rafmacalaba/datause-encoder-data huggingface dataset:
increases in agricultural commodity prices [21]. A USDA report describing the factors leading to the food commodity price rise concludes that the run- up in commodity price reflects a trend of slower growth in production and more rapid growth in demand that led to a tightening of world balances of grains and oilseeds over the last decade [66].

Biofuels are considered to be one among several demand-side and supply-side factors responsible for the increase in crop and food commodity prices in recent years [15, 21, 25, 43, 47, 51, 59]. Quantitative estimates of the impact of biofuels on grain prices range from 20% to 60% (see Table 1). The most pessimistic estimate ascribed 70 to 75% of the price rise between 2002 and 2008 to biofuels [47]. This report uses historical data to estimate the elasticity of world prices of agricultural commodities with respect to the price of energy and related inputs to agriculture and with respect to changes in the value of the dollar. Using these elasticities, this report estimates that between 2002 and 2007, higher prices of energy increased export prices of major U.S. food commodities by about 15 to 20 percentage points, and the depreciating dollar increased food commodity prices by about 20 percentage points. These together, it is argued, translate into a 25 to 30% increase in total price. The author argues that depletion of stocks, shifting for food cropland for production of energy crops, government response in the form of food export bans, and speculative activity, which caused prices to rise, were the consequences of the shocks considered with demand for biofuels being the main cause.

Rosegrant estimates the effect of biofuels using a simulation-based approach [59]. He simulates the market equilibrium under two different scenarios, one without high growth in biofuel and another with high growth in biofuel. For the former, he simulates a scenario in which biofuel grows at a rate which was observed between 1990 and 2000. This is the period before the rapid takeoff in demand for bioethanol. For the latter, he simulates actual demand for food crops as a feedstock for biofuel, from the years 2001 through 2007. Based on these simulations, he estimates that weighted average grain price increased by an additional 30% under the high biofuel scenario, i.e., the actual situation. The increase was highest for maize (39%) and lower for wheat and rice (22% and 21%, respectively). Using a similar approach, Rajagopal et al. estimate that U.S. ethanol production in 2007 may have been responsible for a 15% to 28% increase in the world price of maize and 10% to 20% increase in the world price of soy [54].

Global estimates of both the increase in food commodity prices and the contribution of biofuels to this increase hide variations at the regional level. Mabison and Weatherspoon argue that in South Africa food is processed and then transported several miles before it reaches the 8
Panel B: Hazard Regressions, Impact of Sophistication on Attention to Transaction Costs in Finland

||(1)|(2)|
|---|---|---|
|Illiquidity measure|1.005***|1.013<sup>***</sup>|
||<.0001|<.0001|
|Age|0.995***|0.995<sup>***</sup>|
||<.0001|<.0001|
|Male Dummy|1.371***|1.339<sup>***</sup>|
||<.0001|<.0001|
|Sophistication|1.588***|1.552<sup>***</sup>|
||<.0001|<.0001|
|AdjIlliq × Age|1.000***|1.000|
||<.0001|0.236|
|AdjIlliq × Male Dummy|0.990***|0.992<sup>***</sup>|
||<.0001|<.0001|
|AdjIlliq × Sophistication|0.996***|0.997<sup>***</sup>|
||<.0001|<.0001|
|Stock Controls|Yes|Yes|
|Household stratification|No|Yes|
|Calendar month dummies|Yes|Yes|
|Calendar year dummies|Yes|Yes|
|Observations|1,522,716|1,522,716|
|Wald test|<.0001|<.0001|

52
1991, to 20 percent in 2001. The share of irrigated land is much higher in the west than elsewhere in Turkey. One third of the holdings smaller than 1 ha have access to irrigation. The distribution of agricultural land remained skewed with a Gini coefficient of 0.60. A slight tendency towards the medium ranges from smaller sizes are observed from 1991 to 2001 (TurkSTAT, 1994, 2004b).

