rafmacalaba/datause-encoder-data huggingface dataset:
in our sample are based on two sources. EPL indices for EU-15 member states are based on OECD Employment Outlook (2004) and analogous indices for the new member states are based on Tonin (2005), which replicates the OECD methodology and covers all new member states that are present in our survey.

**Table 2: Collective bargaining coverage and strictness of employment protection**

|Country|Share of<br>employees<br>covered by<br>collective<br>bargaining<br>agreements|Share of<br>firms having<br>collective<br>bargaining<br>agreement|Share of<br>firms having<br>firm-level<br>bargaining<br>agreement|Share of<br>firms having<br>higher-level<br>bargaining<br>agreement|EPL<br>index|
|---|---|---|---|---|---|
|Austria|0.946 (H)|0.978|0.233 (N)|0.962|2.15|
|Belgium|0.893 (H)|0.994|0.353 (N)|0.979|2.50|
|Czech Republic|0.502 (M)|0.540|0.514 (D)|0.175|2.02|
|Estonia|0.087 (L)|0.121|0.104 (D)|0.034|2.33|
|Spain|0.968 (H)|1.000|0.169 (N)|0.831|3.07|
|France|0.671 (M)|0.999|0.587 (D)|0.988|2.89|
|Greece|0.910 (H)|0.934|0.208 (N)|0.859|2.90|
|Hungary|0.184 (L)|0.190|0.190 (D)|0.000|1.65|
|Ireland|0.422 (L)|0.724|0.313 (N)|0.683|1.32|
|Italy|0.970 (H)|0.996|0.429 (N)|0.996|2.44|
|Lithuania|0.156 (VL)|0.242|0.237 (D)|0.008|2.81|
|Netherlands|0.676 (H)|0.755|0.301 (N)|0.454|2.27|
|Poland|0.193 (VL)|0.229|0.214 (D)|0.047|2.22|
|Portugal|0.555 (VL)|0.621|0.099 (N)|0.589|3.49|
|Slovenia|N/A (H)|1.000|0.257 (N)|0.743|2.63|
|Total|0.678 .|0.764|0.330 .|0.655|2.50|
|Euro area|0.845 .|0.942|0.356 .|0.873|2.63|
|Non-euro area|0.241 .|0.277|0.263 .|0.060|2.15|

Note: Figures are employment-weighted and re-scaled to exclude non-responses. Total and euro area country aggregates exclude Germany. Country-level institutional information from Du Caju et al. (2008) between brackets: union coverage: VL = very low (0 to 25% of workers are covered by collective agreements), L = low (26 to 50%), M = moderate (51 to 75%), H = high (76 to 100%); firmlevel agreements: D = company level is dominant in the country, N = company level is not dominant in the country.

# **_4.3. Typology of firms according to wage rigidity_**

We start by noting that our definitions of downward nominal and real wage rigidity are, in principle, mutually exclusive, i.e. a firm cannot be subject to both types of rigidity simultaneously. Nevertheless, a small proportion of the sampled firms gave positive answers to both nominal and real wage rigidity-related questions. We have 146 such firms in our dataset (about 1% of the sample). This overlap is either attributable to measurement error or caused by the different reference periods in the survey questions regarding the two types of 15
# Table 7. The Risk-Taking Channel and the Exchange Rate

# Country-industry logit regressions

|Dependent variable: Country-industr|y monthly issuance above 2000-07 average (1=Yes, 0=No)<br>(1)<br>(2)|
|---|---|
|USREER|-4.474***<br>(0.840)|
|XRATE|-2.399***<br>(0.478)|
|Controls|As in Table 4<br>As in Table 4|
|Observations|79,464<br>76,664|
|No. of Countries|64<br>63|
|No. of Country-Industries|448<br>441|
|PseudoR-squared|0.360<br>0.361|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 35
parable before the influx, and night light density increased after the Rohingya arrived in Bangladesh. We calculate the distance to the camps as a weighted average, considering each individual’s or area’s distance to each of 33 camps with weights based on each camp’s population. This approach enables a nuanced comparison between individuals living closer to and farther from the most populated camps in Cox’s Bazar both before and after the Rohingya arrived.

