rafmacalaba/datause-encoder-data huggingface dataset:
cross-sectional studies estimating quitting equations,"<sup>5</sup> we mix together (transitory) micro- and (permanent) macro-shocks since both types of shocks cause the individual wage to change.. In this paper, however, we are mainly interested in the quitting response of individual workers to permanent macro shocks. These difficulties with using the currently observed individual wage are also the reason why we do not attempt to estimate the quit-rate function of an individual firm, _q,_ = q(wi ,w ,p, _b)._ Our approach to this problem is to add to the right-hand-side of (12) additional individual-specific human capital variables:'<sup>6</sup> The particular specification (13) with 0t satisfying standard error-term assumptions could, for example, arise as the linearized solution of the worker's dynamic programming problem under the following conditions. Suppose we decompose the individual wage and the individual selfemployment benefits as follows where the macroeconomic variables, logw1 , log _b,,_ follow a Markov process, or more specifically a random walk. Moreover, assume that the evolution of the idiosyncratic wage and benefit component is given by "<sup>6</sup> Though in the empirical analysis we do not growth adjust either w or b, in the log-formulation such an adjustment would be entirely captured in the intercept term, a0.

16
construct sampling weights as the inverse of the predicted probability of response in the phone survey. These weights were applied in all our analyses and estimations. As Table 1 shows, use of these weights markedly reduces the unweighted differences in the observable characteristics in the full sample and phone sample. Further, the weighted distribution for some observables in our phone survey is almost indistinguishable from those of the full sample (see Figure 1). In our analysis, we deploy these sampling weights to recover appropriate and representative statistics under the assumption that this list of observable factors can account for systematic non-responses in the phone survey (Wooldridge, 2007; Korinek et al., 2007).

# **3. Descriptive Results and Study Context**

Here we provide some descriptive information on our sample, our outcome variables, the spread of COVID-19 in Ethiopia and our measures of PSNP participation.

# **_3.1. Descriptive Results_**

About 90 percent of our sample households are male headed who are, on average, 39-years old and with three years of schooling (see Table 1). Households have an average family size of six, operate a little less than a hectare of land and own livestock equivalent to three tropical livestock units (TLU).<sup>7</sup> More than half of mothers have never been to school and those that did completed, on average, only two grades.

In evaluations of the PSNP, household food security is measured using a self-reported indicator called the food gap, the number of months the household was not able to satisfy its food needs (Berhane et al., 2014). In both the August 2019 and June 2020 surveys, households were asked to report their food gap over the previous six months; this outcome ranges from 0 (“no food gap”) to 6 months (“acute food gap”). We use both this count outcome as well as a binary food insecurity indicator constructed from these responses and assuming a value of 1 for households that experienced problems in satisfying their food needs and 0 for those reporting no difficulty satisfying their food needs.

More than 51 percent of households reported to have experienced food insecurity in the pre-pandemic period, with the figure higher for PSNP beneficiaries (Figure 2). This incidence of

> 7 Tropical Livestock Units are livestock numbers converted to a common unit. We apply the following formula: 1 TLU = 1 camel, 0.7 cattle, 0.8 horses, 0.5 donkeys, 0.5 mules, 0.1 sheep, 0.1 goats, and 0.01 chicken.

6
under 3 percent of its GDP, leaving a very large scope for asymmetric shocks (Table 3. 1).1° Wage and price flexibility were limited, making adjustment to real shocks difficult.

**Table 3.1. Argentina's Trade Structure (2001)**

||Geographicalcompositionof<br>importsplusexports<br>(percentageofthetotal)|
|---|---|
|Brazil|25%|
|MainLACw/o Brazil(*)|17%|
|USA|14%|
|Europe(**)|20%|
|_Restofthe world_|24%|

# Notes:

(*) Bolivia, Chile, Colombia, Ecuador, Guatemala, Mexico, Penr, Paraguay, Uruguay, and Venezuela.

(**) Austria, Belgium, Switzerland, Czech Republic, Germany, Spain, Finland, France, United Kingdom, Greece, Italy, Netherlands, Norway, Poland, Portugal, Sweden, and Switzerland.

