rafmacalaba/data-use-mentions-extended huggingface dataset:
workers. Balboni et al. (2023) demonstrate that manufacturing firms responded to flooding in Pakistan by diversifying towards suppliers located in less flood prone regions. Similarly, floodaffected firms in Tanzania adjusted their supply networks, and also reallocated resources toward inventory and backup capacity (Rentschler et al. 2021). Firms also reallocate their capital structure: using cross-country firm-level data. Javadi et al. (2023) show that climate change exposure is associated with an increase in firm-level cash holdings as a form of precautionary savings.

On the outputs side, firms tend to reallocate their sales to shield profits from climate risk. In the global automobile industry, Castro-Vincenzi (2023) found that manufacturers reallocated production across plants within a firm in response to flooding, leading to firm-level efficiency losses. Similar effects obtain across firms: Chinese manufacturers reallocated away from domestic buyers following typhoons (Elliott et al. 2019), while in Japan, transactions shifted away from firms in earthquake-affected regions (Carvalho et al. 2020).

Reallocation is costly, because such changes represent a deviation from what were optimal allocations before climate risk: holding cash has an opportunity cost, identifying new suppliers and buyers requires investment, and reallocation of output implies efficiency loss. Despite these challenges, evidence suggests that firms are remarkably resilient; for example, the negative effects of natural disasters appear short-lived (Elliott et al. 2019; Pelli et al. 2023) However, whether preemptive adaptation directly increases such resilience has not yet been studied.

Firm relocation is perhaps the costliest form of private adaptation. Both Indaco et al. (2021) and Balboni et al. (2023) find evidence for firm relocation following flooding events in the United States and Pakistan. Such relocations are an important form of adaptation and can reduce the costs of future climate events but may increase spatial inequality (Cruz and Rossi-Hansberg 2023).

Sectoral reallocation is an essential economy-wide adaptation, because climate change creates new patterns of comparative advantage both within and across countries. Because productivity in services and manufacturing are less
to estimate windspeeds at the province level. This, in turn, implies that all households in each of the 81 provinces were assigned the same wind speed irrespective of a household’s distance from the typhoon’s path.

Figure 2 below presents the total number of typhoons (defined as windspeeds over 119mph) by barangay between 2000-2015, whereas Figure 4, presents the total number of barangays experiencing windspeeds over 119mph by year. Clearly, moving from the southern tip of the Philippines towards the north, more and more barangays experience windspeeds over 119 mph, and many barangays in the northeastern tip of the archipelago experience such extreme windspeeds many times (up to 21 times) between 2000 and 2015 (see figure 2). Also, the number of barangays experiencing windspeeds over 119mph varies significantly by year (see figure 3).

14 The CPI by province, year, and month was downloaded from the PSA OpenStat website: <u>http://openstat.psa.gov.ph/PXWeb/pxweb/en/DB/DB__2M/?tablelist=true&rxid=bdf9d8da-96f1-4100-ae09-18cb3eaeb313.</u> 15 To check the sensitivity of the estimates to a better accounting of the spatial cost of living differences, we have also tried an alternative approach that consists of first regressing the log of nominal PCE on a complete set of interactions across three sets of binary variables identifying the semester of interview of the household within a calendar year (2 in total), the year of interview (5 in total) and the region of residence (17 in total). This amounts to a total of 170 different dummy variables. The residual from such a regression is the “adjusted” lnPCE at any given point in time and region, that is expenditures comparable to the nominal expenditures in the reference time period (2003 semester 1) and region (region 1, which is the Ilocos region). The results were practically the same as to those reported in tables 1 and 2.

16 Annex 1 provides an
(modern facilities and less crowded classrooms), but not better education, as teachers in the 75% of private centers do not attain the average standards in formation and professionalism (UNDP, 2010).

