rafmacalaba/datause-encoder-data huggingface dataset:
theoretical model, distance has a negative and signicant coecient. The third column adds a control for a purchase without a statutory right (coecient of NS purchase). As in the theory, the absence of statutory rights decreases the value of purchased land plots. The last column adds the combined eect of absence of a statutory right and distance of the CBD. This strengthens the negative impact of purchasing a plot without a statutory right (coecient of NS purchase), but this impact attenuates with distance to the CBD (coecient of NS purchase*distance). Accordingly, this negative eect vanishes at about 36 km ( 1 _._ 77 _/_ 0 _._ 049 ). This empirical result nally conrms our rst model predictions: the land price gradient is steeper for statutory than for nonstatutory plots. The land gradients are respectively _−_ 0 _._ 13 and _−_ 0 _._ 08 (= _−_ 0 _._ 13+0 _._ 049 ) for the former and for the latter. Applying (13), the brokers' average of compounded � probability E [Π( _q_ ) _| x < x_ ( _q_ )] over the city is estimated to be 0 _._ 62 (= 0 _._ 08 _/_ 0 _._ 13 ).

Table 3: Land price gradients and tenure (OLS)

|Dependent variable:|Log(land|price) (CF|A/m2)||
|---|---|---|---|---|
|Distance to CBD (km)||-0.13<sup>_∗∗∗_</sup>|-0.12<sup>_∗∗∗_</sup>|-0.13<sup>_∗∗∗_</sup>|
|||(0.010)|(0.0091)|(0.010)|
|NS purchase|||-0.92<sup>_∗∗∗_</sup>|-1.77<sup>_∗∗∗_</sup>|
||||(0.11)|(0.38)|
|NS purchase*Distance||||0.049<sup>_∗∗∗_</sup><br>(0.021)|
|Log(area) (m2)|-0.69<sup>_∗∗∗_</sup>|-0.42<sup>_∗∗∗_</sup>|-0.41<sup>_∗∗∗_</sup>|-0.46<sup>_∗∗∗_</sup>|
||(0.080)|(0.067)|(0.061)|(0.064)|
|Distance to road (km)|-0.12<sup>_∗∗∗_</sup>|-0.12<sup>_∗∗∗_</sup>|-0.13<sup>_∗∗∗_</sup>|-0.12<sup>_∗∗∗_</sup>|
||(0.022)|(0.018)|(0.016)|(0.016)|
|Water dummy|1.04<sup>_∗∗∗_</sup>|0.60<sup>_∗_</sup>|0.48<sup>_∗_</sup>|0.50<sup>_∗_</sup>|
||(0.31)|(0.25)|(0.23)|(0.23)|
|Electricity dummy|0.97|1.16<sup>_∗∗_</sup>|1.12<sup>_∗∗_</sup>|1.13<sup>_∗∗_</sup>|
||(0.52)|(0.42)|(0.38)|(0.38)|
|South bank dummy|0.91<sup>_∗∗∗_</sup>|0.81<sup>_∗∗∗_</sup>|0.68<sup>_∗∗∗_</sup>|0.65<sup>_∗∗∗_</sup>|
||(0.14)|(0.12)|(0.11)|(0.11)|
|Constant|11.9<sup>_∗∗∗_</sup>|12.4<sup>_∗∗∗_</sup>|12.8<sup>_∗∗∗_</sup>|13.3<sup>_∗∗∗_</sup>|
||(0.57)|(0.46)|(0.41)|(0.47)|
|Observations|330|330|330|330|
|_R_<sup>2</sup>|0.457|0.649|0.715|0.720|

