rafmacalaba/datause-encoder-data huggingface dataset:
a period of physical growth during the conict years, the latter cohort would have already gained full adult height (detail discussion is presented in empirical strategy section 4). On average, women in the sample are 151.6 centimeters tall, with the younger treated cohort 0.46 cm taller on average. Similarly, compared to the control cohort, treated cohorts are less likely to have had any incidence of pregnancy loss, report having had fewer live births, were slightly younger at rst birth, attained more years of education, and were less likely to be employed at the time of the survey. However, there is no dierence in economic status (wealth index) between the two groups.

By construction, the older cohort faced zero level of conict during the rst 15 years of life (Panel B.1). Balance in lifetime exposure to conict between the two sets of women (Panel B.2) is reassuring for the empirical strategy used in section 4, which implies that the two groups do not come from dierent types of sampled clusters. On average, women are 33.4 years old with treatment and control cohorts being 27.9 and 41.8 years old respectively (Panel C). Women in treatment groups are more likely to live in a household with a female head, are more likely to be of lower caste, and are less likely to be from Eastern Development region. All other controls are balanced between the groups.

Summary statistics of the children sample is presented in Table 3. Children are divided into treated and control group by their mother's age at the start of the conict. 47% of the children are girls and on average tend to be the second child (Panel C). There is no dierence in control variables (Panel C) and health outcome variables between the two groups except treated children are slightly taller. Panel B reports mother's exposure to conict. Again, it is reassuring that there is no disparity in their mothers' lifetime conict experience.

# 4 Empirical Strategy

Women surveyed in the NDHS 2016 experienced dierent levels of exposure to conict intensity according to their village of residence and year of birth. The identication strategy exploits this variation, specically, the variation in exposure to the conict during the individual's critical period of physical growth.

Height development in humans is characterized by three distinct stages. There is a rapid growth during the rst three years of life, followed by a lower level of constant gain in height until the 12
or after dams have been constructed and conflict has developed between countries sharing a watershed. Any of these sources of endogeneity would bias the coefficients in equation (2).

In Table 8, we address the possibility of endogenous water management institutions using instrumental variables. Column 1 repeats the OLS results from column 3 of Table 7, for comparison. In columns 2 and 4, we report results from two-stage least squares (2SLS) models with different sets of excluded instruments; columns 3 and 5 report first-stage results. The dependent variable in the first stage is the interaction of the endogenous treaty variable with the dummy for the presence of a downstream country. In the first stage, this interaction is regressed on the instruments and the exogenous variables from the main equation. Because the first stage equations also include the country and basin effects, country-level or basin-level instruments are interacted with the downstream country dummy.

Our first instrument is a Herfindahl-like index of population within a GRDC basin, representing the degree of concentration among countries within a basin (equal to 1 for single-country basins). Prior research suggests that the more control any single country has over a basin, the less likely it is to participate in a basin management treaty (Espey and Towfique 2004); distribution of power within a basin appears to be an important driver of treaty formation more generally (Zawahri and Mitchell 2011). We instrument for the interaction between treaty and downstream country with the interaction of _HHI population_ and _downstream country_ in column 2. The magnitude of the downstream country coefficient estimate increases dramatically, and the sign of the instrumented treaty interaction is consistent with the hypothesis that treaties mitigate free-riding, but neither coefficient is statistically significant.<sup>24</sup> In

> 24 We also obtained data from the TFDD on treaty purpose.  If we restrict the treaties variable to count only treaties that specifically mention the management of water allocation (water quantity), the results are qualitatively the same – a positive coefficient on the presence of a downstream country, a negative coefficient on

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In our analysis, we capture expenditure on education, health, social insurance, and social assistance. Taken together, the subcategories we consider represent 82 percent of the government’s total social expenditure, or 30 percent of total expenditures in 2018. Data limitations prevent us to capture accurately these amounts (see section 3 for details).

