rafmacalaba/datause-encoder-data huggingface dataset:
# **8 REFERENCES**

Alderman, H., Hoddinott, J., and B. Kinsey (2006). “Long term Consequences of Early Childhood Malnutrtion”, _Oxford Economic Papers_ , 58(3): 450-474.

- Alderman, H., M.Garcia (1994). “Food Security and Health Security: Explaining the Levels of Nutritional Status in Pakistan,” _Economic Development and Cultural Change,_ 42(3): 485-507.

- Arimond, M., MT. Ruel (2004). “Dietary Diversity is Associated with Child Nutritional Status: Evidence from 11 Demographic and Health Surveys,” _Journal of Nutrition_ , 34(10):2579-85.

- Arimond, M, D. Wiesmann, E. Ecquey, A.Carriquiry, M. Daniels, M. Deitchler, N. Fanou, E. Ferguson, M. Joseph, G. Kennedy, Y. Martin-Prevel (2011). “Dietary Diversity as a Measure of the Micronutrient Adequacy of Women’s Diets in Resource-Poor Areas : Summary of Results from Five Sites,’’ FANTA Technical Report

Barrett, C. B. (2010). “Measuring Food Insecurity”, _Science_ , 327 (5967): 825-828

- CFS (2012). _Coming to Terms with Food Security, Nutrition Security, Food Security and Nutrition, Food and Nutrition Security_ , available at: http://www.fao.org/docrep/meeting/026/MD776E.pdf Accessed 19 July 2013

- Coates, J., A. Swindale, and P. Bilinsky (2007). “Household Food Insecurity Access Scale (HFIAS) for Measurement of Household Food Access: Indicator Guide (v.3).” Washington, D.C.: Food and Nutrition Technical Assistance Project, Academy for Educational Development.

- Firpo, S., N.M. Fortin, T. Lemieux (2009). “Unconditional Quantile Regressions,” _Econometrica_ , 77(3): 953-973.

- Glewwe, P., Jacoby, H., and E. King (2001). “Early Childhood Nutrition and Academic Achievement: A longitudinal Analysis”, _Journal of Public Economics_ , 81(3):345-368.

- Headey, D. and O. Ecker. (2012). “Improving the Measurement of Food Security _”, IFPRI Discussion Paper01225_

- Hoddinott, J. and Y. Yohannes (2002). “Dietary Diversity as a Household Food Security Indicator,” Food and Nutrition Technical Assistance Project, Academy for Educational Development, Washington, D.C.Ivanic, M., and Martin, W. (2008). “Implications of higher global food prices for poverty in lowincome countries,” _Agricultural Economics,_ 39 405-16.

- Ivanic, M., Martin, W. and H. Zaman (2011). “Estimating the Short Run Poverty Impacts of the 20102011 Surge in Food Prices”, World Bank Policy Research Working Paper

- Jensen, R.T. and N.H. Miller (2010). “A Revealed Preference Approach to Measuring Hunger and Undernutrition,” NBER Working Paper 16555.

- Maluccio, J.A., J. Hoddinott, J.R. Behrman, R. Martorell, A.R. Quisumbing, and A.D. Stein (2009). “The Impact of Improving Nutrition During Early Childhood on Education Among Guatemalan Adults,” _Economic Journal_ , 119(537): 734-763.

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control arms. Treatment and control GPs were evenly spread across the two blocks to reduce any potential contamination between intervention and control villages.<sup>3</sup> In Karnataka the design was identical except an additional set of treatment villages was added that received a slightly different treatment- information and advocacy campaign. The number of GPs in each of the three cells in Karnataka (control, treatment 1 (information campaign), treatment 2 (information plus advocacy)) is 70.

Sample sizes were determined using cluster randomized sample size calculations based on a 5 percent significance level and 80 percent power. Figure 1 describes the flow of study participants through the trial.

**Timeline** In MP and UP, baseline surveys were administered between February and April 2006. The interventions were carried out from September to November 2006 and follow up surveys were administered between February and April 2007.  Karnataka had a shifted timeline due to different school year timing. Baseline surveys in Karnataka took place between July and August 2006, the interventions were carried out from February to April 2007 and follow up surveys were done between July and August 2007.

**Baseline survey** One randomly selected public primary school in every GP was chosen for the survey. All teachers teaching grades 1 to 5 are in the sample. In MP and UP, 45 randomly chosen students per school (15 each from grades 2-4) and in Karnataka 30 randomly chosen students per school (15 each from grades 4-5) are in the sample (within each grade, 5 each from Scheduled caste (SC)/scheduled tribe (ST), other backward classes (OBCs) and other category (consisting of general or high castes). Half were chosen to be girls in each grade and category.  The school enrollment registers were used for random selection of students.  Sometimes a given grade had less than the required number of students from a given category for the sample.   In these cases, a total of 15 students were selected from the grade to maintain the overall sample size. This resulted in a slightly uneven distribution of SC/ST versus OBC and general category students.  We used the state government’s definitions for caste (see the list of references).