We use two distinct data sources to investigate the impact of refugee camps on local communities in Cox’s Bazar. First, the study incorporates annual grid-level remote sensing data that tracks night light density as a proxy for economic growth, deforestation, and land use (including grassland, cropped areas, and dense-open forest). We use this data to assess the development effects of the Rohingya’s presence on individuals living around each of the 33 refugee camps. The analysis addresses a grid level of one square kilometer, encompassing the 2,392 cells observed annually from 1992 to 2021 in Cox’s Bazar.

The second data source is the Cox’s Bazar Panel Survey, conducted in 2019 and 2023 after the arrival of the displaced Rohingya. This data represents the Rohingya population plus non-Rohingya individuals living outside the refugee camps. Since data collection began after the Rohingya arrived, only distance to the camps is used to assess the impact on individual outcomes. We enhance our analysis by incorporating survey fixed effects, leveraging the longitudinal nature of the data.

We find that hosting displaced populations in camps produces complex mixed effects on nearby hosting communities. The study reveals that the influx of displaced Rohingya and the subsequent humanitarian aid prompted greater night light density and more deforestation. The increments in night light density, however, are substantially greater than the gains in deforestation. Specifically, the results suggest that when the proximity of a grid to the refugee camps increases by 30 km (18.6 miles), night light density increases by 1.7 percent and deforestation increases by 0.02 percent. Considering that a one percentage point (pp) change in night light density corresponds to a 0.28 pp change in GDP (Henderson, Storeygard and Weil 2012), grid areas 30 km closer to the camps had approximately 0.5 percent higher GDP (1.7×0.28 = 0.47) than grids farther away.

3
**Notes** to Chapter S

1. By supporting surplus production the existence of food aid also supports misallocation of resources at the global level, but we do not pursue this point here.

2. In this case the aid is fully fungible, since the food aid releases an equivalent amount of foreign exchange, so that the food aid is the same as untied financial aid.

3. Maxwell (1986) points out that food aid studies may be divided into formal modelling favoured by academics and a less formal checklist approach used by donors. We focus here on formal studies, but the findings from less formal analysis are similar.

4. Chenery and Strout were not, however, the first economists to provide an intellectual basis for TA, which was given by Johnson's (1963) argument - captured in the expression a "gneralized human capital approach" - about the productivity gains to be realised from institutional development.

- S. There is usually a requirement for counterpart staff - so that aid staff may create a requirement for additiona **l** ocal staff. But in reality counterpart obligations are addidional to existing workload or no counterpart is appointed.

.~~~~~~~~~~~~~~~~~(i 90
Table 1. Dataset Characteristics

||ETHIOPIA<sup>1</sup><br>2011-12|MALAWI<br>2010-11|TANZANIA<br>2010-11|UGANDA<br>2010-11|
|---|---|---|---|---|
|Households in sample|3,969|3,247|3,846|2,633|
|Urban households (share)|0.0106|0.245|0.307|0.163|
|Household size|5.12|4.67|5.09|4.89|
|(sd)|(2.26)|(2.25)|(2.93)|(2.68)|
|Household size, adult equiv.|4.21|3.97|4.13|3.699|
|(sd)|(1.89)|(1.88)|(2.38)|(1.98)|
|Farm operators, all households (share)|0.870|0.794|0.713|0.790|
|Farm operators, rural households (share)|0.876|0.943|0.888|0.882|
|Annual consumption per person, USD PPP, urban HHs|339|2,000|2,246|1,641|
|(sd)|(431)|(2,382)|(1,856)|(1,727)|
|Annual consumption per person, USD PPP, rural HHs|142|748|1,008|675|
|(sd)|(267)|(606)|(820)|(1,040)|

