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Land is an emotive issue for women in Kenya, majority of who still suffer the consequences of not having access to land, leading to economic insecurity. This paper aims at examining the effects of women access to land on household nutritional outcomes among smallholder farmers in Kenya.

The study uses primary data collected from a sample of 384 small-scale women farmers selected using multi-stage sampling technique. For data analysis, household nutritional outcomes were measured using Households Dietary Diversity Scores (HDDS) and Household Hunger Scale Scores (HHS). Stratification multilevel and matching-smoothing approach that controls for pre-treatment heterogeneity bias and treatment effect heterogeneity bias was used in estimating heterogeneous effects of women access to land.

The analysis reveals that women access to land has a significant positive effect on household nutritional outcomes. All households across all propensity scores strata benefited significantly but differently from women access to land in terms of nutritional outcomes.

Econometrically, propensity matching technique used in computing heterogeneity effects captures selection bias due to observable characteristics but it fails to capture selection bias due to unobservable factors. However, robust strategies were employed to ensure minimal estimation bias.

The paper provides insights on the determinants of women access to land and the influence women access to land has on household nutritional outcomes. In addition, by employing one of the conventional impact evaluation techniques, the paper contributes to knowledge by taking into accounts the heterogeneity in the effects of women access to land on household nutritional outcomes.

The purpose of this paper is to analyse the effect of an Italian training program on the re-employment probability of young unemployed workers. The program consists exclusively of workplace training and is coordinated by employment centers, even if it is fully implemented by firms.

The authors develop a discrete duration analysis. In particular, the authors compare the re-employment process of individuals that just finished their workplace training program with individuals that just ended their job. The authors specifically take into account the issue of self-selection adopting the propensity score matching estimation.

The results suggest that this workplace training program improves only the immediate re-employability of trained workers, failing to bestow them with durable human capital improvements. These results appear to be robust to spurious duration dependence and to self-selection. The analysis focuses on unobserved heterogeneity and, accounting for it, the authors show that the training implementation is useful to divide “good” trainees (in terms of unobserved heterogeneity) from “bad” ones.

Therefore, the authors suggest that firms are exploiting training as a screening device and that the implemented program is successful in easing the connection between workers and firms, but it fails to provide a durable improvement in skills and in re-employment prospects.

The evaluation of this program is important because it focus specifically on the workplace component of training, whereas previous analyses focused on generic training, because it evaluates a program targeting youth unemployment which is one the most urgent economic issues and because it helps in understanding the actual processes adopted by firms when implementing workplace training.

This chapter uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. This chapter extends the Monte Carlo results in Athey and Imbens's (2006) to assess the efficacy of the NL-DID estimators in finite samples. Furthermore, the NL-DID methodology recovers the entire outcome distribution in the absence of treatment. Further, we consider the empirical size and power of tests statistics for equality of mean, medians, and complete distributions as suggested by Abadie (2002). The results show that the NL-DID estimator can effectively be used to recover the average treatment effect, as well as the entire distribution of the treatment effects when there is no selection during the treatment period in finite samples.

Labor market programs may affect unemployed individuals' behavior before they enroll. The aim of this paper is to study whether such ex ante effects differ according to ethnic origin.

The authors apply a method that relates self‐reported perceived treatment rates and job search behavioral outcomes, such as the reservation wage or search intensity, to each other. German native workers are compared with migrants with a Turkish origin or Central and Eastern European (including Russian) background. Job search theory is used to derive theoretical predictions. The ex ante effect of the German active labor market program (ALMP) system is examined using the novel IZA Evaluation Data Set which includes self‐reported assessments of the variables of interest as well as an unusually detailed amount of information on behavior, attitudes and past outcomes.

It is found that the ex ante threat effect on the reservation wage and search effort varies considerably among the groups considered.

The study is the first to investigate whether migrants and natives react similarly to the expectation of participating in an ALMP, and whether migrants of different regions of origin react similarly or not.

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption, respectively. For ideal randomized experiments, (i) we propose nonparametric estimators of the sharp bounds on the distribution of treatment effects and construct asymptotically valid confidence sets for the distribution of treatment effects; (ii) we propose bias-corrected estimators of the sharp bounds on the distribution of treatment effects; and (iii) we investigate finite sample performances of the proposed confidence sets and the bias-corrected estimators via simulation.

