rafmacalaba/data-use-mentions-extended huggingface dataset:
This is less likely to be the case in our study as we measure mental health in the last four weeks prior to the interview date, while labor outcomes are measured in the last seven days. To provide further support to our main findings, we regress labor market outcomes on mental illness measured in the previous wave (i.e., wave _t-1_ ) and find qualitatively similarly results, albeit weaker statistical significance for labor incomes (Appendix A, Table A13).

Another concern one may argue is that labor outcomes of refugees in the host country may be determined by their work experience before arrival, which in turn also has an impact on the trajectory of their mental illness. In our model specification, this has been taken into account by controlling for individual fixed effects. While other time-varying experience in the past is not available in the BNLA survey, we split our sample into two groups based on whether they did any paid work in a job, business or on a farm before arrival. The results presented in Table A14 (Appendix A) reveal no difference in the effect of mental health on labor outcomes for both groups.

Finally, we conduct various checks on the IV. Firstly, we add up the total number of traumatic events experienced by refugees as an alternative instrument for mental health. In other words, we consider the intensity of past traumatic experience rather than just exposure. Using this alternative measure produces similar results (Appendix A, Table A15). In fact, every additional traumatic episode has a large impact on both employment and income. Secondly, we test the robustness of our results to deviations from the assumption of perfect exogeneity, using Conley et al.’s (2012) bounding method. This method relaxes the assumption of perfect exogeneity and assumes a flexible second-stage regression that also includes the instrument as a regressor. Assuming that the direct effect of the instrument on labor outcomes ranges from zero—perfectly exogenous—to the reduced form effect, the lower bound and upper bound estimates are both
# **1.Introduction and Motivation**

The way budgets are managed has direct implications on a government’s ability to maintain a prudent fiscal stance and efficiently deliver services. Public financial management (PFM) is the vehicle that enables governments to implement policy. This is broadly recognized by the literature and practitioners alike [1-6] and consequently governments with the assistance of the development community have invested considerable time and resources into PFM reforms. The OECD estimates that every year oversees development assistance to PFM amounts to about US$ 1.6 billion globally. [7] Financial management information systems (FMIS) play a central role in PFM, as they facilitate the budget execution process. [8-10] At their core, FMIS systems enable processing of financial transactions and have embedded a set of controls that ensure the PFM act is implemented. As such, the adequate use of an FMIS should ensure budgetary and commitment control and thereby contribute to adherence to fiscal rules. The repository of financial transactions carried out in government provides the basis for financial accountability of the executive to the legislature and citizens and is thus a fundamental building block in democracy. Conversely, inadequate and opaque capture of government expenditure data introduces opportunity for fraudulent conduct and distances the executive from citizens. FMIS systems are thus central to expenditure control, transparency, and service delivery and by extension democratic processes.

Recognizing the potential benefits, many donors have invested significant resources into FMIS reforms. The World Bank alone has committed over US$ 4,079 million through 134 operations across 74 countries since 1985. By the turn of the century, about six operations were approved annually, averaging about US$ 33 million each. About 28 percent of all projects mapped to the World Bank Governance practice had at least one FMIS component making up about 17 percent of its overall commitment. [10] However, the results have often been disappointing. Frequently implementation of FMIS systems has been found to be operationally difficult, prone to cost overruns and delays, and subject to change management
ones. In fact, the number of municípios increased from 3951 to 5501 during these three decades. To create a consistent panel of agglomerations for the 1970 to 2000 period, we therefore used the Minimum Comparable Area (MCA) concept as implemented by IPEA researchers. MCAs group municípios in each of the four census years so that their boundaries do not change during the study period. All data have then been aggregated to match these MCAs. The resulting data set represents 123 urban agglomerations that consist of a total of 447 MCAs.