> 1For Ethiopia, ”Rural” represents rural populations and ”Urban” represents small town populations. There are no true urban respondents in the sample.
10 TS model with break might have been erroneously taken to be a non-stationary process.<sup>9</sup> Column 1 in Table 2, which replicates Cuddington's (1992) results, shows that all variables would have been classified as DS models by full-sample DF tests; recursive tests, however, reject the null hypothesis of non-stationarity in 8 of the 12 cases, suggesting that a stationary specification with trend and/or level shifts would be more appropriate. Some of the potential breaks consist of trend shifts located around 1960, which might explain the dissimilar parameterizations of aggregate commodity price indices observed when the post World War It period is included in the sample. Interestingly, no evidence of breaks is observed for 1921, the year which has usually been designated as a break in the PSH literature. This gives some support to Cuddington and Wei's (1990) observation that the 1921 break observed in the Grilli-Yang index might be a result of aggregation problems.

These endogenous-break unit-root tests sustain the notion that the presence of structural breaks may have misled the selection of commodity price models in previous studies, and that the rejection of the PSH may be in effect due to model misspecification. An associated effect of the presence of breaks is that, if the DS/TS classification is incorrect. ften the estimated measures of the persistence of the shocks are also dubious. In order to avoid these limitations, Cochrane's non-parametric estimator is used to quantify the persistence of shocks. The next section denves this estimator and performs a simple Montecarlo simulation to prove its robustness in the presence of breaks.'<sup>0</sup> 9To deternine ir an endogenous way the presence of breaks in TS variables, we use standard CUSUM tests, see appendix table 1.

'° Another undesirable feature of paametric techniques for measuring the persistence of shocks, such as the method of unobserved component **s** uggested by Beveridge and Nelson (1981) or the ARMA approach developed by Campbell and Mankiw (1987), is that they concentrate on the short-run dynamic structure of the series, from which the long-run properties are _inferred._ Hence, if high-order negative autocorrelations offset initial positive autocorrelations both methods will fail to detect it, and results will be biased towards low-order DS representations.
# 1 Introduction

Both fertility and HIV prevalence rates in Malawi are among the highest in the world, with the total fertility rate at 5.7 births per woman and the HIV prevalence rate at 10.6 percent.<sup>1</sup> Malawian women make fertility decisions in an environment characterized by high adult and child mortality, exacerbated by mother-to-child HIV transmission. Out of a population of about 15 million, it is estimated that 68,000 die annually from AIDS and that 560,000 children under the age of 17 have lost at least one parent to the disease.<sup>2</sup> There are many policy interventions aimed at reducing HIV in Malawi and other SubSaharan African countries. These include HIV testing programs, information campaigns, and antiviral distribution programs. Evaluating the eects of such policies on outcomes such as number of births, child mortality, and orphan-hood requires an understanding of how women's fertility decisions are aected by the presence of HIV.

An important aspect of the environment in Malawi is that women are typically uncertain regarding their own HIV status. An infected person can live for many years with no symptoms, and testing was not widely available until relatively recently. The median survival time after infection, without treatment, is about 10.4 years.<sup>3</sup> During most of this time, an infected person is in a clinical latency stage and experiences few or no symptoms.<sup>4</sup> In addition to being uncertain about own HIV status, women often express beliefs about HIV risk that dier substantially from actual risk. Studies using the Malawi Diusion and Ideational Change Project data show that individuals in rural Malawi tend to overestimate both the probability of being HIV-infected (Anglewicz and Kohler, 2009) and the HIV prevalence in their community (Anglewicz, 2007). Anglewicz and Kohler (2009) attribute these high risk assessments to overestimated probabilities of transmission. More than 95 percent of respondents believe that transmission from a single instance of unprotected intercourse

> 1Malawi Demographic and Health Surveys (2010)

> 2UNSAID/WHO/UNICEF Epidemiological Fact Sheets (2008)

> 3Todd et al. (2007). Estimate for infected adults in Eastern and Southern Africa

> 4Morgan et al. (2002), for example, nd median time from infection to AIDS to be 9.4 years and median time from AIDS to death to be 9.2 months in rural Uganda.