Source: International Monetary Fund - Direction of Trade.

Indeed, the adoption of Convertibility was not guided by OCA arguments, but by the credibility-enhancing effect that renouncing monetary discretion was expected to have after many years of acute monetary instability, as well as by Argentinean investors' stated preference for dollar-denominated assets. As we shall see below, however, this purported shortcut to credibility left the economy highly exposed to disturbances.

A. WAS THERE AN OVERVALUATION? WHERE DID IT COME FROM?

Argentina's real effective (that is, trade weighted) exchange rate (henceforth REER) experienced a considerable appreciation during the 1990s." Between 1990 and 2001, the REER rose<sup>12</sup> by over 75 percent (Figure 3.1). The bulk of the appreciation developed before 1994. In fact, the REER depreciated after that date and until 1996, but then appreciated again to reach its peak in 2001.

> 10 In fact, on these grounds a peg to the Euro would have made more sense than a peg to the U.S. dollar. See Alesina, Barro and Tenreyro (2002).

> 1 Trade weights are taken from the IMF's Direction _of Trade Statistics_ and correspond to 1995. They refer to goods trade (imports and exports).

> 12 Throughout this note we define the RER so that an increase represents a real appreciation.

17
# **Box 1: Reliance on Short-term Debt in Sub-Saharan Africa**

Short-term debt comprises a large share of private debt flows to Sub-Saharan Africa.<sup>*</sup> Even as developing countries in other regions reduce their dependence on short-term debt, these flows continue to be a large and volatile component of private debt flows to Sub-Saharan Africa (see figure). After a surge in private short-term debt flows to Sub-Saharan Africa during the mid-1990s, these flows turned negative from 1998 to 2002 after the Asian financial crisis. They have again increased in recent years as Sub-Saharan Africa’s growth performance improved. Since 1990, most private debt inflows into Sub-Saharan Africa have been short term.

# **Short-term debt has been a large and volatile component of private debt flows to Sub-Saharan Africa**

<!-- Start of picture text --> US$ bil<br>6 Medium and long-term debt Short-term debt<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ Authors’ calculations, _Global Development Finance_ database, September 2007.

The high share of short-term debt may be partly explained by the severe informational asymmetries and risk perceptions of investing in Sub-Saharan Africa. Similar factors also account for the dominance of foreign direct investment in private capital flows to Sub-Saharan Africa and the small share of arm’slength financing through bond issuance and portfolio equity. In situations characterized by high risks, investors typically prefer to take direct control of their investment through FDI (Hausman and FernandezArias 2001) or resort to relationship-based bank lending that is typically short term or can be secured by some tangible collateral, such as trade credits (See box 2).

A reliance on short-term debt can be risky for the receiving countries. Short-term debt tends to be procyclical in developing countries, increasing when economic growth is cyclically faster and declining when growth rates falter (Dadush, Dasgupta, and Ratha 2000). Favorable conditions attract large inflows, encouraging potentially unsustainable levels of consumption and investment. Changes in risk perceptions, however, can lead to rapid reversals, imposing larger-than-necessary adjustment costs for the receiving countries.

> * Short-term international debt is defined as cross-border debt falling due within a year. The original maturity concept followed by World Bank (2002) is used here. The Bank for International Settlements, however, uses a “remaining maturity” concept—that is, all cross-border debt falling due within one year is counted as short-term debt, regardless of its original maturity. (Dadush, Dasgupta, and Ratha 2000). Although conceptually different, the trends in the two are usually similar.

11
Protecting the energy consumption of households from the two poorest quintiles requires energy subsidies of about 0.7 to 1.7 percent of GDP under the two scenarios.<sup>17</sup> When the calculations are restricted to connected and paying households, the fiscal cost is much lower: 0.3 percent of GDP under the first scenario and 0.7 percent of GDP under the second scenario. However, these calculations assume that households in the poorest quintiles can be perfectly targeted by safety nets. In practice, they are not. The 2004 Poverty Assessment (World Bank, 2004), in particular, finds that some 32.9 percent of the budget on gas and energy compensations accrue to the non-poor. Some 44 percent of recipients are non-poor.