Of the 26 million ha of cultivated land (TurkSTAT, 2006), field crops have occupied slightly over 85 percent of the cultivated area since 1985. The share of the vegetable area is about 3 percent, but has been increasing steadily. Orchards occupy 10 percent of the cultivated land. Land left to fallow is about 5 million hectares. The value composition of the agricultural production diverge drastically from the use of the cultivated land. The weight of crop production has been dominant in the total value of production. The value of livestock products makes a quarter of the total value (TurkSTAT, 2006b). The structure of production is far from reflecting the policy weights that seem to underlay government intervention in agriculture. The policies are generally targeted towards cereals and industrial crops, whereas vegetables and fruits have relatively smaller importance apart from some specialty products. However, the share of fruits and vegetables in total value is slightly over 40 percent. Protection and government support of animal products have not been sufficient to counterbalance the additional costs of feed due to interventions in the cereals. Turkish consumers end up paying higher prices compared with the average meat and milk prices in the EU.

The employment creation capacity of the economy has always been problematic, mainly because the growth in the country’s capital stock has not been commensurate with the rapid expansion of the labor force. Despite improvements in economic indicators since 2002, the unemployment rate remains stagnant at around 10 percent. The rural unemployment rates, both male and female, are the major contributing factors in the stickiness of the overall unemployment rate. The declining trends in the rural labor force participation rates and the share of agriculture in rural employment, combined with increasing rural unemployment rates, signal the start of a major transformation in the use of labor in agriculture. However, agriculture is still helping to overcome the chronic nature of unemployment in Turkey. It eases the detrimental effect of the lack of human capital on growth rates of the labor force, and the inability of the non-farm sector to pull even more labor from agriculture. The illiteracy in agricultural employment is significantly higher compared to the rest of the economy (Çakmak and Akder, 2005) which 17
Table 2: Regional welfare impact

||**Percent**|**US$ billions**|
|---|---|---|
|East Asia and Pacifc|-0.9|-33|
|Latin America and the Caribbean|-0.5|-30|
|Middle East and North Africa|-0.5|-20|
|Sub-Saharan Africa|-0.4|-9|
|Europe and Central Asia|-0.4|-15|
|European Union|-0.3|-55|
|Other Advanced Economies|-0.2|-33|
|United States|-0.1|-11|
|South Asia|-0.1|-2|
|China|-0.1|-5|
|**Total**|**-0.3**|**-211**|

Source: Authors’ simulations. Note: Results are reported relative to the baseline in 2020.

the increase in the cost of traded services occurs simultaneously in all regions so these results cannot be interpreted as the marginal impact of removing certain trade agreements. Last but not least, due to the fact that the underlying modelling framework does not capture financial and monetary linkages, the impact of nominal exchange rate movements, changes in FDIs, innovation, the international reallocation of production and potential increases in NTBs in goods and services markets, these results should be considered as lower-bound estimates.

# **5.1.2 Impact on consumers**

The increase in worldwide barriers to trade is expected to spill over to consumers through both price channels and quantity consumed. First, higher tariffs translate into the effective increase in the price of imported consumer goods putting upward pressure on the aggregate consumer price index (CPI) and ceteris paribus, reducing households’ purchasing power. Second, the increase in the price of imported goods may limit the availability of foreign sourced products and force consumers to substitute with relatively cheaper goods sourced from domestic markets.

Results suggest that the loss of existing preferences would translate into a decline in global real household consumption by 0.3 percent or close to US$148 billion relative to the base17
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||B.BoundTests||||||||
|<sup>70"</sup>|||||||||
||B1.Realinterestrateisathistoricalaverageplustwostandarddeviationsin2005and2006|763|969|944|916|886|854|-30|
||<br>B2. Real GDP growthis athistorical averageminustwo standarddeviations in2005 and20]<br> <br>|710<br><br>|93.7<br><br>|107.7<br><br>|1222<br><br>|1373<br><br>|1528<br><br>|58<br><br>|
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||<br> <br>BS.Onetime30percentrealdepreciationin200516/|635|58.7|546|502|455|406|-13|
||<br>B6. 10percent of GDP increaseinotherdebt-creatingflows<br>in 2005|642|59.4|554|510|464|415|-13|
|«4<br>>|vifInput_fiscal_{Seigniorage{Table{StochSim},Table_SR./Table_GF.fAihistor|ical,£|<||||||>|
# **B Coding sex assigned at birth**