# **Table 4: Size of the Government Expenditure, China 2018**

|**Categories**|**Amount**<br>**in**<br>**Admin.**<br>**(Unit:**<br>**bln**<br>**RMB)**|**% of Total**<br>**Expenditure**|**% of**<br>**GDP**|**% In**<br>**Analysis**|
|---|---|---|---|---|
|**Total Government Expenditure**|**22090**|**100.0%**|**24.5%**||
|**_Primary Government Expenditure_**|21344||23.7%||
|**Social Expenditure**|**8161**|**36.9%**|**9.1%**||
|_in which:_|||||
|_Education Expenditure_|_3217_|_14.6%_|_3.6%_|_Yes_|
|_Health Care Expenditure_|_1562_|_7.1%_|_1.7%_|_Yes_|
|_in which:_|||||
|_Medical Assistance_|47|0.2%|0.1%|Yes|
|_Social Insurance, Social Assistance &Labor_|_2701_|_12.2%_|_3.0%_||
|_in which:_|||||
|_Dibao Transfers_|146|0.7%|0.2%|Yes|
|_Tekun Transfers_|30|0.1%|0.0%|Yes|
|_Temporary Relief_|16|0.1%|0.0%|Yes|
|_Natural Disaster Relief_|13|0.1%|0.0%|Yes|
|_Complements for Basic Pension_<br>_Insurances_|827|3.7%|0.9%|Yes|
|_Pension for Admin. Institutions Staffs_|853|3.9%|0.9%|Yes|
|_Housing Assistance_|_681_|_3.1%_|_0.8%_|_No_|
|**Non-Social Expenditure**|**13183**|**59.7%**|**14.6%**|**No**|
|**_Debt Servicing_**|746|3.4%|0.8%|No|

_Source_ : Statistical Yearbook for China 2019, National Bureau of Statistics 2019; Financia Yearbook of China 2019, Ministry of Financ3, 2019 Compared to other upper-middle-income countries, China’s social spending is relatively low, especially on direct transfers. China’s spending on direct transfers amounts to 0.5 percent of GDP, which is the lowest share among several upper-middle-income countries and below the average of some lower-middle-income countries. (Figure 2). China’s health expenditure as a share of GDP is also one of the lowest among several upper-middle-income countries. However, its expenditures on education and on contributory pensions are closer to the median among these countries.

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typically justify their acts by political or religious motivations toward a larger societal goal. Communal violence, on the other hand, often arises from disagreements about the distribution of resources within a community; it might also be fueled by ethnic, religious, or political arguments. Alongside increased insecurity, the opportunity cost of criminal activity increases as a consequence of the reduced capacity of law enforcement. Therefore, crimes committed in conflict-affected areas also might be motivated by purely personal economic gains. Different groups might be committing crimes and violence for different purposes in conflictaffected areas. These acts might lead to different outcomes depending on the motivations of the perpetrators.

Our second contribution is to quantify the effects of different types of conflict and victimization to understand what kinds of attacks and what types of perpetrators are most detrimental to well-being. Nigeria provides an excellent context to study the effects of different types of victimization due to the various ongoing conflicts. Nigeria is the largest economy and the most populous country in Africa, and it has seen a sharp rise in violent conflict during the last decade. Although the Boko Haram insurgency has gained vast attention from international media, the long-standing militant and criminal activity in the oilproducing south and the increasing tensions between herders and farmers also pose important threats to national security. Our data contain information on the types of violent and other criminal events to which the households have been subjected. Furthermore, the data set contains information about the perpetrator of the event. We find that insurgent attacks are most detrimental to consumption, and both insurgent and criminal attacks increase food insecurity. We do not find similar results for the victims of the farmer-herder conflict. This is in line with the results also being strongest in the North East, which has the highest intensity of Boko Haram activity and, overall, the highest occurrence of conflict events. The insurgency, however, has been active elsewhere in the country, particularly in the North Central area. We also find that events related to property, such as theft or the destruction of assets, are more detrimental to food insecurity and consumption than are violent events.

