rafmacalaba/datause-encoder-data huggingface dataset:
1990-1999 period using Compustat data. There are two main reasons for opting for the data in Maskus et al. (2012) instead of the data from Rajan and Zingales (1998). First, the data are more recent and provide a better proxy of external financial dependence for the period for which we have data on exporter-level dynamics. Second, the data are reported in the same classification available for the EDD, ISIC Rev.3, which limits the measurement errors that may be exacerbated when converting the measure from one classification to another using aggregated data.

# **3.3 Other variables**

When trying to disentangle the mechanisms of influence of financial structures on export, we use data from the World Bank Banking Supervision Survey on the supervision, monitoring and regulation of banks in the countries. The database is based on surveys sent to national bank regulatory and supervisory authorities of 107 countries asking comprehensive questions about bank entry requirements, ownership restrictions, capital requirements, accounting and disclosure requirements, and the quality and actions of bank supervisory personnel. We detail these data and variables below.

We also employ measures of the degree of product information complexity and contractual complexity. Data on the degree of relationship-specific fixed costs or information frictions in contracts are from Nunn (2007) and Rauch (1999). Specifically, as in Nunn (2007), for each product the contract intensity index is equal to the proportion of its intermediate inputs that are relationship-specific and is defined as where _θgj_ is the share of input _j_ used to produce one unit of product _g_ based on the 1997 U.S. input-output tables. _Rj_<sup>_neither_</sup> is the share of inputs that is neither sold on an organized exchange nor reference priced.<sup>8</sup> The proxy for industry information complexity is constructed using data from Rauch (1999). First, we convert Rauch’s original classification of products from SITC Rev.2 to ISIC Rev.3. Second, we aggregate the data to the 2-digit ISIC level by calculating the share of SITC products that are neither sold on an organized exchange nor reference priced (i.e., heterogeneous products). An industry with a higher share of heterogeneous products is more likely to be subject to informational frictions.

In the analysis, we further test whether the impact of financial structures on export differs between normal and difficult times, such as recessions and financial crises. Data

> 8We concord the original data to ISIC Rev. 3 by converting BEA’s 1997 I-O industry classification to NAICS 1997, then the NAICS 1997 to NAICS 2002, and finally NAICS 2002 to the 2-digit ISIC Rev.2 industry level. For a more detailed description of the original data, please refer to Nunn (2007).

9
snapshots in time for Senegal, little research has been done on the dynamics of poverty in this country.

Dissecting poverty dynamics is relevant in the Senegalese context for additional reasons. First, we are interested in identifying the types of households that are most likely to fall into, remain in, or move out of poverty over time for the purpose of designing effective interventions. There have been various shocks (including food and fuel price increases and natural disasters) in the country recently, and these may have affected different households differently, as well as having contributed to the recent economic slump. Indeed, as poverty reduction has slowed in recent years, it is all the more important that we better understand the dynamic mechanisms behind poverty reduction such that more effective interventions can be designed and implemented. Policy makers are likely to be interested in knowing answers to such questions as: what are the profiles of households that remained in poverty? Which households escaped poverty? Who fell back into poverty? As we will illustrate and discuss further below, poverty rates have remained rather stable in Senegal during the latter half of the 2000s. The question arises as to whether this masks any specific poverty dynamics occurring below the surface of the relatively stable poverty estimates deriving from cross section data? If so, how significant were these dynamics?

At first glance, the absence of panel household survey data would suggest that there is little scope to pursue such questions in Senegal—or indeed, in many other developing countries. In an effort to make progress, researchers have pursued second-best options. One estimation strategy is to collect retrospective data on household poverty status over the recent past (Neubourg et al., 2010; Fall et al., 2011), another is to use a household asset index to measure household wealth transitions (Sahn and Stifel, 2000; Booysen et al., 2008). While both of these approaches are 3
Table 1: Descriptive Statistics for Business Training Samples

