rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10522

# **Abstract**

This paper provides a framework to study how different allocation systems of public procurement contracts affect firm dynamics and long-run macroeconomic outcomes. It builds a novel panel dataset for Spain that merges public procurement data, credit register loan data, and quasi-census firm-level data. The paper provides evidence consistent with the hypothesis that procurement contracts act as collateral for firms and help them grow out of their financial constraints. The paper then builds a model of firm dynamics with asset- and earnings-based borrowing constraints and a government that buys goods and services from private sector firms, and uses it to quantify the long-run macroeconomic consequences of alternative procurement allocation systems. The findings show that policies which promote the participation of small firms have sizeable macroeconomic effects, but the net impact on aggregate output is ambiguous. While these policies help small firms grow and overcome financial constraints, which increases output in the long run, these policies also increase the cost of government purchases and reduce saving incentives for large firms, decreasing the effective provision of public goods and output in the private sector, respectively. The relative importance of these forces depends on how the policy is implemented and the type and strength of financial frictions.

This paper is a product of the Development Research Group, Development Economics. 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/prwp. The authors may be contacted at mgarciasantana@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
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# **6 Targeting: The way forward**

Because direct transfers to households are increasingly used as emergency or long-term development instruments, the selection of beneficiary households – targeting – is also increasingly discussed in policy and academic settings. Recurrent shocks related to climate change, forced displacement and the Covid-19 pandemic have highlighted the need to deliver benefits rapidly, cost-efficiently and at scale with limited information and infrastructure to identify vulnerable households. This paper aimed to make conceptual and empirical contributions to this discussion by focusing on the Sahel, one of the poorest and most shockprone regions globally. Consistent with previous reviews, our study emphasizes how difficult it is to compare targeting performance across countries. However, by conducting a harmonized primary analysis across nine data sets from six countries, we are able to contribute to the heated policy debate on targeting, and to make some general remarks and recommendations regarding the design and study of targeting mechanisms.

By relying on multiple performance indicators, we show that the definition of the well-being metric, the share of beneficiaries selected, and the indices used to evaluate targeting play a decisive role in targeting performance. Our analysis suggests that a much greater importance needs to be paid, explicitly, to defining these parameters in a clear and policy-relevant manner.

Even though PMT is more successful at selecting households with the lowest per capita consumption based on a binary classification of households as “poor” and “non-poor,” PMT does not generate results that differ greatly from budget-neutral alternatives such as CBT, or random or universal delivery of benefits, when considering distribution-sensitive measures of performance. The high levels of poverty and relatively low levels of inequality, combined with the geographical targeting applied before household-level targeting, may be driving these results. Also, our analysis shows that while simulations of PMT schemes are widely used by practitioners and academics, ignoring geographical targeting and arbitrarily defining selection thresholds could potentially lead to large biases in the simulated targeting performance.

Our analyses also allow some conclusions on CBT, which are often ignored by studies based on simulated targeting mechanisms using existing household data sets, since CBT is not as easily simulated as PMT selection. We show that CBT selects households that are systematically different from PMT, and that the performance of CBT is lower than PMT’s when assessed based on performance measures related to percapita consumption.

Per-capita consumption is only one of the potential welfare metrics used to define targeting objectives. Reaching food-insecure households is actually a key objective of humanitarian interventions through which hundreds of millions of dollars are spent yearly in the Sahel. Nonetheless, little is known about ways in 23
_Viet Nam._ The 1998 Viet Nam LSMS survey included school and health facility surveys. The health facility survey was limited in scope and detail relative to both the Jamaica and Cote d'Ivoire surveys. It collected information on distance to different enumeration areas, staffing and areas of operation, number of beds, service range, equipment and drug availability, and cost of services and drugs.

## **_Research and Publications_**

- Lavy, V., J. Strauss, D. Thomas, and P. d. Vreyer. 1996. "Quality of Health Care, Survival and Health Outcomes in Ghana." _Journal of Health Economics,_ 15:3, pp. 333-57.

- Peabody, J., P. Gertler, and A. Liebowitz. 1998. "The policy implications of better structure and process on birth outcomes." _Health Policy,_ 43, pp. 1-13.

- Thomas, D., V. Lavy, and J. Strauss. 1996. "Public policy and anthropometric outcomes in C6te d'Ivoire." _Journal of Public Economics,_ 61, pp. 139-54.

- Gertler, P. and J. Litvack. Forthcoming. "Healthcare During Transition," in _Household Welfare and Viet Nam 's Transition to a Market Economy._ Dollar, Glewwe and Litvack eds. Washington, D. C.: The World Bank.

