rafmacalaba/datause-encoder-data huggingface dataset:
Finally, there is the concern related to validation of the hypothesis, which applies to all ex‐ante approaches including this. The only validation or test for this model is to combine ex‐ante and ex‐post analysis (see Bourguignon and Ferreira, 2003), some of which may be possible using newly available sources of data.

# **II. Scenarios and Macroeconomic inputs**

The impact of the twin crises on poverty indices as well as any other distributional measure such as average consumption or income will be estimated in a “with‐without” comparison framework. That is, the difference between “with‐shock” and “without‐shock” scenarios for the same projected year. In the Iraq case, this will correspond to the year 2014. The without‐shock scenario is called business as usual (BaU). This is the scenario in which both population and economy behave as in their usual trend. In other words, economic agents will behave accordingly to what has happened in the past, which in this case is the trend between 2012 and 2013. On the other hand, the “with‐shock” scenario is when both events: internal displacement and collapse of oil prices and the IS‐related macro shocks, hit the economy during the second half of 2014. Thus, the impact is defined as the gap between these two scenarios for 2014.

The macroeconomic variables used as inputs into the micro‐simulation are intended to capture most of the transmission channels discussed above. These variables are: i) changes in aggregate GDP and by economic sector; ii) changes in labor force participation rates and total and sectoral employment levels, iii) changes in public and private transfers; iv) population growth; and v) changes in price of food relative to non‐food. All these macroeconomic inputs are generated in 2014 for both scenarios: business as usual (BaU) and crisis. Additionally, we include in the crisis scenario a series of assumptions for IDP and IS‐ affected areas related to assets, services from dwelling as well as employment and labor and non‐labor income.

## **Changes in aggregate and economic sectors GDP**<sup>**17**</sup>

The aggregate macro‐economic and sector‐specific growth rates for Iraq are shown in Table 1 . The BaU estimates assume the same pattern and trend of sectoral and overall growth in the 2013 to 2014 period as was measured in the 2012 to 2013 period. The estimates for the 2014 crisis year are from the official estimates produced by the Iraqi Central Statistics Organization. The latter estimates imply a negative 6 percent growth rate of GDP for Iraq for 2014, which incorporates the macroeconomic shocks related to war against IS and the oil price decline. Relative to a BaU scenario, where GDP would have grown at a positive rate of 9 percent, the magnitude of the macro shocks on GDP imply a 15 percentage point reduction in total economic activity.

The biggest sectoral contraction is in manufacturing and construction, primarily due to a fall in construction, which employs almost 14% of all Iraqi workers and where most of the poor are employed.

> 17 Macroeconomic data and projections were provided by CSO – GoI (March 2014).

11
pro-cyclical trend relative to macroeconomic indicators and private capital flows. Most empirical studies focusing on the causes of remittances have found prevalent evidence for the altruist motive as opposed to the portfolio one. See Elbadawi and Rocha (1992) for a detailed review, and Agarwal and Horowitz (2002) as a more recent contribution.

Support for the importance of the altruist motive in remitting is also widespread in the literature studying the response of remittances in the aftermath of disruptive events such as natural disasters, political conflicts or specific economic crises. Thus, Clarke and Wallsten (2004) find that remittance inflows increased following a natural disaster in Jamaica. Gupta (2004) obtains a positive impact of an Indian drought on the cyclical component of remittances received by the country. Ratha (2006) indicates that remittance inflows increased after natural disasters in Bangladesh, the Dominican Republic, Haiti and Honduras, as well as in response to conflicts in Albania and in Sierra Leone. They remained substantial during conflict in Ivory Coast (Black et al, 2004). Yang (2008) also finds an increase in remittances following natural disasters. A similar result is suggested by Joseph and Mohapatra (2009) in an ongoing work utilizing data for a large set of developing and high-income countries during 1970-2006. Hysenbegasi and Pozo (2002) find sharp increase in remittances after large macroeconomic shocks and currency crises in the Latin American and Caribbean countries. Yang and Choi (2007) employ household level data for the Philippines and find that in households with overseas migrants, exogenous changes in income lead to changes in remittances of the opposite sign. Halliday (2006) shows that adverse agricultural conditions increased remittances inflows in El Salvador.

On the other hand, Lueth & Ruiz-Arranz (2006) obtain that for the eleven countries in their analysis remittances do not seem to increase in the wake of natural disasters. In the same line, Ratha (2003) mentions that remittance receipts declined in Turkey and the Philippines after the financial crises that hit the countries in the late 1990s, although the decline was less than that of other capital inflows. This evidence prompted Ratha (2003) to assert that the response of remittances to dramatic changes in economic activity was unclear.

