rafmacalaba/datause-encoder-data huggingface dataset:
infrastructure in a given country might be warranted, but it leaves open the question of the exact types and location of investments, new freeways or rural roads, bridges, railways, maintenance versus new investment, etc., that should be prioritized.

Faced with these limitations, two routes appear attractive. The traditional one is to come back to microeconometric empirical assessments of specific projects or instances of infrastructure development. A more recent approach is to explicitly incorporate the lessons from the new economic geography literature in the empirical exercise. We review attempts along this line in the next section before looking at more traditional microeconometric studies.

# **3. Empirical Economic Geographic Studies**

Empirical evidence on the role of infrastructure in the context of the economic geography frameworks mentioned above can be organized in three strands. First of all, I review generic evidence on the theoretical mechanisms underlying these models, namely agglomeration forces, transport costs and the volume of trade.

In a nutshell, two crucial steps need to be integrated to incorporate spatial insights into the discussion of the development impact of infrastructure policies. First of all, the nature of the link between infrastructure and transport costs need to be made explicit, so that we can estimate the impact of new investment, maintenance or upgrading of existing networks on these costs. Second, we need to be able to estimate how changes in transport costs will affect trade and agglomeration of firms and workers, and ultimately what their effect on a number of outcomes such as growth or income distribution will be.

I then turn to papers looking at the relationship between infrastructure and development outcomes by linking the two steps outlined above. I review them in two groups. Those that implicitly incorporate geography by testing the effect of infrastructure on the spatial variations of some variable of interest, generally prices of land, houses or labor, and those that explicitly introduce geographical variables in the analysis.

## **Generic Evidence**

As stated when discussing theory, most of the discussion of infrastructure in the context of new economic geography models is based on the assumption that investments in transport infrastructure reduce trade costs and facilitate trade. This first raises the issue of how to measure transport costs.

_Measuring Transport Costs with a View on Infrastructure Policy_ Traditional methods have focused on simple proxies such as distance, ad valorem shares of trade values (predominantly the cif/fob ratio, which compares the costinsurance-freight value of a good at the point of entry into the importing country to the free-on-board value at the point of shipment for exportation), or real freight expenditures.

26
relocate in response to shocks (Okun 1962.). The short-term measure is particularly useful for monetary policy since supply constraints or adverse demand shocks, even if they are not permanent, reduce the effective slack in the economy and therefore influence the policy interest rate at a given decision point.

_Long-term_ potential growth is a function of the available capital stock, labor input, and current technology (Solow 1962). As such, long-term potential growth captures movements in the slowmoving fundamental drivers of output, assuming allocation of all factors of production to their most productive uses regardless of temporary supply shocks. Long-term potential output sets the underlying trend of short-term potential output as well as actual output (see Box 1 above for various estimation methods).

Other studies have also documented a potential growth slowdown in Russia (Entov and Lugovoy 2013; IMF 2012; Kaitila 2016; Kuboniwa 2011; OECD 2018). This paper fills a gap in the literature by providing a cross-country perspective using a large data set of comparable estimates (World Bank 2018). The analysis is also innovative in its examination of the impact of multiple reforms in a single platform. While the appendix provides the technical details, Box 1 provides an overview of how potential growth is measured.

# **Box 1: How to measure potential growth?**

Estimates of short-term output may be computed using filtering techniques, including univariate and multivariate filters, while estimates of long-term potential output rest on structural models or long-term growth expectations.

**Filtering techniques.** Univariate filters involve estimates of trend output using only GDP series. Multivariate filters take into account the relationship between GDP and other variables (such as inflation or unemployment rates) to help distinguish short-run deviations of output from trends.

**Production function approach.** This approach represents potential output as a (CobbDouglas) production function of the amount of full-employment capital and labor, as well as technology and efficiency of factor allocation that drive total factor productivity (TFP).

**Expectations.** The approaches above are supplemented with long-term growth expectations, such as five-year-ahead growth forecasts from Consensus Economics or the IMF’s World Economic Outlook. These growth expectations reflect both model estimates and forecasters’ judgment. Judgment can be especially useful during periods of major structural changes, which model-based estimates may not be well-equipped to capture.

The key results pertaining to potential growth presented in this paper are broadly robust to the choice of potential growth measures: the faster-than-average post-crisis slowdown in Russian potential growth (see Appendix for more technical detail).

