rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: MDEs with constrained and optimal choice of cluster probabilities

|||**Scenario**|**1**||**Scenario**|**2**||**Scenario**|**3**|
|---|---|---|---|---|---|---|---|---|---|
||Het|Homog|Equal|Het|Homog|Equal|Het|Homog|Equal|
|**Restricted**_qt_||||||||||
|_q_0|0_._408|0_._408|0_._408|0_._388|0_._388|0_._388|0_._272|0_._272|0_._272|
|_q_1|0_._199|0_._209|0_._230|0_._219|0_._231|0_._239|0_._273|0_._283|0_._286|
|_q_2|0_._194|0_._173|0_._131|0_._173|0_._149|0_._135|0_._182|0_._162|0_._157|
|_q_3|0_._199|0_._209|0_._230|0_._219|0_._231|0_._239|0_._273|0_._283|0_._286|
|**MDEs**||||||||||
|_MDE_1|0_._145|0_._047|0_._022|0_._042|0_._027|0_._022|0_._033|0_._025|0_._023|
|_MDE_2|0_._146|0_._051|0_._029|0_._047|0_._033|0_._029|0_._039|0_._032|0_._030|
|_MDE_3|0_._146|0_._051|0_._030|0_._047|0_._034|0_._030|0_._040|0_._033|0_._032|
|**Optimal**_qt_||||||||||
|_q_0|0_._364|0_._352|0_._314|0_._348|0_._328|0_._313|0_._332|0_._315|0_._309|
|_q_1|0_._212|0_._219|0_._238|0_._221|0_._231|0_._238|0_._229|0_._237|0_._240|
|_q_2|0_._211|0_._211|0_._210|0_._210|0_._210|0_._210|0_._210|0_._210|0_._210|
|_q_3|0_._212|0_._219|0_._238|0_._221|0_._231|0_._238|0_._229|0_._237|0_._240|
|**MDEs**||||||||||
|_MDE_1|0_._145|0_._047|0_._023|0_._043|0_._028|0_._023|0_._033|0_._026|0_._024|
|_MDE_2|0_._145|0_._049|0_._025|0_._045|0_._030|0_._025|0_._036|0_._028|0_._027|
|_MDE_3|0_._146|0_._051|0_._030|0_._047|0_._034|0_._030|0_._040|0_._033|0_._032|
|**Scenario Statistics**||||||||||
|_n_||84,175|||81,961|||68,808||
|_G_||4,139|||4,138|||3,982||
|min_ng_||8|||8|||8||
|max_ng_||2,754|||376|||50||
|mean(_ng_)||20.5|||19.8|||17.3||
|_sd_(_ng_)||45.9|||17.5|||8.3||

_Notes_ : This table shows the cluster assignment probabilities and MDEs for the binary outcomes of interest. The parameters of interest are the difference in means between untreated units in each treated group and the pure control units, _βn_ (0 _, t_ ), with _t_ = 1 _,_ 2 _,_ 3 indicating the groups with 20%, 50%, and 80% treated units, respectively. We refer to the corresponding MDEs for the parameters _βn_ (0 _,_ 1), _βn_ (0 _,_ 2) and _βn_ (0 _,_ 3) as _MDE_ 1, _MDE_ 2 and _MDE_ 3, respectively. Scenario 1 exhibits “substantial” heterogeneity, scenario 2 has “moderate” heterogeneity, and scenario 3 presents “limited” heterogeneity. _n_ denotes the sample size; _G_ denotes the number of clusters; min _ng_ and max _ng_ show the smallest and largest cluster; _mean_ ( _ng_ ) is the average cluster size; _sd_ ( _ng_ ) is the standard deviation of cluster sizes. In each scenario, we consider the results obtained with our general formula from Theorem 1 (“Het”), the formulas that rule out between-cluster moment heterogeneity from Corollary 1 (“Homog”) and the formulas that assume homogeneous, equally-sized clusters (“Equal”). Panels 1 and 2 show the constrained and optimal cluster assignment probabilities and their corresponding MDEs. Panels 3 and 4 show the optimal cluster assignment probabilities and their corresponding MDEs.

