rafmacalaba/datause-encoder-data huggingface dataset:
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Smets, L., Knack, S., Molenaers, N., 2013. Political ideology, quality at entry and the success of economic reform programs. The Review of International Organizations 8, 447–476.

Stiglitz, J., 1998. Towards a new paradigm for development: Strategies, policies and processes. Prebisch Lecture at UNCTAD, Geneva.

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18
recruitment and hiring as a tool of human resource management.<sup>11</sup> We contribute to this literature by studying how firms recruit, in a developing country, and by looking at how they respond to an opportunity to recruit in a completely new way.

# **2 Data**

## **2.1 Surveys of job seekers**

We run the job fairs study with a representative sample of young unemployed people in Addis Ababa. To draw this sample, we first defined geographic clusters using the Ethiopian Central Statistical Agency (CSA) enumeration areas.<sup>12</sup> Our sampling frame excluded clusters within 2.5 km of the center of Addis Ababa, and clusters outside the city boundaries. Clusters were selected at random from our sampling frame, with the condition that directly adjacent clusters could not be selected, to minimize potential spill-over effects across clusters.

In each selected cluster, we used door-to-door sampling to construct a list of all individuals in the cluster who: (i) were 18 or older, but younger than 30; (ii) had completed high school; (iii) were available to start working in the next three months; and (iv) were not currently working in a permanent job or enrolled in full time education. We randomly sampled individuals from this list to be included in the study. Our lists included individuals with different levels of education. We sampled with higher frequency from the groups with higher education. This ensured that individuals with vocational training and university degrees are well represented in the study. All selected individuals were contacted for an interview.

We completed baseline interviews with 4,388 eligible respondents. We attempted to contact individuals by phone for at least a month (three months, on average); we dropped individuals who could not be reached after at least three attempted calls. We also dropped any individual who had found a permanent job and who retained the job for at least six weeks. Finally, we dropped individuals who had migrated away from Addis Ababa during the phone survey. In all we were left with 4,059 individuals who were are included in our experimental study.

We collected data on study participants through both face-to-face and phone interviews. We completed baseline face-to-face interviews between May and July 2014 and endline inter-

> 11 Oyer and Schaefer (2010) review the literature on hiring, writing, “The literature has been less successful at explaining how firms can find the right employees in the first place. Economists understand the broad economic forces–matching with costly search and bilateral asymmetric information–that firms face in trying to hire. But the main models in this area treat firms as simple black-box production functions. Less work has been done to understand how different firms approach the hiring problem, what determines the firm-level heterogeneity in hiring strategies, and whether these patterns conform to theory.”

> 12 CSA defines enumeration areas as small, non-overlapping geographical areas. In urban areas, these typically consist of 150 to 200 housing units.

6
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**22**

**Table 2: Residual Entities as a Percentage of Pre-Privatization Assets/Liabilities**

|Residual entity<br>(1)|Year<br>Created<br>(2)|Assets<br>millions<br>(3)|Assets as a %<br>of Assets in<br>Year Prior to<br>Priv.<br>(4)|Liabilities<br>millions<br>(5)|Liab. as a % of<br>Liab. in Year<br>Prior to Priv.<br>(6)|
|---|---|---|---|---|---|
|Chaco|1995|245.3|54.0|233.1|56.8|
|Formosa|1995|135.7|36.7|244.9|83.4|
|Mendoza|1996|666.6|61.2|666.6|71.4|
|Misiones|1995|144.2|28.6|331.8|66.0|
|Prev. Social|1996|292.1|49.7|292.1|56.4|
|Rio Negro|1996|49.2|24.6|47.4|26.2|
|Salta|1996|91.6|73.3|73.4|74.2|
|San Juan|1996|78.6|34.2|175.3|66.9|
|San Luis|1996|29.7|31.6|81.8|90.1|
|Sant del Est|1996|199.6|*81.1|227.3|*84.4|
|Tucuman|1996|261.7|66.7|262.9|70.2|
|Average|.|199.5|**46.1|239.7|**66.1|

*The asset data for Santiago del Estero implied that their residual entity was 135% of pre-privatization assets, an

obviously unrealistic estimate. The figures reported here for Santiago del Estero are, therefore, based on their reported assets in the year of their privatization (1996). See footnote 17 for further discussion of the Santiago del Estero data. **Calculation excludes Santiago del Estero.