We also were interested in studying mental health as an outcome of conflict because economic well-being and mental health are strongly connected (Alloush 2019). Becoming a victim of crime and violence can lead to trauma. Recent studies have established a link between conflict exposure and mental health (Singhal 2018; Jamison et al. 2018). For our measure of mental health, we use the Center for Epidemiologic Studies Depression Scale (CES-D), which was introduced in wave 3 of the General Household Survey (GHS) (Jamison 4
# **E. Change in fiscal balances three years after major events**

# **F. Change in government debt three years after major events**

<!-- Start of picture text --> Percentage points of GDP<br>1.5 Small states EMDEs excl. small states<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Natural disasters Global recessions<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points of GDP<br>8 Small states EMDEs excl. small states<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>Natural disasters Global recessions<br><!-- End of picture text --> _Sources:_ EM-DAT (database); WEO (database); World Bank.

_Note:_ EMDEs = emerging market and developing economies. Panels show differences in average fiscal balance and debt as a percent of GDP in EMDEs compared with those in the year preceding events occurring in year _t=0_ . Events include global recessions (2009 and 2020) or natural disasters resulting in damages of at least 5 percent of GDP. Natural disasters include droughts, earthquakes, extreme temperatures, floods, storms, volcanic activity, and wildfires. In five cases where natural disasters coincide with global recessions, they are attributed to either natural disaster or global recession events and not both, to avoid double counting. Results are robust to alternative assumptions.

A.-B. Difference in average fiscal balance and debt as a percent of GDP in small states compared to year preceding a natural disaster occurring in period _t=0._ Based on a sample of 24 natural disasters in small states for the period 2000-22.

C.-D. Difference in average fiscal balance and debt as a percent of GDP in small states compared to year preceding global recessions occurring in period _t=0_ . Based on a sample of 34 small states.

E.-F. Difference in average fiscal balance and debt as a percent of GDP in EMDEs three years after global recessions and a natural disaster resulting in damages of at least 5 percent of GDP compared to year preceding the shock. Based on a sample of up to 34 small states and 111 other EMDEs.

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# Table 7d: IV estimations using first-differences format

||Baseline OLS<br>estimation with<br>district-industry, state-<br>year, and industry-<br>year FE|FD OLS estimation<br>with state-year and<br>industry-year FE|<br> <br>FD IV estimation with<br>state-year and industry-<br>year FE|Column 3 with log<br>employment change<br>control<br> <br>|Column 3 with log<br>employment change<br>control and extra IV<br>lags|Column 3 using<br>average deviations|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||A. Absolute deviations in|expected and actual loan|s||
|Land misallocation index|0.049++|0.047++|0.129|0.159+|0.146++|0.141|
||(0.025)|(0.024)|(0.111)|(0.093)|(0.058)|(0.091)|
|Labor misallocation index|-0.034|-0.037|-0.126|-0.089|-0.060|-0.042|
||(0.023)|(0.024)|(0.124)|(0.106)|(0.065)|(0.100)|
|Log employment in||||0.221+++|0.222+++||
|district-industry-year||||(0.013)|(0.012)||
|Observations|14852|8525|8525|8525|8525|8525|
|Overid test p-value|||||0.823||
||||B. Squared deviations in e|xpected and actual loans|||
|Land misallocation index|0.041+|0.036|0.229++|0.239++|0.169++|0.205++|
||(0.024)|(0.025)|(0.105)|(0.103)|(0.068)|(0.097)|
|Labor misallocation index|-0.034|-0.032|-0.141|-0.129|-0.045|-0.112|
||(0.020)|(0.022)|(0.105)|(0.105)|(0.069)|(0.096)|
|Log employment in||||0.072+++|0.074+++||
|district-industry-year||||(0.015)|(0.015)||
|Observations|14852|8525|8525|8525|8525|8525|
|Overid testp-value|||||0.492||

