rafmacalaba/datause-encoder-data huggingface dataset:
be lower in high conflict-intensity districts before the conflict, and this rate has decreased faster in the low-conflict intensity district group.

Table A-3 tests for differences in pre-conflict marriage and early marriage trends. Preconflict early marriage trends do not differ systematically across districts more or less affected by conflict. However, the rate of marriage by 21 years old tends to increase somewhat faster before the conflict in districts which experienced more conflict-related casualties. This would tend to lead estimates of Equation (1) to bias upwards the effect of conflict intensity on marriage by 21 years old. There is a significant correlation between pre-conflict, year to year variation in the rate of marriage by 21 years old and future Maoist abductions, but the direction of the differential “trend” is unclear.

Having established that pre-conflict trends are unlikely to drive my estimates of the impact of conflict on primary education and early marriage, but that the long-term marriage analysis may suffer from some bias, I now turn to the difference-in-difference estimation results.

# 5.2. Education

The inspection of Figures 5 and 6 suggested that inequality in primary schooling achievement between districts which experienced different degrees of conflict intensity has tended to decrease with the onset of the insurgency, and especially so for girls. In Tables 4 to 7, I present estimates of the impact of exposure to conflict on educational outcomes to check whether the conclusion of the graphical analysis is confirmed when controlling for unobserved heterogeneity between individual districts and between regions over time, and when using different identification strategies.

# Table 4 goes about here

Table 4 reports findings on the impact of conflict exposure on primary education attainment. The first two columns of Table 4 present estimates of the long-term effect of conflict intensity on primary schooling completion, using the baseline difference-in-difference specification (Equation 1). The last two columns of Table 4 test the robustness of these findings by comparing the change in primary completion rates at any given age between 10 and 18 years old, for districts with varying degrees of conflict intensification between the 2001 and 2006 DHS surveys (Equation 3). Columns (3) and (4) explore the long-term impact of conflict intensity experienced up to- and after 10 years old (Equation 2).

21
households either before shocks occur or soon after can help households recover more quickly and avoid especially adverse and irreversible coping strategies (e.g., Bowen et al. 2020; Goldstein et al. 2021; etc.). Additionally, the fact that a significant share of displaced households can recover quickly without receiving increased assistance suggests a larger amount of resilience among forcibly displaced households than is typically assumed (e.g., World Bank 2017a; Tanner et al. 2021), and further corroborates a growing number of reports suggesting that Yemeni households are potentially more resilient than had been reported due to extensive data scarcity (e.g., Vuylsteke 2021).

The rest of paper is organized as follows. Section 2 discusses the conflict and resulting displacement crisis in the Republic of Yemen; section 3 presents the data; section 4 presents the empirical strategy; section 5 estimates the impacts of forced displacement on food access; and section 6 concludes.

# **Section 2. The Displacement Crisis in the Republic of Yemen**

The displacement crisis in the Republic of Yemen began immediately following the escalation of the conflict in March 2015. At that time, a coalition of nine Gulf and North African countries began supporting the internationally recognized government (IRG) against Houthi forces, which had seized control of the capital and were advancing on Aden in the south of the country (e.g., Tandon 2019). The coalition targeted Houthi positions with air strikes and also supported the IRG forces in ground battles along the front lines of the territory that was under control of Houthi forces (e.g., World Bank 2017b).

Immediately following the initial escalation in violence, there was a large surge in displacement. Official estimates of displacement following the escalation of the conflict were close to 10 percent of the total population; and other sources suggest that closer to 30 percent of the population was initially displaced (e.g., World Bank 2017b). But regardless of the exact data source, the initial surge was large.