34
said it was _Somewhat_ , _Fairly_ , or _Very Reliable_ , as compared with 58% in Venezuela, and only 50% in Mexico. The survey data clearly showed that across the board information services, both in terms of access to information and its quality, were deficient, in spite of the reforms.<sup>57</sup>

# <u>Conclusion: Evidence of an Association between Autonomy and Performance</u>

The cases demonstrate that the ARA model permits managers to take greater control over their organizations and manage them more effectively, though they also demonstrate that some ARAs, notably Peru, Kenya, and South Africa (and perhaps Venezuela before SENIAT’s autonomy was reduced), were more successful than others in improving their performance (Table 4 presents a summary of performance by indicator and ARA).<sup>58</sup>

# **TABLE 4. ARA PERFORMANCE: A SUMMARY COMPARISON**

||**Input Ind**<br>|**icators**<br>|**O**<br>|**utput and Ou**<br>|**tcome Indicat**<br>|**ors**<br>|
|---|---|---|---|---|---|---|
||Personnel<br>Management|Collection<br>Costs|Collections|Registration|Compliance|Taxpayer<br>Services|
|**KRA**|•Quasi-<br>voluntary<br>retrenchment<br>•Overall staff<br>reduction<br>•Selective salary<br>increase<br>•Financial<br>support for staff<br>training|•Very low<br>collection<br>costs|•Declining<br>tax/GDP<br>ratio with<br>falling tax<br>rates (trend)|•Expansion of<br>tax base,<br>especially<br>VAT<br>•Focus on<br>large<br>taxpayers<br>with some<br>success|•Improvement<br>in audit<br>coverage<br>•Improved<br>audit<br>procedures<br>•High audit<br>coverage of<br>large<br>taxpayers|•Some<br>improvement|
|**SARS**|•Salary increases<br>•Special<br>initiatives,<br>including<br>performance<br>bonuses<br>•Slight reduction<br>in staff<br>complement<br>•Excellent<br>training<br>opportunities|•Very low<br>collection<br>costs|•Relatively<br>stable<br>tax/GDP<br>ratio with<br>declining tax<br>rates|•Across the<br>board<br>expansion of<br>tax base<br>•Mixed results<br>with filing<br>compliance|•Improved<br>audit<br>procedures<br>•Increased<br>audit<br>productivity<br>•Declining<br>audit<br>coverage|•Improvements<br>planned|
|**URA**|•Periodic salary<br>increases<br>•Increase in staff<br>complement|•High<br>collection<br>costs|•Substantial<br>increase in<br>tax/GDP<br>ratio|•Focus on<br>large<br>taxpayers<br>with little<br>success|•Moderate<br>audit<br>coverage of<br>large<br>taxpayers|•Some<br>improvement<br>for large<br>taxpayers|
|**SAT**|•No<br>retrenchment|•Low<br>collection|•No major<br>change|•No major<br>progress|•No major<br>change|•Some<br>improvement|

57 Taliercio (2000, 339-341).

58 A recent IMF report (2003: 100) cites Ecuador as a good example of the positive impact of autonomy on tax administration performance.

41
Furthermore, although formally independent, it is materially conducted by present and future World Bank employees (Denizer, Kaufmann, & Kraay, 2013). Finally, the nature of the test is descriptive, because it does not account for nonrandom locations of projects to mission areas. This means that it is possible to draw suggestive, but not definitive, conclusions about project

# **3.2 Survey Data on Development Outcomes**

The second empirical strategy attempts to overcome the limitations of the first by relying on a quasi-experimental setup and on direct measures of economic development. To identify effects of World Bank aid, the strategy exploits the longitudinal dimension of the aid data, obtaining a panel of cellsyears spanning the period 1995–2014. Equipped with these data, it is possible to compare development in cells that received aid at different points in time, exploiting information on the dates of project approval and completion. The regression equation to be estimated by OLS has the form where _i_ , _k_ , and _t_ are indices for cell, country, and year, respectively.

The outcome variable _Yikt_ is a measure of economic development (e.g., electrification or access to water). ActiveAid _ikt_ is a binary indicator of the presence of at least one active project in year _t_ .<sup>26</sup> As the effects of aid will not necessarily materialize immediately in the years of project implementation, regressions also include CompletedAid _ikt_ , which is a binary indicator of the past presence of a project. (The indicator is equal to 1 in every year after completion of the first project.) EverAid _ik_ is a binary indicator that equals 1 if cell _i_ ever received at least one World Bank project in the sample period, and Mission _ik_ is an indicator that equals 1 if at least one historical mission was located in the cell. Fixed effects at the country-year level ( _λkt_ ) are always included.

Specification (3) amounts to a triple-differences setup (difference-in-differences-in-differences): EverAid _ik_ , Mission _ik_ , and their interaction control

> 26A project is defined as active if the year _t_ is between commitment and completion.