2
# **1. Introduction and Motivation**

1.1 A government’s capacity to manage its public finances is central to its ability to deliver services. Financial management information systems (FMISs) are among the basics that facilitate this, as they “support management of public sector budgetary, accounting, treasury, and public debt management processes as well as generate corresponding reporting documents” (Uña and Pimenta 2015, p.282).<sup>1</sup> There are multiple definitions of FMISs varying in scope in the literature (see Dorotinsky and Watkins (2011) for a review), which have been summarized as “computerized systems that track government expenditures and payment processing, and report accordingly” (Schiavo-Campo 2017, p.187). As such, FMISs broadly consist of computer programs, databases, and associated processes, procedures, and technology platforms that enable government finance and accounting staff to conduct their day-to-day operational tasks.<sup>2</sup> The information collected in the system databases as the transactions process enables government finance managers to plan, prepare, and approve budgets, approve payments, monitor and report on financial resources collected, and develop appropriate resource allocation and borrowing strategies. Government auditors can access this transactional data to audit operations.

1.2 The potential of FMISs to contribute significantly to budget management outcomes has been documented widely by the literature (see for example World Bank 1998, Dorotinsky and Matsuda 2001, Diamond and Khemani 2005, Hove and Wynne 2010, Dener et al 2011, Dener and Min 2013, Dorotinsky and Watkins 2013, Hashim 2014, and Uña and Pimenta 2015) and practitioners alike. Among the arguments that are put forward are that improved recording and processing of government financial transactions allows ready access to reliable financial data. This supports transparency and accountability of the executive to parliament and the general public. Further, an FMIS strengthens financial controls, potentially facilitating a comprehensive and current picture of commitments and expenditure on a continuous basis. Once a commitment is made, the system would be able to trace all the stages of the transaction processing from budget releases, commitment, purchase, payment request, reconciliation of bank statements, and accounting of expenditure. This then allows a

1 FMISs are frequently and loosely referred to as Integrated FMISs or IFMIS, regardless of whether they are truly integrated and share common databases.

2 These include originating, receiving, recording, and processing transactions related to budget authority; requests for financial resources; payment of bills, loan, and grants; assessments of taxes, duties, and other levies; recording receipts; and managing deposits in government bank accounts.

**11**

**11**
assumptions of perfect competition and/or constant returns to scale are questionable, and the GTAP platform allows them to be modified.

# **III.4. Conclusions**

There is no one-fits-all solution to trade policy simulation. Instead, using a complex model for policy analysis requires a precise definition of the policy changes that are being considered, careful implementation of these policies in the model, and careful evaluation of simulation results. This is an iterative procedure that functions best when accompanied by an intensive dialog between policy makers and modelers.

For the purpose of analyzing Ukraine-EU FTA scenarios, a general equilibrium approach has important advantages, especially since the focus of the foreseen policy dialog will be on agriculture which accounts for a significant share of production, employment and consumer spending in Ukraine. The general equilibrium modeling framework developed by the Global Trade Analysis Project is a flexible and well-known tool for the assessment of trade policy changes. It also offers a comparatively high level of especially agricultural disaggregation. The most recent database that is available for this framework (GTAP-7) includes Ukraine for the first time, thus making GTAP ideally suited to the analysis of Ukraine-EU FTA issues in agriculture.

26
|If students aren’t disciplined at home, they aren’t<br>likely to accept any discipline at school<br>(1 if Yes; = 0 if No)|0.486|0.501|0.491|.506|
|---|---|---|---|---|
|It is okay to be absent as long as I: complete the<br>curriculum OR leave students with work OR doing<br>something useful for the community<br>(=1 if Yes;=0 if No)|0.682|0.466|0.679|0.468|
|Are any actions taken in case of poor teacher<br>performance?<br>(1 if Yes; = 0 if No)|0.821|0.384|0.857|0.350|
|1st PC score of 4 Positive Attitude oriented<br>variables.|0.000|1.176|0.000|1.187|
|1st PC score of 5 Incentive oriented variables.|0.000|1.248|-0.000|1.244|
|1st PC score of 6 Self-Efficacy oriented variables.|0.000|1.366|0.000|1.370|
|1st PC score of 3 Relationship oriented variables.|-0.000|1.055|-0.000|1.038|
|1st PC score of 7 Reinforcement tendency<br>variables.|-0.000|1.528|0.000|1.545|
|1st PC score of 4 Locus of Control variables.|-0.000|1.066|-0.000|1.081|
|Who is the most important person to assess<br>progress in your professional targets?<br>(= 1 if Teacher says myself; = 0 if Teacher names<br>another person such as Head Teacher)|0.286|0.453|0.277|0.448|
|Teacher<br>Management/School<br>How often does someone from the school<br>leadership observe your classroom?<br>(=1 if'Once per term';=0 if lesser)|0.488|0.501|0.485|0.501|
|Level Governance<br>Does your school regularly recognize or reward<br>teacher performance?<br>(1 if Yes;=0 if No)|0.361|0.478|0.357|0.477|
|What is the one key result HT would assess when<br>rating your job performance?|0.251|0.434|0.256|0.437|

