rafmacalaba/data-use-mentions-extended huggingface dataset:
, Chawla et al., 2002) and divide your original sample into disjointed training and testing sets to assess treatment effects on the original real-world (skewed) data rather than on the artificially rebalanced dataset.

If not, carry out the analysis at both the individual and household level, and compare the key findings. Remember, however, that aside from technical issues, household-level analysis should be preferred for both risk-diversification migration in response to slow-onset changes and ‘survival’ migration in response to fast-onset shocks.

# **4) Does the survey include an agricultural questionnaire?**

If yes, use agriculture-related variables as the most important transmission channels and to explore treatment effect heterogeneity. In addition to the migration outcome, also build an _in situ_ adaptation outcome (e.g., via factor analysis starting from raw agricultural data on inputs, practices, and assets) which you can use as an alternative outcome or as a conditioning variable in order to study the dynamic relationship between migration and local adaptation or the relevance of climate-induced liquidity constraints and immobility traps.

If not, stick with the main migration outcome but be aware that, due to the predominant importance of the agricultural linkage, you may not be able to thoroughly explore treatment effect heterogeneity and mediating channels. In practice, looking only at _average_ treatment effects may be misleading if there is substantial heterogeneity whose underlying signal you are unable to detect (e.g., simplistic case: half sample trapped in immobility, half sample responding positively by sending migrants, null average effect).

# **5) Are you more interested in slow-onset or fast-onset events?**

If you want to explore responses to slow-onset changes, focus on treatment effect <u>heterogeneity, as the literature indicates that average treatment effects can mask substantial</u> 28
boosting Jordan’s manufacturing exports to the EU we conduct a difference-in-differences regression analysis. Figure 19 suggests China is a natural control to use. Formally, we estimate the following regression equation E E 0 1 2016 3 2016 + ε ee ee ee ee EEtt = ββ + ββ ∗PPEEPPtt + ββ EEUU + γγPPEEPPtt ∗EEUU + λλXX where E E is the value of Jordan’s exports in manufacturing at year , 2016 is a dummy equal to one for year in 2016 and onwards, EU is a dummy variable equal to one if destination ee of exports is EU and zero if destination of exports is China in year EEtt , and tt PPEEPPtt 2016 is a dummy equal to one if exports flow to EU as of 2016. tt is the parameter of interest. is the set ee tt PPEEPPtt ∗EEUU of pre-trends controls. Robust standard errors at the country level. For the analysis, we exploit all ee γγ XX data available in WITS disaggregated by sector and destination country, ranging between 20022022.

> 4 Signed in February 2016 at the London Conference, which was hosted by the UK, Germany, Kuwait, Norway, and the United Nations.

17 | P a g e
Last, we add to a growing literature on the role of information frictions in developing country labor markets (Abebe et al., 2021a,b; Abel et al., 2020; Bassi and Nansamba, 2021; Beam, 2016; Banerjee and Chiplunkar, 2022; Caria and Falco, 2022; Carranza et al., 2022). Specifically, we show that labor market entrants have false beliefs about formal wage growth. This is most closely related to Jensen (2010) who shows that secondary school graduates underestimate returns to education. These types of misconceptions are important since they can distort choices and reduce welfare.<sup>10</sup> While the reason for why people hold false beliefs and how to best correct them requires additional research, our results suggest that informing (young) people about events with distant benefits may not be effective if recipients heavily discount the future. In these settings, short-term incentives can offer a promising alternative to change behavior.

# **2 Background**

## **2.1 Youth unemployment in Mexico**

The Mexican labor force comprises of just over 50 million people (ENOE 2017). The unemployment rate prior to the COVID-19 pandemic was roughly 3.5% for the overall population and around 6% for youths (INEGI 2017). Among the employed population 56.5% of the overall population and 60.5% of youths work informally (INEGI 2017; IMJUVE 2017). Informal employment is the dominant entry point into the labor market for youths. Table 1 depicts transition probabilities between non-employment and formal / informal employment across quarterly panel data from the 2018 and 2019 National Labor Force Survey (ENOE) for youths aged 18 to 21. The figures estimate average transition probabilities from one quarter to the next, conditional on initial status type.

