rafmacalaba/datause-encoder-data huggingface dataset:
factors, the economy has actually managed to grow without significant employment expansion. Resources have been reallocated from low-productivity sectors like agriculture to relatively higher-productivity sectors, such as services and construction. For the 1993-2003 period the contribution of total factor productivity growth to the average annual real GDP growth of 6.3 percent was 6.1 percentage points.<sup>10</sup> Considering the almost nonexistent contribution of employment growth to the overall post-transition output expansion, it is important to understand the labor market dynamics in Albania, which will also help in forming expectations regarding the impact of social protection receipts on labor, and poverty outcomes.

Public sector employment decreased from 850,000 in 1991 to 169,000 in 2006. A significant part of this reduction originated from the privatization of state-owned enterprises in early transition. According to annual employment figures presented in Table 2, the public sector accounts for 18 percent of total employment in 2006 versus 58 percent for the agricultural sector. Although employment is partly shifting to the private, non-agricultural sector, job creation has been stagnant, with only 23,000 new jobs created between 2001 and 2004. The most common transition from unemployment to work is into self-employment in agriculture or the informal sector. Very few of the previously unemployed manage to find formal wage employment.

According to the LSMS estimates in Table 3, the national labor force participation rate was between 64-69 percent in 2004, depending on the definition used.<sup>11</sup> The 2004 national

> 10In terms of overall ease of doing business, World Bank’s Doing Business 2006 ranking places Albania at 117 among the 155 countries (World Bank, 2006b). Albania performs poorly (i.e. is in the bottom third) in the following areas: starting a business, dealing with licenses, hiring and firing, protecting investors, paying taxes, and enforcing contracts (World Bank, 2006b). These findings partially explain the lack of substantial job creation in the nonagricultural private sector.

> 11Under the standard definition, adults who are not employed must engage in active job search to be classified as in the labor force and unemployed. Willingness to take a job without active job search is considered inadequate. Under the relaxed definition of labor force participation and unemployment, individuals without jobs are included in the labor force and considered unemployed if they have either searched for work in the last four weeks or have not searched but would be willing to take a job in the next two weeks. In other words, including estimates based on the

11
government of São Paulo, ranked third in terms of assets in 1994, while the other São Paulo state bank, Nossa Caixa, ranked 11<sup>th</sup> . The roots of these banks, both historically and politically, ran deep in Brazil, which has a long tradition of such financial institutions. Banks owned by state governments appeared during the First Republic (1889-1930), and multiplied over the course of the twentieth century.<sup>1</sup> Their ostensible purposes included handling publicsector payrolls, and financing state-level development projects. Yet they increasingly came to serve as major instruments of state-level deficit financing.<sup>2</sup> State banks were a critical component in the machinery of generating employment, patronage, and securing electoral support.<sup>3</sup> Since the costs of these activities were spread broadly, while the banks’ benefits were concentrated more narrowly, they were politically valuable instruments.

By the end of Brazil’s military dictatorship in the mid 1980s most state banks were in dire straits. State banks had repeatedly relied on the Central Bank and state coffers to relieve them of liabilities or to recapitalize them.<sup>4</sup> In the context of worsening economic conditions and public sector deficits a disproportionate share of state bank income derived from inflation-based revenues.<sup>5</sup> Banks used short-term deposits imperfectly protected from inflation to purchase securities that yielded much higher rates of interest than were paid to depositors.<sup>6</sup> Inflation-float revenue became a key source of bank earnings for the Brazilian banking sector as a whole, and especially important for state banks.

> 1 Baer and Nazmi (2000), 5.

> 2 Ness (2000), 72; Baer and Nazmi (2000), 7; McQuerry (2001), 38; Maia (1999), 115.

> 3 Baer and Nazmi (2000), 7.

> 4 Ness (2000), 75. Compare Sola, Garman and Marques (2001) and Lundberg (1999) for a detailed discussion on the use of the monetary reserve by the Central Bank to bail out state banks in the 1980s and 1990s. 5 McQuerry (2001), 38.

> 6 Baer and Nazmi (2000), 6; Ness (2000), 75.

6
describing household ownership of durable goods and characteristics of housing. Asset indices typically follow the same basic form: where Ai is the asset index for household “i”, (a1i, a2i, ... ,aki) are k indicators of asset ownership and housing quality variables, and (b1, b2, ... , bk) are weights used to aggregate the indicators into an index.