**Figure 7: Children attending private schools, by quintile**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>2007<br>0.3 2008<br>2009<br>0.2<br>2010<br>0.1<br>0<br>Q1 Q2 Q3 Q4 Q5<br>Source: Bank staff calculations, ENFT survey<br>% enrolled in private schools<br><!-- End of picture text --> Is the population opting out for private education services? It seems that with the economic slowdown in 2008-09 the share of the households sending their children to private schools noticeably declined respect to 2007 across income levels, except for quintile 1. Interestingly, nowadays the share of children in quintile 2 attending to private schools is lower than that of the lowest quintile. A possible explanation is that a portion of the poorest is settled in areas where public schools are overcrowded or access is limited. The proportion of children going to private centers in quintile 5 is significantly higher than among the rest of the population, suggesting the idea of education for elites that can afford it. However, differences in opting out behavior between quintiles 1 and 4 are not pronounced, and when conducting logit regression, contrary to the electricity case, all the quintile dummies are significant. As it will be discussed, this suggests a different pattern in opting out behavior in the case of education, where differences across socioeconomic classes are less pronounced (except for the richest), than in the cases of electricity or health services.

In sum, again, the education sector in the DR presents faulty public service delivery, which originates a private offer that
.1]|[-1.1, 0.0]|
|Sustainability gap, initial|1.7|1.7|1.7|
|(Percent of GDP)|[0.2, 4.4]|[0.4, 3.6]|[0.1, 4.8]|
|Sustainability gap, changes|-1.1|-0.9|-1.2|
|(Percentage points of GDP)|[-2.1, -0.4]|[-1.6, -0.4]|[-2.5, -0.4]|
|Interest rate-growth differential, initial|-2.5|-1.3|-4.2***|
|(Percentage points)|[-6.6, 0.1]|[-3.5, 0.9]|[-8.9, -0.3]|
|Interest rate-growth differential, changes|0.9|0.4|1.4***|
|(Percentage points)|[0.2, 2.2]|[0.0, 1.0]|[0.3, 3.6]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across all government debt booms in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. ** and *** denote that medians are statistically significantly different between advanced economies and EMDEs at the 5 percent and 1 percent levels, respectively.

27
valorem and specific tariff are indicated, and the actual tariff applied by Russian customs is the maximum of the two. To know the actual tariff, where specific tariffs are specified, we must calculate their ad valorem equivalents. This is a non-trivial task, and some previous efforts have simply ignored the specific tariffs, resulting in an underestimate of the actual tariff rates;<sup>2</sup> and (2) due to the lack of ten digit tariff line data on the value and quantity of imports, most prior calculations were based on more aggregate and less precise data.<sup>3</sup> The first study to accurately assess the structure of the Russian tariff structure, using trade and tariff data at the ten digit tariff line level was Shepotylo and Tarr (2008). They provided calculations for the years 2001-2005 and showed a trend of increasing protection for light industry and the food industry during this period. Jandosov and Sabyrova (2011) and Shepotylo (2011) both accurately showed that the average applied tariff of Kazakhstan would approximately double as a result of the imposition of the common external tariff of the Russia-Belarus-Kazakhstan Customs Union. The initial Customs Union tariff in 2011 was the Russian tariff, and Kazakhstan applied the Customs Union tariff except for about 400 tariff line exemptions for Kazakhstan between July 1, 2010 and July 1, 2011.<sup>4</sup> In widely cited estimates, the WTO (2011) assessed that the un-weighted average bound tariff of the Russian Federation would be 7.8 percent after all commitments are implemented. The World Trade Organization, however, based its calculations on 8-digit trade data, which introduces imprecision.<sup>5</sup> Our estimate of the un-weighted bound tariff of the Russian Federation after all commitments are implemented is 8.6 percent. But the bound tariffs are only maximum tariffs, as Russia

> 2 See, for example, ATF Bank (2011).

> 3 The previous data available were the annual hard copy
pesos|million<br>1977<br>US S|<br>PPP<br>Ps./US$|
|Match using Census Data<br>only (without model<br>differentiation)<br>Passenger<br>cars<br>12,223.3|1,338.3|9.13|436,562.1|47,796.3|9.13|
|Match using Augmented||||||
|Information||||||
|4cylinder<br>cars<br>6,038.0<br> <br>|611.2<br>|9.88|35,407.7<br>|3,584.2|9.88|
|6 & 8cylinder<br>cars6,185.3<br>**------------------------**|561.0<br>**-------------------------**|11.02<br>**----**|487,436.0|44,212.1|11.02|
|Passenger<br>cars<br>12,223.3|**..**<br>1,172.2|10.43|522,843.7|47,796.3|10.94|

Source: see text.
# **PROMOTING THE RURAL FARM AND NONFARM BUSINESSES**

**Evidence from the Yemen Rural Investment Climate**

**Naotaka Sawada**<sup>**1**</sup> **and Jian Zhang**<sup>**2**</sup>

# **Acknowledgments**

We would like to thank the Bank Netherlands Partnership Program and Japan Policy and Human Resource Development for grants funding. We thank Julie G. Viloria-Williams and Pierre Rondot at the stage of rural investment climate surveys in Yemen. We also would like to thank John Lamb, Grahame Dixie, and Mark Cackler for guidance and support given. We also thank Stephen G. Mink, Kathleen Beegle, and Josef Loening for their comments in the concept note stage.

