# **3.1.2 Supply cost**

The production of ethanol from starch and sugar crops is a relative mature technology, widely employed at commercial scale. The economic viability of these processes largely depends upon the price of feedstocks as feedstock accounts for over 50% of the total cost (see Table 4). Although the production costs of fuel ethanol are higher, they have certain cost advantage 7
- Wollburg, P., Tiberti, M., and Zezza, A. (2021). Recall length and measurement error in agricultural surveys. _Food Policy_ , 100:102003.

- Yacoubou Djima, I. and Kilic, T. (2024). Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data. _Journal of Development Economics_ , 168:103249.

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Export growth could be increased both by facilitating export activity and experimentation by large exporters and by reducing the sunk costs to start exporting for smaller firms or occasional exporters. The econometric exercise suggests that reduction the fixed costs and physical burdens to export may play a key role in boosting the export sector in Pakistan. A reduction of tariff complexity in Pakistan may also play an important role. Larger exporters (defined in terms of volumes of exports or number of destinations reached or number of products exported) seem reluctant to change export strategies (it is these firms that are usually most responsible for performance at the extensive sector and product margin). Their success is mainly determined by developments at the intensive margin. This and the fact that they determine the bulk of exports suggest that determinants of developments at the intensive margin, including a macro-economic environment conducive to stability and growth and incentives to innovate are important to ensure good export performance while reducing uncertainty and financing costs would spur innovation. As concerns smaller firms and exporters, the above results suggests that export values are likely to become larger the more continuous the interaction, whether within particular destination countries or within product categories. Hence, strategies helping the survival and/or deepening of exporting relationships of the smaller firms are also likely to be important as a determinant of export performance. These however should not be the result of eliminating competition domestically, but of more effective export support including learning and skill acquisition strategies.

# **References**

Arkolakis, Costas & Marc-Andreas Muendler, 2010. The Extensive Margin of Exporting Products: A Firm-level Analysis, NBER Working Papers 16641, National Bureau of Economic Research, Inc Bernard, A., Jensen, J., Redding, S., & Schott, P. (2011). _The Empirics of Firm Heterogeneneity and International Trade._ Princeton University.

Bricongne, J. C., Fontagne, L., Gauler, G., & Taglioni, D. (2011). Firms and the Global Crisis: French Exports in the Turmoil. _Journal of International Economics,_ 87 (1), 134{146.

Cadot, O., Tumurchudur, B., M. Shirotori. 2009. “Revealed Factor Intensity Indices at the Product Level,” UNCTAD mimeo Davis, S., & Haltiwanger, J. C. (1992). Gross Job Creation, Gross Job Destruction and Employment Reallocation. _Quarterly Journal of Economics,_ August, 107:3 , 819-63 _._ Easterly, W., Reshef, A., and J. Schwenkenberg. 2009. “The Power of Exports,” World Bank Policy Research Working Paper No. 5081 Felipe, J (2010). Inclusive Growth, Full Employment, and Structural Change: Implications and Policies for Developing Asia. Asian Development Bank.

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**Figure 20: Administrative gasoline prices and average actual consumer prices**

<!-- Start of picture text --> 120<br>Actual Official<br>110<br>100<br>90<br>80<br>70<br>60<br>Naira per litre<br>Jun‐14 Jul‐14 Aug‐14 Sep‐14 Oct‐14 Nov‐14 Dec‐14 Jan‐15 Feb‐15 Mar‐15 Apr‐15 May‐15 Jun‐15 Jul‐15 Aug‐15 Sep‐15 Oct‐15 Nov‐15<br><!-- End of picture text --> _Source_ : http://www.nigerianstat.gov.ng/nbslibrary/sector‐statistics/sector‐statistics.