_Other costs_ The rising cost of energy may have significant health and environmental costs as households switch to cheaper sources of fuel. Based on the results of focus group discussions, for example, some rural households in Moldova were found to rarely cook or heat with electricity; they typically use wood, coal, or gas instead.<sup>18</sup> HBS data is not likely to provide a reliable estimate of the switch to dirty fuel following the rising cost of network sources of energy, because there are few observations on wood or coal use. However, it is reasonable to expect that with the price of electricity and natural gas increasing, households (and rural households, in particular, who are more likely to be poor) will indeed switch to less expensive but dirtier sources of energy and this may lead to significant health and environmental costs. For example, burning dirty fuels may degrade indoor and outdoor air quality, and this, in turn may lead to worsening health outcomes. In addition, burning wood promotes deforestation and contributes to the loss of valuable forest functions.<sup>19</sup>

# V. **Policy Implications**

It is clear that Moldova is highly vulnerable to gas price increases, and that upward price pressures are likely to be a fact of life in the coming years. In developing and implementing its policy response, the government should recognize that: (a) gas imports are likely to continue to play pivotal role in the energy balance of Moldova in the long term; and (b) Moldova has in its hands the task of reducing its energy vulnerability by improving energy efficiency, reducing energy waste and developing alternative energy resources. The following recommendations provide a broad roadmap for policy makers in Moldova in this regard.

> also suggest that many of the missing expenditure data are not necessarily due to nonpayment. The two sets of calculations should thus be treated as an indicative range of estimates.

> 17 The most recent Poverty Assessment finds that the poverty rate is close to 40 percent. The two poorest quintiles provide a rough approximation of households in poverty.

> 18 Junge and others, 2004, _Sharing Power: Lessons Learned from the Reform and Privatization of Moldova’s Electricity Sector_ (Washington: The World Bank).

> 19 See Lampietti, ed., 2004, _Power's Promise: Electricity Reforms In Eastern Europe And Central Asia_ (Washington: The World Bank).

15
allocated to the treatment group in the first or second of two sequential list experiments. This approach means that estimates across the two list experiments can be directly compared, serving as an internal robustness check to demonstrate the accuracy of the results. We implement this double list experiment as part of one of the largest World Bank Enterprise Surveys (WBES) to date, covering a nationally representative sample of 2,955 registered firms with at least five employees. WBES are among the most comprehensive and reliable surveys about the activities of small, medium, and large firms, containing detailed information about firm characteristics and beliefs, which allows us to examine the heterogeneity of tax evasion extensively.

The results show that around one-quarter of firms report evading taxes and evasion rates vary substantially on only a relatively small number of dimensions. The ”double” list experiment produced internally consistent estimates of around 26 percent (varying from 25 to 27 percent) of firms admitting to not paying all the taxes they are required to pay (which is an extensive margin estimate of tax evasion). This result is almost identical across both experiments, which provides considerable reassurance of its accuracy, and it represents a lower bound estimate as it is based on self-reported tax evasion, which is likely to be lower than actual tax evasion. A back-of-the-envelope calculation based on this result suggests that revenue lost from tax evasion by registered firms is around two percent of GDP. We use machine learning to identify, among an extensive set of firm characteristics, the dimensions in which substantial and consistent variation in tax evasion exists. This revealed that firms that do not export, face substantial competition from the informal sector, and believe tax administration is a major obstacle to their business activities are the most likely to evade taxes. There was limited variation across both list experiments in reported tax evasion based on other dimensions, highlighting just how widespread this behavior is.

These results provide important insights into how common tax evasion is among firms and the specific firm characteristics and beliefs most associated with evading tax. Many tax evasion reported.

2
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priuary means of solid waste disposal in Jakarta After total missions were calculated for each grid cell, a Euleian mlti-box dispersion modcl, incorportng local meteorologic and topographic features, was developed and used.