Since the escalation of violence in March 2015, the amount of violence, measured by numbers of people killed and number of violent incidents, has increased significantly (e.g., 5
**Table 2: The impact of market remoteness on price volatility**

|**Variables**|**Mean Equation**|**Variance Equation**|
|---|---|---|
|**Constant**|4.0838***<br>(12.25)|0.0522***<br>(3.16)|
|**Ln****_Pt-1_**|0.9098***<br>(119.12)|-0.0002<br>(0.00)|
|**ARCH(1) term**||0.1095***<br>(5.87)|
|**Lean**|0.0156***<br>(3.12)|0.0009***<br>(3.16)|
|**Harvest**|-0.0625***|0.0139***|
||(13.30)|(26.68)|
|**Trend**|-0.0003<br>(1.13)|-0.0000***<br>(5.76)|
|**Exchange Rate**|0.0744<br>(1.61)|-0.0034<br>(1.65)|
|**International Price**|0.0041<br>(0.18)|-0.0017<br>(1.82)|
|**Time distance**|-0.0586***<br>(6.46)|0.0001***<br>(4.05)|
|**Border**|0.0601**<br>(2.57)|-0.0004***<br>(3.28)|
|**Surplus**|-0.0890***<br>(3.19)|0.0016***<br>(5.60)|
|**Markets Dummy**|YES|NO|
|**N**|3163||
|**R2**|0.8285||

Notes: Values in parentheses are t-statistics

*** and ** denote significance at 1%  and 5% levels, respectively.

Results from the ARCH model estimates from maximum likelihood estimation are found in **table 2** . This table presents the results of the model fitted to 28 pooled maize markets. Estimates of the mean equation indicate that price series follow an autoregressive process with a strong monthly autocorrelation. Results establish a seasonal pattern characterized by low prices during the harvest time and high prices during the lean season<sup>16</sup> . These results are

> 16 Because of the existence of natural agricultural production cycles, agricultural prices are affected by seasonality: indeed, there is an intra-annual price variation that tends to repeat regularly ( **Schnepf, 2005** ). Intraannual agricultural price variations imply that prices are at their lower level in harvest time because of the abundance of products on markets but they progressively go up and are at their highest level just before the next harvest season.

16
# **V.  Why Don’t Firms Hire MBAs?**

Of the 277 responses, 113 of the firms said that they hire no MBAs. This is important not just because it shows that a significant number of the respondents did not hire MBAs, but also because we can categorize the reasons for this decision.

## **Figure 1**

**MBA Survey Question 3:  If you do not hire any MBAs, what is the reason?**

<!-- Start of picture text --> 2% 2%<br>17%<br>57%<br>22%<br>Business Does Not Require MBAs<br>Lack of Available MBA Candidates<br>Vacancies Vary Yearly<br>Cannot Afford<br>Other<br><!-- End of picture text --> Figure 1 shows some of the reasons firms gave for not hiring MBAs. First, note that 57% of the firms said that they did not hire MBAs because their businesses did not require MBAs. But this sub-sample can be broken down further. There were 65 firms that responded that they do not hire MBAs because their businesses did not require them. Of these 65 firms, 22 firms said that they trained their own employees or sent their employees to part-time MBA programs. Thirty-six firms said that the nature of their businesses was such that they did not require MBA graduates (such as glass manufacturing), and seven said that they were actually in the process of restructuring or downsizing.

It may be useful to look at the regional breakdown of the 22 firms that reported that they trained their own employees. Of these 22 firms that trained their own employees, eight were from Latin America, and the remaining 14 were evenly distributed among the other regions (with about three each). Though there were significantly more responses from Latin America than from other regions, it is interesting to note that firms in Latin America seem to rely on in-house training more than firms in the other regions.

The second important issue to analyze is the fact that 22% of the firms surveyed said that they did not hire MBAs because of a lack of available candidates. At the regional level, 56% of Middle Eastern respondents said that they did not find available candidates, while 32% of African firms gave the same response.