The surveys were conducted by in-person interviews by a team of trained research assistants with prior experience in administering rural household surveys in the region. Surveys were conducted in local languages: Hindi in MP and UP and Kannada in Karnataka. Instruments were pilot tested prior to use.

# **Outcomes measured in baseline survey**

- Teacher attendance and activity: Four unannounced visits were made per school with a 2 to 3 week gap between two consecutive visits to collect information on teacher attendance and activity. Teacher attendance on each visit is 1 if teacher is present in school, 0 otherwise. Activity is a measure of whether a teacher is

> 3 Although districts with varying literacy rates are chosen to be in the sample in each state, the sample design is not meant to represent the state but to provide enough statistical power for a valid assessment of the impact of the intervention in each state.

7
and Pakistan have the lowest levels of regional inequality followed by India, Brazil, Mexico and Russia respectively.

The United States has seen a decline in regional inequality from 1990 to 1994 after which it has stabilized. Canada's inequality has remained more or less constant from 1994 to 1998. _Pakistan_ has seen a decline in inequality from 1990 to 1998, with a relatively sharp drop in 1994. Its level of inequality is comparable to that of Canada and the United States. The low levels of regional inequality in industrial countries is expected because of their being at an advanced stage of economic development<sup>7</sup> , and with their having few barriers to interregional trade and factor mobility. Pakistan's low level of regional inequality, uncharacteristic of developing countries, may be surprising, but it could be explained by the fact that Pakistan has a small number of provinces with the two richest provinces have over 80% of the country's population and that there is significant inter-provincial migration of labor and capital.

**Figure 4.1a: Regional disparity trends in federal countries**

<!-- Start of picture text --> 0.7<br>i g~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4<br>01<br>041<br>0 .3<br>0.210<br>0-<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>Year<br>-*-Brazil Canada - )- India * Mexico `-Paklstan --- Russia -United States<br><!-- End of picture text --> 18
pharmaceutical sector with Parke Davis, Squibb, Lilly and Burroughs Welcome engaged in its manufacture and marketing.

After the active constituent of the coca leaf was identified in 1859 and named cocaine, this drug emerged as a popular remedy for a range of physiological and psychological illnesses such as allergies, nasal congestion, nymphomania and morphine dependence and it was recommended by the _British Medical Journal_ for anesthesia in eye surgery. Produced and marketed by Merck and the American firm Parke, Davis, cocaine based products such as _Ryno’s Hay Fever and Catarrh Remedy_ and _Agnew’s Powder_ , which contained 99 percent and 35 percent pure pharmaceutical cocaine respectively, gained mass markets in the US and Western Europe.

A second driver of drug use was the need for physical stimulation. Coca, cannabis and other natural plant based stimulants such as betel, khat and tobacco were traditionally ingested by indigenous and indentured laborers. In the Andean region of South America, Spanish colonists encouraged the chewing of coca by indigenous workers in the silver mines, as it boosted physical endurance and depressed the appetite. In the second half of the nineteenth century, the commercialization of coca leaves allowed for the development of a new mass market for stimulant tonics such as Vin Mariani, which was first marketed in Europe in 1863 (Streatfeild 2001). Coca based stimulants also found a receptive market in the U.S., where French Wine Coca, a mixture of wine and cocaine manufactured in Atlanta, was marketed as a ‘braintonic’. It was re-launched in 1886 as Coca-Cola after the alcohol prohibition movement objected to the wine content of the product.

A third factor accounting for the preponderance of ‘drugs’ was their cultural and spiritual significance in religious, pagan, shamanic and cultural ceremonies across the world. From the Dagga cults of West Africa, indigenous Indian communities in North and South America to Hindu festivals in India, coca leaves, opium, cannabis and hallucinogenic plants such as peyote and psilocybin, were used as religious sacraments and venerated as gifts from nature or the gods (Schultes and Hoffman 1992).

Cannabis, coca and the opium poppy were also cultivated as a food source. Hemp, a member of the _cannabis sativa_ family, produces highly nutritious hemp seed and seed oil. It was a staple of rural diets in China, South and Central Asia and the Balkan region for centuries. Hemp was also used for rope, rigging, paper making and textiles. The utility of hemp was first recognized by the Chinese and its cultivation 4
- smaller farms' total farm expenditures were nearly R5 000 per hectare, whereas middle and larger farms spent only R177 and R55 per hectare, respectively.

