Clues to the origin of rising midlife mortality: associations between recent mortality outcomes and county-level economic, social, and employment changes over multiple time periods.

Since the Great Depression, the federal government has implemented agricultural programs by passing what is known as the farm bill. These farm bills typically contain sunset provisions, requiring new farm bills to be passed roughly every five years. These farm bill votes provide ample opportunities for the agricultural lobby to engage in political rent seeking behavior. There is a considerable literature on the impact of direct campaign contributions on farm bill amendment votes. The approach used in this literature is to identify a congressional floor level amendment vote to a farm bill that benefits a crop specific agricultural interest. Through the use of a simultaneous equations probit-tobit model, the relationship between donations and the amendment vote is estimated. This research uses a similar approach. Instead of looking at specific amendment votes, my paper looks at final farm bill votes in the House of Representatives, treating each farm bill vote as a repeated observation of the same event. In so doing, a time series is constructed, allowing for study of how historical events impact the actions of farming lobbies and legislators. Data on direct campaign donations comes from the Federal Election Commission. These data contain information on what industry the donating PAC represents, along with data on the recipients of campaign donations. The donation data is what is referred to in the political economy literature as “hard money” donations. These donations are highly regulated by the FEC, with a maximum contribution limit per PAC. Farming interests are able to bypass these contribution limits by creating more PACs, assuming organizational costs are sufficiently low. These data are merged with information on legislator characteristics, provided by a data repository maintained by Charles Stewart III. These data include chamber seniority, committee membership, committee seniority and party affiliation. A temporally consistent measure of political ideology comes from Lewis, Poole and Rosenthal. Information on congressional votes is provided by Civic Impulse LLC. Production data by crop is acquired from the USDA NASS, while data on farming demographics comes from the Bureau of Economic Analysis. Using reasonable assumptions, these data are converted from county level data to congressional district level geospatial shape files using data provided by Lewis, DeVine, Pitcher and Martis. Data are aggregated to the level of the crop lobby. That is to say, donations from multiple PACs representing the same crop to the same legislator are aggregated together. The unit of observation is a donation from a specific agricultural interest to a specific legislator in a given election cycle in which a farm bill vote take place. The time series consists of the 1985, 1990, 1996, 2002 and 2008 farm bills. Crop lobbies included in this model are the cotton, peanuts, rice, sugar beets and sugar cane lobbies. The model is in the form of a simultaneous probit-tobit model as outlined by Chappell (1982). The probit equation models the vote decision of the legislator on the farm bill. The probability that the legislator votes yes is a function of the amount of campaign donations received from various agricultural PACs, the initial policy position of the legislator (i.e. political ideology) and the farming related demographic attributes of the legislator’s district. The tobit equation represents the donation decision of the farming interest. This decision is a function of legislative power (such as committee membership and seniority), the probability of reelection, and the level of crop production in the legislator’s district. The model is identified through the use of exclusion restrictions. My model extends this framework through the use of a pooled cross section over multiple time periods. By treating each farm bill as a new observation of the same event, this approach allows for the use 1 temporal indicator variables to study the impacts of historical events on farm bill votes. Historical events studied thus far are the impacts of political regime changes in the House of Representatives and the impacts of legal regime changes in campaign finance law. The preliminary version of this model estimates the relationship between each crop lobby and the legislators separately. That is to say, a two equation model is estimated, with the donation of one crop lobby modeled in a tobit equation and the legislator’s vote modeled in a probit equation. The more innovative version of this model estimates the donation equations of all of the crop lobbies, along with the vote equation simultaneously. This better reflects the interconnected nature of the various farming interests and the legislator’s final farm bill vote decision. Results show that committee membership is a highly significant determinant of how much support a legislator receives. Two committees are tracked; membership on the House Agricultural Committee and the House Appropriations Committee. While the farm bill has little to do with appropriations, this is included because funding the programs of the farm bill requires separate legislation, which is drafted by the appropriations committee. This makes appropriations committee members important allies in any policy involving the disbursement of federal funds. In all versions of this model, agricultural committee membership has a positive and highly significant impact on the donation decision. For most crop lobbies, appropriations committee membership is also highly significant and positive. This suggests that farming PACs recognize that they also need the support of legislators that control funding. For most crop lobbies, the level of production of their crop in a legislator’s district has a positive and significant impact on the level of donations received. This suggests that farming lobbies support legislators that represent the districts that their members reside in. The impact of party majority is highly significant for most crop lobbies. The sign on the effect varies between farming interests, suggesting partisan heterogeneity among different groups of farmers. In the vote equation, political ideology, farming demographics and donations have significant impacts on the decision to vote yes, when each lobby is estimated separately and jointly. Results appear to be robust to specification, and the correlation coefficients between the equations imply that the system is, in fact, endogenous. The donation equations of the various farming lobbies are highly correlated with each other. These results extend the literature on agricultural political rent seeking by extending the previous, isolated analyses to a more broad analysis over a pooled cross section. These results suggest that political regime changes have a significant impact on both legislators and special interests, and demonstrate the importance of the appropriations committee to the agricultural sector, which has not been studied by previous research.

