rafmacalaba/datause-encoder-data huggingface dataset:
# **2.1.1 Technology grid**

A critical feature of the survey is how technology is measured. To design modules B and C, the FAT survey relies on a group of technology experts to determine the business functions relevant to the firm and the list of technologies that can be used to implement the key tasks in each function, as described by Cirera et al. (2020). We call the resulting structure the _Technology Grid_ . The grid in FAT has three characteristics. First, it is comprehensive. It includes the main business functions and the full array of technologies in each function, from the most basic to the most advanced technologies available. Second, the business functions and technologies in the grid are relevant to all firms within any given sector. In addition to identifying the key business functions and relevant technologies, technology experts also provided a ranking of the technologies in each business function based on their sophistication. Overall, the FAT survey covers about 300 technologies split into almost 60 business functions, including general business functions (GBF) that apply to all firms, regardless of the sector, and sector-specific business functions (SBFs) applied to agriculture (crops and livestock), manufacturing (food processing, wearing apparel, leather, pharmaceutical, and automotive), and services (retail, accommodation, land transport, banking, and health). Appendix A shows the grid for GBFs and an example of SBFs for the food processing sector.

# **2.1.2 Technology questions**

The survey contains three types of questions about the technologies used by the firm. First, FAT asks whether the firm uses each of the technologies in the grid to conduct the tasks of the particular business function. After determining the technologies that are used by the firm in a business function, the survey asks which of these technologies is the most widely used in the business function. Third, when a firm uses an advanced technology in a given business function, the survey asks how many years the technology has been adopted. This allows us to produce three types of measures of sophistication. One regarding all the technologies that are used, extensive measure (EXT); one regarding the most frequently used technology, intensive measure (INT); and finally, the years of adoption for advanced technologies.

# **2.1.3 Summary technology sophistication index**

As an aggregate indicator to measure sophistication we use a simple cardinal index. Based on the experts’ assessment, we order the technologies in each function _f_ according to their sophistication, and assign each a rank _rf ∈_ 1 _,_ 2 _, ..., Rf_ , from least to most advanced. Because several technologies may have the same sophistication, the highest rank in a function _Rf_ may 4
# **The impact on poverty**

Beyond the impact on inequality, which measures the relative position of households across the distribution, it is important to see the impact on poverty, which depends on the absolute level of income of a household with respect to a poverty threshold. The results suggest that the combination of taxes and social spending was poverty increasing in 2016 **.** The share of the population whose market income (including pensions) was below the per capita US$5.50 PPP-a-day poverty line was 13.9 percent (Table 3). Once the burdens of direct and indirect taxes are considered, these are larger than the direct benefits received from transfers, so that the share of the population whose consumable income is below the US$5.50 PPP-a-day poverty line increases to 15.4 percent. Similarly, if one were to use Eurostat’s relative poverty line of 60 percent of the median equivalized disposable income, the headcount poverty rate increases from 21.8 percent for disposable income to 27.5 percent for consumable income. Most of the increase in poverty is due to the burden of indirect taxes, as households are not being compensated for this burden through direct transfers. Even for extreme levels of poverty (such as those captured by the US$3.20-a-day poverty line), social transfers are insufficient to mitigate the burden of taxes so that the level of extreme poverty after taxes and transfers is higher than before taxes and transfers are considered (Table 3). The poverty gap and the severity of poverty decline when going from market to disposable income for the absolute poverty lines, but once indirect taxes are incorporated into the analysis, part of this effect is reversed. Both the poverty gap and the severity of poverty look worse when using the relative line. Given that households do not observe the value of the benefits of in-kind education and health, we refrain from measuring poverty including those benefits, as standard in the literature.

Table 3. Romania. Changes in Poverty on account of taxes and transfers

||Market<br>income +<br>old-age<br>pensions|Disposable<br>income|Consumable<br>income|
|---|---|---|---|
||(1)|(2) = (1) - direct<br>taxes - social<br>contributions +<br>~~direct transfers~~|(3) = (2) -<br>indirect taxes|
|**Poverty headcount**||||
|US $3.20 PPP-a-day per capita|7.3|3.5|4.9|
|US $5.50 PPP-a-day|13.9|12.1|15.4|
|60% of median equivalized disposable income|22.5|21.8|27.5|
|**Poverty gap**||||
|US $3.20 PPP-a-day per capita|3.4|0.8|1.3|
|US $5.50 PPP-a-day|6.3|3.6|5.0|
|60% of median equivalized disposable income|34.7|37.3|42.6|
|**Poverty severity**||||
|US $3.20 PPP-a-day per capita|2.1|0.3|0.6|
|US $5.50 PPP-a-day|4.0|1.5|2.3|
|60% of median equivalized disposable income<br>Source: Own estimates based on Romanian HBS 2016.|21.8|22.0|26.1|

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further the relevance of education, age, marital status and urban residence in determining an individual’s participation in the labor market as employed. On the contrary, the presence of the spouse in the household and the poverty status of the household limit the employability of an individual.