The comparative efficiency of black, small-scale farming versus white, large-scale farming is very difficult to assess. More than a century of policy interventions has suppressed the profitability of black farming in order to protect white farmers from black competition and to assure the white farm sector of low-wage labor. The only areas where black farming was condoned were the homelands. Given their location, lack of infrastructure, and support services, generally poor soils, and extreme population pressure, it would be unfair to compare small-scale, farming in the homelands with farming in the white areas.

Nonetheless, a few cases exist in which small-scale farmers were given access to support structure roughly comparable to those of their white colleagues. The two case studies presented here, both in which small-scale, black farmers equaled or outperformed larger, white farms, come from tea and sugar farming.

In the tea industry, the case study illustrates that "mini-farming" (where an individual leases a small area planted to tea from a tea estate and is remunerated according to the quantity of acceptable tea produced) show an increase in yields, income, and profitability of both the estate renting out the land and the mini-farmers. Compared to ordinary pluckers, mini-farmers obtained yields on their 0.5 hectare plots averaging 23% more than what the large estate obtained (Van Zyl and Vink, 1992).

The same applies to the sugar-cane case study in the Eastern Transvaal, where black smallholders obtained 116.8 tons/hectare on their plots of 7.1 hectares (on average), while large-scale white farmers adjacent to these smallholders obtained 102.9 tons/hectare on 68.6 hectares (on average). Total costs amounted to R3 286/hectare for the smallholders and R3 448/hectare for the large-scale farmers (unpublished data from representative samples gathered by the University of Pretoria, 1993). Both these case studies confirm that with the same support structures small-scale farming is at least as efficient as large-scale farming in these specific areas and types of farming.

# _Evidence on Causes of Scale Efficiency_

The official definition of the viable farm in terms of size has had a profound negative effect on the relative profitability of farms smaller than the viable size. Given the high levels of official assistance 11
The set of social norms variables at the subnational level include the share of women among highpaying employees in the Central Government at the department level. We interact this variable with the dummy variable of university level of education. We expect high paying public sector jobs to be more attractive for higher educated individuals. The other variables capturing social norms and attitudes towards women are the intrafamily violence against women in each municipality, and the share of males (compared to all) agreeing with gender parity statements at the department level.

The set public policy variables include the number of public preprimary centers in each municipality interacted with a dummy of having a child in 4 to 6 age range, the logarithm of the per capita spending in education in each department interacted with a dummy of having a child 0 to 12 years old, the logarithm of the per capita spending in health in each department interacted with a dummy of living with a person 60 years of age or older, and the municipality road accessibility at the municipality level.

_Id_ are department fixed effects that capture supply and demand conditions of local labor markets not controlled for with the set of FLFP drivers included in the model. We include _Id_ except when the driver of FLFP is defined at the department level. i is an error term. Model (3) is estimated by ordinary least squares and standard errors clustered at either the εε municipality or department level depending on the level of variation of the FLFP driver that is analyzed. All variables are defined as in Section 3 and Table A1 in the Appendix provides details on their construction and data sources.

# **4. Main Findings**

# **4.1 Decomposition of the change FLFP: 2002-2018**

We start by decomposing the observed change in FLFP between 2002 and 2018 using census data and following an Oaxaca-Blinder decomposition approach described in previous section. Table 1 reports the results of the Oaxaca-Blinder decomposition between 2002 and 2018 when FLFP increased 5.69 percentage points. Column (1) reports the FLFP in each year and their difference, column (2) shows the explained component of the decomposition, and column (3) presents the unexplained component. We group the variables in individual characteristics, household characteristics, variables capturing the economic situation of the household, local labor market factors, and geography variables.<sup>11</sup>

> 11 Individual characteristics include age and age squared, dummies of educational level and an indicator variable of belonging to an indigenous group. Household characteristics include a dummy of being married, number of children

11
In a highly publicized recent article, Nathan Nunn and Nancy Qian (2014, hereafter NQ) demonstrate a new strategy to identify what would be the first causal estimate of the effect of United States (US) food aid deliveries on the global incidence of conflict. Using an instrumental variables (IV) strategy popular in recent panel data studies, they report that increasing the quantity of food aid to a given country _causally_ increases the incidence and duration of civil conflict in the recipient country. The IV method NQ employ involves interacting a plausibly exogenous time series variable with a potentially endogenous cross-sectional exposure variable so as to generate a continuous difference-in-differences (DID) estimate of the causal effect of inter-annual variation in the time series variable on the outcome of interest among relatively exposed units compared to relatively unexposed units.

The NQ estimation approach holds intuitive appeal in contexts where a plausibly exogenous instrument is available for an endogenous regressor, but is limited in variation to such an extent as to raise concerns about spurious correlation. If the instrument’s influence on the endogenous regressor is known to vary along an observable dimension, then causal identification of the effect of interest can be recovered by interacting the instrument with this source of heterogeneity, but only under the assumption that control variables fully capture the endogeneity. The assumption is that conditional on the controls, the error term is independent of the interaction of the instrument and the exposure variable, which is only true if either the exposure variable is uncorrelated with the error term or the correlation is constant across time or space. If aid is directed toward countries that experience conflict and the risk of conflict is both grouped together in adjacent years and only experienced by some countries and not others, this assumption is more likely to be violated. We argue that non-parallel trends can cause a violation of this core assumption, and document how the violation manifests within the specific policy context studied by NQ.