The sources for the majority of data employed in this paper are the Brazilian Bureau of Statistics (IBGE) Population and Housing Censuses of 1970, 1980, 1991 and 2000. We used the full Brazilian census counts to get information about total population and housing conditions (urbanization rate). Other data were collected only for a sample of households. We used this census sample information for income, industrial composition, education, piped water provision, 42
Therefore, after controlling for observable differences remaining after randomization, we estimate that a successful ballot increases expected income of PAC applicants by $91 per week, while migrating increases mean income by $274. Given that mean income of applicants with unsuccessful ballots is $104, this represents a 88% increase in expected income from winning the lottery, and a 263% increase in income from migrating.

# **4. Non-experimental estimators**

The natural experiment provided by the use of a lottery to admit Pacific Islanders to New Zealand provides a unique opportunity to estimate the gain in income from migration. Other studies of migration are forced to use non-experimental methods to attempt to deal with the selectivity issues associated with migration, comparing the incomes of migrants to that of non-migrants of similar observable characteristics. In this section we explore how well such methods work in practice, comparing the results obtained from different non-experimental methods to the experimental results described above.

This approach for studying the validity of non-experimental methods has a long history in the labor program evaluation literature. For example, in perhaps the first attempt to do so, Lalonde (1986) compared experimental estimates from the National Supported Work (NSW) Demonstration to non-experimental results calculated using control groups created from household survey data. For this program and treatment, Lalonde found that non-experimental methods did a poor job of replicating the experimental results. Heckman, Ichimura and Todd (1997), Dehejia and Wahba (2002), and Smith and Todd (2005) each further exploit the data collected for the NSW to examine whether particular

- 18 -
formal employment.<sup>6</sup> In fact, among the five MENA countries with available data on labor market outcomes during the pandemic, Jordan exhibited the greatest stability in employment over time (Krafft et al., 2022). Moreover, Jordan undertook strict closure policies especially for camps whereby entering and exiting refugee camps was prohibited, including for humanitarian workers providing assistance to refugees.

# **Data and descriptive statistics**

To analyze the trends of employment across Jordanians and Syrians before and during the COVID19 pandemic we would ideally need panel data that covers this period. Such data does not exist. The Survey of Young People in Jordan (SYPJ) 2020 (Assaad et al., 2021a; OAMDI, 2022) offers a suitable alternative. Collected between August 2020 and October 2020<sup>7</sup> from a nationally representative sample of Jordanian and Syrian youth between 16 and 30 years old, the survey captures the labor market experience of this population before and during the pandemic through retrospective questions about their job history since they entered the labor market. We use data on the characteristics of the individual’s current job as well as data on all their previous jobs since they entered the labor market. Individuals can report as many jobs as they took with no upper limit. The maximum number of jobs reported in our sample was seven jobs.

The other source of micro data for Jordan during the pandemic is the COVID-19 MENA Monitor (CMM) data (OAMDI, 2021). This data was collected over three waves in February, June and August 2021 through a phone survey. It also has retrospective data on individuals’ employment

> 6 Defence order number 6 stipulated, early April, that institutions subject to the labor law must allow their employees who were dismissed or whose services were terminated since the beginning of the pandemic to return to their work < https://www.jordantimes.com/news/local/pm-issues-defence-order-no-6-stipulating-labour-rightsunder-defence-law>.

> 7 Additional data was collected between February
|0.373|
|<br>Note: (1) Data source: EU 2011 Poverty Map (DG-REGIO<br>observations in agricultural indicators are treated as zero;|and World Bank), 201<br>(4) Luxembourg and C|0 Farm Structu<br>yprus Republ|re Survey (Eur<br>ic are not bein|ostat), and EU<br>g analyzed her|National Statis<br>e|tic Institutes (E|urostat); (2) Sy|mbols: ** p<0|.01, * p<0.05,|+ p<0.1; (3) M|issing|

