rafmacalaba/datause-encoder-data huggingface dataset:
job search strategies and work availability. Some LSMS(‐ISA) surveys contain a few additional questions on underemployment (i.e. desire and/or availability to work additional hours).

The second approach, followed by most LFS, starts with a general screening question to determine whether an individual engages in wage employment, followed by more detailed questions about the number of hours worked in the main and second wage job (Benes & Walsh, 2018b). Follow‐up questions are asked to the employed and unemployed/inactive. In addition, information on underemployment is collected.

The third approach, followed by for instance the 2010 Nepal LSMS survey, asks respondents to list all productive activities in which they engaged in the last 12 months and to report the number of months/weeks/hours worked by activity in the last 12 months and in the last 7 days. These questions are followed by questions on underemployment for those employed fewer than 40 hours a week, while the unemployed/inactive are asked to report on job search strategies and work availability. Finally, some additional questions are asked to employees.

A common feature of the three approaches is the focus on wage employment. For instance, the labor module of the 2015/16 Nigeria LSMS‐ISA survey counts 53 questions of which 32 concern wage employment. Of the 44 questions about employment in the 2013 Ethiopia LFS, 14 questions focus exclusively on wage employment. This observation holds more generally. Three elements explain this observation. First, surveys aim to distinguish formal from informal wage employment and this distinction requires several questions about the contractual arrangement and the employer. Second, LSMS(‐ISA) surveys include questions on the first as well as the second wage job in order to capture multiple job holdings. Third, multi‐topic household surveys, such as LSMS‐ISA surveys, often include other modules such as the agricultural module (AG) and the non‐farm household enterprise (NFE) module which capture specific activities of the self‐employed (Box 1). This implies that collecting detailed information on self‐employment in agriculture and non‐farm household enterprises is not the main objective of the LSMS‐ISA labor module, as this information is already collected in other, dedicated modules.

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the revenue-cost ratio decreased to 2.2. Thus, the fear of government collecting high toll revenues as one moves to a 'perfect' road pricing scheme does not seem to be borne out by these simulations. Estimated benefits are just below estimated revenues, suggesting that a benefitmaximizing pricing scheme is on average 'fairer' than it is generally perceived.

53. The Norwegian experience appears to demonstrate that toll financing and welfare maximizing objectives may not be correlated since they may go in opposite directions. That is, a Qow) flat rate, 24 hour toll may maximize revenue but not net benefit. Per contra, a differential pricing strategy would enhance social welfare but not raise as much revenue. Data from Oslo has demonstrated that a 'perfect' road pricing scheme yields significantly higher benefits vis-a-vis a crude 'optimal' cordon scheme. By merely imposing a simple peak period toll, however, a nontrivial proportion of the benefits of a 'perfect' road pricing scheme and several times the benefits of the present flat toll scheme could be reaped too. Further, the cost of toll collection of such a crude 'optimal' scheme appears to be manageable for the size of a city such as Oslo. The simulation results from Norway also show that toll financing would require a lower toll whereas a welfare enhancing pncing strategy would suggest a higher toll. Still, a benefit-maximizing pricing strategy yields a benefit-cost ratio that happens to be the same as its revenue-cost ratio. Perhaps local authorities should only approve toll financing projects which pass the benefit-cos **t** est.

54. The simulation results, which show that road pricing is 'fairer' than formerly perceived, have contributed to informed debate about the possible introduction of road pricing in Oslo. A white paper on road pricing is currently being prepared by the Ministry of Transport and the Norwegian Road Administration.33 As of Janmzy 1992, **.e** toll for **antring the** CBD incresed by one **kmne** (fom _NOKIO to NOKll)._ Furwher, Oslo is looling into the possibility of the use of a dobit card system mther than the current one-way AVI **utannder system.**
# **References**

- Acemoglu, D., and F. Zilibotti. 2001. “Productivity Differences.” _The Quarterly Journal of Economics_ 116:563–606. 3.2

- Alvarez-Cuadrado, F., and M. Poschke. 2011. “Structural Change Out of Agriculture: Labor Push versus Labor Pull.” _American Economic Journal: Macroeconomics_ 3:127–58. 1