28
3 9. There is not a close correlation between the extent that a country was hurt by terms-of-trade changes and the extent to which it is today considered a problem debtor.

# II. The **Growth** of Debt and **Net** Transfers of Resources:

External borrowing and repayment are the dominant items in the capital accounts of most developing countries. Table 1 shows a statistical survey of the debt buildup in the 70 countries that are included in the Report on Adjustment Lending 1 (RAL1) statistical appendix. (These include all the important developing coumtries except China and India.) The years 1978, 1982, and 1986 were chosen to indicate the debt situation after the first oil shock, after the second oil shock (and at the outbreak of the debt crisis), and after 4 years of attempted adjustment to the debt crisis. From 1978 to 1982 the debt expanded rapidly, almost doubling in nominal terms and growing about 30 percent in real terms. After 1982 the growth slowed, in both nominal and real terms. In the distribution of debt between categories of borrowers, the main changes were that the share of debt owed by Latin American Countries (LAC) and the Highly Indebted Countri4 es (HICs) increased between 1978 and 1982, and then by 1986 it drorped back what it had been in 1978. Europe, Middle-East, and North Africa's (EMENA) share in the debt owed has dropped over the eight years; Asia's has risen.

Changes in the distribution of debt to commercial and official creditors reflect developments in the political economy of debt. From 1978 to 1982 the middle-income countries, especially the highly indebted countries (HICs), increased their share in the commercial credit to developing countries, but. not in official credit. With credit readily obtainable from
# **4. Conclusions**

Building on companion research using three rounds of the World Bank’s Business Pulse Surveys in South Asia, this paper quantifies the relationship between informality and firms’ investment and employment decisions. Our work suggests that informal firms face larger constraints on liquidity, access to finance, and perceived uncertainty. Accounting for multidimensionality and the margins of informality, our work suggests that _first_ , informal firms, especially those motivated by necessity and survival objectives, are both liquidity and credit constrained. _Second_ , informal firms perceive higher uncertainty in the context of the crisis. Not surprisingly, the extensive margin of informality, measured via registration or a lack of documentation, is highly correlated with the lack of credit and a higher perceived uncertainty. _Third_ , acute credit constraints and higher perceived uncertainty, especially among necessity firms, lead to a substantially lower probability of investing in digital technologies and fixed assets. Furthermore, informal firms have a lower elasticity of employment as sales begin to recover.

Our results suggest that countries with a large informal sector may need to deploy additional levers to accelerate recovery in the post-crisis period.<sup>15</sup> _First_ , a lack of access to finance by informal firms can be addressed with additional support from microfinance institutions (MFI).<sup>16</sup> At a broader scale, countries with a large informal sector should ideally focus on developing their financial markets to reduce information asymmetries and perceived risks by financial institutions. _Second_ , for dealing with uncertainty, it is important that policy makers clearly communicate the public support program in terms of the scope and time frame, to partially reduce uncertainty and its effect in delaying investment and hiring decisions. _Third_ , policies focused on generating capabilities within firms can contribute to an appreciation for investments in technologies for coping with the crisis. _Finally_ , given that the extensive margin of informality is highly correlated with outcomes on investments, policies aimed at promoting the formalization (e.g., simplified schemes for registration) if accompanied by policies reducing the ongoing costs or increasing the benefits of formality, could allow access to formal financial sources as informal firms try to emerge from the shock.

> 15Policies focused on reducing the costs of formalizing (i.e., registration costs) do not yield significant results in inducing firms to formalize (see Ulyssea, 2020; Bruhn and McKenzie, 2014; De Mel et al., 2013). By contrast, monetary incentives can be helpful (De Mel et al., 2013), although the cost-effectiveness of such interventions needs to be considered carefully.

> 16Although microfinance may not be transformational, it is still a critical vehicle to channel credit to informal firms, especially during crisis (Malik et al., 2020). Furthermore, innovations like asset-based microfinance can allow for investments in a financially-sustainable way to credit-constrained firms (Cai et al., 2021).