Note: Standard errors in parentheses. _<u>∗</u> p <_ 0 _._ 05 _,_ _<u>∗∗</u> p <_ 0 _._ 01 _,_ _<u>∗∗∗</u> p <_ 0 _._ 001 _._ Data: survey on Bamako, Mali, 2009-2012. The table reports the OLS regression of log of land prices at transaction date on controls (including year dummies). All plots are residential and statutory at the time of the survey. A share of plots are Non-Statutory at the time of the purchase (NS purchase) 26
“farming” in the example above, can be interpreting in many different ways. For example, we included a category for “farming” in the original time diary used in the WEAI survey, with the intention that it be used to capture any crop- or livestock-related activity. However, this guidance was not effectively communicated to the survey team in Bangladesh, who instead tended to interpret “farming” as only referring to agricultural activities performed away from the homestead, such as cultivating field crops. As a result, all activities performed by women on the homestead, regardless of whether they were related to agriculture, were classified as “domestic work.” This resulted in the misclassification of time spent tending livestock on the homestead, as well as home gardens. As a result, estimates of “farming” based on this data severely underestimated the true extent of women’s agricultural work, and because other nonfarming tasks were also classified as “domestic work,” severely limited the type of research questions the data could reliably answer. Guidance documents were quickly revised with instructions designed to prevent this mistake from being repeated in other WEAI surveys—enumerators are now instructed to provide respondents lists of specific activities typically associated with farming in the study setting, regardless of where the activity took place. Nevertheless, the lesson from our experience with the WEAI is relevant to researchers interested in measuring time use. Time diaries should be designed to ensure a consistent understanding across respondents (and enumerators) about which specific activities fall within each category. Otherwise, the time use estimates produced from the data may be meaningless for interpersonal comparisons. While a certain degree of ambiguity may, in rare cases, be desirable—if, for instance, a person’s perception of what constitutes an activity is important—in most cases, it is best avoided in favor of precise language or through the inclusion of locally-relevant examples.

Confusion can also arise from problems having to do with the ability to correctly identify the reference period. Similar to the previous example, such risks can be partially mitigated by avoiding ambiguity in how questions are phrased. For example, asking about a fixed reference period, such as “the last seven days,” is much more likely to yield consistent interpretation, than asking about vaguely defined or hypothetical reference periods (e.g., last several weeks, usual day).

8
**_Table 7 Starting informally and subsequent performance: labor productivity_**

||**(1)**<br>**All fi**|**(2)**<br>**rms**|**(3)**<br>**Young**|**(4)**<br>**firms**|**(5)**<br>**Sub-Saha**|**(6)**<br>**ran Africa**|**(7)**<br>**Middle East a**|**(8)**<br>**nd North Africa**|**(9)**<br>**Sout**|**(10)**<br>**h Asia**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Y = Log (sales per**<br>**worker)**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|
|Began operating<br>informally|-0.144*|-0.225***|-0.053|-0.243*|-0.271|-0.347***|-0.258|-0.350***|-0.660***|0.031|
||(0.084)|(0.061)|(0.252)|(0.125)|(0.165)|(0.090)|(0.303)|(0.117)|(0.169)|(0.140)|
|Female top manager<br>Y/N|-0.104**|-0.021|-0.413***|-0.169|-0.128|-0.124|-0.465***|-0.244|-0.220*|0.611*|
||(0.051)|(0.082)|(0.144)|(0.147)|(0.139)|(0.177)|(0.161)|(0.272)|(0.118)|(0.357)|
|Log of age of firm|0.117**|0.083***|0.112|0.094|0.222**|0.144**|0.061|-0.083|0.056|-0.023|
||(0.045)|(0.032)|(0.166)|(0.094)|(0.089)|(0.066)|(0.192)|(0.088)|(0.092)|(0.106)|
|Log of size|0.009|0.005|0.013|0.021|-0.027|-0.003|-0.288**|-0.096*|-0.009|0.015|
||(0.026)|(0.022)|(0.088)|(0.046)|(0.060)|(0.044)|(0.113)|(0.058)|(0.105)|(0.060)|
|Part of a larger firm<br>Y/N|0.267***|0.121**|0.455**|0.216*|0.197|0.183*|0.231|-0.012|0.098|0.063|
||(0.081)|(0.061)|(0.231)|(0.130)|(0.170)|(0.110)|(0.353)|(0.148)|(0.161)|(0.098)|
|Offers formal training<br>Y/N|0.257***|0.088**|0.172|0.140|0.291***|0.137|0.249|-0.100|-0.155|0.015|
||(0.053)|(0.041)|(0.145)|(0.094)|(0.112)|(0.089)|(0.196)|(0.124)|(0.206)|(0.124)|
|Top manager<br>experience in sector|0.002|0.001|0.018*|0.001|0.015**|0.004|0.002|0.015***|0.008|0.003|
|(years)|||||||||||
||(0.003)|(0.002)|(0.010)|(0.006)|(0.006)|(0.005)|(0.008)|(0.006)|(0.011)|(0.004)|
|Direct exports 10% or<br>more of sales Y/N|0.066|0.333***|0.013|0.479***|0.098|0.233*|0.094|0.250*|0.098|0.369**|
||(0.075)|(0.053)|(0.306)|(0.142)|(0.179)|(0.122)|(0.310)|(0.138)|(0.214)|(0.167)|
|Foreign ownership<br>Y/N|0.283***|0.372***|0.309*|0.383***|0.396**|0.520***|0.364|-0.067|-0.410|-0.201|
||(0.082)|(0.060)|(0.180)|(0.116)|(0.163)|(0.102)|(0.464)|(0.215)|(0.786)|(0.433)|
|Has checking or<br>savings account Y/N|0.276***|0.283***|0.466**|0.381***|0.230|0.329***|-0.347|0.374***|0.456|0.061|
||(0.089)|(0.061)|(0.192)|(0.101)|(0.167)|(0.096)|(0.421)|(0.137)|(0.372)|(0.097)|
|Purchased fixed<br>assets Y/N|0.172***|0.145***|0.077|0.043|0.149|0.200***|0.182|0.313***|0.025|0.196**|
||(0.049)|(0.038)|(0.140)|(0.083)|(0.112)|(0.076)|(0.197)|(0.109)|(0.153)|(0.086)|

