rafmacalaba/datause-encoder-data huggingface dataset:
responsive to the sample plot shock across every outcome. A household with 2 additional members, approximately one standard deviation of household size, is less responsive to the sample plot shock on its most important plot by 50% - 94% for irrigation use, 74% - 103% for horticulture, 63% - 75% for household labor, and 20% - 21% for inputs, with all but the input coefficient statistically significant. In contrast, wealthier households are more responsive to the sample shock across these same outcomes. A household with a one standard deviation higher asset index is more responsive to the sample plot shock on its most important plot by 41% - 97% for irrigation use, 39% - 81% for horticulture, 39% - 72% for household labor, and 42% - 58% for input use; however, these results are less precise. In effect, these results suggest that our estimates of separation failures are driven by the behavior of small, rich households, while large, poor households do not change their allocations on their most important plot in response to the sample plot shock. As discussed in Section 4, these results are very difficult to reconcile with a model that does not feature labor market failures.

In sum, these results provide strong evidence for the existence of labor market failures that generate separation failures, which in turn cause inefficient adoption of irrigation.

# **6 Experimental evidence**

Our results leveraging the discontinuity suggest that land and labor market frictions combine to constrain the adoption of hillside irrigation in Rwanda. In this section, we provide evidence from randomized controlled trials on the presence of other competing constraints to adoption of irrigation: management challenges of irrigation schemes, and financial and informational constraints. Additional details on the motivation, treatment assignment protocols, and logistics of implementation of each of these experiments are presented in Appendix I.

First, we test whether failures of scheme management limit farmers’ adoption of irrigation. If farmers faced limited access to water due to problems in the centralized operations and maintenance (O&M) system, this could constrain adoption of irrigation. We sought to alleviate this potential constraint by randomizing empowerment of local monitors to assist system operators and report maintenance needs. We find no evidence this experiment changed cultivation practices. This result is likely be40
# **Leaving, Staying, or Coming Back?**

## **Migration Decisions during the Northern Mali Conflict**<sup>♠</sup>

Johannes G. Hoogeveen<sup>1</sup> Mariacristina Rossi<sup>2</sup> Dario Sansone<sup>3</sup>

**Keywords** : forced migration, Mali, refugee, internally displaced people.

**JEL** : J15; O15; R23.

>  We benefitted from feedback and suggestions from Andrew Dabalen and José Calix and are grateful to participants to the SITES/IDEAs 2016 conference for their helpful comments. The usual caveats apply.

> 1  World Bank. E-mail: jhoogeveen@worldbank.org

> 2  University of Turin, CeRP - CCA. E-mail: mariacristina.rossi@econ.unito.it

> 3  Georgetown University.  E-mail: ds1289@georgetown.edu
(about 0.5). In contrast, the long-term multiplier associated with public infrastructure investment is estimated to be large (about 2.5). These are econometric estimates using the vector auto regression (VAR) methodology<sup>2</sup> . An Inter-American Development Bank study using a multi-country dynamic general equilibrium model simulation suggests that absent the fiscal stimulus, China’s GDP would have been 2.6 percentage points lower in 2009 and 0.6 percentage points lower in 2010 (Cova, Pisani, and Rebucci 2010). An input-output analysis shows that the short-term multiplier is 0.84 and the medium-term multiplier 1.1 (He, Zhang, and Zhang 2009). The IMF estimates that fiscal stimulus raised China’s GDP 1.5–2.25 percentage points in the first half of 2009 (IMF 2009). Other estimates suggest that it boosted real GDP growth from an annualized low of 6.2 percent in the first quarter of 2009 to 11.9 percent in the first quarter of 2010 (Deng and others 2011).

The international community supported emerging economies’ countercyclical policies, the fiscal component of which is captured in figure 1. In 2009 and 2010, the IMF disbursed $57 billion. The World Bank disbursed $48 billion during the two-year period.