4
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MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.4 Fixed effects with instrumental variables (data in logs)**

|Variable|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
||–0.065|0.0002|0.00002|0.0002|0.001|0.003|0.001|0.0000001|0.004|0.103|0.03|
|Income|–1.15|–1.43|(3.35)**|(2.91)**|–0.23|–1.11|–0.3|(8.70)**|(8.12)**|(10.97)**|(12.32)**|
|Ubiti|0.042|6.246|–0.041|3.011|40.797|–12.423|57.96|0.0002|–7.384|–30.302|31.93|
|ranzaon|–0.46|(5.32)**|–0.57|(4.96)**|–1.02|–0.32|(2.12)*|(2.09)*|–0.67|–0.18|–1.16|
|Population|–0.145|0.00001|0.001|0.0002|–0.002|0.093|0.01|–0.0000002|–0.076|–1.312|–0.128|
|density|–0.8|–0.01|(4.75)**|–0.23|–0.04|(2.04)*|–0.51|–1.35|(3.28)**|(4.23)**|(2.97)**|
|Infrastructure|0.0003|–0.0005|0.00005|–0.0004|–0.256|–0.018|–0.047|0.0000003|–0.007|0.222|0.009|
|aid (per capita)|–0.09|–0.22|–0.37|–0.34|(2.05)*|–0.14|–0.16|–1.74|–0.54|–1.16|–0.18|
|Exports|–0.001|0.001|0.0002|0.001|0.016|0.037|–0.03|2.92E–06|0.228|3.58|0.699|
|(% GDP)|–0.64|–0.3|–0.67|–0.69|–0.12|–0.25|–0.22|(8.16)**|(8.43)**|(8.05)**|(7.15)**|
|Agriculture|–0.006|–0.008|0.001|0.001|–0.213|0.097|–0.791|0.000002|0.224|5.128|–0.011|
|(% GDP)|(2.38)*|–0.82|–1.47|–0.26|–1.03|–0.49|(2.20)*|(2.80)**|(2.83)**|(3.67)**|–0.05|
|Manufacturing|0.009|0.025|0.0004|0.011|–0.27|0.206|2.369|0.000002|0.204|1.001|–0.384|
|<br>(% GDP)|(4.38)**|(2.47)*|–0.65|(2.08)*|–0.89|–0.69|(5.60)**|(2.85)**|(2.58)**|–0.75|–1.63|
|C|–1.186|–0.382|–0.006|–1.046|63.256|46.968|6.852|–0.0001|–9.948|–243.622|–25.85|
|onstant|–1.19|–0.53|–0.13|(2.85)**|(3.07)**|(2.09)*|–0.39|(2.43)*|–1.37|(2.06)*|–1.59|
|Observations|1,247|1,299|1,308|1,296|119|116|78|1,474|765|892|1,525|
|Plant fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|Yes|
|Year fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|Yes|
|Hausman p–<br>value|0.0523|0.0405|0.0001|0.0000|0.7338|0.7772|N/A|0.0000|0.0000|0.0000|0.0000|

_Sources_ : As for table 2.1.

When we model our data using the instrumental variables estimation, per capita GDP, urbanization, population density, and certain structural variables continue to explain the most country-to-country variance in infrastructure stocks (table 3.4), although the magnitude of their coefficients changes from the basic fixed effects specification. For the most part, per capita GDP remains highly significant in explaining national variations in infrastructure, although the magnitude of the coefficients decreased from the simpler model. Urbanization becomes slightly less important, with significance in only four sectors, compared to eight under the first fixed effects specification. Population density, too, loses significance in several sectors. The significance of structural variables varies by sector, but export share of GDP is highly significant with respect to generating capacity and ICT.

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Table 6b: Extensions to Table 5 with alternative metrics and by sector

||estim<br>Ba|ation<br>se|urbani<br>DV:<br>Pla|zation<br>Change in u<br>nt|urbani<br>rbanization s<br>Out|zation<br>hare for total<br>put|sec<br>district-ind<br>Orga|tor<br>ustry<br>nized|se<br>Unorg|ctor<br>anized|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Initial urban share|-0.694+++|-0.741+++|-0.727+++|-0.774+++|-0.632+++|-0.669+++|-0.479+++|-0.490+++|-0.791+++|-0.852+++|
||(0.030)|(0.029)|(0.031)|(0.029)|(0.028)|(0.027)|(0.045)|(0.043)|(0.036)|(0.034)|
|Education level|0.057+++|0.060+++|0.065+++|0.070+++|0.039+++|0.034++|0.004|0.001|0.074+++|0.082+++|
||(0.010)|(0.012)|(0.011)|(0.013)|(0.014)|(0.016)|(0.014)|(0.013)|(0.013)|(0.016)|
|Infrastructure level index|0.026++|0.036+|0.032+++|0.034|0.005|0.036|0.000|0.049+|0.036+++|0.028|
||(0.011)|(0.019)|(0.012)|(0.021)|(0.011)|(0.023)|(0.018)|(0.027)|(0.013)|(0.022)|
|Log average wage|-0.019|-0.031++|-0.024|-0.038++|-0.006|-0.017|-0.012|-0.004|-0.019|-0.034++|
||(0.014)|(0.014)|(0.016)|(0.015)|(0.018)|(0.019)|(0.029)|(0.030)|(0.016)|(0.015)|
|Urban/rural wage ratio|-0.020+|-0.016|-0.015|-0.010|-0.007|-0.000|-0.010|0.002|-0.010|-0.005|
||(0.011)|(0.010)|(0.011)|(0.010)|(0.012)|(0.011)|(0.011)|(0.010)|(0.010)|(0.008)|
|Log total district land area|0.007|-0.013|0.008|-0.028+|0.001|-0.014|-0.030+|-0.016|0.011|-0.036+|
||(0.014)|(0.016)|(0.015)|(0.016)|(0.015)|(0.018)|(0.016)|(0.019)|(0.018)|(0.021)|
|Change in urban population|0.235|-0.019|0.479++|0.088|-0.008|-0.006|-0.240|-0.087|0.480+|-0.057|
||(0.219)|(0.203)|(0.226)|(0.193)|(0.157)|(0.188)|(0.175)|(0.212)|(0.244)|(0.221)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|1700|1700|1700|1700|1700|1700|566|566|1105|1105|
|Adjusted R-squared|0.358|0.385|0.350|0.381|0.323|0.343|0.273|0.303|0.382|0.416|

