rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 5: Remittance Flows from the Russian Federation to Central Asia in USD**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>KAZAKHSTAN KYRGYZSTAN TAJIKISTAN TURKMENISTAN UZBEKISTAN<br><!-- End of picture text --> _Source: The Central Bank of the Russian Federation_ The phone-based panel component of the data collection in L2CU also enables monitoring of when migrants return, and for what reasons. Over the months between September 2018 and April 2019, between .6 and 4.2 percent of households had a migrant return in the previous month. About 24 percent of returning migrants did so due to an expiring permit or related issues. About 14 percent returned due to “homesickness”, another 10 percent of returnees came home for “vacation”, and around 12 percent cited “family reasons”. About 9.5 percent returned when they achieved an income target and 10 percent returned for health reasons. Slightly more than 5 percent returned after having found a job in Uzbekistan.

15
.03|0.03|
|Mean dep. var. of mission cells|0.07|0.07|0.07|0.07|0.07|0.07|
|Number of cells|6,819|6,819|6,819|6,811|6,811|6,811|
|Observations|136,380|136,380|136,380|136,220|136,220|136,220|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; elections from Varieties of Democracy; and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Results are from a balanced annual panel over 20 years. Standard errors are clustered at the country level. Controls included in columns 4–6 are the following variables interacted with the Election and/or Turnover dummy: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude).<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 down and these areas experience a temporary drop in aid. The (less robust) increase in election years without turnover could constitute a reward from the incumbent in exchange for political support, but the instability of the estimate makes it to draw conclusions.

The findings in table 9 cannot be explained by a general slowdown of project commitments due to government officials being busy with the electoral campaign, because the regressions include country-year fixed
is intimated to the system operator (also called the state load dispatch center (SLDC)), one-day prior to dispatch.<sup>21</sup> The SLDC verifies this demand against supply-side information provided by generators, such as, capacity at 15-minute intervals, machine maintenance schedule and overall expected congestion in the system. Based on these factors, the SLDC notifies the final drawl schedule for the next day to the generating company and the utility.

Finally, unscheduled power is the difference between forecasted (i.e., scheduled) and real-time demand. The market for unscheduled demand, also called the unscheduled interexchange (UI), is a real-time balancing mechanism that allows utilities and generators to adjust the scheduled demand to the instantaneous demand and supply of power.

Given that utilities and generators can take arbitrage positions across all the different segments of the short-term market, the prices in these sequential markets are expected to be closely correlated. As price determination in the day-ahead market occurs through an auctioning process, we consider the market clearing price of this market segment to be the best economic measure of marginal cost of generation at each 15-minute interval that best encapsulates the marginal cost of generation and transmission constraints.

To test if the market clearing price (MCP) from the India Energy Exchange (IEX) wholesale market<sup>22</sup> reflects the instantaneous price of power in the UI market, the generator-wise dispatch information from Rajasthan’s SLDC is compared to the MCP information of the N2 region of IEX (comprising the state of Rajasthan) for January to December 2018. Aggregating all prices to daily frequency, the correlation between average prices in the two markets to be 0.76. Ryan (2018) using data for 2009-2010, finds a correlation of prices in these two markets to be 0.808 – supporting the view that even though the wholesale electricity market in India comprises of different segments with separate rules and regulatory limits on market participation, the prices discovered in the short-term segments of the wholesale
indicator, as its primary metric and investigates how policy reforms can influence this measure** . In the European Union context, this evaluation generally involves a comparative analysis of the "at-risk-of-poverty" rates with and without the proposed policy reforms. Both poverty rates are determined using the same poverty threshold, set at 60 percent of the national median adult equivalent disposable income as of 2020. It's important to note that the estimates presented in this report are based on the data from CEQ Bulgaria (Robayo-Abril & Cabrera, 2023). The findings from this updated analysis reveal that, despite the policy reforms considered, the impact of fiscal measures on poverty and income inequality remains relatively limited.