PurposeSome US counties are more likely to generate entrepreneurial opportunities than others. This paper aims to determine the linkages between US counties with disproportionately high shares of entrepreneurs and specific attributes of the entrepreneurial support system.Design/methodology/approachNon-farm proprietorship (NFP) has been used as a proxy for entrepreneurship and self-employment. NFP employment data were collected from the US Bureau of Economic Analysis by county. Data on all independent variables were obtained from the US Census and Bureau of Economic Analysis by county and subject to stepwise linear regression analysis.FindingsResults revealed a strong positive relationship between the percent of NFP employment by county and the percent real estate, rental and leasing employment and construction employment as well as percent Hispanic and median age.Practical implicationsIn attempting to encourage NFP employment, policymakers should be more aware of the key predictors that shape county-wide entrepreneurial ecosystems to enhance competitive advantage. Better understanding of the needs and experiences of different types of entrepreneurs and ecosystems can enhance overall quality of life and economic opportunity levels in a community.Originality/valueThe explicit spatial context of this paper has sometimes been overlooked in the traditional entrepreneurship literature, as such, this paper helps fill that gap. The findings provide a disaggregated analysis that can help better understand the key predictors that can drive the local choices of entrepreneurs and help local policymakers to build more competitive communities.

Self-reported physical activity levels during a segmented school day in a large multiethnic sample of high school students

Optimal health insurance for multiple goods and time periods

Rapid population growth and agricultural development are generating a considerable amount of effluents, which poses threats to the quality of rural water resources as well as sanitary conditions. However, with a range of rural wastewater treatment (WT) technologies available, one major problem facing the practitioners is which to choose as the most favorable option suited to specific areas. In this study, a novel decision-making framework is proposed to evaluate and select the optimal alternative in rural areas of Xi'an within multiple consecutive time periods. Firstly, an evaluation index system is constructed and picture fuzzy numbers (PFNs) are used to represent both evaluation levels and experts' refusal due to limitation of knowledge. Secondly, fuzzy analytical hierarchy process (FAHP) is applied to derive weights of criteria, which enables experts to assign fuzzy numbers to express their preferences for comparison judgments. Thirdly, evidence theory is utilized to obtain the aggregated values from multiple time periods. Finally, based on the belief intervals obtained, sequencing batch reactor (A4) is determined as the optimal rural WT technology in Xi'an from 2006 to 2020, whereas the membrane bio-reactor (A2) is the last option. The effectiveness of the proposed framework is further validated by comparative analysis. This research can hopefully serve as useful guidance for the assessment of rural WT technologies in various regions.

We propose a model that seeks the optimal timing and depth of retail discounts with the optimal timing and quantity of the retailer's order over multiple brands and time periods. The model is based on an integration of consumer decisions in purchase incidence, brand choice and quantity with the dynamics of household and retail inventory. The major contribution of the model is that it shows how the optimum depth and timing of discount varies with key demand characteristics such as consumer stockpiling, loyalty, response to the marketing mix, and segmentation. In addition, the optima also vary with key supply characteristics such as retail margins, depth and frequency of manufacturer deals, retail inventory, and retagging costs. The most valuable contribution of the model is that it can provide an optimal discount strategy for multiple brands over multiple time periods. The optimization model runs on a user-friendly personal computer program. An application based on UPC scanner data illustrates the model's uses. Sensitivity analyses of the optimization model under alternative scenarios reveal novel insights as to how optimal discounts vary as a function of the key demand and supply characteristics.