||_Eth_|_iopia D_|_OT_|_Eth_|_iopia WA_|_LN_||_Togo_||
|---|---|---|---|---|---|---|---|---|---|
||Obs.<br>(1)|Mean<br>(2)|S.D.<br>(3)|Obs.<br>(4)|Mean<br>(5)|S.D.<br>(6)|Obs.<br>(7)|Mean<br>(8)|S.D.<br>(9)|
|_Panel A: Characteristics of the_|_househo_|_ld_||||||||
|Household size|689|4.97|2.23|86|7.15|3.62|424|5.59|2.42|
|Women is head of household|689|0.45|0.50|87|0.61|0.49|426|0.26|0.44|
|_Panel B: Characteristics of the_|_woman_|||||||||
|Age|689|31.65|6.39|87|35.56|5.3|424|38.34|6.81|
|No education|689|0.04|0.20|87|0.20|0.41|411|0.08|0.27|
|Completed primary school|689|0.23|0.42|87|0.31|0.47|411|0.34|0.47|
|Completed middle school|689|0.39|0.49|87|0.1|0.31|411|0.36|0.48|
|Completed high school|689|0.21|0.41|87|0.1|0.31|411|0.16|0.37|
|Completed higher education|689|0.13|0.33|87|0.26|0.44|411|0.06|0.24|
|Nb of children at baseline|689|2.06|1.66|87|3.46|1.57|424|0.61|0.82|
|Married|689|0.57|0.50|87|1.00|0.00|424|0.86|0.34|
|_Panel C: Characteristics of the_|_partner_|||||||||
|Partner age|409|42.28|9.31|87|44|8.02|420|44.74|7.65|
|No education|409|0.03|0.16|87|0.00|0.00|422|0.05|0.22|
|Completed primary school|409|0.28|0.45|87|0.26|0.44|422|0.09|0.29|
|Completed middle school|409|0.26|0.44|87|0.15|0.36|422|0.34|0.47|
|Completed high school|409|0.16|0.37|87|0.15|0.36|422|0.21|0.41|
|Completed higher education|409|0.26|0.44|87|0.41|0.5|422|0.31|0.47|
|Farmer/ farm worker||||87|0.13|0.34||||
|Self employed|409|0.56|0.50|87|0.13|0.34|423|0.50|0.50|
|Employed|409|0.31|0.46|87|0.41|0.5|423|0.45|0.50|
|Unemployed|409|0.11|0.31|87|0.3|0.47|423|0.04|0.20|
|Other|409|0.02|0.14|87|0.02|0.16|423|0.01|0.08|
|_Baseline_||YES|||YES|||YES||

Notes: This table reports descriptive statistics of the sample of women under study. The first column of each country reports the number of observations in the sample at follow-up 1 non-attrited for the given variable, the second column reports the variable mean in the control group at baseline, or at follow-up 1 if there is no baseline. The variable married equals one if the woman declares she is formally married. For the number of children at baseline, when we have no baseline we take the number of children born before the beginning of the treatment. The level of education displayed is the highest level of education completed.

14
3 .rgument als^ has strc. analytical underpinnings: the benefits of structural reforms that aim at improving resource allocation through changes in incentives, generally transmitted through relative price changes, are reduced by macroeconomic instability in the form of high and variable inflation and balance of payments crises (Corbo and de Melo 1987; Fischer 1986; and Sachs 1987).

Reforms undertaken as part of a Bank-supported adjustment program add to the country's indebtedness, and should not be attemDted unless there ic sufficient probability that they will succeed. While structural reforms to improve resource allocation initiated before stabilization can turn out to be productive,<sup>3</sup> the weight of the evidence suggests that reforms of this type undertaken in highly unstabla macroeconomic conditions are typically unsuccessful. Reforms that start with low credibility about their sustainability may temporarily get current relative prices right, but consumers and producers perceive that the reforms will noc last and intertemporal relative prices are accordingly distorted. The result is that the responses of consumers and producers can destabilize the whole reform effort (Calvo 1989). Country studies have also shown that many failures of libeializatioLl attempts have been due to the absence or failure of accompanying stabilization efforts (Krueger, 1978 and 1984, Papageorgicu, et al., 1990 and Corbo and de Melo, 1987).

