rafmacalaba/datause-encoder-data huggingface dataset:
in the models producing the data will contaminate our analysis which is based on the data. This might lead to large errors from the regression if, for example, a crop in SPAM is in large quantity in a given pixel, but in reality it is not in the pixel at all, or only in small quantity.

# Results and Discussion

## Spatial disaggregation

The regression results from using crop production from SPAM ‐‐ along with livestock, forest, and fisheries data – are found in Table 1.

For crops, the units are in metric tons, and the dependent variable is in thousands of reais. So, if we were to try to interpret the parameters as prices, the value for the parameter estimate for wheat is 0.456, or R$456 per metric ton (MT), which at the end of 2010 would be equal to USD274 per MT. This is within the price range it could have sold for. Maize price from the regression, on the other hand, is low at R$101 per metric ton. Given the explanatory variables are all modeled values, it is not entirely surprising that the parameters do not reflect prices as well as we might hope. They do, however, allow us to use these parameters to project agricultural GDP to each pixel, which was the goal.

Table 1 shows that most of the agricultural outputs that are important for the Brazilian agricultural sector have statistically significant parameters. In addition to key cereals, cassava, beans, soybeans, and a number of cash crops can be found, along with chickens, cattle, and pigs, and non‐timber forest products.

_Table 1. Regression using pixel‐level production data_

|**Variable**|**Param**|**Std**<br>**error**|**t‐stat**|**Prob**|
|---|---|---|---|---|
|Wheat|0.456|0.083|5.473|0.000|
|Rice|0.061|0.028|2.167|0.030|
|Maize|0.101|0.025|3.982|0.000|
|Barley|‐0.296|0.612|0.483|0.629|
|Sorghum|0.321|0.311|1.035|0.301|
|Other cereals|0.572|0.552|1.035|0.301|
|Potatoes|0.167|0.074|2.263|0.024|
|Sweet potatoes|2.786|0.526|5.298|0.000|
|Yams|‐1.037|1.619|0.640|0.522|
|Cassava|0.081|0.029|2.761|0.006|
|Beans|0.504|0.212|2.381|0.017|
|Other pulses|11.812|9.957|1.186|0.236|
|Soybeans|0.043|0.019|2.204|0.028|
|Groundnuts|1.128|0.664|1.699|0.089|
|Coconuts|0.446|0.077|5.808|0.000|
|Oil palm|0.079|0.098|0.802|0.422|
|Sunflower|‐1.708|3.836|0.445|0.656|

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consumed items, and a one-year recall for clothing, footwear, health, education, and durable goods.

The monthly per capita consumption estimates are put in spatially and temporally real terms by using India’s official poverty lines as deflators.<sup>7</sup> We use two poverty measures. The first is the headcount index, given by the percentage of the population living in households with consumption per capita less than the poverty line. The second is the poverty gap index, given by the mean distance below the poverty line expressed as a proportion of that line, where the mean is formed over the entire population (counting the non-poor as having zero poverty gap). In keeping with our focus on how urban economic growth has impacted rural poverty we confine attention to the rural poverty measures.<sup>8</sup> Much of the research on poverty in India is based on a disaggregation into the urban and rural sectors of each state or union territory (UT). Here we use instead more finely grained data, based on “NSS Regions”. These regions have been used by the NSSO since the 4<sup>th</sup> round, where disaggregation below the state level was first available. Initially, 52 regions were formed, where a region was defined as a group of contiguous districts from within the same state that had similar geographical features and had similar population densities in the 1951 population census. With the subsequent splitting of states and districts, the number of NSS regions grew to 88 by the 68<sup>th</sup> round survey in 2011/12. We only use data for 19 major states, and we also control for state- and district-level splitting by anchoring our panel to the administrative geography of India as of the 55<sup>th</sup> round survey in 1999/00. Consequently, the number of regions we have poverty estimates for is 59, with these available for four years, giving a balanced panel of _N_ =236.

