rafmacalaba/data-use-mentions-extended huggingface dataset:
for displaced women, in particular, should have built-in guidelines for the protection of women and should engage men in promoting more gender-equitable relationships (Heilman & Barker, 2018; van der Gaag et al., 2019). Given the long-term nature of displacement in Colombia, it is important to build capacity for both displaced women and men to access economic opportunities, which can eventually replace social assistance. Program interventions should identify occupations and sectors where they could work given their skills and include support services, such as flexible working hours and childcare to address constraints related to domestic responsibilities.

Finally, the risk of sexual and GBV in situations of displacement has devastating impacts on the individual and on the household. This is a complex issue and requires a battery of interventions for prevention and response. Interventions that have worked in non-displaced settings such as strategies for addressing norms that condone violence against women, designing effective facilities and services for survivors, and engaging men and boys in prevention and response could be piloted in situations of displacement (see Jewkes et al., [2015] and Ellsberg et al., [2015]).

This study has limitations. Large-scale household surveys rarely include questions to identify and measure a norm. Moreover, the choice of proxy indicators for attitudes and behaviors is driven by data availability and some of them could arguably be mapped onto multiple spheres or domains of gender norms. Given data limitations, mixed methods studies can offer more insights as the economics literature on the gender-differentiated effects of conflict-induced displacement evolves. The limited time span covered by the migration question might pose a challenge to capturing changes in gender norms. Nonetheless, the theoretical framework argues that displacement can accelerate change by, for instance, opening up economic opportunities for women in urban areas and reducing exposure to traditional structures that reinforce gender norms (Cislaghi & Heise, 2020; Harper et al., 2020; Marcus & Harper, 2015; Muñoz Boudet et al., 2013). The lack of data for men is another limitation. Gender norms are
# **A4. Crop/tree classifications**

**Table A.1.** Crop/tree classification by type of tree in LSMS-ISA data

|**CROP**|**Type of Tree**|**CROP**|**Type of Tree**|
|---|---|---|---|
|Agbono (Oro Seed)|Fruit Tree|Black Pepper|Tree Cash Crops|
|Apple<br>|Fruit Tree<br>|Cashew<br>|Tree Cash Crops<br>|
|Avocado|Fruit Tree|Cashew Fruit|Tree Cash Crops|
|Bilimbi<br>|Fruit Tree<br>|Cashew Nut|Tree Cash Crops<br>|
|Bitter Kola<br>|Fruit Tree<br>|Castor Beans<br>|Tree Cash Crops<br>|
|Bread Fruit|Fruit Tree|Chat|Tree Cash Crops|
|Buya<br>|Fruit Tree<br>|Clove<br>|Tree Cash Crops<br>|
|Cherry (Agbalumo)|Fruit Tree|Cocoa|Tree Cash Crops|
|Cinnamon|Fruit Tree|Cocoa Beans|Tree Cash Crops|
|Coconut<br>|Fruit Tree<br>|Cocoa Pod<br>|Tree Cash Crops<br>|
|Custard Apple|Fruit Tree|Coffee All|Tree Cash Crops|
|Date Palm|Fruit Tree|Dry Leaves (Kuka)|Tree Cash Crops|
|Durian|Fruit Tree|Gomme Arabique|Tree Cash Crops|
|Fig|Fruit Tree|Gum Arabic|Tree Cash Crops|
|Gishita<br>|Fruit Tree<br>|Iyere|Tree Cash Crops<br>|
|God Fruit|Fruit Tree|Locust Bean|Tree Cash Crops|
|Grape Fruit|Fruit Tree|Macadamia|Tree Cash Crops|
|Guava|Fruit Tree|Monkeybread|Tree Cash
In the fixed effects model, an increase of the lagged overall determinant index by 1 percent is associated with an increase of the annual TFP growth rate by 0.05 percent, after controlling for the lagged TFP level and country and time effects. Suggesting convergence, an increase of lagged TFP by 1 percent is associated with a decrease of annual TFP growth rate by 0.10 percent, holding other variables constant. This implies that countries with a higher level of TFP need to increase the determinant index more than those with a lower level of TFP to achieve the same amount of increase in TFP growth. These results are robust in terms of signs and significance when we use different lags of three and seven years (see appendix D). They are also robust when we use the WDI-based data in the construction of TFP levels and growth rates (see appendix E).