**Figure 1 Countercyclical Fiscal Policy Response by Emerging Economies, 2009–10**

<!-- Start of picture text --> Percept GDP Percept GDP<br>34 10<br>32 8<br>Government revenues<br>Expenditure<br>30 6<br>28 4<br>Fiscal deficit (Right Hand Side)<br>26 2<br>24 0<br>2007 2008 2009 2010<br><!-- End of picture text --> _Source:_ Development Committee 2010.

2 A VAR model estimates the impact of one variable on another assuming that every variable is a linear function of its own past (lagged values) and the past patterns of all other variables, unlike structural models that are based on the formulation of a specific structure of relationships based on economic theory.

12
**Table 16. First Stage Regressions of Formal Labor Markets**

# **Dependent Variable: Covariate Violent Shock**

||**Household**<br>**head and**|**Female**|**Male**|
|---|---|---|---|
||**spouse**|||
|Land Gini|2.8306***|2.8883***|2.7739***|
||(0.9342)|(0.9179)|(0.9575)|
||-0.1989*|-0.2023*|-0.1943*|
|_Dummy_Native Populations in Colonial Times (1535-1540)|(0.1132)|(0.1138)|(0.1135)|
||0.0008|0.0031|-0.0014|
|Rural District Price Index|(0.0310)|(0.0315)|(0.0306)|
|Livestock Income as Share of Total Agricultural Income|-0.1087*|-0.1219**|-0.0955*|
||(0.0554)|(0.0551)|(0.0568)|
|_Dummy_Participation in Goverment Program Familias in|0.0250|0.0229|0.0283|
|Acción (1 if has acces to the progra; 0 otherwise)|(0.0236)|(0.0237)|(0.0251)|
|Ae|0.0011|0.0014|0.0010|
|g|(0.0008)|(0.0009)|(0.0009)|
||0.0007|0.0012|0.0005|
|Education (years)|(0.0030)|(0.0037)|(0.0032)|
|_Dummy_Female Household Head (1 female head of|-0.0461*|-0.0525**|-0.0362|
|household , 0 otherwise)|(0.0269)|(0.0254)|(0.0497)|
||0.0233|0.0289|0.0168|
|_Dummy_Wealth Index Quintile 2|(0.0302)|(0.0309)|(0.0314)|
||00229|00279|00167|
|_Dummy_Wealth Index Quintile 3|.<br>(0.0366)|.<br>(0.0368)|.<br>(0.0373)|
||0.0221|0.0327|0.0104|
|_Dummy_Wealth Index Quintile 4|(0.0369)|(0.0386)|(0.0370)|
||0.0316|0.0227|0.0392|
|_Dummy_Wealth Index Quintile 5|(0.0418)|(0.0445)|(0.0409)|
||00144|00191|00093|
|Number of children under 5 years of age|.<br>(0.0142)|.<br>(0.0145)|.<br>(0.0163)|
||-0.0019|0.0001|-0.0041|
|Number of children between 5 and 18 years of age|(0.0080)|(0.0081)|(0.0083)|
|Number of members 65 or older|0.0105|0.0225|-0.0009|
||(0.0174)|(0.0181)|(0.0202)|
||-0.0075|-0.0086|-0.0062|
|Number of members of extended family|(0.0061)|(0.0080)|(0.0058)|
|Constant|-1.5576|-1.8440|-1.2863|
||(3.1310)|(3.1863)|(3.0895)|
|Observations|6,501|3,311|3,190|
|R-squared|0.1898|0.1898|0.1912|
|F-Test(Kleibergen-Paap)|7.679|8.046|7.203|
|Cluster by community||||
|Regional Fixed Effects||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Source: CLS-WILL: first wave – household questionnaire, Instituto Geográfico Agustín Codazzi (IGAC) and Fernández (2011).

46
individual pattern and are independent and statistically uncorrelatedl. Technically, structural time series model can be cast in a state-space framework, and estimated through the Kalman filter. Estimates of the individual components can be obtained through a Kalman Smoothing algorithm.