By aggregating observed measures of a household’s material living conditions, the asset index captures a dimension of economic status. While it is reasonable to think that assets and housing characteristics reflect a dimension of wealth it is clear that this is not wealth in the formal sense of the value of household assets owned minus liabilities.<sup>1</sup> Data on true household wealth are costly to collect and thus, like expenditures, are frequently unavailable.<sup>2</sup> Asset indices vary in terms of both the set of indicators included (i.e. a1i, a2i, ... ,aki) and how weights used to aggregate the indicators are derived (i.e. b1, b2, ... , bk). How these attributes of indices affect household rankings and our understanding of inequalities in outcomes and welfare levels is the focus of this paper. In this section we first provide a literature overview of the main methodological findings to date.

# _a) Comparing asset indices to expenditure measures_

Chief among the questions about how to interpret the results derived from asset indices is how a ranking by the index compares to one derived from household expenditures. Filmer and Pritchett (2001) compare rankings by household size-adjusted expenditures and an index using data from three household surveys from poor countries where both measures could be calculated. They find that the correlation coefficient between rankings based on the two measures ranges from 0.43 to 0.64 in the three countries. They find general agreement in the households classified in the poorest 40 percent by both measures: roughly 60 percent of households classified as being in the poorest 40 percent by per capita expenditures are also classified in the poorest 40 percent by the asset index. Moreover, they find that differences in schooling outcomes between the richest and poorest quintiles are typically the same or larger when using

> 1 In previous papers (e.g. Filmer and Pritchett 2001) the index is often referred to as a _wealth_ index.  The term wealth in those papers was used to distinguish it from an expenditures based measure.  In order to use a term that more accurately reflects the measure, it is referred to as an _asset_ index in this paper.

> 2 In addition, asset indices typically exclude productive assets which reflect household investments; assets that are not usually collected in the types of datasets for which analysts turn to an index approach.

4
influence on policy is rare. For example, Gregory Mankiw replied “nothing specific comes to mind,” while Steven Levitt said that “I can think of lots of cases we thought we should affect policy, but none where we actually did!” Dani Rodrik also could not point to any specific cases, but said he is amazed “how widely the stuff in the blog is read, and I have had many policymakers tell me about one or other point I had made in the blog.”<sup>22</sup> Among the cases where it does appear blogs influenced policy, the role of the blog has been more as investigative journalism than as disseminator or originator of economic analysis. For example, David Roodman blogged about _Kiva_ , a popular peer-to-peer microlending website, and explained through his post that it operated in a different way than implied, leading to coverage in the New York Times and a modification in Kiva‟s website.<sup>23</sup> Justin Wolfers blogged on Freakonomics about a paper that showed that the process that the U.S. Census Bureau uses to make public-use micro-data anonymous was inducing systematic errors in these data, which led to coverage in the Wall Street Journal and most likely caused the Census bureau fixing the mistake and re-issuing the data.<sup>24</sup> However, even if the vast majority of blog posts do not directly influence policy, it only takes the right reader reading the right post(s) potentially to shape policy in important ways. Tim Worstall has blogged on multiple occasions about the gender pay gap in the United Kingdom, and how the main cause is due to child care. He reports, although he has no documented proof, having been told that as a result of one reader of these posts being involved in the Liberal Democrat Party, the party was urged to, and did adopt, a policy that there should be transferable maternity/paternity leave rather than just maternity leave – a

> 22 Personal communications with authors.

> 23 See http://blogs.cgdev.org/open_book/2009/10/kiva-is-not-quite-what-it-seems.php. Kiva made it appear as if lenders were funding specific projects, who would receive loans if enough people funded them, when in fact the projects shown were almost all funded in advance.

> 24 <u>http://www.freakonomics.com/2010/02/02/can-you-trust-census-data/</u>

28
quality. In addition, when looking at the composition of our measure of rigid expenditure, we also find some evidence that higher expenditure in pensions reduces the probability of fiscal adjustment more robustly than higher expenditure in wages.

The rest of the paper is organized as follows. Section 2 presents a review of the relevant literature to highlight our main contributions. Section 3 presents the conceptual framework. Then, sections 4 and 5 present the estimation methodology and the main results of the paper. Finally, section 6 concludes.