- Artuc, E., D. Lederman, and G. Porto. 2015. “A mapping of labor mobility costs in the developing world.” _Journal of International Economics_ 95:28–41. 11

- Basu, S., and D.N. Weil. 1998. “Appropriate Technology and Growth.” _The Quarterly Journal of Economics_ 113:1025–1054. 3.2

- Bernard, A., and C. Jones. 1996. “Productivity across Industries and Countries: Time Series Theory and Evidence.” _The Review of Economics and Statistics_ 78:135–146. 6, 3.3.2, A.2

- Bryan, G., and M. Morten. 2019. “The aggregate productivity effects of internal migration: Evidence from Indonesia.” _Journal of Political Economy_ 127:2229–2268. 11

- Caselli, F., and S. Tenreyro. 2005. “Is Poland the Next Spain?” NBER Working Paper 11045, National Bureau of Economic Research, Cambridge, MA. 6, A.2

- Chen, C. 2020. “Technology Adoption, Capital Deepening, and International Productivity Differences.” _Journal of Development Economics_ 143:1–26. 2

- de Vries, G., L. Arfelt, D. Drees, M. Godemann, C. Hamilton, B. Jessen-Thiesen, A. Kaya, H. Kruse, E. Mensah, and P. Woltjer. 2021. “The Economic Transformation Database (ETD): content, sources, and methods.” WIDER Technical Note 2/2021, United Nations University World Institute for Development Economics Research, Helsinki, Finland. 2.1

- de Vries, G., M. Timmer, and K. de Vries. 2015. “Structural Transformation in Africa: Static Gains, Dynamic Losses.” _Journal of Development Studies_ 51:674–688. 2.1

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income is positively correlated with total income. It has, therefore, become an important coping mechanism and is probably the primary factor behind the higher incomes in rural areas compared to urban areas. The importance of in-kind income, however, is likely to be a major complicating factor in targeting social benefits. Finally, there is little evidence at Bulgaria's stage of the transition that income distribution has deteriorated significantly.

Policy makers face a difficult task: providing adequate social protection in the midst of a fiscal crisis with very limited information and few instruments. Based on an analysis of the only available household income and budget survey, this paper attempts to identify some important characteristics of low income households that can be used to target more effectively social protection. Three characteristics are potentially of interest:

- Low income households are older and more poorly educated than the average household, thus making them less able to adapt rapidly or ever to the changed circumstances;

* Household size is not a correlate of poverty. In fact, middle-income households have more children. Thus, benefits for children, which are a mechanism to protect vulnerable children in most developing countries where poor households are larger, accrue to better-off households in Bulgaria; and,

- The characteristics of low-income female-headed households are very different
# **4. Intersectoral Linkages and Sectoral and Firm Performance**

In the previous section, we presented evidence on the role of the mining output shock in shaping regional differences in intersectoral linkages. We now aim to understand the mechanisms that link production networks and structural transformation but at a more granular level. We use 2003 NIC and 2014 IBES data to examine (1) how the positive mining employment shock relates to employment growth in other sectors across districts and (2) how the positive mining employment shock across time and geographic areas affects the entry of new firms and the growth of firm employment in different sectors.

