rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table 2: Land ownership (continued)</u>**

|13 or more years of education|||0.044<sup>**</sup>||0.056<sup>**</sup>|
|---|---|---|---|---|---|
||||(2.21)||(2.11)|
|Wife's type of work (ref: not working)||||||
|Services|||-0.041<sup>**</sup><br>||-0.038<sup>*</sup><br>|
||||(-2.27)<br>||(-1.75)<br>|
|Agriculture|||0.072<br>||0.046<br>|
||||(1.65)<br>||(1.24)|
|Industry/manual|||-0.054<sup>**</sup>||-0.044|
||||(-2.02)||(-1.40)|
|DK/missing/other|||-0.022||-0.059|
||||(-0.85)||(-1.66)|
|**Characteristics of the husband:**<br>||||||
|Husband's total number of sons ever born|||0.002||-0.002|
||||(1.19)||(-0.87)|
|Husband's total number of daughters ever born|||0.000||-0.001|
||||(0.08)||(-0.48)|
|Husband's age in years|||0.002<br>||0.002<br>|
||||(0.75)<br>||(0.41)<br>|
|Husband's age in years, squared|||-0.000||-0.000|
||||(-0.27)||(-0.22)|
|Husband's education (ref: none)||||||
|1 to 4 years of education|||0.010<br>||0.010<br>|
||||(0.58)<br>||(0.82)<br>|
|5 to 8 years of education|||0.029<br>||0.013<br>|
||||(1.46)||(1.03)|
|9 to 12 years of education|||0.039<sup>**</sup>||0.011|
||||(2.06)||(0.87)|
|13 or more years of education|||0.068<sup>**</sup>||0.011|
||||(2.44)||(0.54)|
|Husband's type of work (ref: not working)||||||
|Services|||-0.017||-0.029|
||||(-1.44)<br>||(-1.28)|
|Agriculture|||0.071<sup>***</sup><br>||0.005<br>|
||||(4.02)<br>||(0.21)<br>|
|Industry/manual|||-0.030<sup>*</sup><br>||-0.046<sup>*</sup><br>|
||||(-1.95)||(-1.97)|
|DK/missing/other|||-0.011<br>||-0.049<br>|
||||(-0.50)||(-1.53)|
|**Characteristics of the couple/household:**||||||
|Both spouses are working|||0.037<sup>**</sup><br>||0.025<br>|
||||(2.11)||(1.02)|
|Husband-wife education gap|||-0.002||0.000|
||||(-0.98)||(0.25)|
|Household headship (ref: husband is head)||||||
|<br>Wife head of household|||0.012||0.048<sup>*</sup>|
||||(0.76)||(1.96)|
|Other male household head|||-0.014||-0.039<sup>*</sup>|
||||(-122)||(-174)|
|Other female household head|||.<br>-0.031<sup>***</sup>||.<br>-0.083<sup>***</sup>|
||||(-2.94)||(-3.79)|
|Constant|0.333|-0.851|-1.047|2.778<br>1.773|1.031|
||(0.25)|(-0.47)|(-0.65)|(1.51)<br>(1.03)|(0.62)|
|Observations|70,286|70,286|70,099|141,024<br>141,024|140,764|
|_R_<sup>2</sup>|0.03|0.04|0.07|0.07<br>0.10|0.12|

_Note:_ Data for 39 countries from 2010-18. _t_ statistics in parentheses. Dependent variable is wife's land ownership (sole and joint). Coefficients after OLS estimation. Standard errors clustered at the country level.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. _Source:_ DHS.

22
# _Price data_

Our analysis focuses on the price of domestic wheat flour, the form of wheat most commonly purchased by households. Most wheat is consumed in the form of naan, local unleavened bread that is prepared by households after purchasing either refined wheat flour or whole grain wheat (Chabot & Dorosh, 2007). Wheat and other grains represent 48 percent of food expenditure and 70 percent of calories consumed for the national sample.

From 2007 to 2008, wheat flour prices in Afghanistan more than doubled. Figure 1 displays the retail prices of wheat flour in four major urban centers. The price increases were driven by several factors: high international prices; a poor harvest brought on by drought; and export bans in key suppliers such as Pakistan. In the empirical work below, we estimate the marginal effects of the total price increases as we are unable to disentangle the effects of the price increases due to each factor.

Table 2 displays the average prices of wheat flour and other important commodities.<sup>10</sup> We chose these commodities because they represent several key food groups and because milk, lamb, rice, vegetable oil, and wheat flour make up a large percentage of monthly household food expenditure; for example, the relatively poor (20<sup>th</sup> to 50<sup>th</sup> quantile of the total consumption distribution) spend 80 percent of their food expenditure on these five food items. We include kerosene because it is commonly used in cooking.