Determining the assigned sex at birth in administrative tax data is usually a difficult task. Gender is typically not reported on most tax forms. A solution used by scant literature is to infer gender using names and text analysis (e.g., see Scot et al., 2023; López-Luzuriaga and Scartascini, 2023, for recent applications). In this paper, we rely instead on personal tax identifiers known as CUIT (an acronym for _Clave Única de Identificación Tributaria_ , “unique tax identification”), which allows us to infer the gender assigned at birth from the first two digits and the last digit. The CUIT is a national tax identification number in Argentina, assigned by the Federal Administration of Public Revenue (AFIP) to people and businesses.

The CUIT number has a **YY-XXXXXXXX-Z** structure, with the following meaning:

- **YY** is the type: 20, 23, 24, 27 (individuals), 30, 33, 34 (businesses).

- **XXXXXXXX** is the national ID number (DNI) or business number.

- **Z** is a verifying digit.

In the case of individuals, the following administrative rules can be used to determine sex:

- **Female:** YY=27, or YY=23 and Z=4.

- **Male:** YY=20, or YY=23 and Z=9.

- **Unknown:** YY=24. This applies to a few cases for which we will have missing gender.

Following methodological guidance from our team, the municipality’s staff utilized this structural feature of the CUIT in the property register, to infer the gender assigned at birth and the owners’ ages for over 100,000 properties annually. We then linked the gender indicator to the main property tax database to generate a sex-disaggregated analysis.

Following the strategy described above, we were able to correctly assign the gender to 74% of the 108,321 residential property owners using data provided by ARBA from 2018, while the remaining 26% were set to missing.

B-4
The two-stage sampling design of the DHS and most other household surveys, however, violates the assumption of independent observations. Households in a given cluster are more likely to be similar to one another in some respects than households in different clusters. The weighted or unweighted mean of variables are not affected by the two-stage design, but the design does increase the variability of these estimates compared with a simple random sampling design. If the sample design is ignored, the variance of estimated means using standard formulas will be too small. In addition, since the error terms in the regression are correlated across observations in the same cluster, without some procedure to correct these standard errors, the efficiency of the coefficients estimated will be affected (Deaton, 1997).

Unless one fully parameterizes the correlation within clusters, the true likelihood function for the clustered sample cannot be written.<sup>11</sup> However, the model can be estimated without parameterization of the within-cluster correlation using appropriate statistical techniques. These will estimate the β vector that maximizes the likelihood function that we would have if we had data on all individuals in the population. The variance estimates are computed as if the sample was drawn again and again using the same clustering scheme and the estimated β vector computed as the maximum of the “pseudo-likelihood”.<sup>12</sup> Another issue of concern for the estimation of the above model is the possible endogeneity of some of the variables chosen as explanatory variables. For example, if household access to water or sanitation is a choice variable, then it may be correlated with the unobservables attributed to the error term. Endogeneity may also be a concern with the use the mother’s BMI as a proxy for household food security. Failure to control for possible endogenous variables could lead to biased estimates. To test for the possibility of endogenous variables, two estimators can be compared, one consistent (and efficient) under the hypothesis that there is no endogeneity, but inconsistent if there is endogeneity, and the other consistent

> 11 Gragnolati (1999) specifies a variance components model and a random coefficients model in his study of malnourished children in Guatemala.

> 12 The STATA 7.0 manual contains a more detailed discussion of the survey estimation commands that can be used in this case.