In terms of poverty dynamics, we identified several strategies that have an impact on poverty over time. Table 13 below summarizes two important results. First, that there is a positive association between the poverty rate and the share of individuals who live in an agricultural household through time. Thus, an increase in the share of individuals in agricultural households was associated with an increase in poverty through time. On the other hand, household diversification is negatively associated with the poverty rate through time. Suggesting that as diversification of the income sources at the household level increases, poverty decreases. Households that have more diverse sources of income, in terms of agricultural and non‐agricultural income, are associated with a lower poverty rate. This means that households who diversify seem to do better than those who rely only on agriculture. Thus, agricultural households could benefit from diversifying their income by complementing it with non‐agricultural sources. However, it is important to note that this result relies on well‐functioning labor markets, especially labor in the non‐agricultural sectors, that can provide real alternative sources of income to members of agricultural households.

**Table 13.** Regions with higher household diversification have greater poverty reduction

|LABELS|(1)<br>Povertyrate|(2)<br
S., Shelestov, A., 2017. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters 14, 778-782 Kvartiuk, V., Herzfeld, T., Bukin, E., 2022. Decentralized public farmland conveyance: Rental rights auctioning in Ukraine. Land Use Policy 114, 105983 Large, E.C., 1954. Growth stages in cereals: Illustration of the Feekes scale. Plant Pathology 3, 128-129 Lark, T.J., Mueller, R.M., Johnson, D.M., Gibbs, H.K., 2017. Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations. International Journal of Applied Earth Observation and Geoinformation 62, 224-235 Lobell, D.B., Azzari, G., Burke, M., Gourlay, S., Jin, Z., Kilic, T., Murray, S., 2020. Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis. American Journal of Agricultural Economics 102, 202-219 Lopresti, M.F., Di Bella, C.M., Degioanni, A.J., 2015. Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina. Information Processing in Agriculture 2, 73-84 Martin-Shields, C.P., Stojetz, W., 2019. Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict. World Development 119, 150-164 Mashaba, Z., Chirima, G., Botai, J.O., Combrinck, L., Munghemezulu, C., Dube, E., 2017. Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region. South African Journal of Science 113 Matasov, V., Prishchepov, A.V
# **2. Related Literature and Context**

# **2.1 Related Literature**

According to Schumpeter (1939), business cycles are driven by a process of creative destruction by which innovative, high-productivity firms drive relatively unproductive firms out of business. Some prominent macro-models predict that recessions may speed up this process by “cleansing” out unproductive firms and freeing up resources for more productive uses (see e.g. Caballero & Hammour, 1994, 1999). While longitudinal manufacturing firm-level studies provide empirical evidence that the creative destruction process facilitates productivity growth, they also raise questions about how this process varies over time. Decompositions of aggregate productivity growth only weakly support the hypothesis that allocative efficiency increases during cyclical downturns (see, _inter alia,_ Griliches & Regev, 1995, and Baily et al. 1992). Yet such studies typically exclude extreme economic events and it is therefore uncertain whether lessons based on them apply during times of crisis.

In the presence of market imperfections downturns may hamper, rather than facilitate, adjustments, and protract the recovery process (see e.g. Loayza et al., 2005). Distortionary labor market regulations and policies governing firm dynamics appear particularly detrimental to the efficiency of the reallocative process (see e.g. Collier and Goderis, 2009, Haltiwanger, Scarpetta and Schweiger, 2008). Barlevy (2002, 2003), moreover, has pointed out that crises can obstruct the process of creative destruction by exacerbating credit market imperfections, which may hurt efficient firms disproportionately as such firms are likely to have higher financing needs, contradicting the creative destruction hypothesis. Empirical evidence supporting the idea that crises exacerbate credit constraints is provided by Blalock, Gertler and Levine (2008) who, using the same dataset as considered in this paper, show that foreign owned firms, which are arguably 5
efficiency will arise.