Context Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI. Aims The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI). Methods Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants. Key results There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI, the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity. Conclusions Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection. Implications Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.

A branch-and-cut framework for the consistent traveling salesman problem

Exploring the impact of air quality ranking on energy efficiency and its spatial spillovers will help improve the pollution and carbon reduction effects of environmental governance policies. Based on the panel data of 285 cities at or above prefecture level in China during 2009-2019, this study pioneers in adopting difference-in-differences (DID) model with multiple time periods, spatial DID (SDID) model with multiple time periods, and mediating effect to explore the direct influence of ranking on China's energy efficiency, as well as its spatial effect and influence mechanism. Results show that air quality ranking is of significant positive impact on energy efficiency, proved by parallel trend hypothesis, placebo control, and policy heterogeneity. With spatial effect considered, such impact still exists, and ranking of the experimental group has significant positive spatial spillover effect on efficiency of the control group, meaning the ranking also promotes the efficiency of nearby cities in control groups via spatial spillover effect. In addition, air quality ranking greatly elevates energy efficiency via industrial structure and technological innovation, the mechanism of which is of significant positive spatial spillover effect. Based on the above results, some policy recommendations on environmental competition policy, industrial structure adjustment, and low-carbon applicable technology promotion were proposed to promote the energy efficiency of China.

This study examines whether living with other adults enables married and single mothers in New York City to enter the labor market. Multivariate analyses of data on over 8,000 households revealed that living with coresident adults increased the participation of Puerto Rican, Dominican, and Asian single mothers, and enabled all mothers of young children and all foreign-born mothers to enter the labor market. In a separate analysis of extended households, the proportions of elderly and employed coresident adults increased women 's labor force participation, while the proportion of coresident adults who reported child care/family responsibilities as their reason for not being in the labor force decreased women's paid labor activity. Our findings suggest that coresident adults serve different functions within the household, which in turn influence women's labor force decisions in various ways. In recent years attention has focused on the various ways in which families manage social and economic difficulties. One strategy that has received considerable research attention is household extension, or the incorporation of adults other than the husband and wife into the household. Incorporating other adults may benefit the household in a number of ways. Co-resident adults may contribute to the household's pool of financial resources, thereby potentially increasing the economic well-being of household members. Or they may provide domestic labor, which might enable the wife or female householder to devote more time to paid employment. Angel and Tienda (1982) argued that household extension may be an important adaptive strategy for minority families, in that additional income contributions may compensate for low earnings or sporadic unemployment. In this article we analyze the relationship between household extension and mothers' labor force participation among six racial and ethnic groups in New York City. In so doing, we largely replicate an earlier analysis by Tienda and Glass (1985) using current data from one of the nation's most diverse cities. However, we expand upon the earlier analysis in three ways: by including an indicator of the supply of child care in the household, by examining a broader array of racial/ethnic groups, and by analyzing the role that birthplace plays in influencing women's labor market behavior. DETERMINANTS OF WOMEN'S LABOR FORCE PARTICIPATION Family structure has long been recognized as an important determinant of women's labor force participation. Typically, family characteristics--such as marital status, the presence of young children, and the presence of coresident adults--affect the amount of time women can spend in domestic and market activities. Including other adults in the household may ease the strain of balancing domestic and market responsibilities, especially for single mothers and mothers of young children who may have the least flexibility in devoting time to the labor market. Prior research has shown that women who live in extended households are more likely than women who do not live with coresident adults to be in the labor force, yet this relationship varies by race/ethnicity (Barry Figueroa & Melendez, 1993; Stier, 1991; Stier & Tienda, 1992; Tienda & Glass, 1985). Moreover, women's labor force behavior appears to be influenced by the specific characteristics of coresident adults. For example, as the number of coresident adults increases, married mothers' participation decreases, suggesting greater domestic burdens; by contrast, as the proportion of female coresident adults increases so does single mothers' participation, suggesting that coresident women function as surrogate domestic workers (Tienda & Glass, 1985). Living with coresident employed adults increases labor force participation among young mothers, while living with coresident nonworking adults--presumably a source of child care--depresses paid labor activity (Parish, Hao, & Hogan, 1991). …