For this report, only benefits related to reductions in TSP, lead and nitrogen dioxide are estmated. Estimates are not provided for pollutants such as carbon monoxide, ozone, and sulfur dioxide because of a lack of monitoring (ozone), a lack of dose-response information (carbon monoxide), and concentrations that are below the air quality standards (sulfur dioxide). Of the emissions of T.SP, an estimated 30 prcent come from unWortation sources (including gasoline and diesel-powered vehides), 35 percent from fuel combustion Cmcludig residential coolkng), 8 percent from solid waste disposal (ucluding municipal incinerators and open buming), 15 percent from industrial process, and 12 percent from other sources including construction and fugitive dust Figure 1 displays the resultant isopleths for annual averages of TSP, in 1989. Ambient concenrations were deterwined for each grid and combined with the populon data for that grid to calculate the population-micrograms of exposure. As such, this method is a significant improvement over previous smdies that have calculated city-wide averages for the entire metropolitan area based on readings fom one or more fixed site monitors. The results, supported by recent monitoring efforts, suggest that ambient concentations of annual average TSP are 3 to 4 times the levels typically observed in the United States.

For lead concenations in Jakarta, vehicles using leaded gasoline are the primary source, although smeltrs and battery recyclers also contnbute. For nitrogen dioxide, 73 percent of the emissions are frm de t ortation sector, with industry contributing around 16 percent. Figures 2 and 3 display the isoplets for lead and nitrogen dioxide for 1989. Again, the figures indicate ftat the levels are substantially higher than levels observed in the United States, and higher than most existing ambient standards by a factor of 3 or 4.
17 $89 million in the United States."<sup>6</sup> Mauritius accounts for over 80 percent of the latter total due to the relatively high U.S. MFN tariffs (in the range of 15 to 30 percent) that are applied to its textile and clothing exports. Aside from Mauritius, however, the US gains are roughly offset by export losses in Japan. Overall, the trade effects in the EEC predontinate and, aside from Mauritius, African countries are projected to experience combined annual losses of $208 million in the three OECD markets. This situation differs markedly fror that of Taiwan (China) and the Republic of Korea where trade gains of 20 percent or more are projected in the EEC and US markets. Lower export gains are estimated in Japan -- due mainly to the erosion of some GSP preferences Japan gives these two countries -- but the MFN tariff liberalization still increases Taiwan (China) and Korea's exports by 5 to 6 percent.

An important point to note concerning the annual projections in Table 5 is that these trade changes will occur, _ceterus paribus,_ in each and every year after the MFN tariff cut takes place. Table 6 provides a different perspective on these projections by showing each country's annual net gains or losses in the three OECD markets combined, as well as the present '<sup>6</sup> A crucial factor influencing trade gains and loses in the United States is the depth of the MFN tariff cut, since some preferential duties are set at relatively high levels (see, for example, the statistics for Botswana and Cote d'Ivoire in Table 5). As US MFN rates are reduced down to the level of the nonzero preferential tariff, African countries will experience trade diversion. However, further reductions in MFN rates will produce trade creation (gains) that will start to offset the previous losses. This explains why, for example, a U.S. trade gain is projected for Botswana in Table 5 even though the average preferential margin on this country's exports is 1 .1 percentage points. If US MFN duties were cut in half (and not completely liberalized) many of the African countries showing trade gains in Table 5 would record trade losses.
program is theoretically uncertain motivate empirical studies to see what happens in practice.

A more practical reason is that these studies are all in areas where evaluation is most feasible for a variety of reasons. The first is one of sample size. The policies studied are ones where the units of analysis are consumers or firms, allowing the comparison of the impacts on many affected units to a control group of many other units. The second is one of data availability. The regulation studies relied on unusually good existing databases in Mexico (a quarterly labor force panel survey and administrative data from the Mexican social security system) and a comprehensive database on the universe of bankruptcy cases in Colombia. The other studies were designed as ex-ante evaluations, with data collection designed by the researchers. The randomized experiments done to date have generally been conducted by researchers working with NGOs or funding the programs through research grants. This has limited study to either programs which have been run by NGOs willing to work with researchers, or to projects which are cheap enough for research grants to fund.