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

This paper uses two complementary approaches to estimate the effect of right-to-work (RTW) laws on wages and unionization rates. The first approach uses an event study design to analyze the impact of the adoption of RTW laws in five US states since 2011. The second approach relies on a differential exposure design that exploits the differential impact of RTW laws on industries with high unionization rates relative to industries with low unionization rates. Both approaches indicate that RTW laws lower wages and unionization rates. Under the assumption that RTW laws only affect wages by lowering the unionization rate, RTW can be used as an instrumental variable (IV) to estimate the causal effect of unions on wages. In our preferred specification based on the differential exposure design, the IV estimate of the effect of unions on log wages is 0.35, which substantially exceeds the corresponding OLS estimate of 0.16. This large wage effect suggests that RTW may also directly affect wages due to a reduced union threat effect.

Inflation targeting has increasingly become a popular monetary framework since its first introduction in New Zealand at the beginning of 1990. However, the causality effects of this policy on economic performance, particularly in periods of economic turmoil remain controversial. Thus, this paper re-examines the treatment effect of inflation targeting on two important macro indicators which are inflation rate and output growth with the focus on emerging market economies. The global financial crisis, which is known as the great recession since the last decade, is investigated as an exogenous shock to test for the effectiveness of this popular regime.

The difference-in-difference approach in the fixed-model is employed for this investigation using a balanced panel data of 54 countries with 15 inflation-targeting countries for the period 2002 to 2010.

The examination finds that there is no significant difference in terms of the inflation rate and gross domestic product growth over the whole research period between the treatment and control groups. However, the outcome suggests that emerging economies can control the increase in inflation rate when the economy has to cope with the exogenous uncertainties.

This finding indicates important policy implications for central banks in many countries.

Inflation targeting can help emerging countries to reduce an increase in inflation rate in the crisis period without many trade-offs in the growth of output.

This study assessed the heterogeneous impact of the Anchor Borrower Program (ABP) on the welfare distribution of rice farming households in Nigeria.

Self-selection bias and treatment endogeneity were accounted for by employing the Instrumental Variable Quantile Regression (IVQR) model. The estimates obtained from the IVQR model were further compared with those from the conventional quantile regression, and quantile regression using Propensity Score Matching. This was to highlight the extent to which endogeneity bias has been purged from the treatment, in order to establish a consistent causal link between participation in the ABP and the welfare of a cross-section of rice farming households.

ABP farmers had significantly higher rice yields across all quantiles of the yield distribution under treatment exogeneity assumption, and in only two quantiles upon controlling for observable confounders. However, this yield gain did not translate to higher Per capita Consumption Expenditure (PCE). The estimates of the more robust IVQR model provided further evidence that the rice yield and PCE of ABP farmers are not statistically different from that of non-ABP farmers across all quantiles of the welfare distribution.

The negligible impact of ABP was relatively higher for lower-yielding households. Thus, implying that, although the ABP is a pro-poor development intervention, the program has not been sufficiently implemented to significantly improve the welfare of the dominant resource-poor farming households in Nigeria.

This study assessed the impact of ABP beyond the conventional potential mean outcome framework by accounting for heterogeneity in treatment effect.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0083

Education plays an important role in improving Chinese rural laborers’ non-farm incomes. However, in Chinese western rural area, the level of return to education is very low due to the underdeveloped economy and the condition of the education system. For improving the schooling returns level, Chinese central government is paying great attention to the condition of education in the rural western area. To date, no research has examined what educational style is more favorable for improving western rural laborers’ non-farm incomes. To answer this question, the purpose of this paper is to compare the treatment effect of high school education and secondary vocational education on their non-farm incomes. That will provide significant evidence for the government to carry out educational policy.

Base on the Mincer model, several methods is used to estimate the average return to a year education on western rural labors’ non-farm income, including OLS, IV and Heckman tow-steps method, to accounting for the ability endogenous and self-selection bias. And the propensity score matching method is used in estimate the treatment effects of high school education and secondary vocational education.

The results from Mincer model showed that the schooling returns in Chinese western rural area were estimated to range from 2.7 to 3.9 percent, that were lower than the average levels in Chinese whole rural area that are estimated in the other recent studies. By using propensity score matching to roll out the heterogeneous bias, show that the treatment effect from high school education was higher than that from secondary vocational education, indicating that the secondary vocational education is better.

Studies concerning the causal relationship between schooling (high school education and secondary vocational education) and non-farm earnings in the western region of China remain very limited, even empty. This paper will make an update contribution to the literature in the area of education earnings in China.