As for the empirical literature examining the cyclicality direction of remittances in relation to macroeconomic indicators, the conclusions are mixed. In support to the counter-cyclical response, Mishra (2005) finds for 13 Caribbean countries that a 1 percent decrease in real GDP leads to a 3 percent increase in remittances two years later. Similarly, Bouhga-Hagbe (2004) shows that remittances to Morocco are, over the long run, negatively correlated with real GDP in Morocco.

5
recalls 2.5 digits, with this score being .28 digits higher for high non-cognitive ability women (p-value<.01).

Panel C shows evidence on the characteristics of the husbands. High non-cognitive ability women tend to be married to more educated husbands (1.2 years of additional schooling, p-value<.05). There is strong evidence of positive assortative mating on non-cognitive skills. Men married to above-median non-cognitive ability women are 33 percentage points more likely to have above-median non-cognitive ability themselves (p-value<.01).

Panel D focuses on characteristics of the household. High and low non-cognitive ability women have similar availability of adult farm labor. They are also equally likely to be in a polygamous relationship. We measure whether the household is liquidity constrained with a dummy for whether in the last 12 months it tried to borrow money and was turned down, or it did not attempt to borrow because it felt discouraged to do so.<sup>9</sup> According to this measure 54 percent of the households are liquidity constrained, and this figure is similar between high and low non-cognitive ability women.

Panel E focuses on the characteristics of the farm. The average farm has 2.1 hectares, is located 941 feet above sea level, and is exposed to 1,093 mm of yearly rainfall. We constructed an index of soil quality equal to the sum of 7 indicators for desirable soil characteristics. Farms managed by high and low non-cognitive ability women do not differ on any of these characteristics.<sup>10</sup>

> 9 The top three most common reasons for feeling too discouraged to attempt to borrow were: “Believed would be refused”, “Too expensive”, and “Too much trouble for what it’s worth.”

> 10 The measures of soil quality, terrain elevation, and rainfall were obtained from third-party geospatial data sets, which have been linked to household and plot-level data, and are public alongside the IHPS 2013 data. The variable soil quality is the sum of 7 indicator variables that equal 1 if the soil is unconstrained on nutrients’ availability, retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicity, and workability.

10
These studies indicate that investment climate conditions do affect firm decisions. This paper extends the existing literature in a number of dimensions. It is the first to examine a range of investment climate conditions together and to link them to employment growth in SSA. It looks at multiple dimensions of the investment climate simultaneously, minimizing concerns of omitted variable bias. Rather than relying on subjective rankings, it emphasizes the objective measures, i.e. monetary and time costs associated with completing various transactions or interactions with government officials. The paper addresses endogeneity concerns associated with subjective rankings of constraints by using objective measures and by using location-sector-size averages instead of enterprises’ own responses. It also considers whether selection is an issue driving the results. This paper is also the first to undertake a systematic comparison between SSA and the broader set of developing countries, drawing on data from over 100 countries. It also brings in additional evidence that shows while average enterprise employment growth is as high in SSA, the greater concentration in labor intensive micro enterprises raises concerns about productivity growth and the efficiency of resource allocation.

# **3.  Data**

To better address questions about investment climate conditions and their impact on the performance of a wide variety of enterprises, the World Bank launched its program of Enterprise Surveys in 2001. To date, it has interviewed 86,036 entrepreneurs and senior managers in 104 countries. Of particular interest here are the 16,738 interviews in 31 countries in SSA.

The ES have four distinguishing features that make it particularly useful in this study. First, it can benchmark not only subjective rankings of investment constraints to business performance (e.g. the extent to which electricity is rated as a problem), but also objective measures of these constraints (e.g. the frequency and duration of outages, production lost 11
**a) Agriculture Productivity:** Despite rapid growth since 1980, South Asia’s dependence on agriculture remains substantial. While agriculture’s contribution to value added has declined rapidly, it still remains higher than most regions ( figure 18).  More

**Figure 18. Agricultures Share in GDP**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>EAP LAC MENA ECA SAR SSA World<br>Agriculture's GDP share<br>percent<br><!-- End of picture text --> _Source: World Bank 2007_ .

importantly, between 35-50 percent of the labor force remains reliant on agriculture for livelihood, suggesting very low average productivity (figure 19)<sup>9</sup> . Since world commodity prices of energy and fertilizer are likely to remain substantially higher than the levels in 2004, the only sustainable way of reconciling higher input costs with low

**Figure 19. Agricultural Productivity (2000$/worker)**

<!-- Start of picture text --> World<br>SSA<br>SAR<br>2001-03<br>ECA<br>1990-02<br>MENA<br>LAC<br>EAP<br>0 500 1000 1500 2000 2500 3000<br>Productivity<br>Region<br><!-- End of picture text --> _Source: World Bank 2007_ .

> 9 Employment shares range from a low of 35 percent in Sri Lanka to a high of 50 percent for Bangladesh (World Bank 2007).