24
budget) of advanced economies. In absolute terms, the costliest disasters mainly occur in developed countries where the concentration of assets, and thus potential losses, is the highest. However, in such economies, the damage as a proportion of GDP is limited to a few percentage points (see Table 1). Figure 2 shows the estimated average annual direct losses from natural disasters as a percentage of GDP for low‐, middle‐ and high‐income countries. Middle‐income countries are particularly affected by natural disasters in terms of GDP, due to their growing asset base at risk.<sup>2</sup>

**Table 1. Major disasters in the last 40 years**

|**Year**|**Natural Disaster**|**Country**|**Region**|**Estimated Direct Loss**<br>**(US$ million)**|**Direct Loss**<br>**(% of GDP)**|
|---|---|---|---|---|---|
|||**Large Ec**<br>|**onomies**<br>|<br>|<br>|
|2005|Hurricane (Katrina)|USA|North America|125,000|1.1%|
|1995|Earthquake|Japan|East Asia|100,000|3.2%|
|1998|Flood|China|East Asia|30,000|0.7%|
|1992|Hurricane (Andrew)|USA|North America|26,500|0.4%|
|||**Small Islan**<br>|**d Economies**<br>|||
|1988|Hurricane (Gilbert)|St. Lucia|Caribbean|1,000|365%|
|1991|Cyclone (Val and Wasa)|Samoa|Oceania|278|248%|
|2004|Hurricane (Ivan)|Grenada|Caribbean|889|203%|
|1990|Cyclone (Ofa)|Samoa|Oceania|200|178%|
|1985|Cyclone (Eric and Nigel)|Vanuatu|Oceania|173|143%|
|2010|Earthquake|Haiti|Caribbean|8,000|114%|
|2009|Tsunami|Samoa|Oceania|120|22%|

Source: Authors, from CRED EM‐DAT database.

> 2 It should be noted that most small island countries, highly exposed to natural disasters, are categorized as middle‐income countries.

4
is not possible to evaluate the effect of having received the message. Alternatively, for this group of individuals, it is possible to compare the differential effects between the three messages.