The MNL model result of the _women sample_ (Table 9) show that females who are more traditional are less likely to be employed, compared to being out of the labor force. Unlike the pooled sample, the inclusion of other control variables does not change the negative sign of traditionalism, but instead, renders it not significant. Among less traditional women, work centrality (5), tenacity (6) and high achievement (9) are positively associated with being employed following participation in the labor force. The importance of these traits, however, dampens with increasing traditionalism. Although a woman’s optimism by itself plays no significant role in their employment outcome (first half of specification 12), it does positively influence their selection into employment when combined with traditionalism. In the presence of traditionalism and personality traits, women who are older and residing in an urban area have higher odds of employment. Residing in a poor household has a reducing effect on the likelihood of a woman being employed.

Table 10 contains the MNL results of the _men sample._ Here again we see the negative effect of increasing traditionalism on the odds of a man being employed compared to them not being in the labor force. The significant association is retained even when other factors are controlled for (2). Results from the interaction of gender role attitudes and personality traits indicate that among less traditional men, a high internal locus of control has a negative significant effect on their likelihood of being employed (8). Traditionalism appears to positively influence a man with high internal locus of control’s selection into employment, all things being equal. As in the female sample, the inclusion of gender role attitudes and personality traits jointly with other control variables indicate that older men who are married, have at least secondary education, and are residing in male-headed households are more likely to be employed. In addition, religion and the geographic location of a male respondent play vital roles in their employment choice; Muslims and urban dwellers have higher odds of employment. Other factors like spousal presence and household poverty status are, however, negatively associated with employment odds.

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In the survey, up to 4 individuals were randomly selected in each household to answer a total of fourteen (14) questions on gender role attitudes (see Table 1). Response options were strongly disagree-1, disagree- 2, neutral-3, agree-4, or strongly agree-5. An index for gender role attitudes was constructed from these responses following Guiso (2006), Kling et al. (2007), and Friedman et al. (2011). First, all answers were recoded so that higher values correspond to more traditional beliefs. Second, z-scores were generated for each variable entering the index using the mean and standard deviation. Third, the index was created by calculating the means of these z-scores. Finally, the gender role attitudes index was standardized to take on a value between 0 and 100, with higher scores indicating more traditional inclinations.

Like the gender norms variable, the personality traits questions (28 questions in all) use Likert scale response categories. Following Laajaj and Macours (2017), nine personality groups – locus of control, work centrality, tenacity, polychronicity, impulsiveness, achievement, power motivation, organization, and optimism – were constructed from these 28 questions. Each personality group is a simple average of all related traits (see Table 1 for associated personality groups and traits).

# **3.3. Descriptive results**

# **3.3.1. Description of study variables**

Table 2 presents descriptive statistics for the analytic sample for all study variables, i.e., outcome, main explanatory variables and controls (see Table A1 in the appendix for similar results for the pooled sample). The table shows that about two-thirds of the sample is employed, with no significant difference between men and women. About 5% of both men and women are unemployed, while about 27% of individuals are _not in the labor force_ , with slightly higher estimates for men compared to women. There are significant differences in wage and selfemployment between men and women – more women are represented in self-employment, compared to men (52% vs. 33%), while more men are in wage employment, compared to women (35% vs. 15%). The distribution of employment found in this study differs from that of the AHIES (2022), even after restricting the AHIES sample to Ashanti and Northern regions. This could be explained by the differences in sample design and the objectives of the two studies; the current study was designed to provide information on employment and informality in the regions while the AHIES focuses on providing income and expenditure information, with limited emphasis on employment and informality.