18
# **Table 8. Energy and CO2 Intensity by Mode**

|Mode|Pass-mi/Gal<sup>a</sup>|Btu/pass-mi|CO2 g/pass-mi|
|---|---|---|---|
|Van Pool|101.9|1,354|101|
|Heavy Rail|155.3|889|156|
|Commuter Rail|85.8|1,608|177|
|Intercity Rail|66.0|2,091|179|
|Light Rail|120.5|1,146|202|
|Car – 1 Person|27.7|4,983|371|
|Car – Average Trip|43.8|3,154|235|
|Trolley Bus|104.4|1,321|233|
|TransitBus|32.5|4,245|299|

Notes aPassenger miles per Diesel Equivalent gallon

**Highway Motor Coach:** Vehicles designed for long-distance travel, and are characterized by “integral construction with an elevated passenger deck located over a baggage compartment”.

**Heavy Urban Rail:** Self-propelled electric-powered passenger cars operating on an exclusive rail right-of-way, either below or aboveground.

**Light Rail:** Self-propelled electric-powered passenger cars operating on an exclusive or shared above-ground rail right-of-way .

**Commuter Rail:** Electric or diesel-powered locomotives pulling passenger cars, and operating on an exclusive rail right-of-way, for local short-distance travel.

**Urban Transit Bus:** Primarily diesel powered, rubber-tired vehicles for fixed route scheduled service.

**Electric Trolley Bus:** Electric-powered rubber-tired vehicles for fixed route scheduled service within an urban area. **Van Pool:** Small buses and other vehicles operating as a ride-sharing arrangement.

Source: ABA (2007).

An important caveat is that rail extensions in particular locations are not always substitutes for road travel. In dense commuting corridors, a metro system fed by trunk roads from the suburbs, could be appropriate to alleviate the largest bottlenecks.<sup>18</sup> If congestion increases on complementary feeder roads, however, this needs to be taken into account in evaluating the rail upgrade. Similar concerns need to be addressed for light rail and bus rapid transit projects that may increase congestion in the corridors where they run, as well as on feeder roads. Assessing these factors requires use of more detailed models of the transportation network to predict changes in traffic flows throughout the system.

> 18 For example, the Strategic Transport Plan for Dhaka, Bangladesh, initiated in 2004 recommended a mass rapid transit system that consists of three BRT routes, three metro lines and 50 roadways projects (Mahbubun Nabi, 2008). The Sao Paulo Transportation Plan includes strengthening both subway and surface transportation systems (Wilheim, 2004).

36
_Table 7. Schooltype Differences (Base Model) for English and Mathematics Achievement_

||_E_|_nglish_|_AM_|_athematics_|
|---|---|---|---|---|
|_Variable_|_Coefficient_|_Standard Error_|_Coefficient_|_Standard Error_|
|Constant|8.23|.28|5.39|.24|
|Former B|-2.57|.36|-.76|.36|
|High-fee-paying|-.12|.42'|.75|.40'|
|Low-fee-paying|-2.68|.37|-.77|.26|
|District Council|-3.80|.30|-.99|.24|
|School-level variance<br>explained|83 %||_56%_||
|Total variance explained|36%||14%||

_Note: '_ Not significant (p>.05)

# **_Testing Variables in Individual Regressions_**

37. Before proceeding with any further model-building, we tested each of the usable variables in our data set for its contribution toward an explanation of the variation in English and mathematics achievement. The results are reported in Tables 8-10, for student level, class level, and school level variables, respectively.

38. _Student Level Variables._ The best set of student level predictors included the same variables for both subjects: the pupil's age, whether or not s/he had repeated a grade, the family size, and the father's educational level (Table 8).

39. Both the log transformation and the natural metric of pupil's age were tried. The log 3 transformation fit the data better for English and the natural metric marginally better for mathematics", but the log transformations were used for both subjects, for comparability, explaining 16% of between school differences in each subject. The negative coefficients for both variabLs are what one would expect to find, rather than the positive coefficient for age regressed on English in the natural rretric. Where there is significant repetition at primary school, one would expect to find a high correlation between the age of the pupil and the number of school years a pupil has attended primary school. **This** is not the case in Zimbabwe, where there are relatively low repetition rates. Indeed the correlation between these two variables is .03. An age greater than the average Grade 7 pupil's age would normally indicate some sort of hardship in access to primary school.