The Bank periodically evaluates the effectiveness of the adjustment prograns it supports, as well as their design and implementation.<sup>4</sup> It also 3 For instance, some of the reforms undertaken before stabilization in Mexico appear to have been long-lasting.

- 4 Two recent comprehensive reviews are World Bank (1988a and 1990a).
**Table 1. General Government Revenue, in million leks and as a percentage of GDP, 2015**

||Fiscal Data<br>(in million leks)|Fiscal Data<br>(% of GDP)|
|---|---|---|
|**TOTAL REVENUE**|**398,210**|**27.6%**|
|**Grants**|**12,000**|**0.8%**|
|**Tax Revenue**|**355,610**|**24.7%**|
|**From tax offices and customs**|**272,358**|**18.9%**|
|V.A.T|131,203|9.1%|
|Profit Tax|23,547|1.6%|
|Excise Tax|44,900|3.1%|
|Personal Income Tax|31,803|2.2%|
|National Taxes and others|35,582|2.5%|
|Customs Duties|5,323|0.4%|
|**Local Taxes**|**12,563**|**0.9%**|
|Local Taxes|7,066|0.5%|
|Property Tax|4,017|0.3%|
|Simple profit tax of small business|1,480|0.1%|
|**Revenues from Special Funds**|**70,689**|**4.9%**|
|Social Insurance|59,888|4.2%|
|Health insurance|9,201|0.6%|
|Revenues for owners' in value-compensation|1,600|0.1%|
|**Nontax Revenue**|**30,600**|**2.1%**|
|Profit transfer from BOA|962|0.1%|
|Income of budgetary institutions|20,127|1.4%|
|Dividend|2,011|0.1%|
|Services Fees|3,400|0.2%|
|Others|4,100|0.3%|

Source: Albanian Ministry of Finance (2017). Notes: includes central and subnational revenues.

Key features of these key taxes and contributions (further detailed in Annex 3) are as follows:

- _Personal Income Tax:_ In 2014, Albania adopted a new progressive income tax system from a flat tax rate system of 10 percent. Earnings from employment are now taxed at progressive rates depending upon the tax brackets they fall into 0 percent up to 30,000 ALL<sup>5</sup> ; 13 percent from 30,000 ALL to 130,000 ALL; and 23 percent for earnings above 130,000 ALL. All other personal income, except those from employment, are subject to 15 percent tax rate. Several income categories are exempted from taxation such as pensions, scholarship and all type of benefits received from an obligatory social and health insurance scheme. As to the self-employed (with an annual turnover up to ALL 8 million), the above thresholds do not apply any longer, rather a simplified tax on the profit is foreseen.

- _Value Added Tax (VAT):_ The Value-Added Tax in Albania is a proportional tax levied at two different rates: 20 percent as standard rate, 0 percent on special products (See Law no. 92/2014 for more details).<sup>6</sup> Firms that produce or sell with these two rates are also eligible for reimbursement

> 5 ALL: Albanian leks.

> 6General Directorate of Customs (2015).

6
# **Recent labor market performance in Vietnam**

# **through a gender lens**

## **Gaëlle Pierre**<sup>**1**</sup>

Keywords: Vietnam, labor market outcomes, gender wage differentials JEL codes: J21, J31 Sector Boards are SOCPT and GEN

> 1 World Bank; <u>gpierre@worldbank.org. This paper was done under the guidance of Daniel Mont and</u> Christian Bodewig and with the excellent research assistance of Hieu Nguyen. The paper benefited from comments received during the workshop on gender and labor markets held in Hanoi on July 12, 2011. All remaining errors are my own.
program, which seeks to assist the most food insecure households in the most affected localities and states, by providing unconditional cash transfers to beneficiary families in all 18 states of Sudan.