# 2. Literature review

This paper is related to several strands of research in the literature (see Table 9 in the appendix). First, it is related to the large literature on the determinants of fiscal distress and adjustment (see Perotti, 1998; Persson and Tabellini, 1999; and Pinho, 2004 for general surveys). Although this literature is extensive, very few studies have focused on the size of budget rigidities and the difficulty of achieving large fiscal adjustments. This literature has identified major determinants of the likelihood of starting fiscal consolidation and its success _._ Most of the literature concentrates on the composition of consolidation, whether it should be based on expenditure cuts or revenue increases. There seems to be a consensus that adjustments based on expenditure cut tend to be more successful that adjustments based on raising revenues. Adjustments based on expenditure cuts are found to be more effective (Alesina and Ardagna, 1998; Ardagna, 2009; von Hagen et al., 2002; Guichard et al., 2007; Barrios et al., 2010), and the main explanation is that they are often coupled with reforms that enhance the effectiveness of budgetary procedures (European Commission, 2007).

Evidence shows that fiscal consolidation is more likely during periods of weak public finance conditions (Barrios et al., 2010; Guichard et al., 2007; European Commission, 2007; Von Hagen and Strauch, 2001). Stronger economic growth can contribute to successful consolidations (von Hagen and Strauch, 2001), as positive output gaps increase the probability of launching a retrenchment. In particular, governments are more likely to undertake consolidation efforts when the domestic economy is doing well relative to other economies (Von Hagen and Strauch, 2001).

Perhaps closer to the spirit of our paper, some studies have shown that reducing budget items that are politically sensitive makes a significant contribution to successful consolidations (Perotti et al., 1998). While some studies find that cutting social spending and transfers is key to reach a successful consolidation (Guichard et al., 2007), others find that fiscal consolidation should incorporate cuts in welfare spending and government wages to be successful (Alesina and Perotti, 1997). Von Hagen and

- 8 -
Unlike the indicators discussed above, multiple models perform better than average for the USAD poverty gap. Models 8 and 3 have the highest imputation accuracy of 65 percent, followed by Models 1, 2, 7 and 9 (62 percent) and Model 5 (58 percent).

We further plot in Figure 2 the imputation accuracy for mean consumption. Model 3, again, is the best performer that achieves an imputation accuracy rate of 65 percent, followed by Model 9 with an imputation accuracy rate of 46 percent. Adding soil quality and distance to facilities to Model 9 increase imputation accuracy for this model to 50 percent and adding nightlight to Model 9 increases imputation accuracy to 100 percent for Viet Nam.

# _Machine learning as alternative_

We consider machine learning (ML) as an alternative imputation method.<sup>17</sup> The standard ML procedures split a data sample into a training sample (to estimate the imputation model) and an estimation sample (to obtain out-of-sample predictions). In our context, the base survey and the target survey respectively correspond to the training sample and the estimation sample. Employing three common ML techniques, LASSO, Elastic Net, and Random Forest, we show the estimation results in Appendix B, Tables B.1 and B.2 for Tanzania and Viet Nam, respectively. The ML poverty estimates do not work for both countries, except for the estimates of consumption mean that are within one standard error of the true mean for LASSO and Elastic Net in Malawi.<sup>18</sup> These

> 17 See <mark>Mullainathan and Spiess (2017) and Athey and Imbens (2019) for recent reviews of ML in economics.</mark> 18 Lasso linear model and Elastic net linear model are trained in the first round and tested against the second round. Lambda in LASSO is selected by 10-fold cross-validation for out-of-sample prediction. Alpha and lambda in Elastic Net are selected by 10-fold cross-validation for out-of-sample prediction. The final selected variables and prediction models with statistics for Lasso and Elastic Net using postselection coefficient estimates are shown in Table B.3 for Tanzania and Table B.4 for Viet Nam (Appendix B). Random forest model is trained in the first round and tested against the second round. The number of sub-trees is set at 1,000. Both out-of-bag error and validation error are used to determine the best possible model. Importance matrix of the variables is shown in Table B.5 for Tanzania and Table B.6 for Viet Nam.