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Table 8: Marginal Value of Public Funds

||(1)<br>All frms|(2)<br>Low TFP|(3)<br>High TFP|
|---|---|---|---|
|(1) Direct cost|5,116|2,402|2,774|
|(2) Tax cut going to workers|974|-159|1,437|
|(3) 50% of beneft receipt of non-employed<br>who become employed|164|164|-3|
|(4) 50% of additional net wages of<br>non-employed who become employed|255|237|-5|
|(5) Additional tax revenue|438|401|-9|
|(1)-(3)-(5) Net cost|4,513|1,837|2,786|
|(2)+(4)-(3) Willingness to pay (WTP), workers only|1,065|-87|1,435|
|(1)+(4)-(3) Willingness to pay (WTP), workers and frms|5,207|2,474|2,772|
|Marginal value of public funds (MVPF), workers only|0.24|-0.05|0.52|
|Marginal value of public funds (MVPF), workers and frms|1.15|1.35|0.99|

_Note_ : Table shows estimates of the marginal value of public funds (MVPF) associated with the payroll tax cut. In each row, we report per-worker average monthly amounts in HUF for workers aged 55 and above. Row (1) reports the direct cost defined as the tax cut multiplied by the employment rate of the treatment group. Row (2) reports the tax cut received by workers based on the wage effect results reported in Table 6. Row (3) reports the benefits that non-employed individuals who become employed would have received based on the estimated employment effect of the reform and the average unemployment benefit amount. Row (4) reports the additional net wages received by non-employed individuals who become employed based on the estimated employment effect by wage categories. Row (5) reports the additional tax revenue defined as the total estimated income tax and social security contributions paid for workers who become employed. The net cost of the tax cut is the direct cost (row 1) minus the cost saving coming from lower benefits (row 3) and the higher tax revenue (row 5). The willingness to pay from the worker perspective is the wage increase incumbent workers experience (row 2) and earnings of the newly employed (row 4) minus the lost benefits (row 3). The willingness to pay from the worker and firm perspective is the direct cost (row 1), earnings of the newly employed (row 4) minus the lost benefits (row 3). The marginal value of public fund (MVPF) is the ratio of willingness to pay and the net cost. We report MVPF calculations for workers at all firms (column 1), and also separately for workers at low-productivity firms (column 2) and at high-productivity firms (column 3).

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**Table 3:** Composition of non-aid current account deficit, pre aid-surge period (2001-2004) and aid-surge period (2005-2008).

||2001-2004|2005-2008|Difference|
|---|---|---|---|
|||_USD million_||
|Non-aid current account balance|-115.7|-319.7|-203.9|
|Trade balance|-84.5|-207.7|-123.2|
|Exports (fob)|38.7|62.0|23.3|
|Imports (fob)|-123.2|-269.7|-146.5|
|_of which:_petroleum products|-20.2|-65.6|-45.4|
|Services (net)|-33.2|-143.0|-109.8|
|Credits|10.7|33.8|23.1|
|Debits|-44.0|-176.8|-132.9|
|Income (net, excl. interest on public debt)|-5.3|2.6|8.0|
|Current transfers (net, excl. official transfers)|7.4|28.4|21.1|
||_p_|_ercent of GDP_||
|Non-aid current account balance|-18.1|-32.9|-14.7|
|Trade balance|-13.3|-21.2|-7.9|
|Exports (fob)|6.1|6.4|0.4|
|Imports (fob)|-19.4|-27.6|-8.3|
|_of which:_petroleum products|-3.2|-6.6|-3.4|
|Services|-5.2|-14.8|-9.6|
|Credits|1.7|3.6|1.9|
|Debits|-6.9|-18.4|-11.5|
|Income (net, excl. interest on public debt)|-0.8|0.2|1.0|
|Current transfers (net, excl. official transfers)|1.2|2.9|1.8|
|_Memorandum item:_Nominal GDP (USD million)|637.5|966.0|328.5|

_Source:_ Authors’ calculation based on IMF data.

Turning to the use of additional external resources by the government, Table 4 shows that Burundi has spent most of the incremental aid received.<sup>7</sup> The ratio of change of the non-aid fiscal balance to change in aid amounts to 75 percent. Overall, the non-aid fiscal deficit has worsened significantly from 10 to almost 20 percent of GDP. Most of this can be attributed to higher expenditure (excluding interest payments), which increased from 30 percent to 38 percent of GDP between 2001-04 and 2005-08. Section 4 of this paper investigates the breakdown of expenditures and their use in more details.

**Table 4:** Has aid been spent? (percent of GDP, annual averages)

||Pre-Aid Surge<br>2001-2004(average)|Aid Surge<br>2005-2008(average)|Difference|Aid spent?|
|---|---|---|---|---|
|Net fiscal aid inflows|9.3|21.7|12.3||
|Revenue (excluding grants)|20.4|19.1|-1.2|Mostly spent|
|Expenditure (excluding external interest)|30.3|38.4|8.1|75%|
|Overall fiscal balance before aid|-9.9|-19.2|-9.3||

_Source:_ Authors’ calculation based on IMF data.