The differences in commodity prices across subpopulations are typically small (less than 4 percent), but the differences across quarters are striking (Table 3). Using ANOVA (analysis of variance analysis), we find that 75-85 percent of the total variation in wheat flour prices is explained by variation over the four quarters of the survey year; in contrast, approximately 5 percent of the total variation can be explained by variation across provinces.

# _Changes in food security over the survey year_

The potential impact of the commodity price increases can be seen in the raw data. Table 4 displays population averages for some key indicators of food security by quarter of the survey. While the nominal values of total consumption and food consumption are relatively

> 10 Prices are aggregated to the stratum level in order to mitigate potential measurement error in district-level prices.  Strata are based on urban and rural designation within provinces.

9
# **1 Introduction**

Economists are frequently faced with the problem of drawing inferences from data sets which combine cross-sectional and time-series data. In such situations, it has become standard practice to base inferences on techniques which pool the cross-sectional and time-series dimensions in some way. For such techniques to be valid, it must be the case that the error terms are not correlated across different cross-sectional units, either contemporaneously or at leads and lags. This condition is directly analogous to the usual requirement that the residuals from different observations in a single cross-sectional regression be independent of each other. If this condition is not met, estimates of standard errors will be inconsistent, and will not be useful for inference.

This paper begins with the observation that in many applications, especially in macroeconomics and international economics, the assumption of independent cross-sectional units is inappropriate. While it may be reasonable to assume that cross-sectional units are independent when they are households or individuals chosen according to a well-designed sampling scheme from a large population, this assumption becomes less tenable when the cross-sectional units are countries or regions. Countries or regions are likely to be subject to observable and unobservable common disturbances which will cause the residuals from one cross-section to be correlated with those of another. We will refer to such cross-sectional correlations as "spatial correlations" Spatial correlations may arise for a number of reasons. For example, in applications in which real GDP growth rates are the dependent variable, various channels of interdependence such as trade, capital flows or policy coordination mechanisms will induce cross-country correlations in GDP growth rates.' Unless the regressions of interest include right-hand side variables which correctly specify these channels of interdependence, the residuals from these regressions will be correlated across countries. Similarly, in studies of capital flows to developing countries, common external shocks such as US interest rates, or else unobserved contagion effects

> ' See Kraay and Ventura (1995) for a discussion of the roles of trade and capital mobility in the synchronization of GDP growth rates across countries. Ades and Chua (1993) and Easterly and Levine (1995) provide empirical evidence that policies tend to be correlated among neighbours, leading to correlations of growth rates over long horizons.

1
-4-

FY19871/ over 100 percent of GDP with an associated debt service ratio of 40 percent of exports.

The root c Egypt's structural problems is the large budget deficit shich reached 23 percent of GDP in FY1986, excluding debt amortization. Despite the Government' **s** ubstantial progreas in recent years, the budget deficit remains stubbornly high (18 percent of GDP in FY90). The Government increased taxes and improved tax administration. It reformed custom duties by reducing the nominal rates of protection, while raising additional revenues by reducing exemptions and using a more depreciated exchange rate for custom duty evaluation. In addition, it has shown considerable expenditure restraint and has been successful in reducirg Government expenditures bv over 10 percentage points relative to GDP over the period FY1986-89, resulting in a substantial reduction in the budget deficit as a percentage of GDP over the same period. In other economies, a drop in Government expenditures GDP, or a decline in the budget deficit of that magnitude. would be audacious. In the context of Egypt, how rer, this still leaves budget expenditures and the budget deficit at unsustainably high levels. with potential adverse impli^ations for inflation and the financial sector.

# B. Policy Responses

Against the background of widening macro and structural imbalances, the author'.ties have since 1986 initiated a series of policy reforms phased roughly in two stagess (i) stabilization measures aimed at correcting the country's fiscal imbalance and the simplification of its multiple exchange rate system; and (ii) adjustment efforts intended to focus on the liberalization of internal and external trade, the deregulation of public enterprises' management and streamlining of labor and investment controls. The main element of the stabilization measures are detailed in the World Bank (1989), with one major area of success merit reporting here relating to the reform of exchange rate regime. That is to say, the authorities have succeeded in reducing the multiple exchange rate regime consisting of at least five different exchange rates to about three, and in implementing a gradual devaluation of over 25 percent in nominal terms.

M/ If calculated at the new commercial bank exchange rate, total foreign debt exceeded 180 percent af GDP
country under the assumption that every household in a developing country would be given access to simple or more advanced water and sanitation technologies:<sup>14</sup> where _DP_ is the absolute number of deaths prevented and _birth_ is the absolute number of children born per year in a given country. Following the methodology proposed by Mathers et al. (2001), we can convert the number of deaths prevented into a (discounted) sum of lifeyears saved (LYS): where _n_ is the standard (global) life expectancy at age 5 – our proxy for years lost due to death at age 5 - and _δ_ is the discounting rate for future years lived. As suggested by Mathers et al. (2001), we assume a 3 percent discount rate, uniform age weights and a standard life expectancy of 75 at the age of 5.