Youths without employment are almost three times as likely to be informally employed than formally employed in the following period (15.4% vs. 5.6%). At the same time
the Arellano-Bond test of first and second-order serial correlation, the Sargan test of overidentification, and the Hansen test of exogeneity of GMM instruments.

We first check cross-section dependency and stationary properties of all variables by using the Pesaran CD test (2004) and the second generation panel unit root model developed by Pesaran (2007). The CD test suggests the presence of cross-section dependence among variables and therefore, a panel unit that accounts for cross-section dependency is appropriate. The unit root test results using the CIPS test suggest that most of the variables (except FINFRA, and CRE) are non-stationary in their levels or I (1) process (Table-B1, Appendix –B). We then examine the existence of cointegration to establish the long-run relationship by using Westerlund's (2007) panel error correction model (see Appendix-B for details). The cointegration results reveal that the null of no cointegration is rejected by group tests (Gt and Ga) as well panel tests (Pt and Pa) level in favor of the existence of cointegration in the panel (Table-B2, Appendix –B).

# **5.1. Determinants of Physical Infrastructure**

The estimations of the relationship of the physical infrastructure index with its potential drivers are reported in Table 5. The estimations under the baseline model are presented in column 2 with the heading “Model 1”. The results under successive models (Model 2 to 8) with additional and alternative determinants<sup>12</sup> of the physical infrastructure index are presented in the rest of the columns. The results reveal that all the potential determinants with the exception of population density, have expected signs and significant effects on physical infrastructure. Infrastructure is highly path-dependent as its past values are significant in all specifications. This indicates that infrastructure development has an inertia effect and is procyclical.

The coefficient of the size of the economy or economic performance (GSDP) is positive and significant across models indicating that the size of the economy is one of the most important determinants of physical
has permitted a number of refugees in hard-to-reach<br>communities to create bus services with 8+1 passenger<br>vans, thus enabling other refugees to reach urban areas<br>while developing a new public transportation activity. While at<br>least five such companies are known to have registered in<br>one area, a systematic survey is now taking place to quantify<br>theresult throughout the jurisdictions.|� �<br>**ENTIAL OUTCOME**|authorities<br>�Free up capital for investment<br>�Boost exports<br>�Reduce the administrative<br>burden<br>�Reduce the gray economy|

But more than the specific result achieved by each individual reform, the largest impact on the investment climate is the fact that those reforms were passed as a package, pushed by the private sector itself, and that they received a positive response from the government, thus creating a constructive dialogue between the private and the public sectors.

The initiative succeeded in shifting the mindset of many entrepreneurs. For instance "Horizonte”, one of the main Austrian investment funds, had decided to stop investing in BiH because of the intensely bureaucratic behavior of the authorities and problems linked to corruption. After two years of dormant operations, they decided to reopen the fund because they considered the Bulldozer a positive indicator of improvement of the business climate.

The Austrian bank HVB, which participated in the Bulldozer Initiative by submitting two of the selected first 50 reforms, decided to carry out the acquisition of National Profit Banka, more than doubling its presence in BiH by doing so. In his first speech marking the acquisition, the head of HVB mentioned the important role of the Bulldozer process in forming the Austrian headquarters’ view of the business environment of BiH.

At the South East Europe Investment Conference, the BiH Foreign Investment Promotion Agency (FIPA) listed the Bulldozer Initiative as the number one factor of economic stability, noting that it helped create the “fastest
not likely to replace survey data, as these are needed to train the models, they can be used to supplement data collection and provide information either at more frequent intervals or for hard‐to‐reach areas. One particular area for future research that might be especially relevant would be to explore how such sources of data can be leveraged to estimate outcomes during rapidly evolving and dynamic events, exactly when representative surveys and other traditional data collection exercises are especially difficult to implement.