The pattern of change in rural poverty over time is shown in Table 1. The headcount index halved over the two decades considered, falling from 50 percent in 1993/94 to 25 percent in 2011/12. The poverty gap index declined even faster, with a fall from 0.123 in 1993/94 to 0.045 in 2011/12. The reductions in poverty rates are spread fairly evenly between the four survey years, but since there is a ten-year gap between the first two surveys and then gaps of four years and two years, it is clear that the pace of poverty reduction is increasing.

> 7 These are the “Tendulkar” poverty lines, after Suresh Tendulkar who headed an expert group that revised India’s official poverty lines. Among several revisions were a smaller rural-urban cost of living differential than the original Planning Commission poverty lines. At 2011 purchasing power parity (PPP) the Tendulkar poverty lines have a value of $2.09 per day.

> 8 The NSSO follows the Census in defining areas as urban, based on criteria in terms of minimum population, minimum population density, and the percentage of the population working outside of agriculture, or whether an area is a Statutory town (e.g., has a Municipality, Corporation, Cantonment Board, etc.).

7
security outcomes. The key parameters of interest are thus stacked in , the interaction effects of being from the affected region and the post-war-onset month dummies. As we are following mm γγ households for several months, unobserved effects (error terms) can be correlated across time as well as across households living in the same district. Hence, we cluster standard errors at district ( _woreda_ ) level. We also consider alternative definitions of conflict exposure and control group assignments as defined above. Thus, we estimate Equations (1) and (2) for the full sample as well as for a restricted sample consisting of households from the highland regions of Ethiopia.

As noted above, the first two approaches consider Tigray as the treatment region and the rest of the country as a comparison group. This assumes all survey households from Tigray have been affected by the war. Given the depth and breadth of the conflict, this is a reasonable working assumption. It is possible, however, that some survey households were completely immune from impacts of the war. With exposure to war so defined, our estimates measure intention to treat (ITT) impacts. If households who were not affected by the conflict were rather erroneously considered to have been impacted by it, these estimates would be smaller than the average treatment effect (ATE) of the war.

We generate more granular measures of exposure to violent conflict using ACLED’s battle events recorded between August 2019 and May 2021, which coincides with our pre-war and postwar-onset HFPS data. Given the intensity of conflict, exposure is computed by counting the number of battles that took place within 20 and 30 km radius around the homestead of households. These allow us to identify the potential heterogeneities in impacts associated with the intensity of exposure to battles. It also serves to probe the robustness of our main results. We estimate the following specifications: _h_ r h 1 _h_ r _h_ r (3) mm YY = αα + αα + φφ BBW WWBBWWBB + ωω where _h_ r stands for the cumulative number battles experienced within 20 km or 30 km radius from households’ residence. All other terms are as defined before. BBW WWBBWWBB Unlike the coarse measure of exposure to conflict in the two DID approaches, which is based on region of residence at the time survey, the granular measures of exposure defined on the basis of distance between location of battle events and households’ residence is less likely to suffer from misclassification of households into affected and comparison. More specifically, it is highly likely that battles that take place close to a household’s residence would affect its food security 23
**BACKGROUND PAPER TO THE 2009 WORLD DEVELOPMENT REPORT**

**_The Place Premium: Wage Differences for Identical Workers across the US Border_**

# **Michael A. Clemens**

Center for Global Development

# **Claudio E. Montenegro**

World Bank and Department of Economics, Universidad de Chile

# **Lant Pritchett**

Harvard Kennedy School and Center for Global Development JEL Codes F22, J61, J71, O15. We are grateful to Indermit S. Gill and his team at the World Development Report of the World Bank who built and allowed use of the database. We thank Samuel Bazzi for excellent research assistance. We received helpful comments from Christopher Blattman, William Easterly, David Lindauer, David McKenzie, Mark Rosenzweig, and seminar participants at Yale University and the Center for Global Development. This work was partially supported by generous grants from the John D. and Catherine T. MacArthur Foundation and from AusAID. The paper represents the views of the authors alone and not necessarily those of their employers or funders.
**Figure 1: Informal competition and employment growth**