10
- 16 -

**Table 15: Operating Fees (% charged on contributions), 1998 – 2004**

|**Fund**|**Sponsor**|**1998**<br>**(%)**|**1999**<br>**(%)**|**2000**<br>**(%)**|**2001**<br>**(%)**|**2002**<br>**(%)**|**2003**<br>**(%)**|**2004**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|Dimenzió<br>|EMP|6.0|6.0|5.9|5.9|6.0|5.9|6.0|
|HONVÉD|EMP|6.0|6.0|6.0|7.4|7.5|7.5|7.5|
|Postás|EMP|5.8|5.0|5.0|5.0|5.0|5.0|5.0|
|Vasustas|EMP|8.0|7.1|6.3|7.0|7.0|7.0|6.9|
|VIT|EMP|6.0|6.0|6.0|6.0|7.0|7.0|7.0|
|AEGON|FIN|6.0|6.5|5.4|5.6|6.0|6.0|5.9|
|AH|FIN|5.0|5.0|4.7|6.9|7.0|7.0|6.5|
|Budapest|FIN|4.0|4.0|4.2|5.0|6.0|6.0|5.9|
|CS|FIN|5.5|5.1|5.1|5.5|7.0|7.0|6.6|
|ERSTE|FIN|5.4|5.5|6.7|5.8|6.0|6.6|6.9|
|ING|FIN|5.6|6.2|6.0|6.8|7.0|7.0|7.0|
|MKB|FIN|6.1|5.7|5.4|5.5|5.5|5.5|5.5|
|OTP|FIN|5.1|5.0|5.9|6.9|7.0|7.0|6.5|
|Quaestor|FIN|5.0|5.0|4.9|5.0|5.0|7.4|7.0|
|UNIQA|FIN|-|10.0|4.2|5.3|5.5|5.3|5.5|
|Aryankor<br>|INDEP|5.6|5.2|4.9|6.4|7.0|7.0|6.8|
|Életút<br>|INDEP|4.7|6.2|5.9|6.0|6.0|6.0|6.0|
|Évgyűrűk|INDEP|7.0|6.3|6.6|7.7|8.0|7.6|7.1|
|Weighted Average|MKT|5.6|5.7|5.5|6.4|6.8|6.8|6.5|
|Weighted Average|EMP|6.1|5.9|5.8|6.4|6.6|6.6|6.6|
|Weighted Average|FIN|5.5|5.6|5.5|6.4|6.7|6.7|6.5|
|Weighted Average|INDEP|6.3|6.0|6.0|7.2|7.5|7.3|6.9|

Source: HFSA raw data 38. Administration and record keeping account for the largest share of operational costs, as shown in Table 16. This is followed by wages of MPF personnel, the supervisory fee paid to the HFSA, and the guarantee fee. In contrast to most systems in Latin America, the share of marketing-related costs in total costs has been extremely small, although it is possible that some marketing activities are performed by other companies in the same financial group (i.e., the asset manager or the insurance company) and the associated costs are hidden in the asset management fee. It is also possible that the costs associated with contribution collection are not fully reflected in administration costs, because they are partly shifted to employers. In Hungary, arrangements for collecting contributions are not efficient and employers can be required to submit contributions to up to 18 different funds and spend substantial resources in this activity.
24 can offset this effect if they lower returns to schooling, but this is less likely to happen in countries experiencing sharp increases in demand for skills.

By its very nature, this generalized Blinder-Oaxaca approach is, in isolation, incapable of attributing the causal origin of any of these changes to specific exogenous or policy shocks. This is particularly true when broad policy changes, such as a large-scale liberalization of trade, or a permanent change in the exchange rate, are expected to have substantial general equilibrium effects, affecting many variables at the same time. Wide-ranging changes in tariffs, for instance, can affect the distribution of income or consumption through changes in consumer prices, changes in relative wage rates, and changes in employment levels across industries. All of these variables will be changing in the micro-simulations that generate counterfactual growth incidence curves, but which share of the changes is due to the trade liberalization policy is anyone’s guess.