# _Situation Analysis_

Situation analysis (SA) was introduced as a tool for program evaluation by the Population Council in 1989. In general, the SA can be described as "a comprehensive and standardized approach for systematically assessing both the readiness of family planning/reproductive health programs to delivery services and the quality of care received by clients." (Miller and others 1998). The development of the tool was stimulated by indications from DHS surveys that service delivery weaknesses were important in explaining low contraceptive prevalence rates in many countries.

The first SA was carried out in Kenya. Subsequently, SAs have been carried out extensively, including in 11 African countries. The SAs are based on a representative sample of service delivery units within a geographic area of interest. The approach includes structured interviews with managers and facility staff; inventory review and direct observation of clinic facilities and the availability of equipment and consumables; review of service statistics for 12 months; nonparticipant direct observation of family planning client-provider interaction; and interviews with clients of both family planning and MCH services. The general approach has been modified in some cases to address a broader set of concerns.

Situation analyses have provided clear evidence of the poor state of service delivery in many countries. Documented problems include poor contraceptive stocks, lack of basic . infrastructure and equipment, poor adherence to diagnostic and treatment protocols. Through the implementation of follow-up studies, it has been possible to measure changes over time, and to assess the impact of policies aimed at improving service delivery. Data have been used, _inter alia,_ in the design of family planning programs, training initiatives, and in the formulation of sector strategies.

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### AN ANALYSIS OF **THE** AGGREGATE **LONG-TERM** BEHAVIOR OF

### COMMODITY PRICES

# Pier Giorgio Ardeni and Brian Wright University of California, Berkeley

## **1.** INTRODUCTION

The statistical debate on the net barter terms of trade between primary commodities and manufactures has received a great deal of attention in the recent years. Starting from the Prebisch-Singer hypothesis, the empirical research has focused on the long term movements in commodity prices as well as their cyclical variations. Many studies (Spraos (1980), Sapsford (1985), Thirlwall and Bergevin (1985), Grilli and Yang (1988)) have concluded that there has actually been a deterioration in the net barter terms of trade, although to a lesser extent than predicted by Prebisch (1950) and Singer (1950). Works of Cuddington and Urz62a (1987, 1989), on the other hand, have given no support to the deterioration hypothesis, by emphasizing the cyclical variation of secular movements in commodity prices around a steady level.

Notwithstanding this recent flourishing of empirical evidence on long term movements in commodity prices, the debate is still unsettled, as the statistical methodologies that have been used have several shortcomings, some of which make the results unreliable. Spraos (1980) fitted a simple log-linear time trend variable to the data in a regression estimated via OLS. Sapsford (1985), interpreted the results of Spraos (who found no negative trend in the postwar period) in the light of a possible "omitted" structural break in 1950. By introducing a dummy variable and correcting for serial correlation through the Cochrane-Orcutt technique, Sapsford has been able to recover a negative trend in the net barter terms of trade on post-war data, too. Thirlwall and Bergevin (1985), using quarterly data for disaggregated commodity price 1
Infrastructure lock-ins in the transport sector arise not only from the investment decisions for that sector, but also from regulatory and investment decisions on zoning and real estate development. Under the situation where automobile travel is under-priced relative to rail, neglecting externality costs, there is a danger that high-density development may preclude the future expansion of mass transit systems in ways which better account for environmental and congestion costs. This may particularly affect the feasibility of future surface rail and BRT systems, which require certain space and distance from residential concentrations. One approach to avoid such lock-ins is to leave vacant or thinly developed corridors for future transport infrastructure development (e.g., spaces left on both sides of the roads or spaces left in the middle of the two way traffic). This creates an “option value” by leaving open the possibility of constructing a transit corridor in the future, rather than permanently closing off that option. Proper land-use and zoning policies are required to address these lock-in issues (e.g., zoning laws that prohibit or limit construction and development in spaces that entail these options). Obviously, such limitations imply an economic loss in the present and near future years (the loss being the economic value of the activities and services that would emerge in the restricted zones). But that loss needs to be compared to the social gain, including the value of future pollution avoidance, should the option be exercised.

Large upfront capital costs are a key constraint against capital intensive transportation projects, particularly below-ground metros and heavy urban railways. For example, according to World Bank (2006) estimates, the capital costs involved in constructing the Line B metro in Mexico City amounted to $42.8 million per kilometer of capacity (in US year 2002 $).<sup>19</sup> In contrast, according to the same study, capital costs associated with constructing the Mexico City Bus Rapid Transit (BRT) system were a mere 4 percent of this figure, or $1.71 million per added kilometer. Thus, BRT systems are becoming more popular than metro expansions. In addition, the external costs of bus systems, in terms of congestion, pollution, and accidents per passenger mile, while not zero, are still modest relative to comparable costs for automobiles (Table 2). The key conclusion here is that costs of public transport systems that do not appear to be economic per se may in fact be economic, or at least closer to economic (depending on the mode), once relative externality values are taken into account.