<!-- Start of picture text --> Figure 1A<br>0.60 0.50<br>0.40<br>0.20<br>0.00<br>Low informal competition High informal competition<br>-0.20 (residuals; roughly below median) (residuals; roughly above median)<br>-0.40<br>-0.60<br>-0.80<br>-0.74<br>-1.00<br>Figure 2A<br>0.6<br>0.45<br>0.4<br>0.2<br>0<br>Low informal competition High informal competition<br>-0.2<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.4 percentile values) percentile values)<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4 -1.28<br>Employment growth rate (log<br>differences, %, residuals, mean values)<br>mean values)<br>Employment growth rate (%, residuals,<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data.

Note: 1) Employment growth rate is calculated as the log difference in the level of employment (log of final year employment minus log of initial year employment) divided by the number of years between the initial and last year and multiplied by 100 to convert to percentage terms. The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against the residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment (logs) and (log of) GDP per capita. Informal competition variable equals the proportion of firms in the country (excluding own firm) that report competing against informal firms. 3) Group averages (mean values) of the residuals are shown. Close to the median value of the informal competition residuals is used to categorize the two groups in Figure 1A. The cut-off value used for the two groups in Figure 1B is close to the 75<sup>th</sup> percentile value of informal competition residuals.

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<!-- Start of picture text --> R °<br>_~ ~ —— Bank deposit/GDP ratio Te) —— Private credit/GDP ratio<br>5 8 = SecondFirst periodperiod mean mean _~5 = SecondFirst periodperiod mean mean<br>2 o 8 ----/..<br>Do Q oO<br>©—2<br>1960 1980 2000 1960 1980 2000<br>= Density of bank accounts<br>©<br>Oo — Oo oO© = = SecondFirst periodperiod meanmean<br>= __ __ = sgeeees =<br>a st oS<br>Density of bank branches a + —_ f_.<br>—= First period mean<br>N © © Second period mean Oo<br>Nw<br>1970 1990 2010 1980 1990 2000 2010<br><!-- End of picture text -->
https://www.stlouisfed.org/publications/regional-economist/january-2002/the-futures-market-asforecasting-tool-an-imperfect-crystal-ball Fernandez, V. 2007. “Forecasting Commodity Prices by Classification Methods.” Working Paper, Department of Industrial Engineering, University of Chile, Santiago.

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12 other hand, it is estimated that it will lose about 25 percent of its tariff market share after gas supply to the tariff market is opened to competition in 1998 (Royle 1996).

**Table 1 Size of the U.K. Gas Market, 1994-95 (millions of therms)**

|Market|1994|1995|BGE's share<br>1995 (percent)|
|---|---|---|---|
|Tariff market|11,440|1 1,800|100|
|Contract market|10,780|11,200|33|
|Firm supply|5,720|5,900|15|
|Interruptible supply|3,300|3,300|50|
|Power generation supply|1,760|2,000|38|
|Total gas supply|22,220|23,000|67|

Source: Royle 1996 The deregulation of retail markets, an open access policy, and the unbundling of British Gas's supply and transportation have led to the development of short-term gas trading. Until recently most gas was sold under long- and medium-term contracts between British Gas and producers or consumers. After deregulation market participants had to balance their supply and demand in the short term and thus needed an effective way to trade short-term gas. That led to the development of spot markets in entry terminals and in BGT's pipeline system. At present, there are four mechanisms for trading natural gas in the U.K.: bilateral contracts, spot markets, on-system trading, and the flexibility mechanism (figure 2).
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# IV. THE AUGMENTED PRODUCTION FUNCTION AND ER STUDIES OF COLOMBIAN ECONOMIC GROWTH

4.01 We focus on three of the many issues that have been raised in the extensive literature on Colombian growth: (1) Has the introduction of indexed mortgage instruments affected economic growth? (2) Can we draw any conclusions about the effect of financial policy on income distribution? and (3) Are there any additional potential "sources of growth" type problems that can be identified by the inclusion of monetary balances as a factor of production?