Sudan has been experiencing multiple macroeconomic shocks, such as low growth, a wide trade deficit, and the emergence of multiple exchange rates since its separation from South Sudan in 2011. The country has implemented an ambitious agenda of reforms, which included the removal of various subsidies, in an effort to reverse years of structural imbalances and high inflation. Parallel to such reforms was the introduction of a quasi-Universal Basic Income cash transfer program (SFSP) that sought to reach the bottom 80% of the population. The SFSP program was eventually discontinued, as, with the onset of COVID19, political discontent ultimately resulted in a military takeover and a renewed effort by the military-led government to achieve fiscal consolidation.

More recently, the Russia-Ukraine crisis that started in March 2022, has further exacerbated the country’s social and economic problems. In particular, Sudan is expected to face significant negative food security implications as food prices reach historic peaks due to the Russian invasion of Ukraine. All of the economic and social problems that have negatively impacted Sudan are expected to have increased the number of poor in the country.

The worsening situation in Sudan has led to renewed efforts to develop a new social safety net program. To support the efforts of Sudan in the targeting of the Sudan Emergency Safety Nets Project (SESNP) to those most in need, we develop and propose a PMT for Sudan based on the latest household survey, the National Baseline Household Budget and Poverty Survey (NHBPS) 2014/2015.

We find that a PMT should be considered for targeting social protection programs in Sudan. The PMT would assist social programs to reach a higher share of the poor and vulnerable, while reducing expenditure going towards households that are less in need, improving both the poverty impact, as well as the sustainability of the program. We explore various specifications of the PMT and find that our proposed Sudan national PMT performs as well as a rural and urban specific PMT, making it easier to implement and manage in practice. Furthermore, costly and time-consuming employment information does not improve the accuracy of the PMT in a meaningful way. Finally, our proposed Sudan PMT relies on 19 variables that could be collected by a short survey in the field on a large portion of the population.

22
all households-rich and poor-appears to make no less of a dent on income poverty. The authors offer certain caveats and reasons for why they may have underestimated the benefits from the employment scheme-they have ignored externalities arising from the infrastructure the schemes build, for example. Either way, their results should give policymakers and advisers committed to reducing poverty reason to pause.

The studies under discussion here notwithstanding, there are in fact very few examples of good evaluations in the development context. This may be due in part to the high cost of properly evaluating such programs. But there also seems to be a systematic neglect of evaluation; only a tiny fraction of resources used go into seeing if they are properly used. In addition to expanding research efforts in the direction of incorporating behavioral responses into benefit incidence studies, we also need to introduce-ex ante-careful monitoring and experimental methods on a selective basis to better understand what works and what doesn't.

In the meantime, there are ways to take a first stab at such assessments. One should begin by listing all the different social costs that a scheme is likely to entail: administration, costs of participation (such as foregone incomes through displaced labor or displaced transfers from rich patrons), and any indirect costs incurred by the nonpoor (such as the loss to previous beneficiaries from an untargeted scheme). Some of these costs may be weighted differently to others; for example, assessments often attach low or zero weight to small losses to the rich. Some will also be less important than others, depending on the specific circumstances faced (foregone incomes in a rural public works scheme will naturally be low during a drought, for example).