_Key Words: Rural Entrepreneurs; Farm Enterprise; Nonfarm Enterprise; Rural Investment Climate; Yemen_ _JEL Classification: O17; M13; M21; L53 Sector Board: Agriculture and Rural Development (ARD)_

> 1 Corresponding author : Naotaka Sawada, Email: nsawada@worldbank.org,  World Bank, 1818 H Street NW, Washington, DC 20433, USA

> 2Jian Zhang, Email: jzhang7@worldbank.org, World Bank, 1818 H  Street NW, Washington, DC 20433, USA
World Bank 2021). Years of sustained investment in education and health care systems as well as physical infrastructure will likely be needed to undo this damage.

# 3.1.2. Stabilizing short-term macroeconomic fluctuations

Temporary debt accumulation also plays an important role in stabilizing short-term macroeconomic fluctuations. During recessions, borrowing for government spending or tax cuts can provide the necessary fiscal stimulus to support activity (Yared 2019). However, the effectiveness of such stimulus policies depends on fiscal multipliers—the output effects of additional government spending or tax cuts (Huidrom et al. 2020; Ramey 2019).

A large literature estimates fiscal multipliers and documents that multipliers vary widely, depending on circumstances. They range from a 1.1-dollar output decline to a 3.8-dollar output increase for every dollar of additional government spending or reduced revenues. The

> 5 For example, using a panel of OECD countries, Fournier (2016) shows that a 1-percentage-point increase in the share of public investment in expenditure is associated with an increase in long-term output by 5 percent. The study also documents that public investment is more growth-enhancing in areas with large externalities, such as health and research and development. To the extent that debt-financed investment spending stems the slowdown in potential growth, it helps preserve the revenues required to service this debt (Fatas et al. 2019).

> 6 Infrastructure investment can have particularly large growth benefits if it connects isolated communities with input and output markets, allows companies to realize economies of scale by increasing market size, and increases competitive pressures (Calderón and Servén 2010; Égert, Kozluk, and Sutherland 2009).

7
- Menon, N. and van der Meulen Rodgers, Y. (2015). War and Women's Work: Evidence from the Conict in Nepal. Journal of Conict Resolution, 59(1):5173.

- Miguel, E. and Roland, G. (2011). The long-run impact of bombing vietnam. Journal of development Economics, 96(1):115.

- Ministry of Health (2017). Nepal Demographic and Health Survey 2016. Technical report, Ministry of Health, Nepal, Kathmandu, Nepal.

- Murshed, S. M. and Gates, S. (2005). SpatialHorizontal Inequality and the Maoist Insurgency in Nepal. Review of Development Economics, 9(1):121134.

- Nepal, A., Halla, M., and Stillman, S. (2018). Violent conict and the child quantity-quality tradeo. Discussion Paper Series 11690, IZA Institute of Labor Economics.

- Nepal, L. J. (2004). destruction before construction": civil war grips western nepal, making humanitarian aid dicult, but essential.

- Nepal, M., Bohara, A. K., and Gawande, K. (2011). More Inequality, More Killings: The Maoist Insurgency in Nepal. American Journal of Political Science, 55(4):886906.

- OHCHR (2012). Nepal Conict Report. Technical report, United Nations Oce of the High Commissioner for Human Rights, Geneva. Available at https://www.ohchr.org/Documents/ Countries/NP/OHCHR_Nepal_Conflict_Report2012.pdf.

- Onesto, L. (2005). Dispatches From the People's War in Nepal. Pluto Press.

- OPHI (2013). Country Brieng: Nepal Multidimensional Poverty Index (MPI) At a Glance. Technical report, Oxford Poverty and Human Development Initiative, University of Oxford, Manseld Road,
Given the prospect of faster growth in the future than during the period 1982-1999 and the enabling effect of all-weather roads, I hazard the guess that _gb_ will average 2 percent a year over the next 30 years.