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consistent with the idea of a virtuous cycle as discussed above. Our data and methods allow us to assess this in a methodologically rigorous way ─ controlling for how road subprojects are allocated to specific communes and for initial conditions that may affect subsequent outcome trends, and monitoring communes long enough to capture impacts.

The paper concentrates on three sets of questions. The first concerns average impacts on local market development of the rehabilitation of rural roads. The World Bank project coincided with a period of rapid economic development in Vietnam. Access to markets of various kinds increased substantially over the study period. How much of the change observed in communes covered by the project can be attributed to the road intervention? A further, related issue is how impacts vary over time. If the transport cost saving is large enough and the types of expenditures and agglomeration effects noted above are present, then we would expect impacts of the roads project to increase over time. Existing evidence on this point is scarce.<sup>4</sup> Our data allow us to test if that is the case.

Our second set of questions concern cross-commune differences in the project’s impacts on local markets and what explains those differences.<sup>5</sup> Heterogeneity of impacts can be expected to arise according to the economic, social and political characteristics of the community where the road intervention is placed. We aim to test for heterogeneity and try to identify the key contingent factors relevant to impacts.

In this context, a potentially important issue for project design is whether higher levels of initial development enhance or diminish impacts. Should we be targeting places

> 4  In one of the few cases in which impacts of a poor area development project (including rural roads) were tracked over time, the impacts declined over time rather than showing cumulative gains (Chen, Mu and Ravallion, 2007).

6
were obtained from shared taxes (see World Bank, 2001). In developing countries, the role of shared taxes in financing sub-national governments is of lesser significance as general revenue sharing is widely practiced.

# **_c. Designing Fiscal Transfers: Dividing the spoils or creating an enabling framework for innovative and competitive service delivery_**

Intergovernmental transfers are the dominant source of revenues for sub-national governments in most DTEs. In 1999, they constituted on average 24% of total revenues for transition economies (93% in Albania and 4% in Croatia) and 42% (81% in South Africa, 75% in Peru and 7% in China) of the same (in 1997) for developing countries. This ratio ranges from 4% to 81% for individual countries. The transfers constituted 7595% of sub-national revenues in Indonesia, Nigeria, Mexico, Pakistan and South Africa. The design of these transfers is of critical importance for efficiency and equity of local service provision and fiscal health of sub-national governments (see Appendix table 3 for general principles and better practices in grant design). For enhancing accountability it is desirable to match revenue means (the ability to raise revenues from own sources) as closely as possible with expenditure needs at all levels of government. However, higher level governments must be allowed greater access to revenues than needed to fulfill own direct service responsibilities so that they are able to use their spending power through fiscal transfers to fulfill national and regional efficiency and equity objectives. We can identify six broad objectives for national fiscal transfers each of which may apply to varying degrees in different countries.

# _i. To bridge a fiscal gap_

An imbalance between the revenue-raising ability of regional and local governments and their expenditure responsibilities might arise for three reasons. For one, there may be inappropriate assignment of taxing and spending responsibilities such that expenditure needs of sub-national governments exceed their revenue means. Second, it may be more efficient for the national government to collect tax revenues on behalf of the states and turn 22
<!-- Start of picture text --> Goods and Services in Emerging Countries Compensation to Employees in Emerging Countries<br>0.415 0.15 2<br>0.10 ‘ [ 0.10 | H<br>0.000.05. } 0.000.05 | |<br>-0.05 a “0.05 :<br>0.10 -0.10 '<br>0.15 -0.15<br>Recessions Expansions Recessions Expansions<br>Goods and Services in Advanced Countries Compensation to Employees in Advanced Countries<br>0.15 0.15<br>0.10 0.10<br>0.050.00 _ fo| 0.050.00 _ _<br>“0.05 0.05<br>-0.10 -0.10<br>“0.15 “0.15<br>Recessions Expansions Recessions Expansions<br><!-- End of picture text -->
A group of researchers have recently tried to explain the excess volatility of output fluctuations in emerging markets relative to industrial economies. Aguiar and Gopinath (2007) argue that a DSGE model with shocks to trend growth can match the stylized facts of business cycles in EMEs. Neumeyer and Perri (2005) and Uribe and Yue (2006), on the other hand, show that a DSGE model with interest rate shocks and a financial imperfection will replicate the moments found in the data for EMEs. However, these models fall short of providing a deeper understanding of the mechanism through which: (a) the shock to trend growth occurs, and (b) changes in fundamentals may affect country risk.

A full explanation of the causes of business cycles in EMEs goes beyond the scope of the present paper. Our goal is rather modest. We attempt to characterize the business cycles in terms of the duration, amplitude and cost for EMEs compared with industrialized nations, using quarterly data. Following this characterization, we look for a potential explanation for the cost of recessions. More specifically, we explore the association between the cost of recessions and indicators of macroeconomic stability and external imbalances, external shocks and some structural policies and features such as the degree of international integration in trade and finance, and the quality of institutions and the regulatory framework of the countries including in our sample.