# **District-Level Analysis**

We first examine the sensitivity of changes in employment in other sectors to changes in employment in mining across districts. Propagation of the mining employment shock to other sectors is identified based on the assumption that employment growth in sectors that have stronger linkages to mining (for example, heavy manufacturing industries) predominantly takes place in mining districts, that is, districts with at least one mining firm. The regression model is as follows: s ∆ log(Empd)03 − 13 = α +σ∆ log(MEmpd)03 − 13 +φ∆ log(MEmpr ,d ′≠ d)03 − 13 + β 1 ∆ log(IFirmd)93 − 03 + β 2 ∆ log(IFirmd)83 − 93 + β 3 ∆ log(SFirmd)93 − 03 + (1) β 4 ∆ log(SFirmd)83 − 93 + ε d , where ∆ log( _Empd_ )03<sup>_s_</sup> − 13 and ∆ log( _MEmpd_ )03 − 13 measure changes in log employment in sector _s_ in district _d_ between 2003 and 2013 and changes in the mining sector in district _d_ between 2003 and 2013, respectively. To capture the effect of mining activities in neighboring and other districts within the same region, we control for changes in log mining employment in region _r_ less mining employment in district _d_ denoted as ∆ log( _MEmpr_ , _d_ ′≠ _d_ )03 − 13 . The elasticity of sectoral employment with respect to mining employment at the district level is denoted by σ , and φ is the elasticity of sectoral employment at the district level with respect to mining employment at the region level. Industrial productivity in district over the past decades may have driven employment growth in sector and district between 2003 and 2013. In such a case, any dd systematic variation in past employment growth across districts could confound the effect of ss dd mining employment growth on employment growth in other sectors. To filter out this confounding effect, we control for the growth of firms in industry between 1993 and 2003 13
# 2 Background

This section takes a ...rst look at the worldwide MTEF adoption data collected for this paper by presenting stylized facts of MTEF global growth and regional penetration during 1990-2008. It also discusses the rationale behind MTEFs as budget institutions designed to improve ...scal performance. This discussion helps generate theoretical expectations. We subject these conjectures to empirical scrutiny in the next section.

## 2.1 MTEF Phases

We classify MTEFs into three phases, based on the following criteria.<sup>9</sup>

- Medium-Term Fiscal Framework (MTFF): the government has rolling aggregate, expenditure, revenue, and other ...scal forecasts. Features include the availability of a macro-...scal strategy, macroeconomic and ...scal forecasts, and debt sustainability analysis.

- Medium-Term Budgetary Framework (MTBF): budget, spending agency or other reports explain aggregate and sectoral expenditure objectives and strategies, budget circulars detail medium-term expenditure ceilings and revenue forecasts, and budget documents contain some detail about medium-term estimates.<sup>10</sup>

- Medium-Term Performance Framework (MTPF): budget, spending agency or other reports explain program objectives and strategies, and list speci...c agency and/or program output or outcome targets, as well as results.

These three phases are "nested" in the sense that a higher-phase MTEF contains the lowerphase MTEF just below it.

## 2.2 Stylized Facts

Although some forms of medium-term expenditure projections existed in OECD countries as early as the 1960s, the ...rst application of a coherent system of multiyear budgeting occurred in Australia, where an MTEF was introduced in the 1980s (see Folscher 2007). MTEFs

> 9We use these de...nitions to code each country-year in our sample as falling into each of these mutually exclusive categories; see Section 3 and the Data Appendix for a description of variable construction.

> 10We coded countries that introduced a "pilot" MTBF in a few sectors as MTFF since the health sector, our focus in the analysis below, might not be one of the piloted sectors.

6
question in the World Bank investment climate survey of firms is for manufacturing firms that import parts or materials, which is very common worldwide: What was your longest customs delay in the past six months? The average response in Chinese surveys is 12 days, compared to twice as long in African surveys. Ports in China also tend to be very efficient. The cost of exporting a container of goods to the U.S. is quite low, $335. It costs almost five times as much ($1561) to send a container from the typical African country.

The combination of low tariffs, efficient customs, and efficient ports means that large numbers of firms in China are very well connected to the international market, and that has proved to be one of the key advantages spurring the rapid development of these firms. For many African countries, the formal trade regime is as open as China’s. However, problems in customs and ports mean that the actual connection of African firms to the global market is weaker.