> 19 The B line was opened in year 2000. 25 percent of the line is tunnel, 20 percent is elevated, and 55 percent is surface level.

37
25 Table 7: Impact of removing cotton subsidies and tariffs<sup>a</sup> on cotton output, exports and value added, and on national economic welfare (percent and 2001 US$m)

||Change in<br>cotton<br>output<br>volume<br>(%)|Change<br>in cotton<br>value<br>added<br>(%)|Change in<br>value of<br>cotton<br>exports<br>(%)|Welfare<br>resource<br>re-<br>allocation|changes ($m<br>effects of:<br>terms of<br>trade<br>change|) due to<br>TOTAL|
|---|---|---|---|---|---|---|
|**All high-income countries**|**-20.4**|**-15.4**|**-18.2**|**187**|**275**|**462**|
|Australia|25.0|22.2|38.1|12|125|137|
|United States|-24.6|-17.9|-29.0|-15|443|428|
|EU25|-54.0|-53.3|-48.8|124|-109|15|
|Japan|0.7|1.5|61.9|25|-49|-24|
|Korea-Taiwan|11.9|6.9|33.6|21|-84|-63|
|Other High income|**-36.1**|**-36.6**|**-41.7**|**190**|**-293**|**-103**|
|**All developing countries**|**5.7**|**4.3**|**46.3**|**96**|**-275**|**-179**|
|E. Europe and Central Asia|7.0|3.3|35.9|21|-36|-15|
|China|2.0|1.5|75.7|5|45|50|
|Other East Asia|8.7|5.1|65.3|39|-82|-33|
|India|-0.6|-0.4|31.1|-5|-79|-84|
|Other South Asia|6.0|3.5|59.8|9|-20|-11|
|Middle East & North Africa|6.2|6.1|37.4|-7|26|19|
|South Africa|19.4|20.6|46.5|2|-2|0|
|Other Sub-Saharan Africa|32.1|30.6|55.0|32|115|147|
|Argentina|13.6|10.7|66.1|1|6|7|
|Brazil|9.8|10.3|57.6|1|12|13|
|Mexico|13.0|10.5|42.3|11|-136|-125|
|Other Latin American & Car.|9.4|7.3|44.7|-13|-34|-47|
|**World**|**-0.8**|**-1.8**|**7.9**|**283**|**0**|**283**|

a Removal of those distortions left after the eventual phase-out of the quotas under the Multifibre Agreement at the end of 2004.

Source: Authors’ GTAP model simulation results
the estimation sample.

# **3 Methodology for identifying unexpected revenue shocks**

In this section, we propose a new methodology to identify the unexpected component of demand shocks at the firm-level, which exploits forecast errors in the GDP growth of export markets. In each destination, the unexpected component of demand shocks is measured as the difference between the GDP growth actually observed and the current-year growth forecast for that country published in the Spring edition of the World Economic Outlook of the International Monetary Fund. Specifically, the forecast error for a destination-year is defined as: where _FEdt_ denotes the forecast error for destination _d_ in year _t_ , _Gdt_ denotes the GDP growth rate of destination _d_ in year _t_ and _FGdt_ denotes the current-year growth forecast for country _d_ in year _t_ . We then aggregate these destination-year forecast errors at the firm-year level, weighting by the share of destinations in total sales of firm _i_ in the initial year: where _sd_ 0 is the share of exports to destination _d_ in total sales of firm _i_ in 2006 (the first year of our data) and _D_ is the set of destinations for which data on growth forecasts are available. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets. Using the same weights, we also aggregate the forecast growth (WFG) at the firm-year level.

By its construction, the weighted forecast error constitutes an unexpected shock in export demand and is therefore plausibly exogenous to the firm. However, one potential concern is that different firms might select themselves into different export environments in a way that make them differentially exposed to forecast errors. For example, growth-seeking firms might choose to operate more in export markets where the expected growth is higher but also more uncertain, thus exposing themselves to higher forecast errors. In order to check for any indications of potential selection effects of this kind, we present a series of scatter plots showing the correlation between weighted forecast errors and each of a set of baseline firm characteristics (sales, exports, export share, employment, investments in tangible and intangible assets, value added, value added per worker and average worker pay) based on observations in 2006 (or in the 7
# **Table A2: Contribution of Each Explanatory Factor to Wage Differentials, Using Years of Potential Schooling 2003-2007**