To address this point, a third branch of the literature has sought to combine macroeconomic or general equilibrium models with micro-simulations on household survey data. Examples include Bourguignon, Robinson and Robilliard (2002) for the Indonesian crisis, Chen and Ravallion (2004b) for China’s accession to the WTO, and Ferreira et al. (2003) for Brazil’s devaluation in 1998-9. These models are still in their early, experimental phase, and are subject to the usual criticisms leveled against computable general equilibrium models (CGEs). Nevertheless, when the model is run on a single household survey, and its predictions are checked against a separate, ex-post survey (as in the case of Brazil), its distributional prediction performance is superior to those of the previous generation of representative-agent CGEs.<sup>35</sup> A common finding in these exercises concerns the importance of worker and employment flows across sectors, in response to shocks or policy changes that affect relative prices. Developing country labor markets are often _de facto_ very flexible (despite sometimes significant _de jure_ rigidities), because of the existence of large informal sectors. When relative goods prices change in response to a change in the exchange rate (as in Brazil, in 1998) or policy change (as in China’s accession to the WTO), different industries contract and expand in response, and workers to move across these sectors.

> 35 An intermediate approach seeks to identify the causal effects of policy changes econometrically, and then estimate their share within the different components of a micro-simulation-based decomposition. Ferreira, Leite and Wai-Poi. (2007) regress changes in wages and employment levels disaggregated by sectors on (arguably exogenous) changes in tariffs and exchange rates. These trade-mandated changes are then used to generate counterfactual growth incidence curves, which can be interpreted alongside other micro-simulation results.
28 11 Data on per capita GDP income (constant 2000 US dollars) in this study come from World Bank, Development Data Platform (2006).

12 One reviewer suggested that the level of education in the labor-sending country should also be included in the study. However, data on educational attainment are rather sparse for many developing countries, especially those in Sub-Saharan Africa. For example, adding information on educational attainment – such as percent of country population over age 25 with secondary school education (from Barro and Lee, 2000) – would have reduced the size of the data set from 76 to 38 countries.

> 13 Data on population under 14 years of age and real interest rates come from World Bank, Development Data Platform (2006).

14 Data on the costs of remitting US $200 to a particular country come from the International Monetary Fund, Annual Report on <u>Exchange Arrangements and Exchange Restrictions.</u> Data on exchange rate spread come from Western Union, as reported in Freund and Spatafora (2005). Data on the presence of civil war are from Fearon and Laitin (2003).

15 While such an instrumental variables approach is good for dealing with endogeneity, it is unable to control for the unobserved country characteristics that are likely to affect the receipt of remittances by a country.

16 Ordinary least squares (OLS) estimates of equation (1) are available from the author upon request.

17 One reviewer suggested that the level of income inequality (in a country) might also be endogenous in equation (1). However, three factors should be noted: (1) in most countries the level of income inequality (as measured by the Gini coefficient) changes very slowly over time; (2) recent studies of remittances and inequality which are based on large, nationally-representative household surveys have found that international remittances have only a small impact on the level of income inequality in a country (see Adams, 2004; Acosta et al 2006); and (3) in this data set, it proved impossible to identify suitable instrumental variables for income inequality. 18 In this study distance between the labor-sending country and the main remittance-sending region (US, OECD (Europe) or Persian Gulf) is measured for each labor-sending country as the miles between the capital of that country and the main region from which it receives remittances. For example, for Latin American countries it is the distance to the United States, for North African countries it is the distance to OECD (Europe) and for South Asian countries it is distance to the Persian Gulf. For those countries (like Egypt and Jordan) which send migrant workers to both the OECD (Europe) and the Persian Gulf, the distance variable is calculated on the basis of where that country sends the largest proportion of its migrant workers.