Notes: See Table 5.
Our dependent variable is logged per capita FDI inflows in constant US dollars of 2000, winsorized at the tail 5 percent.<sup>13</sup> We add a constant to this quantity because some countries have negative FDI inflows (negative FDI inflow represents repatriation of previous investment). Dropping the negative FDI inflow observations leads to qualitatively similar results. However, we are not aware of significant economic reasons to exclude the observations.

Our objectives are twofold. First, we wish to empirically investigate the key variables attracting FDI. Second, we wish to see if FDI flowing into SADC behaves similarly to that flowing into other countries. To these ends, we follow the discussion in the previous section and regress each country’s FDI inflows on a set of country characteristics associated with the quality of government, along with some basic measures of the level of development and other country characteristics such as population size, demographic characteristics and trade policies. We include SADC in these regressions and then ask whether SADC’s FDI inflows behave differently from those in other countries.

We proxy general institutional quality by a commonly-used measure of the more general quality of government: the rule of law index from ICRG. **_Rule of law_** is an ICRG survey result gauging the state of law and order in each country. It ranges from 1 to 6, with higher values connoting greater general respect for the rule of law.<sup>14</sup> It contains a law component, which captures the strength and impartiality of the legal and political establishment in judicial matters, and an order component, which captures the extent to which residents of a country accept established legal and political institutions as the solely legitimate way to make and implement laws and to adjudicate disputes.

We also adopt ICRG corruption index as our **_control of corruption_** index. This indicator is most commonly used in the related economics literature. This variable is

> 13 The winsorization is done to prevent the influence of outliers and measurement errors.  This variable is highly skewed.  The qualitative results are similar when we winsorize at the 1 or 5 percent levels.  However, giving the winsorization threshold is around 0 at 5 percent level, the interpretation of magnitude is a lot easier--log(FDI per capita + constant) is close to log(FDI per capita) when the constant is 1.4 instead of around 92, and log point is close to percentage point in interpretation.

> 14 ICRG data has the advantage of covering the majority of countries from 1982 on. For details, see Knack and Rahman (2007).

13
were directly affected by the collapse of CMEA-based trade. One noteworthy difference between the region is that the CEECs that were GATT members were constrained in their ability to raise tariffs, having bound these at low levels upon accession (tariffs were not relevant in the centralized trade context). Once central planning was abolished, CEEC governments started out with very low tariffs and were constrained in their ability to raise them by GATT compensation requirements.<sup>2</sup> This turned out to be an advantage, facilitating the negotiation of an Association Agreement with the EU (see below), and illustrating one of the main potential benefits of GATT membership.

Data on per capita exports of MENA countries, to the world and to the EU, reveal that <!-- Start of picture text --> Non-Fuel Exports<br>U Share EL WORLD<br>450T<br>D400T1<br>Xn 350 1 MENA | TLC Middle East<br>g. 300k<br>Q 250 North Africa<br>lU 200 rli<br>,_150 - _ 4<br>150<br>89 93 89 93 89 93 89 93<br>1. North Africea -Algeria Morocco and Tunisia-<br>2. Middle East - Egypt. Israel, Jordan, Kuwait. Lebanon, Syria and UAE.<br>3. MENA - North Africa and Middle East.<br>4. CEEC - Bulgaria Czech Repubic. Hungay, Poland. Romania and Slovak Republic.<br><!-- End of picture text -->

Figure 1 MENA countries have not been very dynamic in terms of non-oil, non-traditional exports. Per capita 2 As a result, several countries used import surcharges during the initial adjustment process. Such temporary surcharges are permitted under GATT, but are subject to conditions (demonstration that measures are necessary to deal with a balance of payments problem) and surveillance.