# **5. Simulations and Scenario Analysis**

For illustration purposes, in this section we simulate the change in TFP growth rate for 78 low- and middleincome developing countries (that is, countries with GDP per capita in 2014 lower than $12,056, constant USD 2010). We present the simulation results in averages by region or income group. More generally, the Long-Term Growth Model (LTGM) toolkit can be used to generate country-specific projections for TFP growth for a much larger set of countries. This allows of the LTGM users to replace the assumption of an exogeneous path for TFP growth by one that is based on improvements in innovation, education, market efficiency, infrastructure, and institutions, feeding into the overall determinant index.

We provide four scenarios below. They present different ways and extents of improving the TFP determinant index, to regional or world benchmarks (or leaders). We use the fixed-effect regression results to relate changes in TFP growth to changes in the overall determinant index. The corresponding increase in TFP growth depends directly on the speed of progress in the country’s TFP determinants and inversely on the extent of
# **Table 1. Share (percentage) of employment/work rates with added definition not detected in 3-month definition, conditional on employment in a project, by project characteristics, and country, women aged 15-64**

||**Tunisia**|**Egypt,**<br>**Arab**<br>**Rep.**|
|---|---|---|
|**Additional employment market definition**|||
|**Non-farm enterprise in or out of an establishment**|||
|All outside|18.9|12.0|
|Inside (one or more)|11.7|16.4|
|Total|15.0|14.0|
|**Livestock size**|||
|All small/medium|55.7|48.3|
|Large (some or all)|52.5|26.4|
|Total|55.2|38.6|
|**Crop land area**|||
|Below median||18.7|
|Median or above||16.2|
|Total||17.0|
|**Additional work extended definition**|||
|**Livestock size**|||
|All small/medium|34.5|10.4|
|Large (some or all)|22.7|9.7|
|Total|31.1|10.2|
|**Crop land area**|||
|Below median||10.8|
|Median or above||3.7|
|Total||6.7|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: Restricted to participants in the project type.

Market livestock work tends to be particularly underreported (55% of additional market employment was not
underreporting their income and receiving, on average, much more of their actual income as "envelope wages" or non-declared cash in addition to their official wage. This can lead to a spike in the minimum wage in the wage distribution and undermine the effectiveness of the minimum wage policy. Envelope wages remain an important problem in Central and Eastern European transition economies despite some improvements. Empirical research confirmed that the spike in the minimum wage is partially explained by the underreporting of income (tax evaders are overrepresented among minimum wage workers) in Hungary (Tonin, 2011), Latvia (Gavoille & Zasova, 2023), and Poland (Kiełczewska et al., 2021). Therefore, assessing tax compliance and underreporting of income, particularly among minimum wage workers, is essential for promoting effective social policy and designing effective minimum wage policies. It can help ensure that the benefits of social programs are targeted to those eligible and promote greater equity in the tax system. It can also inform policy design by identifying the factors contributing to tax evasion and developing measures to reduce them, promoting a more effective and equitable fiscal system.

**The document is organized as follows.** Section II explains the methodology and data sources used. Section III presents the main results of the study for Romania. Finally, Section IV concludes.

# **II. Methodology and Data**

**Several methods assess tax evasion in the literature, each with advantages and limitations. To obtain a more comprehensive understanding of the extent of tax evasion in Romania, it is essential to** . **understand which methods or combinations of these methods are more reliable**

**One method compares tax returns and tax audit data, although it is important to note that audits are not always conducted randomly** . Analyzing tax audit data can provide detailed information on noncompliance data. This information can be enriched by linking the audit data to population census data. However, this method is not always feasible, given the restrictions on access to
within these EAs. An important feature of these surveys is that households are georeferenced, which facilitates the mapping of each household’s geographic location to its respective agricultural zone. This enables the integration of information on zonal agricultural season cycles with households’ self-reported agricultural cycle data.