40. Children who had repeated a grade (determined simply by the number of school years they'd attended primary school) had a stronger negative relationship on mathematics achievemert than on English achievement, reducing the score by .5 point.

- 3 For English (t=9.7) for the log of age against **(t=** 2.0) for the natural metric; for mathematics (t=6.5) for the log of age against (t=7.5) for the natural metric.

14
_<u>Facts:</u>_

- _Between 1972 and 2002 income per capita in Sub-Saharan Africa fell by 8 percent, from USD 625 to USD 575 in constant 1995 values._

- _Between 1972 and 2002 manufacturing value-added per capita in SubSaharan Africa fell by 13 percent, from USD 98 to USD 85 in constant 1995 values._

- _In 2001/2002 the share of Sub-Saharan African in world population was 11 percent, the share in world income was 1.1 percent, the share in world manufacturing value-added was 0.8 percent, and the share in world manufacturing exports was 0.7 percent._

_Source: World Development Indicators_<sup>1</sup>

# **1. Introduction**

Africa’s poor economic performance is a well established fact. The process of globalization, or international economic integration, has largely passed the continent by. Foreign investors do not see Africa as a promising location for investment, and many Africans share this view and keep a large share of their wealth outside Africa.<sup>2</sup> The growth of world trade in recent decades has been driven by the increasing international outsourcing of production, a process that has made it possible for poor countries with relatively unskilled workers to participate in (parts of) high-skill or high-tech industries. There has been extensive outsourcing primarily to Asia, but very little to Africa. Further, with the exception of Mauritius, Africa’s own manufacturing sector is small and stagnant, undertakes little investment, and has not managed to break into exports markets. The optimistic beliefs of the 1960s that the sector would act as an “engine of growth” in the continent have thus far not been fulfilled.<sup>3</sup>

> 1  World Bank (2004a).

> 2 Africans hold 40% of their wealth outside Africa according to Collier, Hoeffler, and Pattillo (2001).

> 3 Manufacturing is typically far from the largest sector in African economies, nevertheless it has often been considered central for economic development. Reasons why the manufacturing sector is often perceived as “special” is that this sector is a potential engine of modernization, a creator of skilled jobs, and a generator of positive spillover-effects (Tybout, 2000).

1
|`SVN`|`2618.50`|_`0.07`_|
|---|---|---|
|`SWE`|`20894.17`|_`0.58`_|
|`TUR`|`19070.35`|_`0.53`_|
|`TWN`|`6318.91`|_`0.17`_|
|`USA`|`122388.23`|_`3.38`_|

As can be noted, the share of the German GDP absorbed in China (on the total GDP produced by Germany), around 2.8%, is obtained either by looking for the `CHN` row in the previous output, or by taking the ratio of the dollar values obtained throughout the options `origin(deu) destination(chn)` and the value of the total German GDP, that can be obtained using: ``` .*WhatistheGermany ́sGDP?
.icio,origin(deu) Value-Addedbyorigin/destination: Origin:DEU Output:Value-Added Millionsof$%oftotal Value-Added3620310.26100.00 ``` Other examples of questions that could be answered (see the reported syntax) with the analysis of supply-demand linkages through `icio` are:

- _What is the GDP (value-added) produced by each country?_ `icio, origin(all)`

- _How much value-added does each country produce in a given sector (e.g., sector code 19)?_

``` icio,origin(all,19) ```

- _What is the aggregate final demand of each country?_ `icio, destination(all)`

- _Where the value-added produced in the Italian sector 19 is absorbed?_ `icio, origin(ita,19) destination(all)`

- _Which final demand sectors in China are the most important for the absorption of US-made value-added?_

``` icio,origin(usa)destination(chn,all) ```

- _Where the GDP produced in each country is absorbed (and save the output as “supply_ _~~d~~ emand.xls” in the current working directory)?_

``` icio,origin(all)destination(all)save("supply_demand.xls") ``` 9