**Table 3: Fiscal Situation of Provinces in 1996**

|Sample|Avg. Expenditures<br>(million of pesos)|Avg. Revenues<br>(millions of pesos)|Deficits<br>(millions of pesos)|
|---|---|---|---|
|1. All Provinces|2,195.2|2,263.3|68.1|
|2. Provinces Except<br>Buenos Aires|1,075.0|1,105.2|30.2|
|3. Provinces that have<br>Privatized Provincial<br>Banks|698.8|719.9|21.2|

Source: Ministry of Economy (National Directorate for Fiscal Coordination with the Provinces)

# III THE **PRIVATIZATION PROCESS AND ITS FISCAL IMPLICATIONS**

**The** quality of the bank's portfolio, in practice, determines the size of the residual entity.

Although a poor portfolio largely reflects past performance and, therefore, **has** nothinig to do with the privatization process per se, opponents are likely to claim, at least in **the** court of public

> 22 Notes: Prev Social is the former Banco de Prevision Social de La Provincia de Mendoza; Sant del Est is the forner Banco de La Provincia de Santiago del Estero. Source: Fondo Fiduciario. Assets in the year prior to privatization (used to construct data in columns 4 and 6) come from B.C.R.A. balance sheet data.

9
Table 1 reports the summary statistics of key variables in three periods. The agriculture sector includes agriculture, forestry, fishing, and hunting. The industry sector includes mining, manufacturing, construction, and utilities. The services sector includes all the rest of the industries.

China experienced rapid structural transformation during the study period. Nearly 70% of employment was in agriculture in 1990 on average. The share declined to 64% in 2000 and 60% in 2005. Average employment share in industry and services remained stable, though the simple average masks huge variations across locations. Moving out of agriculture is also manifested in the sharp decline of agricultural GDP share, which fell from 27% in 1990 to 16% in 2005. Average industry GDP share dropped from 45% in 2000 to 41% in 2000, then increased to 47% in 2005. Services continue to account for a larger share of GDP.

|Table1. Summary statistics|
|---|

||Ye<br>|ar=1990<br>|Year<br>|=2000<br>|Ye<br>|ar=2005<br>|
|---|---|---|---|---|---|---|
|Variable|Obs|Mean|Obs|Mean|Obs|Mean|
||Pa<br>|nel A: Structural t<br>|ransformatio<br>|n<br>|||
|Agri_emps|329|69.22%|345|64.05%|344|60.42%|
|Indu_emps|329|16.03%|345|15.92%|344|16.64%|
|Serv_emps|329|10.72%|345|10.85%|344|10.77%|
|Agri_gdps|298|26.55%|320|23.59%|262|16.42%|
|Indu_gdps|298|45.02%|320|41.35%|262|47.05%|
|Serv_gdps|298|28.43%|320|35.07%|262|36.53%|
|<br>||Panel B: School e<br>|nrollment<br>||||
|High school enrollment|329|8.15%|345|22.41%|344|24.82%|
|College enrollment|329|1.85%|345|5.30%|344|7.16%|
|<br>||Panel C: City cha<br>|racteristics<br>||||
|Population|329|3391397|345|3598137|344|3693000|
|Years of schooling|344|6.75|344|8.28|344|8.56|
|Age|344|31.00|344|33.42|344|34.52|
|Han ethnicity|344|85%|344|85%|344|84%|
|GDP per capita|337|2071.81|322|8460.51|318|14886.54|
|Rural population share|301|66%|322|62%|324|66%|
|||Panel D: FDI v|ariables||||
|FDI emp share|341|0|341|9.52%|339|13.16%|
|∆FDI_emp share|341|0|341|9.52%|337|3.52%|
|FDI output share|341|1.66%|341|11.89%|341|13.79%|
|∆FDI_output share|341|0.94%|341|10.23%|341|1.91%|

Note: This table reports summary statistics at the city level in three periods.