For our individual level analysis, we further restrict the sample to keep middle and high school students who are currently enrolled in school. That is, our sample consists of students enrolled in 5<sup>_th_</sup> through 12<sup>_th_</sup> grades. To account for potential spatial correlation within EAs, we cluster the standard errors of all our regressions at the EA level.

# **4 Empirical Framework**

We estimate the effects of temperature during the exam year on standardized total test scores of each student using the following regression specification.

where the outcome variable _Yisjt_ is the standardized test score for student _i_ , in stream _s_ , in school _j_ , and in year _t_ . The variables _Tjt_<sup>_m_denotethenumberofdaysinschool</sup><sup>_j_and</sup> year _t_ where the daily maximum temperature was in the _m_<sup>_th_</sup> of the seven bins used in our analysis.<sup>12</sup> Following the literature, we set 18-21<sup>_◦_</sup> C to be the reference bin. Similarly, variables _Prec_<sup>_n_</sup> _jt_<sup>denotethenumberofdaysinschool</sup><sup>_j_andyear</sup><sup>_t_wherethedailytotal</sup> precipitation was in the _n_<sup>_th_</sup> of the four bins used in our analysis.<sup>13</sup> We estimate several versions of this specification, dividing the year into school days and non-school days as well as growing season and non-growing season days.

The variable _Female_ is a dummy which takes the value of 1 if the student is female, and 0 otherwise
surveys advertised through Facebook in 2022/2023.

The main report on the in-person survey<sup>4</sup> outlines the relevant context of the West Bank and Gaza as well as theoretical frameworks linking mental health outcomes to socio-economic outcomes, experience of adversity, and exposure to violence and trauma. Taking this context and theoretical relationships as given, the analysis that follows explores differences between the in-person and Facebook surveys.

We can reasonably expect estimates from internet surveys and in-person surveys to differ, even if they aim to measure the same things. In the West Bank and Gaza example, the Facebook survey estimates more severe outcomes (i.e., higher exposure to conflict, worse mental health) then its in-person counterpart for most topics and populations. In this report we explore three possible mechanisms that may have contributed to this pattern of differences: (1) nonresponse bias, (2) sensitivity bias, and (3) misreporting to influence resource allocation.

We find that estimated outcomes tend to be more similar for people in Gaza, possibly because of greater homogeneity in socioeconomic experiences and exposure to violent conflict and broader interest in a survey on the effects of the May 2021 violence. We also assess the extent to which our results are robust to different ways of controlling for observable characteristics, and find that neither alternative weights nor sample restrictions erase the systematic differences between surveys.

> 3 These included the Palestinian Central Bureau of Statistics (PCBS), Zentrum Überleben, and the International Security and Development Center (ISDC).

> 4 World Bank Group et al (2022), available here. The PPCS data [World Bank (2023)] can also be accessed directly <u>here.</u>

2
We also discuss how these parameters can be estimated using individual income data and econometric techniques that exploit both the longitudinal and repeated cross-section features of our data set on individual incomes from Mexico (Sections III-V).<sup>6</sup> Finally, we use a tractable consumption-saving model with labor income risk and incomplete markets (Section VI) that yields closed-form solutions for equilibrium consumption and welfare as a function of the preference and income parameters. This theoretical framework, based on the work of Constantinides and Duffie (1996) and Krebs (2007), focuses on the persistent component of labor income and abstracts from the i.i.d component, an abstraction motivated by results in the literature demonstrating that workers can effectively self-insure against transitory income shocks, as we have already mentioned.<sup>7</sup> One of the main insights of this literature is that in equilibrium consumption responds one-for-one to permanent income shocks.<sup>8</sup> In this paper, we exploit this property of equilibrium consumption to derive an explicit formula for social welfare as a function of the underlying income parameters.

The analytical framework we develop in this paper has the merit of linking income dynamics, income mobility and social welfare in a simple and transparent manner − allowing for a clearer analytical and quantitative discussion of these interrelated concepts, and specifically the role of income variability, than has generally been possible in the past. We discuss in detail how different determinants of measured income mobility may have quite different Fields and Ok (1996) discuss, however, the literature has recently made important advances in studying the “multi-faceted concept” of mobility and a number of different theoretical measures, each capturing a different aspect of mobility have been introduced. We have no contribution to make to this discussion and simply use the Hart Index as our basic measure of mobility.