# _Knowledge of the program_

Table 6 shows more modest results on parental leave knowledge compared to experiment 1. This can be partly explained because knowledge of the program in this sub-sample is much higher than in Experiment 1, and also because all individuals in this experiment received a message with basic information about the program. We only find a significant and negative effect on knowing about the 100% wage replacement rate for men.

Table 6. Effects on knowledge, experiment 2

||||**Pane**<br>|**l A: Men**<br>|||
|---|---|---|---|---|---|---|
||Overall|Wages|Start date|Duration|Beneficiary|Sharing|
|Role model (M2)|0.020|-0.105|-0.056|-0.105|0.010|0.016|
||(0.059)|(0.075)|(0.065)|(0.070)|(0.071)|(0.068)|
|Planning (M3)|-0.026|-0.141**|-0.001|-0.034|-0.037|-0.014|
||(0.066)|(0.072)|(0.067)|(0.072)|(0.077)|(0.069)|
|Observations|304|304|304|304|304|304|
|R-squared|0.235|0.231|0.217|0.225|0.233|0.195|
|Control mean|0.723|0.479|0.287|0.394|0.564|0.266|
|Diff M2-M3|0.0459|0.0362|-0.0545|-0.0713|0.0466|0.0304|
|p-val|0.432|0.608|0.407|0.301|0.513|0.645|
||||**Panel**<br>|**B: Women**<br>|||
||Overall|Wages|Start date|Duration|Beneficiary|Sharing|
|Role model (M2)|-0.000|-0.010|0.012|-0.004|-0.006|-0.027|
||(0.021)|(0.038)|(0.033)|(0.032)|(0.035)|(0.040)|
|Planning (M3)|-0.013|-0.020|0.026|-0.036|-0.049|0.014|
||(0.022)|(0.038)|(0.032)|(0.033)|(0.036)|(0.039)|
|Observations|936|936|936|936|936|936|
|R-squared|0.097|0.095|0.135|0.094|0.097|0.068|
|Control mean|0.926|0.661|0.774|0.797|0.748|0.387|
|Diff M2-M3|0.0133|0.0107|-0.0141|0.0325|0.0429|-0.0410|
|p-val|0.542|0.782|0.665|0.328|0.231|0.294|

Notes: The table shows the results of regressing the variables regarding knowledge of the parental leave program on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Post-intervention survey.

# _Gender norms_

Table 7 shows the effects of the intervention on gender norm attitudes for individuals in experiment 2. Among fathers, the message about couple planning (M3) helped reduce traditional gender norms compared to the information and role model messages. It increased the level of agreement with the statement `It is 18
Table 1: Data for the VAR model

|Variable|Defnition|Transformation|Source|
|---|---|---|---|
|_Yt_|Real GDP , seasonally adjusted|Log frst diference, annualized|Haver Analytics|
|_πt_|Consumer price index, seasonally adjusted|Log frst diference, annualized|Haver Analytics|
|_it_|Central bank policy rate|Level|Haver Analytics|
|_ERt_|Nominal broad efective exchange rate|Log frst diference, annualized|Haver Analytics|
|_Oilt_|Average of Brent, West Texas Intermediate, and Dubai|Log frst diference, annualized|Haver Analytics|
|_Equityt_|Equitymarket capitalization,USD|Share of 2018-21 total|Haver Analytics|

21
# Early Warning, Early Action: The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net Programme

Mareile Drechsler<sup>1</sup> and Wolter Soer<sup>2</sup>

**JEL codes:** H84 Disaster Aid, Q54 Climate, Natural Disasters and Their Management, Global Warming, I38 Government Policy, Provision and Effects of Welfare Programs

**Keywords:** Early Warning, Disaster Risk Management, Disaster Risk Financing and Insurance, Productive Safety Net Programme, Shocks and Vulnerability to Poverty