# **_4. Infrastructure finance and pricing_**

Everyone who comes to China is impressed with the infrastructure. It is not surprising that the flagship cities of Beijing and Shanghai have good infrastructure. What is more remarkable is that large numbers of cities one has never heard of have similarly good transport infrastructure and reliable power supply. The rapid expansion of infrastructure has been an important factor sustaining China’s growth. Between 1998 and 2006 capacity of the power sector grew at 10% per year, keeping pace with the needs of the economy. The rail network, the most extensive in the world, increased its line length by more than 10,000 km. Most rapid has been the expansion of the expressway system 10
Table 4: Overall Treatment Effects - Secondary Outcomes

||Awareness|Ease|Reward|Supplier|Credibility|
|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Treatment group mean|-0.086<sup>_∗∗∗_</sup>|-0.036|-0.118<sup>_∗∗∗_</sup>|-0.068|-0.044|
||(0.03)|(0.04)|(0.04)|(0.04)|(0.04)|
||0.006|0.326|0.002|0.101|0.283|
|Control group mean|0.821|0.767|0.798|0.739|0.730|
|Observations|1210|1210|1210|1210|1210|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Note: This Table shows the overall impact of the treatment relative to the control group. Model Names (Awareness, Ease, Reward, Supplier, Credibility) represent our secondary outcome variables. Awareness: Based on question C12a that states I am fully aware of my firm’s tax obligations. Ease: Based on question C12d that states The process of declaring and paying taxes is easy. Reward: Based on question C12e that states Recognizing and rewarding tax compliant behavior is better than punishing non compliance. Supplier: Based on question C12f that states When my firm chooses suppliers/service providers, it considers whether they pay taxes. Credibility: Based on question C12g that states The risk of legal action for firms evading or underpaying taxes in Tanzania is high unawareness of their tax obligations, they could be shielding themselves from liability. In case any wrongdoing was uncovered, they could claim lack of awareness as a defense.

In general, respondents stated moderate to high levels of willingness to pay tax, however this varied considerably depending on the survey question asked. For instance, over 90 percent of the respondents agreed that it is important for people to pay taxes, whereas less than half of respondents thought that people should be punished for not paying tax.

Willingness to pay tax was relatively higher among firms with monthly turnover above one million Tanzanian Shillings, firms in wholesale and retail trade and when respondents were directly involved in decisions related to firm’s legal obligations. The relationship between these three dimensions and willingness to pay tax holds even after accounting for the influence of other factors like age, level of education, or the regions in which these businesses operate.

The presence of a tax official led to respondents being more likely to say they were willing to pay tax. This pattern was more pronounced among respondents who were employees of the business, when firms operated in sectors other than wholesale and retail trade, and, among firms located in Central, Northern and Western Tanzania. Even though the presence 17
security particularly salient.<sup>1</sup> The intervention, carried out in partnership with one of the largest humanitarian aid organizations in Jordan, randomized rental subsidies across 2,870 refugee households residing in Irbid and Mafraq governorates in Jordan. The randomization was implemented over geographic unit clusters and designed with sufficient statistical power to detect moderate impacts on important life outcomes, including living standards measures, labor market performance, and subjective well-being.

The housing subsidy covered, on average, a year of rent, combined with funding for landlords to upgrade the refugees’ housing quality. Subsidies were offered for recipients’ current housing arrangement, and thus their physical location (and by extension, their neighborhood) was typically held constant. To measure effects on refugee recipients as well as Jordanian neighbors, the study collected both in-person and phone surveys over a total of three and a half years since the start of implementation, with refugee households surveyed three times and neighbors once. The data collection succeeded in attaining relatively high sample tracking rates, overcoming some challenges associated with the COVID-19 pandemic. Although 86% of the refugee sample was surveyed at least once, there was some differential attrition between the treatment and control refugee samples, and therefore we estimate bounded effects following Lee (2009) (and the text highlights the results for which both estimated bounds take the same sign unless otherwise noted). These data are analyzed largely following the econometric models and primary outcomes specified in an AEA pre-registration (AEARCT#0006141) and associated pre-analysis plan, while making note of additional and exploratory results.

Another core contribution of this study is to examine the host community reaction to refugee assistance, utilizing a detailed survey of the attitudes and experiences collected among a representative sample of the Jordanian neighbors (N=2,146) of both treatment and control households. To our knowledge, this is among the first studies to experimentally examine how humanitarian assistance to refugees affects the views of the local communities who do not directly benefit. Specifically, we examine whether refugee-targeted transfers — in this case via rental payments

> 1Statistic calculated directly from data on the universe of Syrian refugees in Jordan registered with UNHCR.