21
|Education Years of Manager|||-0.004|
|---|---|---|---|
||||(0.009)|
|% Planted with Bananas|||0.284***|
||||(0.073)|
|% Planted with Coffee|||0.312***|
||||(0.110)|
|% Planted with Fruits|||0.509**|
||||(0.214)|
|% Planted with Other Cash Crops|||0.941***|
||||(0.188)|
|% Planted with Other|||1.523|
||||(1.028)|
|% Planted with Pulses and Oils|||0.149***|
||||(0.044)|
|% Planted with Roots|||0.303***|
||||(0.048)|
|% Planted with Vegetables|||0.119|
||||(0.125)|
|Intercropped|||0.352***|
||||(0.035)|
|Family Labour Days per Acre (log)|||0.255***<br>(0.025)|
|Hired Labour Days per Acre (log)|||0.116***<br>(0.027)|
|Reverse Dummy for Family Labor|||0.992***|
||||(0.124)|
|Reverse Dummy for Hired Labor|||-0.022|
||||(0.082)|
|Any Pesticides Applied|||0.197**|
||||(0.085)|
|Any Manure Applied|||0.152**|
||||(0.075)|
|Any Fertilizer Applied|||-0.107|
||||(0.171)|
|Improved Seeds|||0.183***|
||||(0.059)|
|Any Erosion Control Or Water|||0.094*|
|Harvesting Structure On Parcel|||(0.048)|
|Constant Term|4.345***|4.358***|2.374***|
||(0.020)|(0.031)|(0.185)|
|Number of Observations|6,999|6,999|6,999|

Specifications 2 & 3 Include Household and Community-Season Fixed Effects Note: *** p<0.01, ** p<0.05, * p<0.1 19
|l Wages<br>2002-2010<br>(6)<br>-0.00116<br>(0.00216)<br>0.860<br>66,891<br>-0.000124<br>(0.00271)<br>0.860<br>66,891<br>-0.00389<br>(0.00719)<br>66,891<br>**e)**<br>0.299***<br>(0.0151)<br>390.33<br>66,891<br>Yes<br>Yes|
|---|
|Surveys<br>Nomina<br>1995-2010<br>(5)<br>-0.00195<br>(0.00216)<br>0.862<br>80,604<br>0.00203<br>(0.00236)<br>0.862<br>80,604<br>-0.00521<br>(0.00576)<br>80,604<br>**Working Ag**<br>0.373***<br>(0.0181)<br>42.69<br>80,604<br>Yes<br>Yes<br>Yes|
|Labor Force<br>ployment<br>2002-2010<br>(4)<br>-0.00237<br>(0.00408)<br>0.941<br>67,286<br>-0.00142<br>(0.00533)<br>0.941<br>67,286<br>-0.00793<br>(0.0136)<br>67,286<br>**P (% Pop. in**<br>0.299***<br>(0.0151)<br>392<br>67,286<br>Yes<br>Yes<br>Yes|
|vailable in the<br>Total Em<br>1995-2010<br>(3)<br>-0.00191<br>(0.00392)<br>0.932<br>81,025<br>0.00202<br>(0.00420)<br>0.932<br>81,025<br>-0.00512<br>(0.0105)<br>81,025<br>**: Share of ID**<br>0.373***<br>(0.0181)<br>422.22<br>81,025<br>Yes<br>Yes<br>Yes|
|nicipalities A<br>vestment<br>2002-2010<br>(2)<br>-0.00192<br>(0.0193)<br>0.703<br>40,452<br>-0.0179<br>(0.0229)<br>0.703<br>40,452<br>-0.00575<br>(0.0578)<br>40,452<br>**ep. Variable**<br>0.299***<br>(0.0151)<br>296.56<br>40,452<br>Yes<br>Yes<br>Yes|
|es for the Mu<br>Gross In<br>1995-2010<br>(1)<br>-0.00284<br>(0.0177)<br>0.690<br>47,728<br>-0.0177<br>(0.0175)<br>0.690<br>47,728<br>-0.00725<br>(0.0452)<br>47,728<br>**D**<br>0.373***<br>(0.0181)<br>213.41<br>47,728<br>Yes<br>Yes<br>Yes|
|**Table (II.3)**<br>Estimat<br>**Dependent Variables (in logs)**<br>**Time Period**<br>**Panel A. Reduced Form**<br>Predicted Infows<br>R-squared<br>Observations<br>**Panel B. OLS**<br>Share of IDP (%Pop in Working Age)<br>R-squared<br>Observations<br>**Panel C. 2SLS**<br>Share of IDPs (%Pop in Working Age)<br>Observations<br>**Panel D. First Stage**<br>Predicted Infows<br>First Stage F-statistic<br>Observations<br>**Controls for all Panels**<br>Year FE<br>Firm FE<br>Additional Controls|

52
15

# **Country effects: the role of public information**

The regressions above could be falsely attributing to privately held information a value that does not exist if information from private sources were collinear with and equally available from public sources. Clearly, public information sources do influence the foreign investment decision. Firms obtain information through analytical country reports by international organizations such as the World Bank and the International Monetary Fund and also by a growing range of private consulting firms; and the media provides continuing reports on a range of political, economic, and social attributes of countries. As a consequence. investors form assessments of countries from such widely available informnation on a large number of factors including market size, growth prospects, costs of production, the state of infrastructure, and the country's political and institutional development.