Notes: See Tables 7a-7c. Column 1 repeats the levels specification with panel fixed effects, and Column 2 shows a comparable first-differences form. Column 3 instruments for the change in total land and labor misallocation within the district's manufacturing sector with the change in the unorganized sector's land and labor misallocation. Coefficients in the first stage for the change in total land misallocation are 0.684 (0.102)+++ for the change in unorganized sector land misallocation and 0.006 (0.144) for the change in unorganized sector labor misallocation. Comparable coefficients in the first stage for the change in total labor misallocation are 0.248 (0.136)+ and 0.740 (0.157)+++. Column 4 introduces the employment change control. Column 5 introduces lags of ASI land and labor misallocation as added instruments. Column 6 considers average deviations as the outcome.
tions to resume operations. While calibrated to an annual unemployment rate of 4% in the steady state, which implies not so severe frictions, the mass of temporarily laid off workers congests the labor market when the shutdown is lifted, leading to a higher wage in the hiring market and a lengthy rehiring process. The capital stock dynamics in the bottom right panel accompanies the persistently high unemployment. Consumption, in turn, overshoots the quarter after the lockdown in reflection of the rise in wages and the fall in investment. In short, incorporating rest unemployment is important for the model to match the quick dissipation of recall unemployment in the US data in Hall and Kudlyak (2020).

Lastly, we discuss the role of debt-financed unemployment insurance and costless firing of workers. As a reminder, non-essential entrepreneurs in our baseline can lay off their workers during the lockdown without any severance payment or firing cost. The government issues new debt to provide full wage replacement to the unemployed. The “no wage subsidy” case in Figure 14 is the case in which non-essential businesses have to pay their wage bills while in lockdown. This keeps the unemployment rate low during the lockdown, since idled workers are still employed, the unemployment rate goes up by 1 percentage point when the lockdown ends and remains persistently higher than in the benchmark. The GDP also recovers more slowly. The reason for the excess unemployment lies in the interaction between the balance sheet effect exacerbated by the wage bill payment during the lockdown and the capital rental constraint. Unlike in the benchmark, the additional financial wealth that must be devoted to wage payments during the lockdown (and no-revenue) period deteriorates the non-essential firms’ balance sheet more significantly, leading to a smaller labor demand after the lockdown and to a rise in unemployment.

# **5 Complementary Impulse: Reallocation Shock**

Previous sections considered three plausible channels through which cross-country differences in the recovery from a lockdown may emerge: difference in lockdown size, duration, and policies supporting firms and workers. Here, we consider another channel, which we label as reallocation shock. The idea is that the pandemic renders some goods and services less appealing (e.g. cruises) and others more appealing (e.g. teleconferencing and online shopping) to consumers more _permanently_ .

Since in our model all firms produce the same good, a tractable way to mimic this kind of reallocation shock is to temporarily change the persistence parameter _ψ_ governing the evolution of entrepreneurial productivity. We force a larger-than-normal fraction of non-essential entrepreneurs to re-draw their entrepreneurial productivity in period 1 independently of their wealth or entrepreneurial productivity. In other words, we set 1 _− ψt_ =1 _>_ 1 _− ψ_ for period 1 30
An important characteristic of Malawi’s land tenure system is the co-existence of matrilineal and patrilineal inheritance systems. As the ability to decide on long-term transfer of land rights has been shown to affect investment decisions (Deininger and Jin 2006), individuals’ ability to influence decisions on intergenerational land transfers is likely to affect their willingness to make non-observable investments. Place and Otsuka (2001), Lunduka (2009), Lovo (2016) and Bhaumik _et al._ (2016) indeed argue that in matrilineal and matrilocal systems, the discrepancy between females’ land ownership and males’ social position and superior access to markets for inputs and outputs impedes land-related investments.