Revenue (excluding grants), while improving in absolute terms, decreased slightly as share of GDP, mainly as a result of lower GDP shares of taxes on international trade and non-tax revenue. It seems, therefore, that up to now for Burundi there is no indication that the higher aid flows lead to a decrease in revenue as suggested by Gupta et al. (2004) who find that foreign aid in the form of grants is associated with lower domestic revenue mobilization.

> 7 Net fiscal aid inflows are defined as the sum of budget grants, loans and debt relief minus public debt service and arrears clearance. They differ from net aid flows to the economy due to off-budget aid and aid transferred directly to the private sector.

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Most of the data concerning high‐skilled migration that we discuss here are taken from global bilateral migration database as described in Docquier et al. (2009), in addition to the recently completed update of the Database on Immigrants in OECD Countries (DIOC) for 2010 (Arslan et al. 2014).<sup>1</sup> These databases report the stock of migrants by education level from each home country that have moved to each destination country. High‐skilled workers, our main focus, are defined as those with at least one year of tertiary education. The data come from decennial national censuses conducted in 1990, 2000 and 2010 in the destination countries (where available) or from labor force surveys (when necessary). The data presented cover people of working age (25+) and are matched according to individual countries’ definition of migration, which typically pertains to country of birth.<sup>2</sup> Figure 1 compares migration to OECD countries along multiple dimensions. There were about 28 million high‐skilled migrants residing in OECD countries in 2010, an increase of nearly 130 percent since 1990. By comparison, the growth rate for low‐skilled migrants in the OECD countries from 1990 to 2010 was only 40 percent. (Throughout, we use the term ‘low skill’ to designate migrants with only primary education and ‘other skill’ to refer to those not classified as high‐skilled via their education.) While OECD countries constitute less than a fifth of the world’s population, these countries host two‐thirds of high‐skilled migrants (OECD Factbook 2013; Artuç et al. 2015).

The exceptional rise in the number of high‐skilled migrants to OECD countries is the result of several forces, including increased efforts to attract them by policymakers as they recognize the central role of human capital in economic growth, positive spillovers generated by skill agglomeration, declines in transportation and communication costs, and rising pursuit of foreign education by young people. Among the resulting effects are the doubling of the share of the tertiary‐educated in the labor force and fierce competition among countries hoping to attract

> 1 The 2010 update was developed by the OECD, the World Bank and the International Migration Institute at Oxford University. The details of the databases, their construction, methodology and the main patterns are discussed in Arslan et al. (2014). There is a parallel DIOC‐E database for non‐OECD countries

> (http://www.oecd.org/els/mig/dioc.htm) described by Artuç et al. (2015).

> 2 To achieve harmonized and comparable series over the 20‐year period, we exclude Chile, Estonia, Israel, Slovenia, and South Korea from our analysis. Throughout this article, we use ‘OECD’ to refer to the 29 remaining members, irrespective of their date of joining the OECD.

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identify the geographic location of each work site. We used geographic information system (GIS) software and Hydro-SHEDS data for water accumulation levels and flow direction to delineate the catchment area for each infrastructure project. This was done by first moving the irrigation coordinate location to the highest accumulation grid value, and then determining the catchment area using a flow direction grid.

Second, we drew the appropriate radius of project influence, determined using the number of hectares that the infrastructure was estimated to affect<sup>2</sup> , along with Glob Cover Version 2.2 (Figure 2b) that was used to identify agricultural land. Thirdly, the team identified communities that were upstream from the infrastructure location within the water users’ association (demonstrated by the large area marked “WUA” in Figure 2a) and downstream from the infrastructure location within a radius slightly larger than the radius of influence, also within the WUA.

As the infrastructure improvement would only influence those communities downstream (because water flow would not be affected upstream) and within the radius of influence, the upstream communities and communities beyond the determined radius serve as appropriate controls. This strategy eliminates the issue of nonrandom project placement, as the control group is restricted to households in communities within the same water users’ association. Additionally, as the institutional strengthening component of PSI was implemented at the level of the WUA, and for each rehabilitation project, the treatment and control group for this analysis are by definition within the same WUA, this component of PSI is held constant and does not

> 2 PSI officers and Ministry of Agriculture officials estimated this amount based on the size of the water source, the size of the project, and the number and location of households likely to benefit.

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