With this preliminary, it is instructive to return to the actual examples in Section 4.1. The estimated value of _B_ 1<sup>+(</sup><sup>_ρ_= 0</sup><sup>_._10</sup><sup>_,_0)fortheaffluentvillageinHimachalPradeshis</sup> Rs 3.28 million a year. A conservative estimate of the aggregate commercial benefits is Rs 2.05 million, leaving Rs.1.23 million to be found in other forms. As noted earlier, the villagers had rather good access to education and health facilities even before the road was completed, so help from those quarters may well be limited. Data of the kind provided by the Orissa Survey are lacking, moreover, which adds to the uncertainties in the calculations that follow. There are about 2,000 persons in some 350 households, with a school-age population of, say, 500. In the light of the findings for Orissa, the annual savings in travel-time and reductions in days lost due to bad weather are unlikely to exceed 5 days each per child. Suppose the daily wage rate for juveniles in counting. Since the discount rate also affects the present value of costs, it is essential to modify _ρ_ ; but the per capita benefits may not be scaled up. In what follows, we do neither.

19
# **Introduction**

Most of the aid effectiveness literature has concentrated on explaining the impact of aid on economic growth with unclear and ambiguous results. Given the variety of ex-ante objectives for aid, it is to be expected that ex-post evaluations of aid efficacy lead to mixed results when conducted as if growth were the only objective. This is why in this paper we take a new approach and evaluate aid’s impact on its main objective. We analyze firms’ perceptions of the areas which aid was set out to improve. Using firm perception data not only allows us to analyze the impact of aid on its direct objective but it also allows us to identify the areas firms categorize as obstacles for their growth and whether aid is addressing these obstacles. Firms are the drivers of innovation, job creation, and growth in an economy. Firms in their everyday business transactions face obstacles such as corruption, inadequate labor force, and lack of infrastructure and therefore are the best agents to judge these and other areas. Being able to concentrate on the problems firms are facing in developing countries can prove to be extremely important in using aid more effectively and having a greater impact on the development of these countries.

We classify aid flows from the OECD CRS database into seven areas—access to finance and land, business environment, infrastructure, labor force, rule of law, economic and political stability, and trade—and match them to firms’ perceptions of these seven areas from the World Bank Enterprise Survey Database. By doing this we attempt to answer two main questions related to aid effectiveness:

1. Does aid target those areas which firms have identified as obstacles for their growth?

2. Are firm perceptions better in those areas that have received more aid in the past?

2
packaging securities based on mortgages, which resulted in these firms also holding large inventories of these hard-to-value securities. In the cases of Bear Stearns and Lehman Brothers, the results were disastrous.

High leverage was widespread in the U.S. leading up to the GFC, from households to firms to financial institutions. Between 2000 and 2008, household debt approximately doubled from $7.2 trillion to $14.1 trillion, while debt in the financial sector more than doubled from $8.7 trillion to $18 trillion according to Federal Reserve data.<sup>2</sup> Some of the investment banks were leveraged on the order of 30:1, so a three percent portfolio loss could potentially destroy their net worth.

A key event that triggered the GFC was the reduction in housing prices that started in 2006, which created losses on MBS and related securities, as well as questions about how to value them. By August 2007, losses of confidence and freezes impaired the operations of these and other financial markets. Issues in the interbank lending market and syndicated loan market created liquidity problems for some banks that could no longer easily borrow or sell portions of the loans they originated. These problems worsened considerably after a number of financial institutions failed or needed rescues, including Bear Stearns, Indy Mac, Countrywide, and finally Lehman Brothers, which declared bankruptcy in September 2008. The public became well aware of the problems associated with mortgages and the securities based on them, and public confidence eroded.

The commercial banks suffered liquidity problems from the market freezes and capital losses on their mortgages and securities based on mortgages, resulting in significantly reduced credit supplies. These reduced credit supplies were key factors in initiating the Great Recession.

> 2 <u>https://www.wsj.com/articles/from-dutch-tulips-to-internet-stocks-bubbles-often-burst11620379809?st=6lbwxzg8iuywthu&reflink=article_email_share</u>

9
# 3.3 The labor market

As much as 90 percent of the labor force in Pakistan operates in the informal sector (defined as individuals without formal employment contracts and without access to social security).