The very basic usage of case management systems consists in measuring what happens in courts. To begin with, judiciaries could create indicators that evaluate the efficiency, access, quality and integrity of each court. A full list of indicators is available at “Court Performance Around the World: A Comparative Perspective,” which includes, among others, the following variables: number of cases filed, disposed and pending; clearance rate and congestion rate; average duration of a case; and number of judges per habitant across regions (Dakolias, 1999). Court-user surveys help create indicators of quality, integrity and public trust in the judiciary. Databases can also be used to diagnose the main issues and problems the judiciary faces. Many courts in developing countries are worried about their large backlog of cases, which limits citizens’ access to justice and erodes their trust in formal institutions. Databases can be used to identify the main causes of delay and adjournments in legal cases.

Similar diagnostics can be used to evaluate an array of outcomes that a judiciary cares about, such as who uses formal institutions and how they make use of them, the level of user satisfaction via court-user surveys, which legal needs in a region remain unfulfilled, the main barriers to accessing justice and how to address them. Apart from diagnosing problems, data systems should also be used to coordinate and deploy resources in an efficient and effective manner. For example, the assignment of cases to judges can be automated so that they take into account the workload and backlog of cases for each judge, but also to ensure the random assignment of cases to judges. Databases can also be used to evaluate the capacity of judges and mediators. Then governments can train or incentivize those who are less capable or less willing to improve performance. Finally, databases could be used to determine which courts and cases may benefit more from ADR. Advanced systems can detect which cases are more likely to be successfully resolved through mediation and automatize the redirection of these cases to mediation.
- Deghi, A., Katagiri, M., Shahid, S., Valckx, N. (2020). Predicting Downside Risks to House Prices and Macro-Financial Stability. _International Monetary Fund_ WP/20/11.

- Del Negro, M. & Otrok, C. (2007). 99 Luftballons: Monetary Policy and the House Price Boom across U.S. States. _Journal of Monetary Policy_ 54(7), 1962-1985.

- Durst, N. J. & Wegmann, J. (2017). Informal Housing in the United States _. International Journal of Urban and Regional Research_ 41(2), 282-297.

- Drehmann M. & Juselius M. (2014). Evaluating Early Warning Indicators of Banking Crises: Satisfying Policy Requirements. _International Journal of Forecasting_ 30, 759-780.

- Engsted, T. & Pedersen, T.Q. (2015). Predicting Returns and Rent Growth in the Housing Market Using the Rent-Price Ratio: Evidence from the OECD Countries. _Journal of International Money and Finance_ 53, 257-275.

- European Union, International Labour Organization, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations Economic Commission for Europe, The World Bank (2013). _Handbook on Residential Property Prices Indices (RPPIs)_ . Luxembourg European Union.

- Fan, G. Z., Ong, S. E., & Koh, H. C. (2006). Determinants of House Price: A Decision Tree Approach. _Urban Studies_ 43(12), 2301-2315.

- Fletcher, M., Gallimore, P., & Mangan, J. (2000). Heteroscedasticity in Hedonic House Price Models. _Journal of Property Research_ 17(2), 93-108.

- Follain, J. R., Jr. & Malpezzi, S. (1981). Are Occupants Accurate Appraisers? _Review of Public Data Use_ 9 (1), 47-55.

- Gardner, D. & Pienaar, J. (2019). Benchmarking
et al., 2004 and Bradford, 2003), and quantity-impact measures (using NTB data as in Leamer, 1990 and Harrigan, 1993 or as residuals of gravity-type equations as in Mayer and Zignago, 2003). However, to our knowledge, there exists no attempt to estimate those in a consistent way for a wide variety of countries at the tariff line level. Because trade policy is determined at the tariff line level (Men Shirts made of cotton) and not at the more aggregate industry level (Apparel), it is important to measure its restrictiveness at the most disaggregated level. Otherwise aggregation bias could lead to misleading conclusions.<sup>4</sup> We estimate AVEs of NTBs as follows. Using data on two broad types of NTBs —Core NTBs (price and quantity control measures, technical regulations, as well as monopolistic measures, such as single channel for imports) and agricultural domestic support— at the tariff line level for each country, we estimate their impact on imports following Leamer’s (1990) comparative advantage approach (see also Harrigan, 1993 and Tre fl er, 1993). The logic of this approach is to predict imports using factor endowments and observe its deviations in the presence of NTBs. This is done for each HS six-digit tariff line in which at least one country has some type of NTB (around 4800 tariff lines). The estimated impact of NTBs on imports varies by country (according to country speci fi c factor endowments). We then convert the quantity impact of NTBs on imports into a price equivalent(or AVE) by simply moving along the import demand curve using import demand elasticities estimated earlier.