This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Many thanks, first and foremost, to Ato Tadesse Bekele, Engida Mena and Zinet Ahmet at the National Disaster Risk Management Commission (NDRMC) for their support of this project. Thanks also to John Aylieff, Samir Wanmali, Alemtsehai Alemu and Ezgimelese Tecleab at the World Food Programme (WFP) for their cooperation, provision of data and instructive comments. Stephen Anderson and Mark Lawrence at the Food Economy Group (FEG) provided helpful background information regarding the HEA methodology and LIAS tool. Many thanks are also due to Zerihun Mekuria at FEWS NET, who shared LIAS data used in this analysis and provided key background information on the sources and uses of LIAS data. Catherine Porter (Heriot-Watt University) conducted economic analyses and simulations of indicative drought-related poverty used in this paper. Background data and information were provided by Kelly Johnson, Segen Tewelde, Adeyabeba Mitiku Beyene, Elizabeth Farmer and Laura Campbell. Many thanks to Daniel Clarke and Ruth Hill, who have been very generous with their time and greatly helped in shaping this paper through many instructive discussions. Finally, many thanks to Richard Choularton (WFP) and Sarah Coll-Black (World Bank) for the provision of peer reviewer comments.

1 Finance and Markets Global Practice, World Bank Group, Washington DC, USA. E-mail: mdrechsler@worldbank.org.

2 Social Protection and Labor Global Practice, World Bank Group, Washington DC, USA. E-mail: wsoer@worldbank.org.
Distributional Impacts of Taxes and Benefits in Post-Soviet Countries Alan Fuchs<sup>a</sup> , Mikhail Matytsin<sup>a</sup> , Natsuko Kiso Nozaki<sup>a</sup> and Daria Popova<sup>b</sup> a The World Bank b Institute for Social and Economic Research, University of Essex

**Keywords** : tax-benefit policy, fiscal incidence, welfare state, income distribution, redistribution; CIS; post-Soviet countries; Former Soviet Union

**JEL classification** : H22, I38, D31
by the model while for Morocco 50 crops and 7 livestock products are incorporated. Egyptian model consists of 8 regions while Moroccan model has 5 irrigation zones in 6 regions.

Using multi-model approaches have started in the last five years. These models use partial equilibrium models as a module to represent the economic side of the water allocation problem, while they rely on other types of models such as biophysical or hydrological models to introduce heterogeneity among farmers or uncertainty. Sunding _et al_ (2002), for example, represents a multi-model approach. The economic part of the model, namely Californian Agricultural Resource Model (CARM), depends on partial equilibrium concept and _“provides various measures of economic impacts, including impacts of supply cuts on producers’ surplus, producers’ revenue, production, employment, and irrigated acreage”_ (Sunding and Chong, 2006:242). The CARM objective function maximizes the sum of producer and consumer surplus from California agricultural crop production The model also takes uncertainty into account and derives differences between short–run and long-run. Heterogeneity among producers is incorporated into the model by introducing farms at different sizes. Lastly barriers to trade such as the prior appropriation system and riparian rights systems are also modeled (Sunding and Chong 2006). Sunding _et. al._ (2002) combine CARM with an agro-economic model, which supplies water productivity data, and with a detailed rationing model which measure the immediate impacts. (Sunding and Chong 2006).

Perret and Tauchain (2002) also use a multi-model approach to evaluate the effects of different irrigation pricing and distribution schemes in South Africa. Their model consists of five modules: Cost, farmer, irrigation scheme, crop and charging system modules. These modules interact to determine a number of micro-economic, socio-economic and technical variables to assess economic performance issues as well as hydraulic performance and water scarcity issues. The output is calculated by cost and crop modules Hence water demand is determined by these modules. Water supply on the other hand is determined by schemes module that lists different water availability options. Perret and Tauchain (2002) use this module to make simulations for Dingleydale - New Forest irrigation scheme.