Next, one should take stock and make a judgement about whether these costs are high relative to the benefits to the poor. In general, benefits will be dispersed widely among the poor, and so there will be an issue of how gains at different levels should be weighted; the most widely accepted judgement is that gains to the poorest should receive highest weight (Atkinson discusses this issue in 37
**Table 7: Sensitivity Analysis for DPT, High Income Countries, IV with country fixed effects**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||DPT rate|DPT rate|DPT rate|DPT rate|DPT rate|DPT rate|DPT rate|DPT rate|
||(log)|(log)|(log)|(log)|(log)|(log)|(log)|(log)|
|Convention on the Rights of the Child-yes/no (CRC)|0.037<br>(1.95)|0.048<br>(2.41)*|0.027<br>(1.35)|0.048<br>(1.87)|0.046<br>(2.14)*|-0.006<br>(0.37)|0.026<br>(1.62)|0.018<br>(1.02)|
||0.235|0.289|0.045||0.26|0.034|0.006|0.21|
|GDP per capita (in constant 2000 US dollars, log)|(5.69)**|(6.84)**|(2.14)*||(7.26)**|(0.98)|(0.15)|(4.95)**|
|ICRG-Brrti Qlit|0.085|0.083|0.079||0.085|0.044|0.037|0.019|
|ueaucac uay|(8.03)**|(7.60)**|(7.38)**||(7.98)**|(5.23)**|(4.72)**|(0.89)|
|Polity2x|0.045<br>(3.79)**||||||||
|Population Density (log)|1.255<br>(1.22)|0.801<br>(0.76)|-0.01<br>(0.91)||0.675<br>(0.64)|-0.451<br>(0.57)|0.23<br>(0.09)|2.04<br>(1.84)|
||-1.363|-0.915|-0.008||-0.811|0.392|0.039|-2.042|
|Total Population (log)|(1.34)|(0.87)|(0.7)||(0.78)|(0.49)|(0.01)|(1.88)|
|YEAR|-0.002|-0.003|0.002|-0.002||0.001|0.002|-0.001|
||(1.4)|(2.08)*|(1.29)|(1.54)||(0.76)|(1.23)|(0.93)|
|||-0.021|-0.007||-0.022|-0.009|0.019|-0.005|
|Democracy||(3.29)**|(2.59)**||(3.37)**|(1.86)|(1.22)|(1.18)|
|GDP per capita (in constant 2000 US dollars, log) (Lag)||||0.252<br>(5.79)**|||||
|Log DPT Immunization Rate (Lag)||||||0.606<br>(20.05)**|||
|B-spline on [1989,1995)|||||0.036<br>(1.84)||||
|B-spline on [1995,2000)|||||0.016<br>(1.08)||||
|B-spline on [2000,2006)|||||0.01<br>(0.85)||||
|ICRG-Bureaucratic Quality (Lag)||||0.072<br>(6.65)**|||||
|Democracy (lag)||||-0.018<br>(2.77)**|||||
|Log Population Density (Lag)||||0.05<br>(0.05)|||||
|Log Total Population (Lag)||||-0.174<br>(0.16)|||||
||22.27|19.356|0.992|9.228|12.022|-4.853|-0.772|29.662|
|Constant|(1.72)|(1.45)|(0.44)|(0.68)|(0.98)|(0.48)|(0.02)|(2.17)*|
|Observations|633|635|635|606|635|635|720|405|
|Number of group(countryc)|48|48|48|47|48|48|51|29|

23
Few tasks are more important in the social sciences than discovering the sources of human wellbeing. While this remains a contested issue (Frey and Stutzer 2000; Clark et al. 2008; Frey 2008; Bjørnskov et al. 2010; Deaton 2012), the question of whether “money buys happiness” attracts particular attention, no doubt because of the seemingly paradoxical finding—first reported by Easterlin (1974, 1995)—that income growth is not associated with corresponding increases in happiness and well-being (Clark et al. 2008; Easterlin et al. 2010). However, recent work has emphasized that subjective well-being does seem to fluctuate with banking and financial crises (Deaton 2012; Bjørnskov 2014; Montagnoli and Moro 2014) and macroeconomic factors like inflation, unemployment, and gross domestic product (GDP) (Oswald 1997; Di Tella et al. 2001, 2003; Stevenson and Wolfers 2008; Kahneman and Deaton 2010; Sacks et al. 2012a), providing some support for the claim that income is correlated with happiness and well-being.