In this study we analyze the Sggregate commodity price index (CPI) constructed by Grilli and Yang (1988). They proposed also two alternative deflators: the U.S. Manufacturing Price Index (USMPI) and the United Nations Manufacturing Unit Value (UNMUV) based on internationally traded manufacturing prices.

The paper is organized as follows. In the next -oction the Structural Time Series approach is briefly outlined. The remainder is then devoted to the statistical analysis of the aggregate deflated commodity price index (CPI). We show that the autocorrelation funcdon of the log of the deflated index is consistent with a stationary ARMA representation and that the evidence of non-stationarity in the data is not as clear-cut as previously claimed. We then perform unit root tests in some univariate representations. Whereas for the Dickey-Fuller tests we can reject the hypothesis of a root of unity, for the Augmented Dickey-Fuller tests (with 4 lags) we are unable to reject the same hypothesis. Several structural and ARIMA models are then estimated and compared. The former appear to have better fit and forecasting performance than the latter. Moreover, the deterministic trend model proves superior in terms of fitting to the stochastic trend model, although in both cases the trend appears to be significantly negative over the entire time series.

# 2. THE STRUCTURAL TIME SERIES APPROACH .

The approac! roduced by Box and Jenldns (1976) is based on the idea that a "parsimonious" model from the class of autoregressive integrated moving average Iln the Beveddge and Nelson decomposition method, the trend and the cycLical components have the same variance, i.e. they are perfectly correlated.

4
- 33 - **Insured and Uninsured Losses from Weather Related Natural Disasters**

<!-- Start of picture text --> 100<br>90 ....... . ?- -<br>80<br>70<br>60<br>-e C ;- NN '-Uninsured<br>50 Insured<br>40<br>30<br>10<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br><!-- End of picture text --> Source: Munich Re, Guy Carpenter, WorldWatch The above chart shows the economic (unisured) losses and insured losses from 1980 to 1999 for weather related disasters, globally. From weather related disasters alone (excluding earthquake), 1998 was the year with the highest total economic losses worldwide. 1999 was the third highest year for total economic losses as well as weather related insured losses. Generally, hurricane and windstorm losses in a given year represent about 70% of total losses, however, in 1999, earthquakes represented almost 40% of insured losses from events in Colombia, Turkey, Greece and Taiwan.

1999 was also a year with significant flood damage in Central Europe and South East Asia as well as hailstorms in Australia and gale storms in Europe (Lothar, Martin). Windstorm damages include for the U.S. and the Caribbean those from hurricane Floyd, the Orissa cyclone in India, tropical cyclone Bart in Japan, as well as major tornadoes in Oklahoma. Total economic losses from weather related and earthquake events combined approximated $100 billion in 1999 compared to $93 billion in 1998, and for all events, insured losses were higher in 1999 as well ($22 bn.)compared to 1998. The intensifying loss trends, requiring matching insurance risk capital, can be attributed to increased urbanization, population growth, rising growth of both number and value of properties in catastrophe prone areas, as well as climatological and environmental changes.

Given the context of global disaster risks as well as the risk exposure in the Caribbean, the following chapter examines the operation of the property insurance market in the Caribbean. Based on the nature of catastrophic risk, the organization and structure of the domestic market in the Caribbean represents the first line of defense for successful risk management. The following chapter thus identifies some of the domestic industry and regulatory factors which could constrain effective capacity in the management of such catastrophic risks.
Two informal vessels operate out of St Lucia’s ports in Vieux Fort and Castries. Contrary to practice elsewhere, their monthly sailing schedule is announced well in advance. The South-bound round trip is:

## **St. Lucia – St. Vincent – Grenada – Trinidad – St. Vincent – Barbados – St. Lucia**

The round trip takes two weeks, and there are about 25 round trips per year.

Commonly, the informal vessels are owned and operated by their captain. In other instances they may be owned by a local business entrepreneur who may operate and own more than one ship. Almost all either employ local agents on the islands to call, or negotiate directly with those wishing to move cargo. Much business is conducted informally, and relies on the consignees knowing how to contact the captain, or which agency to approach.

