Amen Jalal

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Hello! I am a 4th year PhD student in economics at the London School of Economics (LSE), where I am affiliated with the Hub for equal representation (HER) and the Economics of Environment and Energy (EEE) Programme at STICERD.

I have a Bachelors in economics from Yale University.

Before starting the PhD, I worked at the World Bank as a research assistant at DIME and ID4D.

I am also a Graduate Student Fellow at CERP, Pakistan.

I have ongoing work on labor market frictions and gender inequality in low income countries. I also work on studying the impacts of, and adaptation to climate change in low income countries.

Works in progress

Salary disclosure in job ads

Experiment on-going

Supported by G2LMLIC

Click for abstract Salary is a central characteristic of jobs, and varies considerably across firms for similar positions. However, globally, salary information is scarce at the hiring stage, making it difficult for workers to direct search. This study assesses how salary disclosure in job ads affects workers’ sorting and firms’ wage setting behavior. Using firm surveys and administrative data from Pakistan’s largest online job search platform—where I see salary ranges even when they are hidden from jobseekers—I find that larger and better paying firms are more likely to hide salary information. In particular, such firms use salary non-disclosure as a ‘self-screening' tool to exclusively attract ‘suitable’ workers. This practice may disadvantage women, who tend to have lower labor market exposure and thus may be less able to extract wage signals from job descriptions, or may prefer to know the bargaining space before negotiating. To study these issues, I partner with the job platform to run an experiment in which treated ads are induced to post salary ranges while control ads can choose whether to disclose this information. In response, workers may reallocate search towards jobs for which learning about salaries was previously difficult, but that are ex-post revealed to pay well, e.g., jobs in larger firms. I capture reallocation of search by exploring relevant heterogeneity in firm and job characteristics, and leveraging a saturation design that randomly exposes some labor markets to high (75%) and others to low (25%) treatment intensity.


How the rural poor cope with a climate catastrophe: Evidence from Pakistan’s 2022 floods

(with Pol Simpson)

One year follow-up completed

Supported by the IGC, STEG and Center for International Development at Harvard University

Click for abstract Extreme weather events are increasingly common as a result of climate change. Yet little is known about how exceptional climate shocks affect the lives of those most vulnerable to them, or about the barriers they face to moving out of harm's way. In this project, we study the effects of the 2022 flooding in Pakistan, which has affected 33 million households and left one third of the country under water. We leverage pre- and post-flood panel data on a random sample of 5,000 low-income, rural households across 6 districts of Sindh, who vary in their local exposure to the 2022 floods. We study (i) how floods impact these households, (ii) what decisions they make to cope with the immediate consequences of this shock, and (iii) what forces shape their forward-looking adaptation decisions. We exploit plausibly random local variation in flood water inundation – i.e., precipitation interacted with topography – conditional on historical likelihood of inundation and district fixed effects. Our outcomes include flood damages (e.g. loss of income or assets, health impacts, and disruption of social networks and trade), coping strategies (e.g. drawdown of savings, sale of assets, new loans, increased labour supply, changes to educational or nuturitional investments) and adaptation (e.g. diversification of networks or assets, and migration).


The illusion of time: Job search and female labor force participation

(with Oriana Bandiera and Nina Roussille)

Draft available soon

Supported by HER, STICERD, and RISF at LSE

AEA registry

Click for abstract This paper documents a large gap between college-graduating women’s intended and realized labor force participation, and proposes an explanation. To do so, we field a panel survey and an experiment on >1,500 college students in Pakistan. A month before graduation, women believe they have about the same likelihood as their male peers of working six months later (~75\%). By contrast, we uncover large employment gaps: only 37.9\% of women were employed six months later, compared to 64.4\% of men. Traditional supply-side (e.g. GPA, major, job preferences, search effort) and demand-side factors (e.g. interviews, wage and job offers) leave this gap virtually unchanged. However, we find that women’s employment is much more sensitive to the timing of their applications than men’s. We provide a theoretical framework and empirical evidence on why timing matters: the unexpected increase in women's reservation wages over time. To causally estimate the effect of timing, we experimentally shift students' job applications closer to graduation. We find that this treatment increases women's employment by 22.3\% (7.5 ppt) six months later, and has no effect on men. Finally, we explore behavioral and cultural mechanisms through which the treatment operates.


Can market competition reduce corruption? Evidence from cash transfers in Pakistan

(with Muhammad Haseeb and Kate Vyborny)

Draft available soon

Supported by the World Bank’s ID4D and the Gates Foundation

Policy brief

Click for abstract We study whether market competition between public officials can reduce corruption. We exploit exogenous changes to the market structure of payment delivery agents in Pakistan's Benazir Income Support Programme to assess impacts on corruption in the delivery of these cash transfers. We find that a payment reform that led to exclusive reliance on payment delivery agents increased reports of side payments paid involuntarily to access the cash transfer. However, higher market competition between these rent-seeking agents reduced extensive and intensive margin demand for bribes.


What happens when cash transfers suddenly stop? Quasi-experimental evidence from Pakistan

(with Nasir Iqbal, Mahreen Mahmud, Kate Vyborny)

Draft available soon

Supported by IFPRI and IPA

Click for abstract A growing body of evidence shows mostly positive impacts of cash transfers for women on a range of outcomes. However, there is limited work, empirical or theoretical, on what happens when long running unconditional cash transfers stop. Cash transfers may stop for a given household either because their economic position has improved and they no longer meet the eligibility criterion, or because of cuts to the funding pot resulting in a more stringent eligibility criterion. Since cash transfer programs are costly and may not be expected to provide support permanently, understanding how households cope when cash transfers stop is crucial. In this study, we use a regression discontinuity approach to examine the impact of the discontinuation of cash transfers on households in Pakistan who have been receiving transfers over a ten year period.


General equilibrium effects of a billion trees: Evidence from Pakistan

(with Veronica Salazar-Restrepo)

Analysis ongoing

Supported by the IGC

Click for abstract Several countries are investing large sums of money in nation-wide tree planting programs as part of their climate mitigation and adaptation strategies. However, there is limited evidence on the impacts of such programs on livelihoods and ecosystems. These programs may disrupt ecosystems and agriculture, deplete water supplies, displace local communities, and lead to more deforestation in other areas. Conversely, planting the right species of trees at the right place can sequester carbon, regenerate forests, and provide ecosystem services like flood prevention. In this project, we focus on Pakistan's Billion Tree Tsunami Afforestation Programme (BTTAP), which planted 1 billion trees in the province of Khyber Pakhtunkhwa. First, we employ the AVOCADO remote-sensing algorithm to measure forest regrowth at high resolution (30m pixels) in order to measure the effectiveness of the program (Decuyper et al. 2022). Second, we also gather environmental data on wind, fires, temperature, precipitation, and pollution and combine it with administrative data on socioeconomic outcomes in order to measure ecological spillovers of the program in neighboring areas. Finally, we incorporate these spillovers in a general equilibrium framework to analyze whether the program displaced existing economic activities like agriculture.


Teaching