||**#1**<br>**(Both sexes) **|**#2**<br> <sup>**(Both sexes,**</sup><br>**2003)**|**#3**<br>**(Both sexes,**<br>**2007)**|**#4**<br>**(All men)**|**#5**<br>**(All women)**|
|---|---|---|---|---|---|
|**Residual**|**48.3**|**46.4**|**50.0**|**49.4**|**44.7**|
|**Personal Attributes**|**26.1**|**24.2**|**26.0**|**21.7**|**30.8**|
|_Gender_|_1.3_|_1.1_|_1.4_|_--_|_--_|
|_Experience_|_1.2_|_1.0_|_1.1_|_2.2_|_0.0_|
|_Years of completed schooling_|_23.6_|_22.1_|_23.5_|_19.5_|_30.8_|
|**Sector**|**4.2**|**5.8**|**3.6**|**9.9**|**0.8**|
|**Occupation**|**15.7**|**18.7**|**13.6**|**11.7**|**19.1**|
|**Region**|**5.6**|**4.8**|**6.7**|**7.1**|**4.3**|
|**Year**|**0.2**|||**0.1**|**0.3**|

17
Peria (2007). Where any discrepancies were found, respondents were contacted by telephone or email for further clarification.

Scatter plots of the comparison between the Financial Access Survey commercial bank deposit account penetration measure and measures from World Bank (2008) and Beck, Demirgüç-Kunt, and Martinez-Peria (2007) can be seen in Figure 2. In the two comparisons, differences come from changes over time and from the fact that the two previous efforts requested data on all deposit money banks which often included cooperatives and credit unions in the total, as is the case of Spain, Italy, and Austria.

# **_2.3 Advantages and Limitations of the Data_**

Our approach has three important limitations which impact the comparability of our measures across countries and the uses to which they can be put: <u>Differential reporting: Not all countries report data for the same types of institutions which has</u> implications for comparing data across countries. Specifically, data from non-banks seemed to suffer from greater underreporting bias than data from commercial banks. This bias expresses itself in two ways. First, and most common, many countries did not report anything for certain categories of institution (cooperatives for instance) though that type of institution was clearly operating within their jurisdiction. Second, in just a few cases, countries reported incomplete data for a given category (for instance, if the data reported comprised urban cooperatives but not rural cooperatives). Desk research showed this issue was not pervasive and was of small magnitude in most cases. For example, data from the Microfinance Exchange (MIX) showed that the number of loans and deposits from unregulated NGO-MFIs (which were unlikely to be represented in data sent to us by the central bank, since these institutions were not regulated) were a small fraction of those from regulated MFIs in most countries, which were again a small fraction of total loans. Having data disaggregated into multiple categories and data on which specific institutions are included in each category also helped mitigate these problems.

10
**Figure 3: Contributions of the Petroleum Figure 4: Petroleum Rents (2000-2008) Sector (2000-2008)**

<!-- Start of picture text --> 100<br>90 80,000 50<br>80 70,000 45<br>40<br>70 60,000<br>35<br>60 50,000 30<br>50<br>40,000 25<br>40<br>30 30,000 20<br>15<br>20 20,000<br>10<br>10 10,000 5<br>- 0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Petroleum Revenue as % of Total Public Revenue<br>Petroleum Rents Rents as % GNI<br>Petroleum Exports as % of Total Exports<br>Percentage<br>USD Millions Percentage of GNI<br><!-- End of picture text --> <!-- Start of picture text --> Sources: IMF 2009.<br><!-- End of picture text --> _Source:_ Wealth of Nations 2010.

# **Political Economy Context**

11. The Federal Republic of Nigeria (FRN) has an area of 923.800 sq. km. and 148 million inhabitants. There are 389 different ethnic groups, but the largest are Hausa-Fulani, Igbo, Yoruba, and Kanuri. The Nigerian economy is heavily dominated by oil and gas production and agriculture is the second economic activity representing 30 percent of the national GDP and employs twothirds of economically active population, though the sector has deteriorated sharply since the 1970s due to inadequate governmental policies and the shift to an oil economy.

12. Nigeria was a British colony until 1960 when it became independent under a federal constitution. The country has experienced several military regimes (1966–79 and 1984–99) and a 30month civil war (1967–70). In 1999, Nigeria returned to a democratic system and has since held three presidential and parliamentary elections, resulting in the first-ever handover from one civilian president to another in 2007.

13. In terms of political organization, Nigeria has a three-tier government. The country has a Federal Capital Territory, Abuja, and 36 states, which are subdivided into 774 local governments. The National Assembly is bicameral. For most of its history Nigeria has had a local government structure parallel to the central government. Nonetheless, the number of tiers and local governments, as well as the distribution of public revenue and responsibilities among these, has changed substantively throughout its history (Adedokun 2004). During the early colonial period, local-self government was institutionalized by the Native Administration Ordinance No. 4 of 1916. This led to the emergence of different practices of indirect rule across the three traditional regions - Eastern, Western, and Northern. In the 1950s, the Eastern and Western regions increased the number of elected councils responsible for local government, while the Northern region retained a system of local government based on the traditional authority of Emirs. These systems did not change significantly after independence.

11