While this informal approach has worked well in the past, new businesses are not so well informed, and there is a clear problem on some of islands of matching ships with business. Consultants have found that in Grenada there is often no advertising of schedules, and sometimes even no knowledge of the existence of the service.

The only informal vessels offering container, reefer or chilled cargo services are a ro-ro vessel operating out of Portsmouth (Dominica), which will transport containers, and a converted ferry with a compartment of 400 cubic feet for chilled cargo, based in St. Vincent. (Isik, 2012).

# **c. Competition**

The 140 routes that serve the Caribbean routes brought an estimated 19.7 million TEU in capacity to the Caribbean region in 2011, with about 40 carriers, and a fleet of roughly 140 vessels. Of that amount, 90% was solely for the Hub countries, and only 5% reached the small island states, 2.6% the continental countries of Belize, Guyana, and Suriname, and 2% for Haiti. The highest amount of estimated TEU capacity is concentrated in the hubs of Jamaica and the Dominican Republic (Table 36).

Small island states were served by an estimated total of 7 carriers with an estimated 15 routes<sup>22</sup> in 2011. Though it is assumed that the small island states handle mainly feeder traffic, there were a number of mainline routes that also went to Europe (Le Havre in France), and to northern South America. CMACGM is one of the largest shipping lines in the world, and provides connectivity between the US, France, and the Caribbean. Tropical’s capacities are highly approximate, assuming a weekly frequency of two vessels with 400 TEU. In fact, all of the shipping lines serving the small island states originated from outside the Caribbean, with five from the US, one from France (also serving Guadeloupe), and one from the UK.<sup>23</sup> The hub countries consist of two types: major global transshipment centers receiving large vessels crossing the Pacific Ocean and then the Panama Canal, or crossing through the Atlantic Ocean in either and east-west or north-south direction. An estimated 130 routes went through the hub countries,

> 22 13 routes can be found in the Containerization International Yearbook of 2012 with their actual stops, which will henceforth be termed the “known” routes.

> 23 See Appendix II for details.

85
Table A.14: Different effects of mining in countries with many women working in services.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.023*|0.017**|0.006|-0.010|
||(0.014)|(0.007)|(0.010)|(0.016)|
|inactive (20 km)|0.022**|0.000|-0.003|0.023**|
||(0.009)|(0.003)|(0.007)|(0.011)|
|highservice*active (20 km)|-0.005|-0.022**|0.010|0.012|
||(0.019)|(0.009)|(0.017)|(0.025)|
|highserv|-0.300*|0.161***|0.141|-0.174|
||(0.165)|(0.055)|(0.099)|(0.154)|
|Observations|372,635|372,635|372,635|372,635|
|R-squared|0.208|0.081|0.139|0.357|
|F test: active-inactive=0|0.000812|4.376|0.543|2.566|
|p value|0.977|0.0365|0.461|0.109|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. *** p<0.01, ** p<0.05, * p<0.1 40
Uganda has a relatively diverse mix of staple foods, including maize, cassava, sweet potatoes, and matooke (plantains or cooking bananas). Benson et al. (2008) concluded that the likely impact of rising food prices on Ugandan households would be modest for several reasons, most notably Uganda’s relative isolation from global food markets, the large share of food staples that are not traded internationally, and the high levels of autoconsumption among rural Ugandan households. That said, Benson et al. (2008) also identified three groups that consume most of their calories from maize and are likely to be adversely affected by higher maize prices: the urban poor, residents of internally displaced persons (IDP) camps, and residents of institutions such as schools, prisons, hospitals, and military bases. Benson et al. (2008) also noted the need for continued monitoring of the situation, especially the possibility of persistently high maize prices and increasing prices of other staple foods.