The further analysis in this section, therefore, explores two sets of questions. First, once firms' perceptions of different countries are controlled for, do the effects of past experience and rivalry continue to be important? In other words, are the results of the base model robust to differences in publicly perceived attractiveness of countries considered? Second, do PAST and RIVAL influence investment to varying degrees in the different countries?

To answer the first question, we augmented the basic regression with country dummies which capture in a summary forn the relative attractiveness of the different countries. Since the coefficients on the dummy variables represent the average perception of the country, we take these to represent the publicly available information. An alternative specification would include specific country features, such as infrastructure, market size, and labor costs. As Head, Ries, and Swenson (1995) have argued, a full elaboration of country characteristics is difficult, and hence a country
_Figure 2. The evolution of average leave days granted to mothers and fathers, by region_ <!-- Start of picture text --> Mothers Fathers<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br>Year Year<br>East Asia & Pacific Europe & Central Asia High income: OECD<br>Latin America & Caribbean Middle East & North Africa South Asia<br>Sub-Saharan Africa<br>500 80<br>400<br>60<br>300<br>40<br>Leave Days Leave Days<br>200<br>20<br>100<br>0 0<br><!-- End of picture text --> Figure 2 plots the average allocation of leave days to each parent by region for the period from 1970 to 2021. It shows that, in 1970, the allocation of leave days to mothers was, on average, the highest in OECD high-income economies (mean of 102.5 days and median of 84 days), followed by countries in the Europe and Central Asia (ECA) region (mean of 88.8 days and median of 105 days). More than fifty years later, the average allocation of leave to mothers, which includes those parental leave days that are not specifically reserved for either parent, is quite notably the highest in Europe and Central Asia at 506 days (median of 379 days). The average in OECD high-income economies follows at 332.8 days (median of 311). Average values are, to a degree, skewed by some countries with extremely generous leave policies; for example, the total number of leave days for mothers in Belarus is 1,221 days and in the Slovak Republic it is 1,137 days. It is for this reason that the median values are presented in parentheses.

In other parts of the world, the allocation of leave days to mothers in 1970 was well below the averages of OECD high-income economies and countries in the ECA region. Furthermore, the gap between the rest of the world and these two regions has increased notably over time. One region where the gap has been reduced is the East Asia Pacific (EAP) region. In 1970, this was the region with the lowest average number of leave days allocated to mothers (with an average of 25.5 days and a median of zero days). By 2021, the average length of leave to mothers in this region had increased to 126.4 days (median of 90 days). As the
**Table 2: Comparisons of means across early and later reforming regions before reforms in 2000**

||EarlyReformers|Late Reformers|t-test probability<br>difference is<br>significant|
|---|---|---|---|
|Work outside the home|0.776|0.763|0.887|
||(0.056)|(0.059)||
|Work all year|0.584|0.651|0.469|
||(0.066)|(.077)||
|Paid work|0.693|0.649|0.76|
||(0.105)|(0.097)||
|Occupations with higher human capital|0.211|0.197|0.79|
||(0.070)|(0.045)||
|Years of education|1.34|1.27|0.898|
||(0.277)|(0.124)||
|Age of first marriage|15.84|16.48|0.27|
||(0.807)|(0.395)||

Standard errors are in parentheses. None of the differences are statistically significant.

# **IV. The Data**

The data set we use comes from the Demographic and Health Survey data collected in Ethiopia in 2000 and 2005. In both years, the data set covers all regions and chartered cities in the country. The 2000 and 2005 surveys covered 15,367 and 14,070 15-49 year old women, respectively. Averaged across both years, about 23% were single (never married), 60% were married, 7% divorced, 3% separated, 5% widowed and 1% cohabiting.

Educational attainment in the sample is low. The average years of schooling of women in the sample in 2000 and 2005 are 1.3 and 3.4, respectively. This variable varies significantly across geographic areas (mainly because of varying level of urbanity) and by marital status (younger age cohorts are more likely to have attended school and for a longer period of time). However, there is no systematic difference across early and late reforming areas.

16