# A. Indexation and Economic Growth

4.02 Carrizosa et. al (1982) have argued that the introduction of indexation had little effect on the level of economic growth. They suggest that because of credit fungibility, the central effect of increasing formal sector mortgage financing was a substitution of financing from alternative sources of funds. This point of view has been contended by Currie and Rosas (1986) who argue that mortgage indexation played an important part in Colombia's economic growth by stimulating housing as a lead sector in the economy.

4.03 Whether mortgage indexation lead to an increased growth rate, particularly in the manner described by Currie (1974) is perhaps an intractable econometric question. Although the lower average level of GDP invested in housing after the introduction of indexation does provide some support to the credit fungibility argument raised by Carrizosa et. al. Our analysis suggests a different channel through which the introduction of mortgage indexation might have indirectly affected growth: by stimulating the competition for financial resources they helped reduce the burden of inflationary taxes on monetary balances. This effect, in turn, led to a reduction in the costs of an important factor of production. According to this perspective, this cost reduction facilitated investment rather than affected its composition, and thereby affected growth.

4.04 To get an approximation of the effects of financial technology on overall growth, it is convenient to assume that the production function is linearly homogeneous, and that the necessary conditions for producer equilibrium apply. With these assumptions output elasticities sum to one, and the effect of the estimated increasing returns to scale on total factor productivity is eliminated. Comparing the adjusted coefficients from linearized versions of equations (4) and (6), i.e., production functions with and without monetary balances, we find that in equation (4) technical change or, in Abramovitz's terms, the measure of our ignorance, accounts for 72 percent of growth in total factor productivity. In equation (6), in contrast, technical change accounts
implying that inside the band of no-arbitrage the premium follows a random walk. Furthermore, the estimated sum of the ARCH and GARCH parameters lies between 0.90 and 0.99, with a value of 0.95 for the majority of stocks.

Having computed the TAR models, we can compare the estimates obtained in financial markets with those previously found in goods markets. To do so, we use the results reported in Obstfeld and Taylor (1997), who estimate a similar model for Mexico, South Africa, and a number of developed countries.<sup>21</sup> Comparing their results (reported at the bottom of Table 2) with ours shows that, as expected, the band of inaction is larger in their case. Furthermore, arbitrage takes place much more rapidly in financial markets than in goods market. While the typical half-life as estimated by the TAR model is less than a day in financial markets, it can be more than a year in goods market. Similar results can be found when the half-life is based on the AR model.

If non-linearities are present in the evolution of the cross-market premium, convergence speeds should be slower when estimated by a linear (AR) model than those obtained from the TAR model, as is indeed the case for each of the stocks in the table. Moreover, the wider the band-width, the higher the persistence estimated by the linear model, as Figure 1 shows. Additionally, the difference between the half-life estimated by the AR, and that obtained from TAR models outside the band, is itself proportional to the linear half-life. These results, which provide further evidence of how the presence of nonlinearities influences the results from a linear estimation, are consistent with similar tests reported by Imbs et al. (2003) for goods markets.

One could argue that the rapid arbitrage we find is related to the fact that these results are based on the most liquid stocks in the sample for each country. However, while liquidity certainly plays a role in the degree of integration, the previous conclusions carry through to more illiquid stocks, as we show in the next section.

> 21 Obstfeld and Taylor (1997) consider disaggregated as well as aggregated consumer price indexes (CPIs) for a number of U.S. cities and a number of, mostly, developed countries at a monthly frequency during 1980-1995. The results reproduced in Table 2 refer to their results for the aggregated CPIs.

13
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UNAIDS. 2008. Report on the Global AIDS Epidemic. UNAIDS, Geneva.

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