28
**Table A.1.2: Child Labor Statistics by Survey Assignment and Season: 7-day Recall Period**

|||**Pooled Dat**<br>|**a**|**Shor**<br>|**t Raining S**<br>|**eason**|**H**<br>|**arvest Sea**<br>|**son**|
|---|---|---|---|---|---|---|---|---|---|
||Self-<br>reported|Proxy-<br>report|Diff|Self-<br>reported|Proxy-<br>report|Diff|Self-<br>reported|Proxy-<br>report|Diff|
|**_Partic_**|**_ipation in_**|**_Household_**|**_Farm Activities (_**|**_%)_**||||||
|All|0.629|0.609|0.020|0.489|0.484|0.004|0.773|0.734|0.039|
||[0.017]|[0.012]||[0.025]|[0.017]||[0.022]|[0.016]||
|Boys|0.677|0.688|- 0.010|0.567|0.575|0.008|0.792|0.800|-0.008|
||[0.022]|[0.015]||[0.031]|[0.022]||[0.025]|[0.018]||
|Girls|0.580|0.532|0.048*|0.410|0.397|0.013|0.753|0.668|0.085**|
||[0.022]|[0.015]||[0.033]|[0.023]||[0.031]|[0.024]||
|**_Hours_**<br>|**_Spent on_**|**_Household_**|**_Farm Activities_**|||||||
|All|8.571|7.809|0.762*|6.167|6.338|- 0.171|11.043|9.292|1.751***|
||[0.317]|[0.228]||[0.395]|[0.314]||[0.484]|[0.328]||
|Boys|9.984|9.175|0.809|7.952|8.286|-0.334|12.089|10.062|2.028***|
||[0.477]|[0.322]||[0.624]|[0.460]||[0.676]|[0.393]||
|Girls|7.151|6.483|0.668|4.358|4.463|-0.105|10.000|8.538|1.462**|
||[0.349]|[0.268]||[0.450]|[0.361]||[0.550]|[0.421]||
|**Hours**|**Spent on**|**Household**|**Chores**|||||||
|All|11.526|11.447|0.079|11.733|11.428|0.305|11.315|11.467|-0.152|
||[0.365]|[0.248]||[0.525]|[0.353]||[0.435]|[0.328]||
|Boys|9.550|9.375|0.175|9.802|9.547|0.255|9.290|9.205|0.085|
||[0.407]|[0.297]||[0.590]|[0.447]||[0.482]|[0.400]||
|Girls|13.515|13.447|0.067|13.695|13.216|0.478|13.332|13.683|-0.351|
||[0.527]|[0.335]||[0.727]|[0.460]||[0.619]|[0.433]||

**Source:** Authors’ analysis based on an agricultural household survey conducted in 2016. **Note:** Standard errors are in brackets. Proxy respondents include head of households and spouses. *p<0.1, **p<0.05, ***p<0.01

28
net enrolment between 97 and 98). As a consequence, the income gradient observed in 1997/98 is lost in 1999/2000.