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# **Infrastructure in Conflict-Prone and Fragile Environments: Evidence from the Democratic Republic of Congo**

# **1. Introduction**

The rehabilitation of damaged transport infrastructure is often a high priority for governments and donors in post-conflict and conflict prone fragile states. The justification seems compelling - improved connectivity can rekindle economic activity, revive fragile economies, and spur economic growth that could stave off future conflict. For example, in the conflict-afflicted Democratic Republic of Congo (DRC), spending on transport infrastructure was approximately US$230 million per year during the mid2000s, and increased to US$275 million per year in 2008 and 2009 (Pushak and Briceño-Garmendia, 2011). The investment in road transport infrastructure in DRC will continue to be high as the World Bank, European Commission, African Development Bank, Belgium, DFID, Japan, the Republic of Korea, and Canada together donated US$1.19 billion, for roads (African Development Bank 2013). This pattern is evident in other conflict-prone economies too. The New Partnership for Africa’s Development (NEPAD) has proposals for 9 highways across the continent, at an estimated cost of US$ 4.2 billion (Review 2003), all of which pass through fragile states (as defined by the OECD), and in most cases, the portions of the roads that need the most rehabilitation lie within these countries. In Afghanistan, the U.S. Agency for International Development (USAID), provided over US$1.8 billion between 2002 and 2007 to reconstruct roads (GAO 2008), while the US Department of Defense has allocated about US$300 million from the Commander’s Emergency Response Program (CERP) funds for roads. The emphasis on spending for transport infrastructure raises questions about whether these funds are efficiently allocated, and whether they are effective in fragile states. In this paper, we study this much neglected question using data from DRC acknowledging that transport and conflict are just two of many other determinants of wealth, growth and poverty.

The DRC provides an apt case study on the effects of infrastructure in the context of conflict. The history of DRC has been characterized by frequent conflict, international exploitation, and economic stagnation. Since Sir Henry Stanley and King Leopold II of Belgium first drew the Congo Free State’s borders in 1877, international and regional powers have competed for the country’s resources and wealth. Upon achieving independence in 1960, after undergoing one of the darkest colonial periods in history, the country was again pillaged, this time by domestic forces, under the reign of President Mobutu. Since the overthrow of Mobutu in 1996, DRC has been in a nearly constant state of conflict and civil war, mainly in the mineral-rich eastern part of the country. Given its violent history, its network of roads—where they exist—have fallen into a dreadful state of disrepair. Constant conflict, poor governance, and lack of 2
Table 3: Number of days worked with NREGS

||(1)<br>NREG|(2)<br>S days|(3)<br>NREGS|(4)<br> days (log)|
|---|---|---|---|---|
|Rainfall (deviation, lag)|-67.245<sup>_∗∗∗_</sup><br>(16.498)|-66.185<sup>_∗∗∗_</sup><br>(16.803)|-3.128<sup>_∗∗∗_</sup><br>(0.881)|-3.102<sup>_∗∗∗_</sup><br>(0.841)|
|Rainfall (deviation)|-30.766<sup>_∗∗∗_</sup><br>(6.795)|-33.383<sup>_∗∗∗_</sup><br>(6.883)|-1.006<sup>+</sup><br>(0.576)|-1.096<sup>_∗_</sup><br>(0.523)|
|Self-reported shock|1.918|1.774|0.144|0.141|
||(4.148)|(4.500)|(0.093)|(0.102)|
|Additional controls|No|Yes|No|Yes|
|Observations|740|740|740|740|
|_R_<sup>2</sup>|0.055|0.070|0.087|0.100|

Notes: Estimation in FGLS. Dep. var.: No. of days a household worked for the NREGS in the past 12 months. Time trend and region-time trends included, but not reported. Cols. (2) and (4) additionally control for area cultivated (acres, log), wealth index of the household, and if household benefits from credit/training program. Standard errors (clustered at the sub-district) in parentheses.<sup>+</sup> _p <_ 0 _._ 10, _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001.

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