China’s high school enrollment and college enrollment rate grew dramatically during 1990-2005. Average high school enrollment nearly tripled from 8% in 1990 to 22% in 2000 and increased to 25% in 2005. College enrollment was less than 2% in 1990, the number more than doubled to 5% in 2000 and to 7% in 2005.

13
_How Fail_ **_Hai Chuis Indu4wry Grow,? IS_** Unfortunately, the price indexes derived from statistics of industrial output value point in the opposite direution. Table 7 shows that implicit price increases reported for the collective sector fall short of comparable state sector data in every year since 1978. Typically, reported inflation is the coUective sector is half or less of ieported intlation in the state sector. Comparison of annual inflation rates for state and collective industrial ou.put witiin the same branch produces the same result. Between 1978 and 1987, nearly three fourths of the instances where comparable data exist (91 of 126 cases; 9 cases are excluded because of data incomparability), the implicit inflation is higher in the state sector. In recent years, this result is even clearer: during 1984/87, we find 35 instances of higher implicit branch price increase; in the state sector compared with only 8 instances of higher price increases in the collective sector (two items are discarded as incomparable)S These conclusions are not acceptable, particularly since data from the coal sector suggest that price indicators extracted from the reported growth of industrial output in constant and current prices already understate industrial inflation for the state sector. The figures shown in Table 7 suggest that output statistics for the entire collective sector, which now accounts for nearly one-third of industral production, systematically understate the impact of inflation. If this is true, the most likely mechanism is that rorted output yalue at fixed prices forChir1. collective industries Systematically overstates the growth of real out= during the 1980s, particularly in recent years of strong inflationary pressures.

# F. **Are Reported Gains in Energy Productivity Unrealistically Large?**

Data reproduced in Table 8 indicate that China's industries achieved very substantial gains in energy productivity during the early 1980s. These figures, which exclude village industries (see below) indicate annual gains averging 6.7 percent in real output per ton of standard coal equivalent. This compares well with figures for energy productivity in mining and manufacturing for major industrial nations showing average annual productivity growth of 2.6, 3.4 and 7.0 percent for West Germany, the United States and Japan respectively during the period 1973-86.' If correct, these figures indicate a highly effective response to energy shortages within Chinese industry, which accounts for more than two-thirds of China's energy consumption.

Many observers have noted that China's energy prices are much lower, in relative terms, than comparable prices in cther nations and in the world market, and also that domestic energy prices have not risen in paralle with global market trends. Despite the inflexibility of official prices, the data in Table 6, showing that avemge coal prices paid by thermal power plants nearly S. **.. ece** commenu am **based** on **a workshea** DEFLATE2.wkl.

6. **Based on** _a_ **scpare workshee** NENOCOMP.wkl, **which is not included** in this **paper.**
# **<u>Table 3: Effects of Priming on Vaccine Take-up</u>**

|Dependent variable:<br>|(1)<br>|(2)<br> <br>Received|(3)<br>vaccine|(4)|
|---|---|---|---|---|
|Vaccine condition & Fear|-0.028|-0.010|-0.015|-0.001|
||(0.017)|(0.032)|(0.020)|(0.035)|
|C300||0.216***||0.222***|
|||(0.031)||(0.030)|
|C800||0.285***||0.282***|
|||(0.035)||(0.036)|
|Vaccine condition & Fear * C300||-0.026||-0.030|
|||(0.044)||(0.046)|
|Vaccine condition & Fear * C800||-0.023<br>(0.043)||-0.012<br>(0.042)|
|Observations|2482|2482|2214|2214|
|R-squared|0.002|0.092|0.024|0.113|
|Control mean of dependent variable|0.748|0.575|0.741|0.570|
|Covariates|||X|X|
|Fixed effects by village (80 villages)|X|X|X|X|
|_p-values of F test:_|||||
|Vaccine condition & Fear + Vaccine condition & Fear *|C300 = 0|0.212||0.278|
|Vaccine condition & Fear + Vaccine condition & Fear*|C800=0|0.263||0.672|