The reminder of the paper is organized as follows. Section 2 describes the methodology used to estimate ad-valorem equivalents of NTBs, whereas section 3 presents the methodology used to estimate the trade restrictiveness indices. Section 4 describes the data, whereas section 5 describes the empirical results. Section 6 concludes.

> 4 For example, imagine that the Apparel industry
tions of-or uncertainty about—future fiscal or monetary policy. These expectations may have an immediate effect on output or prices. As the VAR cannot identify expectations, these effects will be estimated as a direct effect of debt on output. 5.2. Estimation and results Favero and Giavazzi (2007) estimate the system (3) and (4), but found only minor changes in the macroeconomic effects of shocks to government expenditure and tax revenues. Here we estimate (3) and (5) using data from five developing countries and contrast our results with Favero and Giavazzi’s (2007). The five countries—Brazil, Chile, Estonia, Malaysia and South Africa— were selected based on data availability and consistency. The estimation of the system (3) and (5) is only valid if data on the endogenous VAR variables in Y; are consistent with time series for sovereign debt d; in equation (5). As time series for sovereign debt are usually compiled using different methods and sources from high-frequency fiscal and macroeconomic data, (5) does not obtain consistent estimates for most developing countries. The selected sample of countries were the only five in our sample of developing countries for which data were consistent.’ These countries represent a variety of regions (Asia, Africa, Eastern Europe, and Latin America), degrees of openness to trade and exchange rate regimes.

5.2.1 Consistency of debt series Figure 13 shows the evolution of the debt-to-GDP ratio for the five countries studied, together with the evolution of debt-to-GDP implied by the highfrequency macroeconomic time series. Solid lines in Figure 13 represent the <u>~ TWe included countries whose simulated</u> debt series deviated from actual debt by 2 percentage points of GDP or less, on average.

24
unadjusted labor variable. This finding unambiguously suggests that the effect of the increase of education, now captured by the labor quality index, was the main driving force behind TFP growth during the past decades.

By contrast, quality-adjusted capital only explains about 28 percent of growth, compared to 32 percent explained by the unadjusted variable. Consistent with earlier findings, the decrease of capital accumulation in explaining GDP growth reflects the deterioration of the quality of the country’s physical capital base. This is most likely associated with the disastrous effect of the civil war and a negative investment climate, among other factors. The finding of a negative rate of TFP growth of about 6 percent for the period 1971-2002 is a somewhat odd result. Rather than ‘technological regress’ it should be interpreted as an indication of the declining efficiency of the economy, due to the conflicting political and social environment of the country. Notice that TFP growth is consistent with the earlier regression results. In most specifications the constant term was found to be significantly negative.

How stable are these findings? The TFP estimate was found sufficiently robust. A sensitivity analysis based on alternative assumptions on the factor shares yielded TFP growth estimates ranging from –4 percent (capital share 0.4 and labor share 0.6) to –1 percent (capital share 0.5 and labor share 0.5). The associated changes of the contribution of labor and capital was negligible. Applying alternative data sources to calculate the residual was not found to be helpful. The robustness tests of the regression analyses clearly indicate that both the labor (based on IGSS data) and capital variable (5 percent depreciation) provide a higher explanatory power than other sources.

# **7.3 Disaggregation by Education Level**

Table 13 shows the results of the extended growth accounting exercise for the period 1951-2002. The human capital variables now enter directly into the production function by level of education. They capture improvements in the country’s skill base, which were formerly measured by quality-adjusted