21
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27 Table 3: Deviation of national NRA around its trend value,<sup>a</sup> key farm products,<sup>b</sup> developing and high-income countries, 1965–84 and 1985–2004

||Deviati<br>|on of nationa<br>|l NRAs aroun<br>|d trend<sup>a</sup><br>|W<br>|eighted avera<br>|ge of NRAs (<br>|%)<br>|
|---|---|---|---|---|---|---|---|---|
||Developing|countries|High-incom|e countries|Developing|countries|High-incom|e countries|
||1965–84|1985–04|1965–84|1985–04|1965–84|1985–04|1965–84|1985–04|
|Rice|32|64|66|229|-20.1|1.9|136.8|419.3|
|Wheat|33|47|80|91|5.5|10.0|16.1|29.3|
|Maize|36|33|53|58|-3.4|0.6|7.5|13.9|
|Soybean|46|117|75|61|2.7|-1.2|0.1|6.6|
|Sugar|53|66|179|173|17.2|15.5|106.5|141.2|
|Cotton|38|33|42|28|-16.0|-12.5|33.1|32.2|
|Coconut|22|20|na|na|-11.5|-20.8|na|na|
|Coffee|41|27|na|na|-37.3|-12.2|na|na|
|Beef|45|52|128|127|-12.4|2.7|22.3|47.0|
|Pork|81|60|92|77|23.6|-7.5|35.6|14.7|
|Poultry|109|74|164|197|26.3|12.3|24.0|25.8|

a Deviation, measured in NRA percentage points, is computed as the absolute value of (residual – trend NRA) where national trend NRA in each of the two sub-periods is obtained by ordinary least squares linear regression of the national NRA on time. Estimates shown are an unweighted average of national NRA deviations each year, averaged over the number of years in each period.

Source: Authors’ compilation based on NRAs from Anderson and Valenzuela (2008).
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# 1. Introduction

A significant number of countries around the world are facing a “learning crisis”: a large share of children complete primary school lacking even basic reading, writing, and arithmetic skills (World Bank, 2018). This phenomenon is not new; research as far back as the 1980s highlights this problem, with early evidence provided for Brazil (Behrman & Birdsall, 1983).

How is it possible that after five to six years of sitting in a classroom almost every day for anywhere between four to seven hours, a large portion of students are still not able to read, write, or do basic arithmetic? To start answering this question, consider three examples. Each describes a scenario that is likely to lead to no learning gains among students. These scenarios were actually documented as part of our ongoing study of teaching practices in low- and middle-income countries.

In a classroom in Afghanistan, the teacher takes out a textbook and reads the lesson objective out loud. He asks one student to read a passage describing the learning objective, then asks another student to read the same passage, and yet a third student. By the end of the class, a total of eight students have read the same passage, spending more than 20 minutes on this activity.

In a classroom in Dar es Salaam, Tanzania, the teacher arrives 20 minutes late. During that time, students are unsupervised and have no learning activities in which to engage. Once the teacher arrives, she puts addition questions on the board and asks students to solve them individually. After sitting at her desk for 10 minutes, she asks a single volunteer to go to the board to solve one of the problems. When the student shows difficulty in solving the problem, the teacher becomes impatient and asks another student to come solve it. After the second student solves the problem correctly, the teacher asks the students if they understand. They respond in synchrony that they do and the class ends.

The final scenario takes place in Rawalpindi, Pakistan. The teacher asks students to divide 4 by 2. After some time goes by, he asks one student to solve the problem on the board. This student solves it correctly and all the students cheer. After that, the teacher erases the answer and asks another student to come to the board to solve the exact same question. The teacher follows the same process for five additional students.

These are not isolated examples. A growing body of evidence suggests that to a large extent the learning crisis is a reflection of a teaching crisis (Bold et al., 2017, Molina Fatima, Trako, & Wilichowksi, 2018a; Molina, Trako, Hosseini, Matin, Masood, & Viollaz, 2018b; 2
Sen, A.K., 1985. _Commodities and capabilities_ . Amsterdam: North-Holland.

Sen, A.K, 1997. _On economic inequality._ Oxford: Clarendon Press.

Sen, A.K., 1998. Mortality as an indicator of economic success and failure. _Economic Journal,_ 108(446), p.1-25.

Senik, C., 2004. When information dominates comparison: learning from Russian subjective panel data. _Journal of Public Economics,_ 88(9-10), p.2099-123.

- Slesnick, D.T., 1998. Empirical approaches to measuring welfare. _Journal of Economic Literature_ , 36(4), p.2108-65.

Solnick, S., and Hemenway, D., 1998. Is more always better? A survey on positional concerns.

_Journal of Economic Behavior and Organization,_ 37(3), p.373-83.