Your fathers’ mistakes: Critiques of GDP and the search for an alternative

The sequelae of increasing childhood obesity are of major concern. We assessed the association of BMI in late adolescence with diabetes mortality in midlife. The BMI values of 2,294,139 Israeli adolescents (age 17.4 ± 0.3 years), measured between 1967 and 2010, were grouped by U.S. Centers for Disease Control and Prevention age/sex percentiles and by ordinary BMI values. The outcome, obtained by linkage with official national records, was death attributed to diabetes mellitus (DM) as the underlying cause. Cox proportional hazards models were applied. During 42,297,007 person-years of follow-up (median, 18.4 years; range <1-44 years) there were 481 deaths from DM (mean age at death, 50.6 ± 6.6 years). There was a graded increase in DM mortality evident from the 25th to the 49th BMI percentile group onward and from a BMI of 20.0-22.4 kg/m2 onward. Overweight (85th to 94th percentiles) and obesity (the 95th percentile or higher), compared with the 5th to 24th percentiles, were associated with hazard ratios (HRs) of 8.0 (95% CI 5.7-11.3) and 17.2 (11.9-24.8) for DM mortality, respectively, after adjusting for sex, age, birth year, height, and sociodemographic variables. The HR for the 50th through 74th percentiles was 1.6 (95% CI 1.1-2.3). Findings persisted in a series of sensitivity analyses. The estimated population-attributable fraction for DM mortality, 31.2% (95% CI 26.6-36.1%) for the 1967-1977 prevalence of overweight and obesity at age 17, rose to a projected 52.1% (95% CI 46.4-57.4%) for the 2012-2014 prevalence. Adolescent BMI, including values within the currently accepted "normal" range, strongly predicts DM mortality up to the seventh decade. The increasing prevalence of childhood and adolescent overweight and obesity points to a substantially increased future adult DM burden.

Purpose Some US counties are more likely to generate entrepreneurial opportunities. This paper aims to determine whether US micropolitan counties with disproportionately high nonfarm proprietorship (NFP) employment levels are systematically linked to specific attributes of the entrepreneurial ecosystem. A limited amount of research has been conducted on the geography of entrepreneurship in small to medium-sized micropolitan counties where rates of growth and change can be quite dramatic. Design/methodology/approach NFP employment data from the US Bureau of Economic Analysis (BEA) is used as a dependent variable proxy for entrepreneurship. NFP data are widely used in the entrepreneurship literature. Data on all independent variables were obtained from the US Census Bureau’s American Community Survey and BEA by county and subject to stepwise linear regression. Findings Results revealed a strong positive relationship between the percent of NFP employment by micropolitan county and percent construction employment, percent real estate, and rental and leasing employment, and the percent elderly. It is argued that the combination of predictors captures primarily a self-employment of opportunity (e.g., thriving land and real estate markets). Practical implications In attempting to encourage NFP employment, policymakers should be more alert to the key predictors that shape micropolitan entrepreneurial ecosystems when attempting to enhance competitive advantage in small- to medium-sized communities. Better understanding how micropolitan counties function relative to larger metropolitan places can help local policymakers more efficiently enhance the overall quality of life in smaller communities. Originality/value The focus on smaller micropolitan communities and the explicit spatial context of this paper has sometimes been overlooked in the traditional entrepreneurship literature and this research helps to fill that gap.

Using two independent data sources—the intrafirm trade data from the US Bureau of Economic Analysis and the related party trade data from the US Census Bureau—I construct and compare measures of US intrafirm exports and imports. I find that, in general, the two datasets provide similar measures of US intrafirm trade, particularly for exports. Understanding the differences that do exist in measurement will likely require study of the confidential micro data at both the Bureau of Economic Analysis and the Census Bureau.

The US Bureau of Economic Analysis (BEA) estimates that the return on investments of foreign subsidiaries of US multinational companies over the period 1982–2006 averaged 9.4 percent annually after taxes; US subsidiaries of foreign multinationals averaged only 3.2 percent. BEA returns on foreign direct investment (FDI) are distorted because most intangible investments made by multinationals are expensed. We develop a multicountry general equilibrium model with an essential role for FDI and apply the BEA's methodology to construct economic statistics for the model economy. We estimate that mismeasurement of intangible investments accounts for over 60 percent of the difference in BEA returns. (JEL F23, F32)