Last, we combine investment and maintenance costs and durability estimates of various categories of water and sanitation infrastructure (WHO/UNICEF, 2000; Hutton and Haller, 2004; Clasen et al., 2007; see Table 9 for details) with the national coverage levels displayed in Table 3, to compare the expected costs under the two scenarios to the expected benefits in terms of life-years saved. The estimated costs per life-year saved under a given scenario can be computed as where _costS_ , _costW_ are the investment and (discounted) maintenance costs for sanitation and water infrastructure over the estimated life time. _Covs_ , _CovW_ are the fractions of household with appropriate sanitation and water access prior to the interventions, and _HH_ is the total number of households in the country. Similar to equation (5), δ is the discounting rate for future years lived, which we fix at 3%. _T_ is the number of years the water and sanitation infrastructure of interest is expected to last.

> 14 Child mortality reflects the likelihood of a child born in period _t_ dying in the period _t_ to _t+5_ . Under the simplifying assumption that the previous cohorts are of approximately the same size as the one born in a given year, it is easy to see that deaths prevented in a given year can be expressed as in equation (4).

14
**_In general, Russia has, until late 2007, wisely chosen to reduce spending volatility below the levels of oil revenue volatility._** This was achieved by diverting a stable flow of resources from oil revenues to the budget, and allocating the remainder to a stabilization fund. In fact, there is some evidence that Russia’s fiscal policy has in recent years been suitably counter-cyclical, despite the natural pro-cyclicality of its oil-dependent fiscal revenues; most recently, however, with fiscal relaxation in 2007, it appears that countercyclicality was reversed (see, for example, Spilimbergo 2005, and Bogetic and Fedderke 2008, Figure 9).

**Figure 9: Russia: Strength of Anti-Cyclicality of Fiscal Revenues and Expenditures** (correlations with the real output trend; values above zero indicate degree of _anti_ -cyclicality)

<!-- Start of picture text --> Strength of Total Revenue and Total<br>Expenditure Anti‐Cyclicality<br>0.8<br>0.6<br>0.4<br>0.2<br>Total Revenue<br>0<br>Total Expenditure<br>‐0.2<br>‐0.4<br>‐0.6<br>‐0.8<br>Strength of Correlation<br><!-- End of picture text --> Source: Bogetic and Fedderke (2008).

**_The prudent fiscal policy stance was also supported by important fiscal reforms_** . Russia introduced many fiscal management innovations in recent years, including: treasury management of the budget, a medium-term fiscal framework (a 3-year budget), explicit long-term budget planning modeled after best practice in New Zealand and Australia, and the introduction of oil revenue management funds modeled after good international practice. Since January 1, 2008, the Oil Stabilization Fund was split into the Reserve Fund and a National Welfare Fund, which will accumulate oil revenues beyond the Reserve Fund maximum of 10 percent of GDP. Simultaneously, the external position improved markedly, with a five-fold increase in the trade balance as compared to its crisis level (fig. 10b).

11
- _Firm surveys such as ICAs should be designed with the specific objective in mind to address endogenous placement and (especially) endogenous technology choices._

## **_Key Working Objectives_**

- Build on Briceño-Garmendia and Klytchnikova (2006) to define gaps and adjustment to be made to current household survey data collection. Define strategy implementable at the World Bank / multilateral donors’ level to generate such data, taking into account institutional / political constraints related to the ownership of such surveys. This should give special care to reach a complete coverage of infrastructure sub-sectors, for which linkages are often not well understood (e.g. rail, ports, airports, etc.).

- Retake and evaluate existing ICA work and define gaps and adjustment to be made to current data collection to make infrastructure (one of) the prime objective of such surveys.

- One crucial objective to be kept in mind when designing surveys should be to address correctly issues of innovations and productivity changes induced by infrastructure investment (e.g. new information technologies), that is the endogenous technological choice issue.

- The sectoral coverage should also be as inclusive as possible. For example, transport should not include only roads but also ports, airports, railways; telecommunications should encompass different modes such as fixed and mobile telephony, internet access, etc.

- Once this is done, generate pictures of interactions between infrastructure sectors and development outcomes (from macro to micro) on the basis of household/firm survey data.

# **1.c. Economic Geography**

Ideally, suitable empirical tests of new economic geography models will rely on household and firm-level data, together with spatially disaggregated infrastructure indicators. In a sense, these models represent an opportunity to apply microeconometric techniques on a large enough scale to generate interesting policy conclusions.