- Townsend, P., 1979. _Poverty in the United Kingdom_ . Harmondsworth, Middlesex: Penguin Books.

van de Stadt, H., Kapteyn, A., and van de Geer, S., 1985. The relativity of utility: evidence from panel data. _Review of Economics and Statistics,_ 67(2), p.179-87.

- Van Praag, B.M.S., 1968. _Individual welfare functions and consumer behavior_ . Amsterdam: North-Holland.

Van Praag, B.M.S., 1991. Ordinal and cardinal utility: an integration of the two dimensions of the welfare concept. _Journal of Econometrics,_ 50(1-2), p.69-89.

Walker, I., and Smith, H., eds., 2001. _Relative deprivation: specification, development and integration_ . Cambridge: Cambridge University Press.

- World Bank, 2005a. _World development report: equity and development_ . New York: Oxford University Press.

World Bank, 2005b. Note on construction of expenditure aggregate and poverty lines for IHS2. Processed. Development Research Group, World Bank, Washington, DC.

25
default on its debt or to monetize its debt, or be forced to undertake a major fiscal retrenchment to avoid default or monetization.

A starting point for analyzing fiscal sustainability is the current levels and medium-term projections of general government fiscal balances and public debt. It is relatively straightforward to determine the impact of such fiscal balances, if extrapolated forward into the future and combined with some basic macroeconomic projections, on the evolution of public debt levels, with debt sustainability being defined as a situation where public debt as a ratio of GDP is stable or falling and/or does not exceed some critical threshold.<sup>2</sup> Section 2 of this paper examines the general government fiscal balances and public debt levels in Romania and the medium-term projections of these variables. The general government fiscal data include the local governments.

The data captured in the general government budget, however, often provide an incomplete and hence misleading picture of the sustainability of public finances over the long term. This is starkly illustrated by a comparison of current public debt levels with calculations of the longterm net worth of the government in Romania, taking into account the long-term costs of aging. Romania’s outstanding public debt amounted to 32 percent of GDP in 2010, a relatively moderate level. In contrast, Velculescu (2010) estimates the inter-temporal net worth of the Romanian government at negative 252 percent of GDP, based on a finite horizon approach and negative 1,097 percent of GDP based on an infinite horizon approach.

Risks to fiscal sustainability may emanate from activities of the public sector, which are not currently part of the general government budget, but which might eventually impose fiscal liabilities on the budget. These could include contingent liabilities of government, such as loan guarantees, or the quasi fiscal deficits (QFDs) of state owned enterprises. The QFDs of the state owned enterprises are discussed in Section 3.

> 2 An example of such a threshold is the ceiling of 60 percent of GDP for public debt set by the Stability and Growth Pact of the EU for countries which aim to join, or are members of, the Eurozone.

3
were either in hiding to avoid a mandatory draft, migrated or dead (Tadjbakhsh, 1996). Gomart (2003) notes though that families that were more prone to poverty were families with few working age men or female-headed households with many small children.

# **4. Data, Main Variables and Identification Strategy**

# **4.1 Data**

This analysis in the study uses data for the 2003 and the 2007 Living Standards Measurement Studies for Tajikistan (henceforth, TLSS). The surveys are nationally representative surveys of households and communities. The sample frame used a two-stage method based on the 2000 Census of Tajikistan. More information about the surveys can be obtained from the World Bank web-site dedicated to Living Standards Measurement Studies (LSMS). The 2007 survey was prepared by the World Bank in collaboration with UNICEF and carried out by the National Committee for Statistics (Goskomstat).

The surveys include data on household consumption of a wide range of food and non-food items; the socio-demographic composition of the household; labor market activities, such as participation in the labor force during the last 14 days and number of hours worked<sup>10</sup> ; the health and education of household members; sources of household income such as individual wages, both cash and in-kind; and transfers to the household from various sources. The 2007 data contain extensive information on the migration of individual household members, including those who are currently present or away, as well as remittances and transfers, such as inter-household and government transfers. The survey differentiates between main and secondary jobs held by individuals.

The analysis of education employs data from the 2003 and 2007 surveys to get an understanding of the long-term impact of armed conflict on education of the cohorts who were of school age during the

> 10 While studies quoted earlier in the paper emphasize the differences between the long and short-run employment outcomes and AWE (e.g. Fernandes and de Felicio 2005), such studies were based on the panel data which allows for such comparisons. My study of labor market outcomes in Tajikistan is based on the cross-sectional data for 2007. The cross sectional nature of the data limits the scope of the analysis of employment to short-term outcomes.

10