> 38 See: http://microdata.worldbank.org/index.php/catalog/2914

22
representative household survey and exogenously measured weather shock indicators. After taking into account household characteristics as well as idiosyncratic shocks and carefully dealing with transfers and aid in response to drought, we quantify vulnerability to poverty during 2004–2016. The results indicate that weather shocks have severe impacts on the welfare of households and push non-poor households into poverty. In particular, drought during the growing season decreases consumption per capita without aid by 5–12 percent. Excess rainfall during the first two months also has an adverse effect on consumption per capita. Leveraging this link between consumption and weather shocks, we conducted 4,000 simulations to estimate vulnerability to poverty following extreme weather events. Households’ vulnerability to poverty is generally higher than static poverty. This is particularly true during a good weather year when the static poverty measures are lower than the share the population that has a more than 50 percent chance of falling into poverty. In years of extreme droughts such as 2016, recorded poverty rates tend to be higher than vulnerability, which indicates that the magnitude of drought these years is 27
for 37 countries for the time period 1970-1990. They present a model of production under heterogeneous technology, where the implemented technology is chosen jointly with the level of inputs. The empirical formulation allows for the dependence of parameters of the function, as well as the inputs, on a set of state variables, where state variables are defined to be a set of variables which characterize the environment in which the production decisions are

> 21 For ease in displaying the figure, data from the United States were excluded in Figure 5. Including the US would not alter the finding of the lack of a strong relationship between the two series.

> 22 Since livestock and treestock are included in agricultural GDP, it would be more appropriate to examine the relationship between agricultural GDP and _total_ agricultural capital, which includes these components. We compromise here for the purpose of a more direct comparison with Figure 8. Both fixed capital and capital of agricultural origin (livestock and treestock) are included in the productivity analysis which is reviewed in the next sections.

11
relies on the expenditure of households on the products expected to be affected by the policy reform. It thus only captures the first-order effects of competition on welfare. Second, the current version of WELCOM only analyzes the distributional effects of changes in market concentration through the price channel, potentially missing other impacts from alternative channels, such as labor markets or the value of assets, the diversity of alternatives available for purchase, the reallocation of resources, and new consumers who had previously been priced out under imperfect competition. Notably, competition can improve the quality of the products and services provided without noticeable changes in prices. However, because WELCOM relies on household surveys (which typically do not include detailed information on the quality or brand of the goods and services consumed), the tool is not able to capture the potential welfare effects through quality. Third, the tool does not take into consideration firms that may behave outside the conventional norms established in the microeconomic literature, such as public monopolies that provide subsidized services.

This paper also illustrates an application of the tool by simulating the distributional effects of a rise in competition in two markets in Mexico known because of their high levels of concentration: mobile telecommunication and corn products. The application of WELCOM to Mexico relies on nationally representative household income and expenditure data from the 2014 round of the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH, National Household Income and Expenditure Survey). This data set includes expenditure categories that could be matched with the two markets of interest.

> 10 WELCOM was developed by Poverty and Equity Global Practice in the World Bank. More information about this tool, including installation and documentation, can be found at http://dasp.ecn.ulaval.ca/webwel/welcom.html.

3
15 through coefficient smaller than 0.8 in times of rising prices is likely to suggest attempts to keep domestic prices artificially low, or else domestic prices were very high initially and price stability was maintained against volatile world prices (as seems to be the case with LPG in Japan).