Another example of multi-model approach is the WATERSIM Model developed by International Water Management Institute (IWMI) and International Food Policy Research Institute (IFPRI). WATERSIM models water and food production and the environmental issues at the global scale (Fraiture, 2007). It is an integrated model that combines different aspects of water management issue. The main focus of the model is on the effect of the changes in food demand on water demand at the global scale. There are two models in the 30
# 5. ENTERPRISE-BASED TRAINING

# 5.1 **Tvpes** of enterprise-based training

The term enterprise-based training is used here to denote all.forms of training for which the enterprise takes responsibility. The actual training may take place at the work-place (on the production line, in the office, on the ward), in which case it will be described as on-the-job training; it may take place at the work place in dedicated facilities (workshops or classrooms), in which case it will be'described as vestibule training; or it may take place in off-site facilities, typically a nonformal training center, a technical institute, or a proprietary school. The reason for including the off-site training components here, rather than leaving them to the next two sections, is that they tend to have a different impact when the trainee is employer-sponsored than when he is not.

Surveys in most countries show that the great majority of training programmes fall into two categories: short courses of less than six months' duration, usually much less, and longer, structured, programmes primarily aimed at entry-level workers. The former will be called ad-hoc training and the latter apprenticeships.

The term apprenticeship may conjure up an image of a young person undergoing pre-employment training of three to five-year duration, with dayrelease to a training center for more general instruction, the training scheme being broad-based in nature and supervised by some national body under legislation following an ILO model.

Such a conception is much too narrow and the customary restriction of the definition of apprenticeship to this ideal model has the damaging effect of causing national policymaking to overlook other forms of practical entry-level training which belong in spirit to the same category, most notably the apprenticeship systems in the traditional sector whose "discovery" was noted in Section 2, but also the structured entry-level training for occupations not covered by ILO-style legislation or explicit master-apprentice contracts, for example, for the health professions, accountancy, and the specialized technical occupations in electricity generation and distribution and telecommunications in countries where apprenticeship "does not exist". The three types of apprenticeship, which are treated separately below, will for convenience be termed regulated, indigenous and de facto.

# 5.2 Ad-hoc enterRrise-based training: strengths and weaknesses

Ad-hoc training in its various forms--on-the-job, vestibule and offsite--constitutes the most important form of manpower development after general education and its cost-effectiveness is generally least open to doubt. There are usually no problems with the selection and placement of trainees, and there is no need to make a special provision for work experience, with the quality of training inputs. Except in'moribund private enterprises or the more insulated parastatals, ad-hoc training can be expected to have the following strengths and weaknesses: 33 -

-
is also correlated with transport costs, rising transport costs are associated with rising and not declining revenue productivity contrary to the model’s prediction. Revenue productivity would still rise with local market expansion when we control for the cost of power and the cost financial services as we do Table 10B, but it would no longer be correlated with transport costs. At the same time revenue productivity is seen to decline with rising cost of power or rising cost of financial services.

Focusing on textiles, garments and the footwear industry, only one of the predictions of the model holds when we pool data across all three in estimating the baselines specification in Table 10. This is that aggregate revenue productivity rises as the local market expands, which continues to hold when we control for the cost of power and the cost of financial services in table 10c. But now revenue productivity also declines with rising transport costs just as it does with rising costs of power and rising costs of financial services.

Looking at estimates industry by industry, controlling for the costs of power and the costs of financial services makes the production data on the footwear industry (ISIC 1920) fully consistent with all three predictions of the Melitz‐ Ottaviano model. For we see in Table 10B that productivity declines with rising costs of power while it also increases with local market expansion while declining with increasing transport costs and rising costs of entry licenses. This bears sharp contrast to estimates of the basic specification in Table 10, where the only significant influence on productivity in the same industry is seen to be that of market expansion.

Controlling for the cost of power and financial services also removes the association between the cost of entry licensing and revenue productivity in the textile industry as reported in Table 10. At the same time productivity declines with rising cost of power while increasing with local market expansion just as it does in the footwear industry and, indeed, in most of the 11 industries covered by our data.

The contrast between the results of Table 10B and Table 10 is similar when it comes to the garments industry, where controlling for the cost of power and the cost of financial services also removes any correlation between productivity and licensing fees. Productivity declines with rising costs of power in this industry also but the effect of local market size is not there anymore.

25