_A number of key issues remain to be addressed:_

- _Can we enrich these models to include a better, more realistic representation of (all) infrastructure sectors, without making them intractable?_

- _Technically, what is the best way to test these models? How do we go about addressing threshold effects in a dynamic context? How do we deal with multiple equilibria? There clearly needs to be more work on these issues._

- _Panel data are an absolute requirement to test models, which by nature imply dynamic changes in the distribution of economic activity as infrastructure investment occurs._

## **_Key Working Objectives_**

- Sustain a large theoretical effort to develop new economic geography models that include more explicitly the different infrastructure sectors. This implies

38
education there. More evidence is obviously needed.

Finally, although not all workers benefit equally from agglomeration effects, it also appears that not all workers contribute equally to these effects. There is a large body of literature on human capital externalities suggesting that workers enjoy higher wages when surrounded by more educated workers. Estimates of urban external returns to education in cities are typically between 50 and 100 percent of the corresponding estimates of private returns to education, particularly for university graduates. These findings are robust to a number of estimation concerns and suggestive of large effects.<sup>10</sup> It is beyond the scope of this paper to review this literature extensively. See Moretti (2004) and Duranton (2006) for more in-depth surveys and discussions. Early findings in the literature were generated from US data, but they have been confirmed for most large developed economies. These findings have also been replicated for Malaysia (Conley, Flyer, and Tsiang 2003), China (Liu 2007), Russia (Muravyev 2008), Chile (Saito and Gopinath 2011), and, in further unpublished work, for a number of other countries, including Colombia, India, and Turkey.

# _Worker learning and dynamic agglomeration effects_

There is emerging evidence from US and European data that wage growth also depends on city size/density. To demonstrate this, one can estimate a regression along the lines of regression (1) using wages in the first difference instead of in levels as the dependent variable: where _∆_ is used to note time differences between _t_ and _t_ + 1. Among a number of papers, Peri (2002) and Wheeler (2006) confirm that wage growth is stronger in larger cities. Although this result appears robust in many advanced countries, evidence from developing countries is still 15
Policy Research Working Paper 5416

# **Abstract**

The causes and consequences of foreign direct investment (FDI) in developing countries remains a subject of debate among researchers and policymakers alike. The authors use international data and a new micro-data set of firms in thirteen Southern African Developing Countries (SADCs) to investigate the benefits and determinants of FDI in this region. FDI appears to have facilitated local development in the SADC region. Foreign firms tend to perform better than domestic firms, tend to be larger, are located in richer and better-governed countries and in countries with more competitive financial intermediaries, and they are more likely to export than domestic firms. They also exhibit positive spillover effects to domestic firms. Relying on a standard model to predict the country-level FDI inflows per capita, the authors find that SADC is attracting their expected level of FDI inflows, at least relative to its income level, human capital, demographic structure, institutions, and economic track record. There are some differences between SADC and the rest of the world in FDI behavior: in SADC, the income level is less important and openness more so. The authors use two comparison groups to compare with SADC to shed light on why other regions have attracted more FDI per capita than SADC. The factors that explain SADC’s low FDI inflows are economic fundamentals (e.g., previous growth rates, average income, phone density, and the adult share of population).

This paper—a product of the Finance and Private Sector Development, and Trade and Integration Teams, Development Research Group; and Private Sector Development Division, Africa Region Department—is part of a larger effort in the departments to understand the causes and consequences of foreign direct investment in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lxu1@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption.

Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1,600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job.

# **4 Data Collection and Survey Instruments**

**Timeline and survey instruments** We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes. In an effort to ensure that our temporary interventions had no unintended negative mental health consequences on our participants, we also conducted a final short followup survey six weeks after the interventions concluded (Appendix Figure A1. We had 2% attrition at endline and followup, neither differential by treatment arm (Appendix Table A1).

**Main outcome variables** Our primary outcome of interest is psychosocial well-being, which we assess through an index of seven mental and social health measures, henceforth referred to as the psychosocial (PS) index: depression, stress, life satisfaction, locus of control, sociability, self-worth, and stability. Our measures of depression, stress, life satisfaction, and locus of control are drawn from standard screening tools (PHQ-9, Cohen’s Perceived Stress Scale, Diener’s Satisfaction With Life Scale, and the Levenson Multidimensional Internal Locus of Control Scales, respectively) adapted for sensitivity to the Rohingya camp context. The PHQ, our depression screening tool, has been validated against antidepressant medication (L ̈owe et al., 2006) and employed in the cross-section among refugee populations (Poole et al., 2018) as well as in experimental evaluations of psychotherapy programs in South Asia (Patel et al., 2017; Bhat et al., 2021).

For sociability, we inquire about the number of interactions that participants have had 11