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on Development |
| By: | Benjapon Prommawin; Nattanun Svavasu; Spol Tanpraphan; Voravee Saengavut; Theepakorn Jithitikulchai; Witsanu Attavanich; Bruce A. McCarl |
| Abstract: | Climate change has caused widespread alterations in the environment and agricultural production. This paper investigates how higher temperature impacts agricultural production value of Thai farmers and the potential adaptation through diversification strategies. We use historical weather data coupled with farm household socioeconomic survey data to carry out econometric regression analysis and perform projections from the IPCC AR6 scenarios. We find that higher temperature reduces agricultural output value and that this will be worse as the planet keeps warmer. We also find that households engaged in diversified production activities are better adapted to higher temperature. The adaptation outcomes increase with access to irrigation and smaller farm size. Our findings support the country’s policies to encourage integrated farming and diversified crop-mixes strategies for Thai farmers. |
| Keywords: | climate change; Agricultural diversification; Agricultural households; Cimate resiliencer; Irrigation; Sustainable development |
| JEL: | Q12 Q54 O13 O44 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:pui:dpaper:184&r= |
| By: | Datta, Sandip (University of Delhi); Kingdon, Geeta G. (University College London) |
| Abstract: | This paper employs a hurdle model approach to ask whether the extent of gender bias in education expenditure within rural households in India changed over time from 1995 to 2017-18. Our most striking finding is that there has been a change over time in the way that gender bias is practiced within the household. In 1995, gender bias occurred through a significantly higher probability of school-enrolment of boys than girls, but by 2017-18, gender bias was practiced via significantly higher conditional education expenditure on boys than girls, and this was largely achieved via pro-male private school enrolment decisions. Households practicing gender equality in school enrolment by 2017-18 is a desirable trend. However, girls' significant disadvantage vis-à-vis boys in terms of lower education expenditure, achieved via their lower private school enrolment rate by 2017-18, is problematic if lower expenditure is associated with lower levels of cognitive skills (literacy, numeracy, etc.) since both individual economic returns and national economic growth accrue to cognitive skills and not independently to completing a given number of years in school. Household fixed effects analysis shows that the observed gender biases are a within-household phenomenon rather than an artefact of differences in unobservables across households. |
| Keywords: | gender bias, education expenditure, education and gender, India |
| JEL: | I24 I24 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp15394&r= |
| By: | Deepika Kandpal (Department of Economics, Delhi School of Economics); Dibyendu Maiti (Department of Economics, Delhi School of Economics) |
| Abstract: | The quest for social status is the driving force behind many human decisions including the expenditure on conspicuous goods. Recent evidence shows that conspicuous consumption patterns vary across social groups. Further, rank-based status signalling models suggest that the income distribution of peers affects conspicuous consumption behaviour. Using recent nationally representative microdata from India, this paper investigates the caste-based inequality in conspicuous consumption patterns and the role of income distribution of reference groups in explaining these differences. We find that social identity and economic inequality are essential determinants of conspicuous expenditure. Dalits and Adivasis spend around 7% more on conspicuous items than upper caste households. Consistent with the status signalling models, we find that this gap is significantly influenced by the disparities in the average income of the reference group, within-group income inequality and the share of peers with similar income, denoted by local density. Specifically, local density is found to have a strong influence on household conspicuous consumption decisions. Key Words: Conspicuous consumption, Income distribution, Signalling, Social groups, Social Status JEL Codes: D12, D31, J15 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:cde:cdewps:327&r= |
| By: | Pia Heckl (Department of Economics, Vienna University of Economics and Business) |
| Abstract: | This paper studies gender differences in the labor market reallocation of workers in Mexico as a response to trade liberalization with China. To measure exposure to import competition, I exploit variation in the initial industry structure of Mexican local labor markets. I show that aggregate outcomes mask heterogeneous responses based on gender. Although the employment rate drops for both men and women, the former enter into unemployment while the latter leave the labor force. The results suggest that the drop in the female labor force participation rate is driven by their exit out of formal and especially informal work. |
| Keywords: | Trade, Gender Inequality, Labor Market, Informal Work, Mexico |
| JEL: | F16 J16 J21 J46 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp327&r= |
| By: | Merfeld, Joshua D. (KDI School of Public Policy and Management); Newhouse, David (World Bank); Weber, Michael (University of Chicago); Lahiri, Partha (University of Maryland) |
| Abstract: | Better understanding the geography of women's labor market outcomes within countries is important to inform targeted efforts to increase women's economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation. |
| Keywords: | small area estimation, data integration, geospatial data, labor force participation, unemployment, Mexico |
| JEL: | J21 C13 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp15390&r= |
| By: | Emily Aiken; Guadalupe Bedoya; Joshua Blumenstock; Aidan Coville |
| Abstract: | Can mobile phone data improve program targeting? By combining rich survey data from a "big push" anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.11400&r= |