We are interested in documenting the differences in the business cycle facts for Latin American countries, East Asian fast‐growing economies and other emerging market economies as well as OECD countries. More specifically, we assess whether these differences are attributed to economic fundamentals or any unknown factor. Also, are differences over time in (the duration, amplitude and cost of) recessions or the fundamentals have changed? What is the relationship between the depth of recessions and economic rigidities? Are recessions more sizable and adjust to slow when labor markets or output markets are more rigid?

3
# **Table VIII: Children's Health Measures Instrumental Variables Regressions - Instrument: Program Dummy**

|**Dependent Variable**|**Full Sample**<br>**Mean(Std.Dev.)**|**Model 1**|**Model 2**|**Model 3**|
|---|---|---|---|---|
|_Parasite count_||-0.289<br>[0.146]**|-0.292<br>[0.148]**|-0.294<br>[0.149]**|
||0.302(0.625)|-0.786|-0.796|-0.802|
|||-0.081|-0.086|-0.086|
|_Diarrhea_||[0.043]*|[0.042]**|[0.041]**|
||0.133(0.340)|-0.493|-0.525|-0.522|
|||-0.389|-0.364|-0.366|
|_Anemia_||[0.123]***|[0.118]***|[0.118]***|
||0.386(0.487)|-0.819|-0.766|-0.770|
|||17.980|23.374|23.520|
|_McArthur Communication Development Test score_||[6.924]**|[6.951]***|[6.980]***|
||15.363(20.076)|0.960|1.249|1.256|
|||13.766|16.434|16.650|
|_Picture Peabody Vocabulary Test percentile score_||[9.147]|[7.970]**|[8.193]**|
||31.859(25.422)|0.361|0.431|0.436|

Notes: (i) Model 1: no controls; Model 2: age, demographic, health-habits and economic controls; Model 3: age, demographic, health-habits, economic and federal-programs controls.

(ii) Reported results: estimated coefficient, clustered standard error at census-block level in brackets and elasticity (coefficient*outcome mean/share-firm-floor mean). * significant at 10%; ** significant at 5%; *** significant at 1%.

(iii) Missing values in covariates were imputed with zero, and a corresponding dummy variable was then added to the regressions.

35
born ethnic or linguistic minorities, second generation immigrants, as well as first generation immigrants (the highest rate is found among immigrants from CEE and former Soviet Union; in Spain, Portugal and Greece this rate exceeds 50%). In Eastern Europe, the picture is broadly similar: local born minorities feature the highest dependent informality rate of about 11%, followed by second generation immigrants and immigrants from CEE and former Soviet Union with 7%, whilst this rate is just 4% among the natives. Moreover, in Eastern Europe, as well as in Spain, Portugal, and Israel, ethnic and linguistic minorities are more likely to work informally also after controlling for a variety of characteristics (Table 9).

In Western Europe, the only minority group with above-average proportion of noncontracted employees consists of immigrants from CEE and former Soviet Union: 12% of employees of this origin works without contracts, whilst for the natives this rate is 7%. In the Nordic countries no clear pattern emerge, probably because the sub-sample of informal immigrant employees is too small.

Table 4 also compares informality rates of immigrants depending on whether they do have an ̳automatic‘ working right due to nationality (based on country- and year-specific rules on free movement of labor within EU). The differences by legal status are smaller than those by geographic origin. Somewhat surprisingly, informality rates are slightly higher among immigrants covered by the ―free movement of labor‖ provisions in all parts of Europe except the South. It appears that in other parts of Europe persons not covered by the provisions are either not likely to work as non-contracted employees or they are not captured by the ESS surveys. On the other hand, persons covered by the provisions are more likely to move without a job in hand, and hence more likely to end up with an informal job. The situation is strikingly different in Spain, Portugal, and Greece, where informality rate among non-covered immigrants is twice as big as among covered ones (33.7% vs. 16.5%). After controlling for individual characteristics and industry of employment, non-covered immigrants in Southern and Eastern Europe are more likely to work informally than natives and, in the South, also than covered immigrants (Table 9).

The above findings are supported by Figures A2 and A3 (in the Annex), which display proportions of ethnic minority population and population with immigrant background among formal employees, informal employees and informal self-employed in each country using data of from three ESS rounds conducted in 2004-2009.

As expected, informality sharply declines with the size of establishment. Estimated across all 30 countries proportion of non-contracted employees is 16% in establishments with less than 10 workers, 8% in units with 10 to 24 workers, 5.5% in units with 25 to 99 workers, and 4% in those with 100 or more workers. Interestingly, in the South the informality level 19