_Source:_ Authors estimate _Notes:_ These are based on the analysis sample of 2,482 women. Robust standard errors clustered by village (80 villages) are in parentheses. Covariates are Clinic condition, Woman's age, Age squared, Muslim, Any education, Marital status = Single, Currently pregnant, Have children, Has paid work, Monthly HH earning per capita (naira), Distance to clinic (km), Transport to clinic (naira), Opportunity costs to clinic (naira), Ever used clinic, Received tetanus vaccine before, Likely to contract tetanus, Number of people who die of tetanus, Very worried about Tetanus, Tetanus is very bad, Very important to be protected from tetanus, Vaccine efficacy, Vaccines protect from disease, Needles are scary, Vaccines have side effects, Vaccines give diseases, Vaccines give HIV, Heart rate (beat/min). Control mean of dependent variable is the mean under Vaccine condition in (1) and (2) and under Vaccine condition and C5 in (3) and (4). * significant at 10%; ** significant at 5%; *** significant at 1% 35
The coefficient of the DEFT variable is 0.56 meaning that 56% of the CPS fiscal deficit tends to be financed through money creation. The coefficient of DEFT on ITAX is highly significant and justifies the presumption that deficit financing is a significant factor explaining the need for the inflation tax. The negative coefficient on the D76 variable indicates that since that year there has been a tendency to use less inflationary financing for any amount of DEFT and in consequence that there has been a shift towards more debt financing.

We have also tried to use the Primary Deficit of the CPS (PD, not including any interest service) as the explanatory variable for ITAX and found the surprising result that it works much better than the total deficit (DEFT). The results are reported in Regression III.2.

# **REGRESSION III.2**

ITAX 0.936 - 1.21 D76 + 0.70 PD (1.5) (-1.98) (7.47) MA(1) = -1.12 ( T value:-2.4) R2 Adj.: 0.69 D-W.Stat.:1.59 Sample Period:1963-1987The results of Regressions III.1 and III.2 suggest the possibility of a tendency to finance the Primary deficit with money and to roll over the interest expenditures in the form of new debt. In order to check for that possibility we have included the interest expenditure of the CPS as an additional explanatory variable, together with the Primary Deficit. If all deficits, independently from its source received the same treatment we should expect the coefficient on the interest expenses (IE) to be the same as the coefficient on the Primary Dnficit. This does not turn out to be the case, as the results of Regression 3 show. As shown, the Primary Deficit retains the same coefficient of 0.7 while the interest expenditures turns out to be insignificant in explaining the inflation tax. We conclude that over the sample period authorities have used the inflation tax to finance primary expenditures while interest expenditures that result from the existing stock of Government Debt have tended to be refinanced through the issue of more debt.

21
disasters, and the desert locust crisis of 2020 to name a few. These shocks have had devastating impacts on the lives and wellbeing of Yemenis. Many of these crises are inter-related and are rooted in the ongoing conflict. This includes an unprecedented scale of internal displacement caused by the fighting. To date, 4.3 million Yemenis have been displaced within the country. Overall, the economic conditions of Yemenis are very precarious, and the latest Humanitarian Needs Overview (HNO) indicates that 23.4 million Yemenis are in need of humanitarian assistance, 19 million of whom require food assistance.<sup>1</sup> It is estimated that prior to the conflict around 13 percent of the Yemeni workforce was unemployed, but these numbers were substantially higher for youth and women, and masked the inactivity of those who were out of the labor force (ILO 2015). While the male labor force participation rate was 65.4 percent prior to the conflict, this was at 6 percent for women, one of the lowest in the MENA region (ILO 2015). Moreover, most employed women were working in the agriculture sector, engaged in unpaid work, or were economically inactive because they were fully engaged in household chores (Al-Ammar and Patchett 2019). A rapid damage needs assessment conducted by the ILO at the start of the war documented the effects of the conflict on women’s participation in the economy, reporting job losses estimated at an average of 28 percent among female respondents, compared to 11 percent among male respondents (ILO 2016).