Going forward this calls for a need for continued close interaction between theory and evaluation – we want to know not just whether or not something works, but why, and how? It also suggests that widespread rigorous evaluation of the many other types of FPD programs and policies implemented by governments and supported or advised by international financial institutions requires a much greater commitment to evaluation, and in particular, to planning ahead so the evaluation process (including data collection) can start before the program is implemented. It also suggests unexploited benefits exist from small modifications in currently collected sources of data which do not presently have policy evaluation in mind. For example, surveys of firms should include questions on participation in particular types of policies or projects (e.g. does your firm participate in a business cluster developed by the government under its regional clustering program), and include enough identifying information to link with administrative records on banks, firms, or consumers participating in such programs. And unfortunately even when such data is collected, access to the microdata is often limited in many countries, so greater data accessibility is also needed.

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organized as a two stage procedure. In the first stage, telephone screening confirms eligibility and schedules the interview. In the second stage, a face to face interview with the top manager of each firm is conducted. When needed, follow-up questions and corrections are implemented, in person, by phone, email or web interface.

Finally, the ES team has created quality control procedures and programs intended to minimize coding and processing errors. Coding errors commonly occur due to the misinterpretation by the enumerator of the answers, especially with questions about numbers, or during the data entry stage. The ES implementation methodology includes comprehensive systems of checking the answers for logical consistency and completeness. Furthermore, outlier tests are implemented to capture potential typos. Several layers of extra verification, including independent double entry, callbacks, and multiple logical and consistency tests are common during the digitalization of the data.

# **3. Definition of Credit-Constrained Firms**

Using the finance section of the Enterprise Surveys questionnaire, we construct four major groups that measure the extent firms were credit constrained during the fiscal year referenced in each survey. The first group called **Fully Credit Constrained (FCC)** includes the firms that meet all the following conditions simultaneously:

- A. Did not use external sources of finance for both working capital and investments during the previous fiscal year;

- B. Applied for a loan during the previous fiscal year;

- C. Do not have a loan outstanding at the time of the survey which was disbursed during

the last fiscal year or later.

9
Table 1.1.

Turkeys Unemployment, Rural Areas, 1974-1974 Labor Force Survey Data

||R|ural are|asa, age|12 and ab|ove, last|<sup>week</sup>|
|---|---|---|---|---|---|---|
||June|1973|Octo|ber 1973|April|1974|
||000|DCt|000|yCt|000|OCt|
|mployed<br>b|8,937|99.6|9,042|99.8|89979|99 9|
|Employedb|8.937|99.5|9,042|99.3|8,979|99.8|
|Unemployed|32|0.4|21|0.2|8|0.1|
|UnemployedFd|47|0.5|65|0.7|16|0.2|
|Labor Force|8,969|100|9,063|100|8,987|100|
|Labor Forced|8,984|100|9,107|100|8,995|100|

a 401 rural settlements with less than 2,000 inhabitants b Including persons with a job but not at work, negligible **C** Seeking employment d Including unemployed not seeking employment Sources Statistical Yearbook of Turkey, 1981, Table 150.

Table 1.2.

Economically

|Turkey: Unem<br>Pop|ployment<br>inTotal/Population<br>ulation<br>Census Data|||
|---|---|---|---|
|19|65a<br>1970<br>1 9|7 5<sup>b</sup><br>1 9<br>|8 0<sup>b</sup>|
|000|Oct<br>000<br>pct<br>000|oct<br>000|Pct|
|Employed<br>13,501|99.6<br>17,098|98.4 18,522|96.4|
|Unemployedc<br>56|0.4<br>285|1.6<br>690|3.6|
|Economically<br>active<br>13,557|100<br>17,384|100<br>19,212|100|
|a 15 years and above|<br>b12 years<br>and above<br>|cSeeking<br>employm|ent|
|Sources:<br>1965, Statistic<br>1975, 80,ib|al<br>Yearbook of Turkey,<br>1968,<br>id.,1985, Tables<br>43 and 44.<br>-22-|Table 140||