The government attempted to deregulate the price of gasoline in January 2012, more than doubling the retail price. Immediately after the price increase, trade unions staged an eight‐day strike, estimated to have cost the economy more than US$1 billion (Reuters 2012). The government backed down and lowered the gasoline price to ₦97 (US$0.60) a liter within two weeks. The price remained at that level until January 2015, by which time the price denominated in U.S. dollars had declined to US$0.52 per liter. Nevertheless, perhaps partly because of the presidential elections originally scheduled for February 2015 (later postponed to March), the government lowered the price of gasoline to ₦87 (US$0.47) per liter in January. In dollar terms, the retail price has continued to decline because of currency depreciation. At the end of July 2015, the pricing agency estimated the unit fuel price subsidy to be ₦45 (US$0.22) for gasoline and ₦63 (US$0.32) for kerosene (pppra.gov.ng).

Subsidies are channeled through two different mechanisms. One involves direct budgetary transfers to those companies submitting approval for reimbursement, which is administered by the Petroleum Products Pricing Regulatory Agency. The second is through withholdings by the NNPC of oil revenue obtained through joint ventures with international oil companies, referred to as implicit subsidies in the IMF’s country reports. The IMF’s estimates of the two types of subsidies are given in Table 17. The very large subsidies paid out in 2011 became the subject of a series of government investigations.

**Table 17: Fuel subsidies in Nigeria, percent of GDP**

|Subsidy type|2011|2012|2013|2014|
|---|---|---|---|---|
|Budgetarytransfers|4.7|2.2|0.9|0.7|
|Implicit subsidies|1.5|1.1|0.9|0.7|

_Source:_ IMF 2013b, 2015e, 2015f. Note: The budgetary transfer in 2012 includes payment arrears from 2011.

The audit reports on the oil and gas sector published by the Nigeria Extractive Industries Transparency Initiative, the most recent of which covers the year 2012, and the forensic audit into the affairs of the NNPC carried out by PwC for the Auditor General for the Federation covering the period January 2012 to July 2013, provide more details about the way the NNPC provides fuel subsidies. Unlike all other
that urban economic growth helped reduce urban poverty but brought little or no overall benefit to the rural poor; in fact, the main driving force for overall poverty reduction was rural economic growth. We confirm this finding for the data up to 1991, but the picture looks different after 1991. As before, urban growth reduced urban poverty, and rural growth reduced rural poverty. But we find much stronger evidence of a feedback effect from urban economic growth to rural poverty reduction than we had found in the pre-1991 data.

The relatively weak performance of India’s agricultural sector and the widening disparities between urban and rural living standards remain important concerns, including for India’s poor. However, it is encouraging that rising overall living standards in India’s urban areas in the post-reform period appear to have had significant distributional effects favoring the country’s rural poor. While the attribution to the reforms is hardly conclusive—since we can have no comparison group, to observe India after 1991 but without the reforms—these findings are consistent with the view that India’s efforts to create a more open and productive market economy have come with a reversal in the historical pattern of weak feedback effects of urban economic growth on rural living standards.

This may be a surprising conclusion given that the most dynamic sectors of India’s postreform economy have been more intensive in skilled than unskilled labor. However, the more relevant observation is that the non-farm sectors that use unskilled labor more intensively— notably trade, construction and the “unorganized” manufacturing sectors—have seen higher employment growth in the post-reform period. It is a plausible conjecture that this is the main reason why we have found stronger spillover effect on the rural distribution of urban economic growth since 1991.

This encouraging finding comes with a warning, however. While the rural poor have benefited more from urban economic growth in the post-reform economy, it can be expected that the reverse also holds: India’s rural poor will be more vulnerable in the future to urban-based economic shocks.

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shown in Arthi et al. (2018). It is also consistent with the reporting in our data. For those participating, the average hours per month worked under short recall (averaged across rounds) is 96.4 for primary jobs and 83.4 for secondary jobs. The hours per month for the long recall are notably higher, 111.9 for primary jobs and 115.7 for secondary jobs.

# _4.2 Heterogeneity by self and proxy reporting_

Next, we examine heterogeneous impacts of long recall by proxy/self-reporting, gender, and age. Because a respondent is thought to have more complete knowledge of their own labor contributions than those of other household members, use of long recall when reporting on behalf of others could be more taxing and more susceptible to omission. We present these results in Table 3, Panel B, estimating effects by proxy status using a fully saturated regression model, such that the reported interaction is the long recall effect for each group compared to no effect. Beneath each column, we also report the means in the short-recall group separately for self and proxy reports, the scaled differences of the treatment effects, and the p-value for the difference between the two groups. In considering these patterns we emphasize that a household’s primary respondent, and therefore proxy or self-reporting status, was not randomly assigned and therefore we cannot make causal claims of the effects of proxying itself on reported measures.<sup>15</sup> However, we can show how absolute and relative gaps between long and short recall differ by proxy status in our sample.