> 6For an interesting exercise which compares results on poverty vulnerability (the propensity to move into poverty) obtained using panel data on incomes with those obtained from repeated
at the 10% level, but the next section will present evidence that the differences are, indeed, important and likely significant over the longer term.

One exception to the null or negative results for mature and old cohort of plants is the large increase in plant count for mature and old plants. Since the synthetic cohort structure restricts our sample to the set of existing plants as of 2000, this necessarily implies a reduced exit rate among these firms. This is counterintuitive given that we might expect the increased product market competition precisely to challenge weak incumbents. We return to this puzzle in section IV using plant-level data where exit decisions can be studied more directly.

Annex Tables undertake three tests of the possibly confounding effects of firm geographical selection and endogenous highway placement by exploiting new segments vs upgrades (A.3a), the unfinished NW-EW highway as a placebo (A.3b), and straight-line highway layouts as an instrument (A.3c) and confirms the findings of the previous literature that neither is importantly biasing the results.

6
|
|,<br>NDP10 Cumulative balance (in billions of 2008/09 Pula)||_-11.0_|-20.0|-26.6|-30.9|-33.1|-34.1|-33.8|
|Non-mineral fiscal balance (% of non-mining GDP)|_-28.7_|_-35.5_|-32.4|-28.9|-24.3|-20.4|-18.1|-15.7|
|<br>Government Investment Account (in billions of Pula)|_27.6_|_20.5_|12.6|6.7|5.3|6.6|8.5|10.0|
|Government Investment Account (% of GDP)|_31.6_|_23.1_|12.5|5.9|4.2|4.6|5.5|6.0|
|External debt & guarantees (% of GDP) /3|_..._|_13.0_|20.0|20.0|20.0|20.0|20.0|20.0|
|Domestic debt & guarantees (% of GDP) /3|_..._|_5.8_|4.4|4.3|5.4|6.6|7.0|5.9|
|Total debt & guarantees (% of GDP)|_..._|_18.7_|24.4|24.3|25.4|26.6|27.0|25.9|

Source: Author’s calculation. 1/ Estimates.

2/ A negative figure indicates an increase in the cash balance and vice versa.

3/ Data for 2008/09 do not include guarantees in the absence of information.

4/ Net of government gross assets (GIA balance
be downgraded on account of pandemic-related business disruption, and that it would become more expensive or even impossible to raise cash after that. With falling revenue, most SMEs are now in major financial distress (Bartik et al., 2020; Zhang, 2020).

While there is much uncertainty about the longer term impact of the pandemic, evidence from past crises points to potential scarring effects in the long run. Reallocation effects of the COVID-19 shock are likely to persist long after the pandemic recedes (Barrero et al., 2020). All firms are vulnerable to persistently low demand, but in large systemic crises good and bad firms may be weeded out alike (Hallward-Driemeier and Rijkers, 2013; Foster et al., 2016). More recent estimates from Bosio et al. (2020) using Enterprise Survey data suggest that in multiple shock scenarios, firms suffer liquidity shortages regardless of age, size and productivity levels. Uncertainty can further deter economic activities. Balla-Elliott et al. (2020) find that post-lockdown delays in business reopening in the United States can be explained by low levels of expected demand rather than by health concerns. During past crises, firms were less likely to undertake radical innovation and disproportionately cut back on intangible investment Granja and Moreira (2019); Duval et al. (2020). There has been an unprecedented level of uncertainty documented during this pandemic and firms have been found to respond by significantly cutting expenditures on innovation and general management improvements, which will likely affect future productivity growth (Baker et al., 2020).