Table 4.11: Moldova: Gender and enrolment rates 1997-2000 (percent of children attending school)

||97/98|98/99|99/00|
|---|---|---|---|
|Males||||
|Preschool(6)|9.4%|8.3%|5.0%|
|Primary(7-10)|90.5%|92.5%|94.4%|
|LowerSec.(11-15)|95.6%|94.9%|96.8%|
|UpperSec.(16-18)|51.6%|54.9%|53.7%|
|IFemal2es||||
|Preschool(6)|13.4%|8.8%|9.7%|
|Primary(7-10)|92.7%|94.8%|95.5%|
|LowerSec.(11-15)|97.4%|97.2%|97.0%|
|UpperSec.(16-18)|56.6%|56.9%|56.0%|

Source: Moldovan Household Budget Survey (1997-2000).

Table 4.12: Moldova: EnroDment rates by Quintiie 1997-2000 (percent of children attending school)

|Agegroup/Quintiles|97/98|9$/99|99/00|
|---|---|---|---|
|Age7-10|91.6|93.7|94.9|
|Poorest|89.7|94.0|94.5|
|2|91.3|94.1|95.1|
|3|91.7|94.9|96.3|
|4|93.1|93.0|94.0|
|Wealthiest|93.3|91.3|94.5|
|Age11-15|96.5|96.0|96.9|
|Poorest|94.9|92.2|93.3|
|2|97.1|96.3|96.4|
|3|95.8|97.5|98.0|
|4|98.4|96.4|99.0|
|Wealthiest|96.3|98.3|99.2|
|**Age**16-18|54.1|55.9|54.8|
|Poorest|39.4|39.2|46.7|
|2|51.7|55.3|57.3|
|3|55.3|54.5|52.3|
|4|61.5|67.4|54.8|
|Wealthiest|64.5|71.7|65.4|

23
# **I. Introduction**

Is the spread of COVID-19 and the mortality it triggered related to different countries’ government mobility restriction policies, democratic institutions, and cultural norms? We examine this important, timely, but underexplored question. Still ongoing, the COVID-19 pandemic has already ranked as one of the most adverse economic and human shocks in a century (Barro and Ursua 2020; Fukuyama 2020). Its consequences appear in every aspect of our lives. In the United States, for instance, aggregate consumer spending dropped precipitously by 31% in the first few months since its onset (Coibio et al. 2020). The number of active business owners in the U.S. plummeted by 22 percent from February to April 2020 (Fairlie 2020). Among all infectious disease outbreaks, this pandemic left the largest impact on the stock markets both in the U.S. and around the world (Baker et al. 2020; Ding et al. 2020a). In addition, the pandemic has had serious effects on income inequality, impacting especially the young and low-income earners (Adams-Prassl et al. 2020), Blacks and the Hispanics in the U.S. (Fairlie 2020; Benitez et al. 2020), employees in firms with high female employment shares and therefore women (Alon et al. 2020), and those employed in low-wage services (Bartik et al. 2020a, Chetty 2020). This pandemic could also have long-term adverse effects by affecting quality of schooling (via distance learning), beliefs about disastrous economic shocks in the future (Kozlowski et al. 2020), or confidence in political institutions and leaders, confidence in public health systems (Aksoy et al. 2020), or individuals’ social trust (Aassve et al. 2020).

Yet countries differ greatly in terms of the pandemic’s spread and mortality. In terms of the number of accumulated confirmed COVID-19 cases as measured in the John Hopkins University (JHU) COVID-19 data on November 19, 2020, several countries, such as Brazil, India, and the United States have had more than 5 million of cases. In contrast, 11 countries or regions have had fewer than 100 confirmed cases. For the 187 sample countries or regions in the JHU data, COVID-19 cases per million residents range from about 3 to 78,634, with the highest observed in Andorra, Bahrain, Belgium, Montenegro, and Qatar, all above 47,500. Meanwhile, Cambodia, Lao PDR, Tanzania, Qatar, and Vietnam have the lowest cases, all fewer than 19 per million residents. Countries around the world have also reported very different COVID-19 mortality rates (i.e., the number of COVID-19 deaths divided by the number of cumulative cases), ranging from 0 to 29 percent. What explains the vast differences in the pandemic outcomes?

The unprecedented challenges of the pandemic compel every country, no matter the 2