To the extent that they give individuals control over assets, inheritance regimes may also affect spouses’ outside options.<sup>5</sup> Women have been documented to be more vulnerable to temperature shocks in patrilineal systems, possibly due to lower levels of tenure security (Asfaw and Maggio 2018). Moreover, higher household consumption in matrilineal as compared to patrilineal systems has been attributed to differences in women’s tenure security across inheritance regimes (Telalagic 2014).

With the objective of integrating customary and statutory rights in innovative ways, Malawi passed a new land law in 2016. The law aims to demarcate territories controlled by individual traditional leaders as a precondition for documented rights to individuals.<sup>6</sup> In addition, it introduced sweeping changes of inheritance practices that have been highly controversial with scholars who argue that such change could disenfranchise women on a massive scale (Peters 2010).

# **3. Data, descriptive statistics and empirical approach**

The descriptive analysis of our data shows that (co)-owners often do not have the full bundle of rights and that women lag men in all rights. Comparing descriptive statistics between surveys relying either on selfreporting (IHPS) or on responses by a proxy (IHS4) suggests that similar figures in the aggregate conceal important discrepancies between surveys. Compared to the data provided by individuals directly, proxy respondents (i) overstate levels of male (co-)ownership, (ii) understate levels of female (co-)ownership, and (iii) for parcels that are reported as (co-)owned, introduce an upward bias regarding the rights held by women and conversely, a downward bias regarding the rights held by men. The latter finding is consistent with informational imperfections as well as strategic responses. Econometric analysis to ascertain the net effects investment and land use is thus needed and we discuss the empirical framework to be used.

> 5 While historically, matrilineal inheritance regimes are related to the physical force required for food provision (BenYishay _et al._ 2017), there is some evidence of short-term changes in Malawi which are particularly relevant as divorce rates are high (Cherchye _et al._ 2016). 6 The law aims to demarcate traditional land management areas (TLMAs) and establish customary land committees (CLCs) with at least 50% female participation, normally at Group Village Headman level.

8
enables us to perform counterfactual exercises in which we administer different aggregate tax rates to different managers. In other words, we make the aggregate tax rate a function of the managerial ability �.

��� The next step is to estimate the aggregate tax function �� ��� for each size group that we observe ��� in the Enterprise Surveys. We estimate the tax function �� ��� using the data from the 2008 cross-section as it covers twice as many firms as in 2014. Given that there is a huge variation in risks reported across years, our choice of 2008 cross-section provides a less noisy estimate of the tax function. The tax function encompasses both losses dues to crime and corruption. For crime-related taxes, we aggregate the losses reported due to crime-related events (conditional on experiencing an event) and the expenditures on security arrangement. Losses due to corruption are inferred from the share of total sales lost as informal payments and gifts to public officials. The limited data we have means that we can quantify a small share of risks faced by firms in Afghanistan as they do not include the comprehensive impact of all factors. Figure 3 shows the crime-related taxes for the different size groups. The output losses due to crime-related events rise with firm size and decline for the largest firms. However, this decline is more than offset by the high security expenditures by them. In aggregate, we find that the computed taxes rise steadily with size, from 0.7% of sales for the smallest firms to 4.1% of sales for the largest firms in the Afghan economy.

**Figure 3: Output Loss (Taxes) Due to Crime by Firm Size**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>less than 5 5 to19 20 to 99 100 or more Aggregate<br>Number of Employees<br>Security Payments Crime‐Related Losses Total<br>Loss (% of Sales)<br><!-- End of picture text --> Figure 4 shows crime and corruption related taxes together with their sum. Corruption related taxes rise from 1.7% of sales for the smallest firms to 3 percent of sales for the largest firms, though the relationship is not strictly monotonic across firm size. However, the aggregate taxes rise steadily with firm size with the tax rate faced by the largest firms being almost three times as large as that faced by the smallest firms.

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