The starting point for computing pass-through coefficients is collection of retail prices. Retail prices of gasoline, diesel, kerosene, and LPG were collected in January 2009, January 2012, July 2012, and January 2013. Figure 5 shows the distribution of the prices in January 2013. The prices in U.S. dollars are given in Table B.2 in appendix B. The prices of gasoline across the countries varied by a factor of 190, diesel by a factor of 250, kerosene by a factor of 10, and LPG by a factor of 74. The large differences between the minimum and maximum prices are because of exceptionally low prices of gasoline and diesel in República Bolivariana de Venezuela, and very low LPG prices in the Arab Republic of Egypt and República Bolivariana de Venezuela. Without these countries, the price differences are reduced to a factor of about 10. Of the four months for which retail prices were collected, these variations were largest in January 2013. When distribution curves from the three months in 2012 and 2013 are overlaid, they match between the 20<sup>th</sup> and 95<sup>th</sup> percentiles for gasoline, 25<sup>th</sup> −95<sup>th</sup> for diesel, virtually the entire range for kerosene, and 0−45<sup>th</sup> for LPG. World prices in U.S. dollars were comparable in the three designated months for gasoline, diesel, and kerosene, but the price of LPG fell by one-third in the Americas between January 2012 and January 2013.

Figure 5: Distribution of Fuel Retail Prices in January 2013 in U.S. Dollars <!
Court to settle the dispute. Thus, in such a case there is no need to form a "Labor Reconciliation Council" nor can there be any involvement of People's Committees.

# _b) Evidence_

Most private sector companies hire workers on very short-tern contracts. This allows employers to deal with labor redundancies and adjust employment levels within the legal framework by simply not renewing contracts. A case study in Haiphong shows that most fulltime workers in the private footwear, garment and food processing sectors are on 6 months to I year contracts, and that no employer ever had to reduce its labor force by breaking a contract (Belser, 1999). The MPDF survey (MPDF, 1999) shows that out of 95 interviewed firms, only 20% sign more than 1 year contracts for most of their labor force and that a majority of the companies sign 6 months to 1 year contracts.

Foreign investors who have signed joint ventures agreements with State-owned enterprises have less flexibility. Usually, most of their workers are on life long contracts. The story summarized in the next section shows that although the Labor Code establishes a clear procedure for employment termination and dispute settlement, such procedures remain unfamiliar in Vietnam. In addition, the case study brings to attention the fact that unions can ultimately refer disputes over employment termination to the Provincial People's Committee. This means that, in such cases, it is an executive branch of the Government which is responsible for interpreting the Labor Code. This contrast with countries where Governments can impose compulsory arbitration through independent courts. The procedure in Vietnam thus resembles the situation in Spain, for example, where collective dismissals require the approval of the Ministry of Labor, which usually grants its approval only if unions agree.

24
**Figure 13. Out-of-sample forecasts of consumption anticipated actual trends well**

<!-- Start of picture text --> 120<br>90<br>115<br>70<br>110<br>50<br>105<br>30<br>100 10<br>95 ‐10<br>2011 2012 2013 2014 2015 (est.)<br>HH Financial Situation Better ‐ Worse [RHS] Actual (IMF May 2016)<br>This paper IMF Article IV (2011)<br>WB Afghanistan in Transition (2013) WB Pathways to Inclusive Growth (2014)<br><!-- End of picture text --> A final validation of the out-of-sample forecasts in this paper comes from the preliminary results of the latest poverty assessment for Afghanistan according to which the share of the population living below the poverty line increased from 35.8 percent in 2011 to 39.1 percent in 2013 (Afghanistan Central Statistical Office, CSO-ALCS, 2015). This particular poverty assessment used survey-to-survey imputations, as opposed to actual expenditure data, so that the poverty headcount it provides, could be less precise than its predecessors. However, if the figures are taken at face value they imply an even more dramatic deterioration of the economic situation than anticipated by the out-of-sample forecasts in this paper.

# **9. Conclusion**

This paper proposes an empirical approach to understand the relationship between conflict and household wellbeing in Afghanistan, and uses the results to predict the consequences of Afghanistan’s ongoing transition. The approach combines a dynamic model of conflict intensity at the province level with a cross-sectional model disentangling the impact of conflict-related indicators on consumption expenditures at the household level. One of the contributions of the paper is to assemble the database on conflict at the province level. Although much of the data used to this effect is in the public domain, the added value is to bring the data together on a spatial platform and to check
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