2
in the first differenced residuals of Equation (8) in estimations for the full sample.<sup>17</sup> After extensive experimentation with alternative instruments sets and subsamples, we find evidence of no second order serial correlation only when we estimate Equation (8) for a subsample of young plants with 8 or 9 annual observations. The results presented in Table 4 are based on that subsample. The output experience coefficients change their sign, though age becomes statistically insignificant. Export experience continues to be positively associated with TFP, with each additional year of export experience increasing TFP by about 9% while the coefficient on the cumulative exports index is positive but not statistically significant.<sup>18</sup> The most noticeable result in Table 4, however, is that the estimate of<sup>ρ</sup> is very close to 1. Using a conventional _t_ test of H0:<sup>ρ</sup> =1 against H1:<sup>ρ</sup> <1, we fail to reject the null hypothesis in both regressions with p-values of 0.25 and 0.47.

To obtain more information on the time series properties of the variables used in this model, we test for the null hypothesis of unit roots by estimating simple AR(1) specifications by OLS.<sup>19</sup> Our tests cannot reject the null hypothesis of a unit root in our TFP series, with p-values of 0.13 (with year dummies) and 0.46 (without year dummies). The tests overwhelmingly reject the null hypothesis of unit roots in the output and export experience variables. Although unit root tests have low power to distinguish between a random walk and a highly persistent AR(1) process, the evidence suggests that assuming that<sup>ρ</sup> =1 is a reasonable approximation. While estimating

> 17 By construction, the first differenced residuals of Equation (8) follow an MA(1) process; therefore, if ν _t_ is i.i.d. we should find evidence of first order but not of second or higher order correlation in these residuals. The m1 and m2 statistics reported in Table 4 test, respectively, for first and second order serial correlation in the residuals.

> 18 For comparison purposes we also estimate for this subpanel the regressions corresponding to Table 3 and find an effect of export experience on TFP that is larger than for the full sample: e.g., the coefficient on the number of years exported in the fixed effects specifications is 5.7%.

> 19 Bond et al. (2002) show that the _t_ -test on the OLS coefficient of the lagged value of the series has high power when the variance of unobserved heterogeneity is relatively small.

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procurement (Pimenta and Rezai, 2015: 332). If so, a bias would be created against finding a positive (negative) link between bidding (kickbacks) and the PEFA procurement indicators. It is easier to pass laws (e.g. establishing a complaints mechanism) than to implement them fully (e.g. ensuring a complaints mechanism functions as intended), so reforms prompted by poor performance would likely affect the _de jure_ indicator (LEGAL) more than the _de facto_ indicators (METHODS, ACCESS and COMPLAINT). Reverse causation may thus be partly responsible for the fact that LEGAL is never significant in any of the regressions, and may reduce the coefficients (in absolute value) of the _de facto_ indicators, even where they are nevertheless significantly related to bidding and kickbacks.

Another potential threat to a causal interpretation of our results is that our procurement system indicators and procurement outcomes indicators could be capturing a much broader relationship between “good institutions” and good governance outcomes. Some countries may score highly on a range of indicators measuring recommended practices on tax administration, legal systems, private sector regulation, etc., and on a range of governance outcomes (tax compliance, corruption in various sectors, delays in issuing permits, etc.), while other countries score poorly. If so, our indicators for procurement policies and outcomes - as merely one of many elements of governance systems - could be proxying for these broader systems. Then, well-governed countries may score highly on PEFA procurement indicators and on procurement outcomes in Enterprise Survey questions, even if there is no causal effect of the former on the latter.

However, there is a surprising degree of variation in country performance across policy areas or sectors within countries. For the Enterprise Surveys, Kenny (2009) reports considerable variation in corruption experiences across economic sectors within countries. Nor are country rankings on different types of corruption measured by the Enterprise Surveys very highly correlated with each other. Moreover, (Spearman) rank correlations among the various aspects of public financial management, as measured by PEFA, are quite modest, averaging +.10 and ranging from -.34 to +.42.

If the results in Tables 1-5 merely reflect something about the quality of governance more broadly, and not about procurement systems and outcomes more narrowly, then they should be spuriously “robust” to substituting less conceptually relevant indicators from the same data sets. We report results from these falsification tests in Table 6. First, we replicate Tables 1 (BID regressions) and 2 (KICKBACK) regressions, substituting PEFA question 18 (on payroll controls) and its four sub26