**There are several advantages of using the CEQ and not individual microsimulation of the MSA reform scheme** . The CEQ approach considers the whole fiscal system, taking into account not only social transfers

12 The Bulgaria NSI publishes the Price Index of a Small Basket (PISB), which comprises about 100 goods and services considered socially useful and vital for living. The aim is to gather representative country data on prices paid by households for these items and calculate indices reflecting their changes over time. The PISBs focus on goods and services essential for the biological and social existence of individuals or households with relatively low incomes, using expenditure data from the lowest income 20% of households. The methodology aligns with the calculation procedure for the Consumer Price Index (CPI). However, this Index of consumer basket measures only the change of prices and, therefore, is defined as a "pure price change" index. They do not measure the cost of living and are not the cost of living indices.

> 13 In countries where official poverty is measured using consumption, national extreme poverty lines are anchored to the cost of a food basket.

> 14 Reference budgets have been developed across the EU by _the Herman Deleeck Centre for Social Policy, University of Antwerp, and DG Employment, Social Affairs and Inclusion,_ with the purpose of assessing
A question arises why women are poorly remunerated especially in the informal wage sector. A potential explanation is that jobs in the latter sector tends to involve work of a low-skilled or elementary nature,<sup>32</sup> and physical strength may matter more in such jobs than in factory employment or more mental-labor intensive jobs (e.g., Fan and Lui, 2003; Galor and Weil, 1996; Goldin and Sokoloff, 1982).<sup>33</sup> For instance, using agricultural wage data available in the _Vietnam Living Standard Survey_ (VLSS) 1998, Brassard (2004) finds that the wage gap between genders is particularly high for physically demanding work, such as land preparation and crop harvesting. Following Fan and Lui (2003), I ran four separate Mincerian regressions by ownership categories for Vietnam assuming that the coefficient estimates of the female dummy captures the unexplained part of the gender gap. The results reveal that, controlling for personal and other characteristics, the gender gap is largest in the informal wage sector as the average female worker earns about 23.6 percent lower wages relative to her male counterpart. The gender gaps are found to be about 15.0 percent for foreign and domestic private sectors. Other things equal, disproportionately drawing women from the low-wage informal sector (where the gender gap is very large) to a higher wage sector, the EPZ has the potential to contribute to narrowing the gender gap (Glick and Roubaud, 2006). 32 The largest sources of informal wage employment for males were construction (36 percent), agriculture/forestry/fishery (27 percent) and transportation (7 percent) whereas those for females were agriculture/forestry/fishery (37 percent), other services (10 percent) and sales (9 percent) in 2004 (the VHLSS 2004).

33 Some economists relate the evolution of relative wages of females to women’s changing comparative advantage as economies develop (Fan and Lui, 2003; Galor and Weil,
**Figure 2:** Mozambique’s hidden debt, 2006 – 2014

<!-- Start of picture text --> 8<br>Hidden debt (in 2014):<br>6<br>• USD 1.36bn<br>• 7.7% of GDP<br>4<br>2<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Initially reported external public debt<br>External public debt added through revisions<br>(USD billion)<br>Public external debt stock<br><!-- End of picture text --> _Notes:_ The figure shows the initially and most recently reported public and publicly guaranteed debt stocks for Mozambique between 2006 and 2014 in billion USD. The grey bars show the initially reported debt stocks. The red bars show additional debt stocks added through revisions in subsequent reporting. The data is from our new database (see Section 3 and Appendix A).

Our measurement approach allows to quantify Zambia’s hidden debts by comparing debt figures across different vintages of the World Bank International Debt Statistics (see Figure 4). The reported stock of Zambia’s external public and publicly guaranteed debt for 2021 amounted to USD 12.5 billion when it was initially published in the World Bank’s International Debt Report 2022. In the latest vintage, the same figure is given as USD 15.7 billion, indicating an upward revision of USD 3.2 billion or 14% of GDP.

# **3 Data, measurement and interpretation**

In this section, we introduce our new dataset of debt data revisions and explain how we quantify hidden debt and hidden debt revelations. Throughout this section, we focus on the main principles of data construction and measurement and refer interested readers to Appendix A for further details.

## **3.1 A new database of debt data revisions**

To systematically quantify hidden debt and its
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firms therefore tend to favor personal contacts in recruitment whereas larger firms are more likely to use formal channels in addition to informal ones (World Bank, 2013). Participants of the control group who followed the standard curriculum completed an internship prior to graduation and are therefore more likely to have gained personal contracts in the private sector. Graduates of the entrepreneurship track likely lacked these contacts and might have been more likely to seek recruitment in larger companies.