Women have also faced further setbacks throughout the war. A World Bank phone survey asked respondents about coping strategies adopted to ease financial distress (World Bank 2023b). Alarmingly, 3 percent of respondents said that female children were married to ease financial stress over the last three months alone (Figure II.1). This was almost double for displaced households, and higher in Houthi areas than those under IRG control. Child marriage has been a concern in Yemen even before the current conflict started, as 16 percent of women between the ages of 20 and 49 were married before 15 years of age (MOPHP et al. 2015). Notably, in 2013 the median age of marriage was lower among younger women, potentially indicating an improvement in the incidence of child marriage in the future. However, today any of these changes are likely to be reversed, as more households are forced to marry their female children for financial relief amid devastating living conditions.

_Figure II.1: Percentage of households who married female children (younger than 15) to ease financial burden_

> 1 Yemen Humanitarian Response Plan 2022 (April 2022).

4
**-** **<u>Table 3. Country Level Determinants of the Average Predicted Probability of Product Innovation by Incumbent Firms</u>**

|**Explanatory Variables:**|**(1)**<br>**Dependent Va**|**(2)**<br>**riable: Country-**|**(3)**<br>**Average Predict**|**(4)**<br>**ed Probability o**|**(5)**<br>**f New Product**|
|---|---|---|---|---|---|
|Average tariff index|-0.104|-0.246|-0.247|-0.105|-0.124|
||(1.48)|(2.95)**|(3.93)**|(1.20)|(1.74)|
|Average core NTB coverage ratio|-0.039|0.018|0.019|-0.039|-0.096|
||(0.17)|(0.06)|(0.07)|(0.20)|(0.45)|
|Real manufacturing GDP growth|-0.402|-0.499|-0.504|-0.402|-0.419|
||(1.42)|(1.94)|(1.91)|(1.64)|(1.56)|
|log of cumulative patent density (per<br>capita)|0.030|0.055|0.054|0.031|0.027|
||(1.98)*|(3.56)**|(3.46)**|(2.38)*|(1.71)|
|Index of entry/exit regulation|0.018|-0.012|-0.013|0.018|0.013|
||(0.41)|(0.22)|(0.22)|(0.40)|(0.25)|
|Index of institutional quality|-0.061|-0.064|-0.065|-0.061|-0.066|
||(2.18)*|(2.79)**|(2.19)*|(3.19)**|(2.28)*|
|Index of infrastructure|-0.004|-0.125|-0.13|-0.004|-0.051|
||(0.08)|(2.46)*|(1.92)|(0.10)|(0.79)|
|log of business density||-0.005|-0.006|||
|||(0.19)|(0.26)|||
|log of real GDP per capita (PPP)|||0.009<br>(0.13)||0.058<br>(1.09)|
|Imputed log of business density||||-0.001<br>(0.04)|-0.006<br>(0.16)|
|Constant|0.811|1.083|0.992|0.814|0.293|
||(4.45)**|(5.60)**|-1.58|(5.16)**|(0.59)|
|AdjustedR-squared|0.15|0.42|0.40|0.13|0.15|
|Observations|57|39|39|57|56|

z statistics in parentheses; standard errors are bootstrapped with 50 random iterations for all estimations.

* significant at 5%; ** significant at 1%

Note: We imputed the values for the trade-policy indicators for the countries without such data, so as to increase the sample of countries that appears in the econometric estimation reported in Table 3. Table 1 presents the summary statistics for these variables with and without the imputed values, which have very similar means and standard deviations.

20