12
29 otherwise have been lost to death and to various degrees of morbidity and disability.35 The willingness-to-pay approach is likely to be distorted by differences across households in income; for the same set of preferences, richer households are likely to have higher willingness to pay than poorer households. In the end, it is difficult to place a value on a human life; even were it simple to do so, it would still be difficult to place a value on the social gains associated with general improvements in health, in terms of creativity, entrepreneurial capacity and other X-efficiency type measures (Leibenstein, 1957) or on the avoidance of the costs to the families and communities of those individuals who suffer poor health.

On the cost side, researchers face great difficulties in obtaining good data, particularly in many develt,ing countries where information systems are weak and public provision of much care through centralized systems lacking budget accountability has destroyed incentives to measure costs. The best work on costs has been done in connection with efforts to analyze the costeffectiveness (i.e. the least-cost way to achieve a certain goal) of different programs that fall under zhe rubric of primary health care.<sup>36</sup> Data on hospital costs and on costs of overall systems are even rarer.<sup>37</sup> The potential for breaking new ground in cost-benefit analysis is huge in the long run, but poor in the short run. In the current environment, academic researchers doing applied research inevitably are drawn to areas of 35 See Barnum (1987) for application of weights to epidemiological data from Ghana.

36 See Abel-Smith (1989) for a discussion of cost-effectiveness. He cites work by Mills (1985).

37 But see Barnum and Kutzin, 1990.
Policy Research Working Paper 8458

# **Abstract**

This article estimates how households were affected by the mostly non-violent capture of Yemen’s capital in 2014 using a survey conducted as the capital was captured. Although socioeconomically advantaged households were initially better able to cope with the shock than other households, the capture resulted in a robust decline in expenditure for the entire population within three months. Struggling households turned to a number of coping strategies to meet their basic needs- they increasingly made purchases on credit, increased their reliance on self-employment to deal with a decline in the economic climate, and reduced both the quantity and quality of foods consumed. Furthermore, women were affected by the capture more so than men. These results demonstrate that the capture of territory without wide-spread violence can result in a nearly immediate decline in standards of living and further illustrate the manners in which households were able to cope with the shock.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/research. The author may be contacted at standon3@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
focuses on explanations in terms of labor market imperfections by testing the relevance of family labor and separability of labor supply and demand. Section five concludes with implications for policy and research.

# **2. Motivation and relevance**

This section draws on conceptual and empirical literature to document strong empirical evidence in support of a negative farm size–productivity relationship and to discuss potential channels underpinning it. If an inverse relationship between productivity and farm size can be attributed to labor market imperfections, one would expect the relationship to weaken as better labor market functioning and increased demand for non-agricultural labor lead to choice of more capital-intensive modalities of agricultural production. Longitudinal data spanning a long enough period should uncover such patterns.

# **2.1 The farm size-productivity relationship**

A negative relation between farm size and output per unit of land in un-mechanized agriculture has been confirmed many times in the literature (Eastwood, Lipton, and Newell2010). It was first noted in Russia (Chayanov 1926), in Indian farm management studies (Bardhan 1973; Srinivasan 1972), and in a host of other countries using simple ordinary least square (OLS) regressions (Berry and Cline 1979) that likely failed to fully control for land quality (Bhalla and Roy 1988). Yet, even when these shortcomings are addressed, the inverse relationship has remained surprisingly robust. Correcting for measurement errors may weaken the relationship, but it does not invalidate it (Lamb 2003). In fact, use of panel estimation techniques (Assuncao and Braido 2007) or careful controls for soil quality affected the magnitude but not the existence of the relationship (Barrett, Bellemare, and Hou 2010). Although biased estimates of area cultivated by farmers may reinforce the inverse relationship, it persists even when objective, GPS-based measures of area cultivated are used (Calogero, Savastano, and Zezza 2011). Use of profits rather than output per hectare in India has the same result—namely, the relation weakens but does not disappear (Rosenzweig and Binswanger 1993). The relationship is also not limited to South Asia; a recent study finds that in Mexico, large farms have a lower value of output per hectare than small farms and produce further from the efficiency frontier (Kagin, Taylor, and Yunez-Naude 2016).

Conceptually, imperfections in land, labor, and credit or insurance markets affect the relationship between farm size and productivity. If, as suggested by most empirical studies, the agricultural production function is characterized by constant returns to scale (Deininger, Nizalov, and Singh 2013), imperfections in two markets—most often those for land and labor—will generate a systematic relationship between size of cultivated area and output (Feder 1985). Family labor has long been shown to resolve informational asymmetries in rural labor markets (Bharadwaj 2015). If hired labor is used, owner-operators, who are residual claimants to profit, will be more likely to exert effort than will wage workers, who, in light of the 4