# **8. Conclusion**

This paper provides new experimental evidence on the medium-term impacts of entrepreneurship education on self-employment and labor market outcomes up to four years after university students graduate. The study sheds light on whether entrepreneurship education can successfully foster actual, nascent or latent entrepreneurship. It also analyzes whether a time lag between intentions and actions explains why the impacts of entrepreneurship education on employment outcomes is rarely documented. Based on short-term and medium-term follow-up surveys, as well as data on employment history and individuals’ attempts to set up projects going back to graduation, the study provides rare information on the time-paths of potential impacts of entrepreneurship education on self-employment and labor market outcomes.

The results show that small program impacts on self-employment were mostly concentrated in the year after graduation but were not sustained in the medium-term. Results show that participants were more likely to try to realize a project at some point. As a result of these additional attempts, they were more likely to set up a project and thus more likely to have had an active project at some point as well. However, they were not more likely to have an active project at the time of the endline survey, nor were they more likely to succeed in their attempts. Overall, patterns of heterogeneity are weak and the lack of medium-term impact on selfemployment holds across groups. Results on intermediary outcomes are also consistent. Nearly 4 years after graduation, program participants still have better knowledge on the content of a business plan, but less so than at midline. In contrast, changes in personality dimensions, aspirations and entrepreneurial traits
driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **I Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme,
|**Table6a.Busine**|**OLS Regres**<br>**ss Management Pr**|**sion Analysis**<br>**actices**|**Table6b.Business**|**Management Prac**<br>**OLS Regres**|**tices**<br>**sion Analysis**|
|---|---|---|---|---|---|
||**Business**<br>|**Financial**||**Business**<br>|**Financial**|
||**development**<br>**index**<br>|**planning index**<br>||**development**<br>**index**<br>|**planning index**<br>|
||coef/se|coef/se||coef/se|coef/se|
|Female owner|0.007|0.117|Female owner|2.540*|2.371***|
||(0.270)|(0.148)||(1.387)|(0.620)|
|High education|0.899***|0.253|High education|0.911***|0.268|
||(0.254)|(0.178)||(0.258)|(0.179)|
|Small firm (6-10)|0.341|-0.144|Small firm (6-10)|0.272|-0.206|
||(0.386)|(0.192)||(0.380)|(0.202)|
|Medium
the last 5 km in the PSU/EA|7|

# **4. Using Harmonized Household Surveys to Produce Stylized Facts**

The diverse design of transport questions, especially the fragmented evidence on household ownership and expenditure of transport vehicles and services across countries, adds complexity and challenge to data harmonization and cross-country analysis. The World Bank’s Global Consumption Database can be taken as an example of synthesizing the evidence on household expenditure, and SSAPOV, a database of harmonized nationally representative household surveys in Sub-Saharan Africa, contains the harmonized information on ownership of vehicles.

## **4.1. Transport Expenditure: Global Consumption Database**

The Global Consumption Database is a one-stop source of data on household consumption patterns in developing countries, provided by the World Bank’s Development Data Group. It 17
Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Romania, and United Kingdom.

> 29 Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Romania, and United Kingdom.

> 30 For details on methodology see Lehmann and Muravyev (2009).

> 31 For description of data sources and discussion of information relevancy of these indicators see Section 1 and Annex 3.

> 32 This method estimates the size of the shadow economy from both likely causes and effects of the informal activity. The underlying variables comprise the size of the government, the unemployment rate, government effectiveness, the GDP per capita, currency (M0 over M1), the growth rate of GDP per capita, the labor force participation rate, the fiscal freedom index, the share of direct taxation, the business freedom index, the growth rate of total labor force (Schneider et al., 2010).

16
Benincasa et al. (2024) find that extreme weather events erode balance sheets as firms that experience monetary losses from shocks become more leveraged and are seen as less credit worthy. The study also notes that firms affected by weather shocks are more likely to invest in fixed assets as they replenish damaged capital (Benincasa et al., 2024). New investments can also lead to vintage effects where replenishment of capital means newer equipment with a lower environmental footprint. On the other hand, firms may become environmentally aware and therefore increase green investments or improve green management practices. A closely related study by Benincasa et al. (2024) covers European and MENA economies but relies on a self-reported measure of monetary damages from extreme weather events in contrast to the geocoded data on precipitation shocks used in this study.