Firms are modeled as making the choice to engage in a formal or an informal contract after weighing the advantages and disadvantages of each employment type. Major benefits of the formal sector are access to financial services and government services. A major benefit of being in the informal sector is that firms can avoid direct taxation by the government.

This firm’s decision is captured in heterogeneous firm models as presented in Ulyssea (2018), where firms differ by productivity. Very productive firms tend to be larger than less productive firms and thus benefit from returns to scale. More productive and larger firms, therefore, tend to enter the formal market while less productive firms produce informally. In Appendix 1A, we present a simplified version of the model in Ulyssea (2018). This theoretical result has been supported by several empirical papers finding that reducing labor taxes increases formal employment (Rocha, Ulyssea, and Rachter 2018; Bosch and EstebanPretel 2012; Ulyssea 2010).

Integrating the model from Appendix 1A into PAKMod would be a conceptually convincing solution. However, any calibration would be very uncertain, and would require firm-level data on the distribution of firm sizes (both formal and informal).

Therefore, we follow Ahmed (2012) and assume that informal and formal labor can be combined in a 1 constant elasticity of substitution (CES) function to total labor, � . σσ FF<sup>σσ</sup> II II<sup>σσ</sup> 3.3.1 Data creation tt tt tt CC = �ββ<sup>FF</sup> CC + ββ CC The International Labour Organization provides data for the share of formal employment in total employment (ILO 2020b). This variable, denoted as , allows calculating the historical values for formal and informal employment, KK,II tt
information on demographics, resource endowments, agricultural production, and input, output, and credit markets participation for 1700 randomly selected households in 14 villages ( _kebeles_ ) in six districts ( _woredas_ ) in South Wollo and East Gojam zones of Amhara (Deininger _et al._ 2008). Information on agricultural production was collected for the main season ( _meher_ , i.e., September-February) of the 2007 agricultural year. We use data from 1587 households who engaged in crop production and have complete information on the variables of interest.

Observed loan applications and lenders’ decisions and responses to qualitative questions on borrower’s needs and perceptions are used to infer a households’ semi-formal credit regime,<sup>3</sup> in line with previous studies on credit markets (Barham _et al._ 1996; Feder _et al._ 1990; Guirkinger and Boucher 2008; Jappelli 1990). Loan applicants were classified into three groups, namely those who had (i) applications fully or partly rejected (quantity rationed), (ii) received the requested amount but wanted to borrow more under

> 3 Note that this method does not allow to measure the intensity of credit constraints (Diagne 1999).

7
32 WHO (2000), Global Water Supply and Sanitation Assessment, Geneva, available at <u>http://www.who.int/docstore/water_sanitation_health/Globassessment/GlobalTOC.htm</u>

# **Bibliography for PPI, the macro story**

Briceno, C., A. Estache and N. Shafik (2004), “Infrastructure Services In Developing Countries: Access, Quality, Costs and Policy Reform”, The World Bank, (forthcoming) Easterly, W. and L. Serven, editors (2003), **The Limits of Stabilization,** Stanford University Press, Stanford Estache, A. and M.E. Pinglo (2004), “Are returns to Public-Private Infrastructure Partnerships in Developing Countries Consistent with Risks since the Asian Crisis”, World Bank, PR Working Paper series, Number 3373 Guasch, J.L. (2004), **Granting and Renegotiating Infrastructure Concessions: Doing it Right** , WBI Development Studies Gomez-Ibanez, J.A. (2003), **Regulation of Private Infrastructure: Monopoly, contracts and discretion** **_,_** Harvard University Press Kessides, I. (2004), **Reforming Infrastructure: Privatization, Regulation, and Competition,** Oxford University Press Ramamurti, R. and J. Doh (2004), “Rethinking foreign infrastructure investment in developing countries” , _Journal of World Business_ , 39, pp151-167 U.K. Department for International Development, 2002, **Making Connections: Infrastructure for Poverty Reduction,** London

# **Bibliography for the Fiscal space debate**

Ballasone, F. and D. Franco (2000), “Public Investment, the Stability Pact and the Golden Rule, Fiscal Policy, vol. 21, 2, pp207-229 Blanchard, O.J. and F. Giavazzi (2003), “Improving the SGP through a Proper Accounting of Public Investment”, European Economic Perspective newsletter, CEPR