19 In this data set the simple correlation between the percent of urban population and the poverty headcount is -0.518.

20Using a different data set and different assumptions, Adams (2006) also finds that an inverted U-shaped curve exists between the level of country income and the receipt of remittances.
# Figure 6: Per Capita State Revenues from a 3 Percent Sales Tax

_(states are arranged with the highest poverty rates on the left and lowest on the right, according to the CONAPO index of marginality)._ <!-- Start of picture text --> 1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>MxP per capita<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source:_ Bank staff calculation.

To complement the authority to impose these taxes on the same bases as federal taxes, the states also want the federal government to reduce its rate somewhat, to create tax room. The concept of tax room relates to the general objective of keeping the total rate—state plus federal—at a politically tolerable level, and also to the practical administrative necessity of not driving taxpayers to shift the reporting of their income or sales to a different category where the state tax does not apply. In the VAT-sales tax area, even though regime switching would not be legal, putting a state sales tax on top of a uniform federal VAT (as in the proposal discussed in 2002) would give firms and their customers a clear incentive to under-report final sales and claim them as intermediate sales, thus avoiding the state tax. On the other hand, if the federal VAT on final sales were reduced by say 2 percent (as was discussed in 2001) and replaced with the state tax of 2 percent on final sales, then there would be little incentive for firms to misrepresent the nature of their sales. Reviving this approach might help passage of the state sales tax.

Who would pay for the creation of tax room? In 2001 the federal government wanted the states and municipalities to absorb all the loss from the VAT reduction (on final sales) through a reduction of their share in _participaciones_ , to make the transaction revenue neutral for the federal government. And this demand contributed to the failure of the deal. The creation in 2001 of the special (optional) regime in the federal (corporate) income tax law with a lower rate for small payers (REPECO, gross annual revenue below MxP 1,570,000) did not come with any change in the _participaciones_ formula (some think that its simplicity and lower rate will actually increase federal collections from this group), and so it faced no opposition from the states. Then in December 2002, Congress amended the Income Tax Law to give the states the authority to 11
of their wealth when they are flooded (Hallegatte et al., 2017). This result is confirmed in the case of the 2015 flood in Accra.

# **6.1. Types of damages to households**

Among all affected households, 63.9% reported having their dwelling damaged by the flood and 52.7% reported asset losses/damages. Impacts through infrastructure services – primarily roads and electricity – were also commonly reported. Impacts on water and sanitation, work places, schools and health were not as common.

_Table 4: Types of damages reported by households exposed by the 2015 flood_

||Total|
|---|---|
|Dwelling|63.9%|
|Assets|52.7%|
|Paths or roads|21.3%|
|Electricity|7.3%|
|Water facilities (taps, tanks, pipes)|3.7%|
|Sanitation Facilities|3.8%|
|Work place|4.2%|
|Schools|4.3%|
|Illness in household|2.7%|
|Observations|393|

In the community survey, local leaders reported that when flooding happens, a wide range of facilities and services are generally affected. They indicated that schools tend to close during floods, water and electricity services are affected, roads become inaccessible, and some businesses are forced to close.

However, few households were affected only indirectly: most people who report having been affected by the flood also reported having their homes flooded, or assets lost. Most of the reported damages were localized (impacts to the household’s own dwelling) – Among affected households, 71% reported experiencing localized damages only. Just 4.3% reported only non-localized damages (impacts elsewhere) while 24.7% reported both types.

# **6.2. Quantification of household impacts**

Cost associated with housing repairs and asset losses represented the largest share of total losses, averaging 67% and 29% of total losses, respectively. Labor losses incurred from missed work days and medical costs caused by the flood were less common, and totaled only 4% and 0.2% of total losses, respectively. Poorer households experienced relatively larger asset losses while richer households experienced relatively larger housing repair costs – most likely due to the higher capacity of richer households to repair housing damages. More information on the composition of type of losses can be found in Appendix 6.

As a metric of disaster impacts, average losses per household is not only of limited value, but it can even be misleading, as losses are highly heterogeneous. Affected households lost approximately 509 cedis on average due to the flood, representing about 4% of the value of total annual household expenditures or 15