Despite this quickly expanding literature, there has been no systematic cross-country evidence about the impact of the pandemic on firms. Some cross-country evidence exists but only focuses on small and relatively selected samples of firms. For example, using survey responses from early April across nearly 500 listed firms in 10 emerging markets, Beck et al. (2020) find that the vast majority of firms have been negatively affected by COVID-19 and reacted by reducing investment rather than payrolls. Surveys of businesses
# 2. Institutional Mapping

The purpose of the institutional mapping section is to contribute to the measurement of opportunity structure discussed in the paper. The measurement of the institutional element of opportunity structure is not easily captured using individual or household surveys and therefore requires a mixed-methods approach, combining various participatory tools and processes in group discussion exercises to generate local indepth data on the operation of formal and informal institutions together with the national tracking of legislation, regulation, and procedures. Participatory group analysis will enable data to be collected on those intermediate indicators where preexisting indices do not exist, and additionally enable a degree of triangulation on those where they do.

Table 1 summarizes the information that can be elicited from both the institutional mapping and from the questionnaire. Institutional mapping can be sequenced with the administration of the questionnaire, providing an opportunity to identify focus groups through sub-sampling of the questionnaire households (see discussion below).

There are various participatory tools that can be used to capture information not already available through pre-existing indices or sources within different domains/sub-domains. These are listed in the final column of table 1. Where there is more than one possible tool, the most appropriate tool is identified as primary. The selection of tools depends very much on the context and the information being sought.

Groups should consist of between five to twelve participants (ideally about eight) and reflect social stratification in any particular context. Key informants with in-depth country knowledge should be able to provide the researchers with the most important social groupings. Researchers should place particular emphasis on ensuring that marginal groups are included in the process. Mixed group interviews can also be conducted to assess levels of consensus, but these should be in addition to separate groups.

81
large government facility that grants bank credit for 4.6% of gross domestic product (GDP). A concurrent employment insurance program allows firms to cover salaries while employees are not reporting to work due to the pandemic, amounting to 0.62% of GDP.<sup>2</sup> We collect transaction-level information on the universe of bank credit to all firms, including loan applications from firms and corresponding approvals or rejections from banks. Further, we complement this data with information about firms’ use of the employment program. We match the financial data with administrative tax data for the universe of formal

> 1Credit and employment programs are part of a wider array of policies implemented during the COVID-19 pandemic to deal with the crisis. For comprehensive recounts, see Harvard’s Kennedy School, the IMF, Cirera et al. (2021), and Feyen et al. (2021).

> 2Chile’s credit guarantee program is similar to many credit programs implemented in Asia, Europe, and Latin America during the pandemic. For a review of some of those cases, see Anderson et al. (2023) and Hong and Lucas (2023a,b). Those programs differ from the well-known U.S. Paycheck Protection Program (PPP) in that the latter offers loans that effectively convert into grants to firms instead of loan guarantees.

1
which each provision appears in the 300 RTAs covered by the DTA database).<sup>6</sup> One drawback of the DTA database is that it does not cover RTAs that are no longer in force.<sup>7</sup> So, when controlling for the presence of an RTA between two countries (see section 4), we complement the DTA database with the CEPII data covering active and inactive RTAs. The _RTA adjusted_ dummy is equal to one if a RTA is observed (from CEPII and/or DTA dataset) and zero otherwise. As for the depth of RTAs not mapped in the DTA database, we construct two proxies of depth. First, we (arbitrarily) set the depth measures for these RTAs as equal to

> 4See Horn et al. (2010) for definition of WTO+ and WTO-X provisions. Core provisions are defined in Hofmann et al. (2017) and include all WTO+ provisions plus clauses that regulate competition policy, bilateral investment, movement of capital and intellectual property rights.

> 5It should be noted that in this paper we aim to capture the impact of the _overall_ depth of RTAs on firms with different characteristics. Different provisions in trade agreements, however, have different effects on aggregate trade outcomes (Fernandes et al., 2021) and are likely to have heterogeneous effects on firms’ exports. For example, provisions that lower fixed entry costs on the destination market, such as provisions aimed at improving trade facilitation or reducing regulatory divergence, can make it easier for small firms to export. Fernades et al. (2021) finds some preliminary evidence of this focusing on provisions on technical and sanitary standards in trade agreements involving a sample of Latin American countries. Other provisions may have just the opposite effect. For instance, requirements in DTAs to meet higher environmental or labor standards could make it easier for larger exporters relative to smaller firms to export in the destination market, reinforcing the competition effect that we stress in this paper.

>