In this article, we contribute to this literature by examining how macroeconomic shocks affect individual well-being. Using the case of an unanticipated and rapidly implemented currency devaluation in Botswana—a middle-income country in sub-Saharan Africa—we examine how individual evaluations of well-being respond to such a monetary policy shock. We do so by analyzing microlevel data from the _Afrobarometer_ , which happened to be in the field conducting interviews for a survey at the time when the citizens of Botswana were exposed to the news of the national currency devaluation. Specifically, two days into the survey—late in the day on May 29, 2005—the central bank of Botswana and the Ministry of Finance and Development Planning issued a public statement saying that the national currency would be devaluated by 12 percent, with effect from the following morning.<sup>1</sup> Our analysis exploits the fact that the chance occurrence of the devaluation creates a clear demarcation between respondents surveyed before the devaluation and respondents surveyed in the days following the devaluation.

> 1 Press Release, 17:00 hours, Sunday 29 May 2005, issued by the Ministry of Finance and Development Planning.

2
# **V. Results: Distributional Impact of the Net Fiscal System in Albania**

**The net fiscal system contributes to reducing inequality in Albania** . Before any fiscal intervention, the Gini coefficient stands at 0.37 and falls to 0.32 after the combined effect of taxes and contributions, and social spending.  The largest reduction in inequality takes place from the effect of in-kind transfers (health and education). Direct taxes and transfers, and in-kind transfers (education and health) are equalizing. Indirect taxes (VAT and excises), in contrast, have an unequalizing effect but very small. Figure 3 shows the change in the Gini coefficient going from households’ market income (which treats pensions as deferred income) to net market income (removing direct taxes and contributions), to disposable income (adding direct transfers to households), to consumable income (removing indirect taxes) and to final income (adding in-kind transfers). Box 1 considers an alternate scenario in which pensions are treated as government transfers instead of deferred income; nevertheless, given that pension system is currently primarily contributory, this scenario is not elaborated further.

**Figure 3. Albania: Change in Inequality from Market to Final Income (Gini coefficient)**

**Figure 4. Cross-country: Change in Inequality from Market to Final Income (Gini coefficient)**

<!-- Start of picture text --> 0.38<br>Ethiopia (2010-11)<br>0.37 Jordan (2010-11)<br>0.37 Guatemala (2011)<br>0.36 Indonesia (2012)<br>0.36 Honduras (2011)<br>Sri Lanka (2009-10)<br>0.35 0.35 0.35 Uganda (2012-13)<br>Ghana (2012-13)<br>0.34 Peru (2009)<br>El Salvador (2011)<br>Nicaragua (2009)<br>0.33<br>Paraguay (2014)<br>0.32 Albania (2015)<br>0.32 Tanzania (2011-12)<br>Dominican Republic (2006-07)<br>0.31 Bolivia (2009)<br>Venezuela (2012)<br>0.30 Russia (2010)<br>Colombia (2010)<br>0.29 Ecuador (2011-12)<br>Chile (2013)<br>Tunisia (2010)<br>Mexico (2010)<br>Iran (2011-12)<br>Croatia (2014)<br>Uruguay (2009)<br>Costa Rica (2010)<br>United States (2011)<br>Brazil (2008-09)<br>Poland (2014)<br>South Africa (2010-11)<br>Argentina (2012-13)<br>-0.200 -0.150 -0.100 -0.050 0.000<br>Market Income plus pensions Net Market Income Disposable Income Consumable Income Final Income<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

Source: Own estimates for Albania using the Albania HBS 2015. For other countries, see the Commitment to Equity Institute Data Center on Fiscal Redistribution, 2017 (based on information from sources in the footnote)<sup>34</sup> and the references section of this paper.

34 ARGENTINA (Rossignolo, 2018); ARMENIA (Younger and Khachatryan, 2017); BOLIVIA (Paz Arauco et al., 2014); BRAZIL (Higgins and Pereira, 2014); CHILE (Martinez-Aguilar et al., 2018); COLOMBIA (Melendez and Martinez, 2015); COSTA RICA (Sauma and Trejos, 2014); DOMINICAN REPUBLIC (Aristy-Escuder et al., 2018); ECUADOR (Llerena et al., 2015); EL SALVADOR (Beneke, Lustig, and Oliva, 2018); ETHIOPIA (Hill et al., 2017); GEORGIA (Cancho and Bondarenko, 2017); 18