Across the first four outcomes, losses from long recall are larger for proxied individuals, in both absolute and relative (to their short recall mean) terms, although we lack sufficient precision to say whether these differences are statistically significant for the two intensive margin measures. Long recall loss for working at all among those who self-report is 5 percentage points, or 5% considering a short recall mean of almost 100%. The effect among those proxied is large: 42 percentage points or 54% of the short-recall mean for proxied individuals. These large differences between self and proxy reports may be due to increased recall bias in proxy reports or to the fact that primary respondents are engaged in different types of labor activities that may be less vulnerable to omission. When considering total activities and months worked (columns 2 and 3), the proportional effect is larger than for working at all (15% compared to 5%) suggesting that recall bias may differentially affect the reporting of a second job or the specific months worked for those

> 15 The survey respondents are almost exclusively household heads and, as reported in Table 1, are overwhelmingly male and significantly older than the proxied respondents.
- 13 -

24. Agricultural producers are quite heterogenous. The amount of crops produced ranges from 2 kg to over 50 tons, with an average of 280 kg. The market sales are also different, from 2 kg to 9,100 kg. Labor force is measured by the number of household members aged 16 to 60. The use of advanced inputs, such as fertilizer and improved seeds, remains minimal in the Comoros. Only 12 and 11 percent of households used them, respectively. Intermediary services by cooperatives or traders also seem to be unavailable in most places. It is also noteworthy that farmers often do not have any transport means in the Comoros.

# **<u>Table 1. Summary statistics</u>**

|<br>Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Quantity of crop produced (kg)|_Q_|2,587|280.26|1104.56|<br>1.98|51613|
|Volume of crop sold (kg)|_S_|1,340|225.11|419.88|<br>2.30|9133|
|Transport costs to bring produce to Moroni<br>|_COSTMORONI _|2,587|55.43|42.88|<br>0.08|100.54|
|(US$/ton)|||||||
|Transport costs to bring produce to a large city|_COSTCITY_|2,587|0.89|0.83|<br>0.03|2.89|
|(US$/ton)|||||||
|Market Access Index (0 to 1)|_MAI_|2,587|0.60|0.27|<br>0.08|1.00|
|Labor force (age 16 to 60)|_L_|2,587|2.63|1.32|<br>1|10|
|Use of improved seeds|_SEED_|2,587|0.12|0.32|<br>0|1|
|Use of fertilizer|_FERT_|2,587|0.11|0.32|<br>0|1|
|Access to intermediary services:|||||||
|Crop collection by cooperatives|_COOP_|2,587|0.02|0.14|<br>0|1|
|Direct access to large market|_MKT_|2,587|0.16|0.37|<br>0|1|
|Crop collection by intermediate traders|_TRAD_|2,587|0.01|0.10|<br>0|1|
|Household size|_SIZE_|2,587|5.41|2.37|<br>1|15|
|Dummy variable for male household head|_MALE_|2,587|0.80|0.40|<br>0|1|
|Age of household head|_AGE_|2,587|45.93|13.82|<br>16|90|
|Ownership of transport means:|||||||
|Vehicle|_CAR_|2,587|0.06|0.24|<br>0|1|
|Motorcycle|_MOTR_|2,587|0.04|0.20|<br>0|1|
|Bicycle|_BICY_|2,587|0.01|0.09|<br>0|1|
|Instrumental variables:|||||||
|Slope (degree)||2,587|6.48|3.69|<br>0.17|16.70|
|Elevation (meter)||2,587|305.83|246.00|<br>2.00|881.00|
|Longitude (degree)||2,587|<br>43.92|0.51|<br>43.23|44.53|
|Latitude(degree)||2,587|<br>-12.05|0.31|<br>-12.38|-11.37|