This paper extends the analysis by Benson et al. (2008) in three important directions. First, it uses a continuous measure—the net benefit ratio (NBR)—rather than discrete categories to assess the impact of higher food prices on household well-being. A continuous measure is advantageous because it captures the important differences in impact between households that are marginally net buyers or sellers and those who depend on food markets for most of their consumption (net buyers) or income (net sellers). Another advantage of the NBR is that it measures the impact of higher food prices relative to a household’s overall expenditure level. Second, this paper disaggregates the food price data, using region-specific prices for five staple food crops in the calculation of the NBR. This disaggregation is critical because of strong evidence of large regional variation of staple food prices in Uganda. It also has the advantage of using more recent price data, covering the period through October 2008 for most crops. Third, this paper estimates the short-run change in poverty in Uganda as a result of higher food prices. We estimate both the incidence (headcount) and depth (poverty gap) of poverty, disaggregating by region and rural/urban area of residence.

We estimate an increase in the poverty headcount ratio of approximately 2.6 percentage points in the short term as a result of the recent food price increases. This is equivalent to as many as 700,000 more Ugandans living below the poverty line. Even more significant is the increased depth of poverty, with the poverty gap index increasing by 2.2 percentage points, which is a 25% increase over the most recently available estimates. Despite the perception that Uganda is relatively insulated from distant world markets, food prices increased sharply in 2008 not only for cereals like maize and rice that are traded on 3
values from some third party such as a tax assessor, or the self-appraised property values of homeowners. Regarding the latter, several studies have pointed towards a large variance of self-appraised values which in large enough samples like ours, positive and negative errors tend to cancel each other out (e.g., Follain & Malpezzi 1981; Goodman & Itter 1992). The difference between the transaction and the listed property price is dependent on multiple factors including the overall state of the housing market, the demand for housing, the time the property remains on the market before willing and able buyers come forward, as well as cultural aspects pertaining to e.g., negotiation. Despite these issues, in the absence of transaction price data sets, listing prices offer a good proxy to estimate the state of the housing market as researchers have consistently found rather low deviations between listed price and transaction price (Arnott 2009; McGreal & Taltavull de La Paz 2013; Haurin et al. 2010).

# 3.4 Descriptive Statistics

Across the _final data set_ of the five EMDEs in our sample, we observe different patterns in terms of availability of apartments versus single family houses (Figure 1). While in Costa Rica, Pakistan, and South Africa, the number of apartments and houses are well distributed, Albania and Morocco have many more apartments than houses available within the data. In South Africa and Costa Rica, the right-hand side of the distribution is dominated by rather expensive single-family houses and only few, expensive apartments. Similar patterns are observable in the price-size relationship (Figure 2). While in Pakistan the price and size differences between houses and apartments are marginal, South Africa – and to a lower extent Costa Rica – have noticeable price differences between houses and apartments, which could potentially be attributed to composition effects as houses and apartments are not equally located in all places. The strength of observed correlation between house price and size also varies across countries (Figure 3). In South Africa, this correlation is weakest – among apartments, houses, and overall. This suggests that size may not be the primary driver of price, and that other attributes collected (e.g., location) may have more explanatory power.

Equally insightful are frequency distributions of smaller-sized apartments and houses within the data (Annex 4). Across countries, units smaller than 200 sqm are usually apartments. In Costa Rica and Pakistan, however, there are a significant number of smaller-sized houses, compared to the other emerging economies in our sample. In Pakistan, houses are dominated by 5-Marla<sup>14</sup> houses (equal to about 126 sqm), which are considered a typical house for a small family. 5-Marla houses are particularly prominent in Lahore, Rawalpindi, Islamabad, and Peshawar.

> 14 <mark>The Marla is a traditional unit of area that is used in India, Pakistan, and Bangladesh, with one Marla being equal to 25.29 square meters.</mark>

20
<!-- Start of picture text --> Assigned to public space Assigned to reserved space<br>~~<br>no}<br>£<br>39 ° °<br>soecrLoge)<br>OL<br>ve<br>sg ®<br>a5<br>B20}”58£ ---+--------¢---- -----t---------+----.@<br>o§<br>ores<br>ge<br>Lw<br>=°o1<br>a1<br>Share of public space Share of reserved space Share of public space Share of reserved space<br>riders who are men riders who are men riders who are men riders who are men<br><!-- End of picture text -->