The study also tests whether some of the channels highlighted in the global literature also apply to the MENA region. Overall, we see that negative precipitation shocks decrease labor productivity. The channel could be through a change in the composition of workers -- negative precipitation shocks are found to increase the share of temporary workers, potentially explaining the drop in labor productivity. Negative precipitation shocks also increase losses from power outages with some indication of increases in water outages although the finding is not as robust. Negative precipitation shocks limit access to finance by decreasing the likelihood that firms use banks to finance working capital and have access to overdraft facilities. A new channel uncovered in this study is governance. The intensity of negative precipitation shocks increases the amount of time senior management spends dealing with regulations and expectations of bribe solicitations. For the cross-section of 2019/2020 MENA surveys, there is a green module included in the survey instrument that captures green investments and green management practices. This study finds negative precipitation shocks increase the likelihood of adopting water management, more climate friendly energy on site, and machinery and equipment upgrades, suggesting scope for adaptation.

The study makes several policy recommendations. Firms in MENA economies are vulnerable to climate shocks. Building infrastructure
additional redistribution depend on gross labor income shares rather than on disposable labor income shares.

I assume that the marginal utility of consumption takes the form: Harsanyi [1955] establishes that, for the purpose of welfare analysis, it is appropriate to interpret _γ_ as the coefficient of relative risk aversion (rather than, for example, the intertemporal elasticity of substitution or a parameter that governs the degree of altruism). I choose _γ_ = 0 _._ 5, a highly conservative estimate of risk aversion. The macro literature typically uses _γ_ = 2, and much larger values are often used in the finance literature, which documents that large levels of risk aversion are necessary in order to account for the equity premium puzzle.

Estimated tax shares, _sT_ , are based on data from the World Development Indicators (WDI). Estimated labor shares, (1 _− α_ ), are taken from the Penn World Tables (PWT). I assume that _μ_ = 0 _._ 6, which is highly conservative given estimates of the administrative costs of cash transfer programs.

As a conservative benchmark, I assume that the targeting constraint does not allow any targeting of poor households, and that the government must transfer the same amount to every household. This is conservative because the welfare gains from cash transfer programs are higher when they target those who have higher marginal utilities of consumption.

Finally, there is substantial controversy regarding the labor supply elasticity. The micro literature typically finds an elasticity in the vicinity of 0.3, while the macro literature finds that a much larger elasticity of between 1 and 2 (see Keane [2011]). I take a conservative approach and assume that ̃ _εL_ = 2. Note that a high elasticity of labor supply implies that taxes are more distortionary.

12
in Panama over the 1997-2003 period. Although international migration is likely to be an important element in the dynamics of the Panamanian labor market, it is not considered in this analysis due to the difficulties of modeling this flow in a single-country setting. Labor mobility across formal and informal activities is not limited, but informal workers earn significantly lower salaries (on average, 20 percent of formal sector wages), giving rise to potentially large productivity effects when demand for one of the activity types rises. Finally, the model assumes no change in the degree of resource utilization, or fixed employment. This assumption is consistent with the available econometric evidence for Panama (see Galliani, 2006), which shows that the unemployment rate has been fairly steady at around 6 percent and has not changed much during economic upswings or downturns.

The volumes of government current and investment spending (including investments in the canal) are fixed as shares of real GDP, while the deficit (in real terms) is also fixed. Public revenues adjust to clear the government balance by means of a flexible household direct tax rate.<sup>7</sup> The investment-to-GDP ratio is fixed at the base year value and a flexible marginal propensity to save out of household disposable income ensures that total saving equals total investment. The current account balance is fixed by the available quantity of foreign saving and the exchange rate is the numeraire, which means that domestic prices are determined relative to a fixed-cost basket of foreign goods.<sup>8</sup> The model is solved in a recursive dynamic mode, in which subsequent end-of-period equilibria are linked with a set of equations that update the main macro variables. There are three determinants of real GDP growth in the model: labor supply growth, capital accumulation, and increases in productivity. The volumes of both types of labor grow exogenously at the rate of growth of the working age population (ages 15-64), obtained from World Bank population forecasts. The capital stock in each period is the sum of