rafmacalaba/datause-encoder-data huggingface dataset:
manufacturing industry; administrative workers, and workers in the service sector. The criteria for constructing the latter group was a set of possible alternative occupations available to the teacher.

In addition to the _mixed-skilled_ group, ENEU allows us to construct a group based on a comparable teacher ́s educational background. Thus, two groups were added to the previous comparable group definitions: those individuals who have _Upper Secondary Education but are not teaching ( Upper Secondary in Education not teaching_ ) and those individuals who have a B.A. in education but are not teaching ( _University degree in Education not teaching_ ). The annex provides a detailed description of these groups.

# _Formal years of schooling, age and gender_

The teacher years of schooling were computed as the total number of formal years of education reported. Tables 1a, 1b and 2 show that in urban areas, teachers have more years of schooling than other groups do, such as _the low skilled group_ and _the agricultural group,_ but teachers have less years of education than other _professionals with a B.A in Education not teaching_ . In urban areas, the teachers' average years of schooling increased by 2 years from 1988 to 1999. The distribution of teachers' years of schooling in basic education is less dispersed compared to _the mixed-skilled group_ . By region, average teachers' years of schooling in basic urban schools is similar to those in rural areas. However, there is a difference that increases with the level of instruction. Basic education teachers have on average 14 years of formal schooling, which is just below OECD countries (16 years) but slightly above other Latin American countries (12 years).

**Table 1: Years of Schooling in Urban Areas**

|||**1988**|||**1994**|||**1999**||
|---|---|---|---|---|---|---|---|---|---|
|**Type of Occupation**|**Mean **|**Median **|**S.D. **|**Mean **|**Median**|**S.D.**|**Mean**|**Median**|**S.D.**|
|Primary Teacher in Public School|12.6|11.0|3.9|14.0|14.0|3.0|14.6|17.0|2.9|
|Primary Teacher in Private School|12.5|11.0|2.2|14.3|17.0|3.0|14.4|15.0|2.9|
|Lower-Secondary Teacher in Pub. School|14.7|16.0|2.8|15.9|17.0|4.1|16.2|17.0|3.5|
|Lower-Secondary Teacher in Priv. School|14.1|15.0|2.5|15.6|17.0|2.4|15.2|17.0|2.9|
|Upper-Secondary Teacher in Pub. School|16.4|17.0|1.3|16.2|17.0|2.2|16.7|17.0|2.0|
|Upper-Secondary Teacher in Priv. School|15.8|16.0|1.7|17.3|17.0|9.3|16.7|17.0|1.6|
|University Teacher in Public School|17.0|17.0|1.5|17.6|17.0|1.2|17.6|17.0|1.5|
|University Teacher in Private School|17.0|17.0|1.2|17.6|17.0|4.9|17.4|17.0|1.6|
|**Prof. With Upper-Secondary in**|11.1|11.0|0.6|11.3|11.0|0.9|11.3|11.0|0.9|
|**Education not teaching**||||||||||
|**Prof. With an University degree in**<br>**Education not teaching**|18.4|18.0|0.5|17.0|17.0|0.0|17.0|17.0|0.0|
|**The mixed-skilled group**|**9.0**|**9.0**|**4.5**|**9.5**|**9.0**|**4.8**|**9.9**|**9.0**|**4.5**|
|The agricultural group|4.8|4.0|4.2|5.6|6.0|4.2|6.1|6.0|4.5|
|The low-skilled group|6.2|6.0|3.6|6.6|6.0|3.7|6.9|6.0|3.7|

Source: Own calculations based on ENEU survey 7
**Table 36. Governance drivers of political risk: Public agency capacity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.103*|0.0890|||0.211*|0.191*|||
||(0.0556)|(0.0548)|||(0.110)|(0.111)|||
|Export % of sales|-0.000232|0.000497|-0.000324|0.000370|-0.000605|0.000859|-0.000732|0.000693|
||(0.000730)|(0.000749)|(0.000749)|(0.000759)|(0.00143)|(0.00151)|(0.00151)|(0.00159)|
|Share of sourcing via imports|0.00169**|0.000878|0.00172**|0.000896|0.00321**|0.00189|0.00331**|0.00186|
||(0.000777)|(0.000770)|(0.000803)|(0.000794)|(0.00155)|(0.00158)|(0.00165)|(0.00167)|
|High-income source country|-0.207**|-0.155*|-0.227***|-0.164*|-0.423**|-0.341*|-0.482***|-0.380**|
||(0.0813)|(0.0836)|(0.0839)|(0.0843)|(0.167)|(0.177)|(0.177)|(0.184)|
|>250 employees|-0.0606|-0.0495|-0.0896|-0.0745|-0.107|-0.0775|-0.180|-0.134|
||(0.0684)|(0.0714)|(0.0688)|(0.0721)|(0.130)|(0.141)|(0.135)|(0.148)|
|Cumulative inv. >US$10M|0.0491|-0.0422|0.0451|-0.0459|0.0947|-0.0932|0.0815|-0.106|
||(0.0667)|(0.0676)|(0.0664)|(0.0669)|(0.129)|(0.136)|(0.132)|(0.140)|
|Years in country|0.000218|0.000378|0.000481|0.000600|0.000281|0.000108|0.00148|0.000980|
||(0.00233)|(0.00221)|(0.00226)|(0.00217)|(0.00472)|(0.00470)|(0.00469)|(0.00479)|
|Level of foreign ownership (%)|-0.000824|0.000246|-0.000940|0.000342|-0.00153|0.000504|-0.00143|0.000941|
||(0.00114)|(0.00121)|(0.00110)|(0.00115)|(0.00226)|(0.00248)|(0.00223)|(0.00243)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.072|0.034|0.090|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Capacity of public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

53
# Source: Authors’ elaboration

The main idea of the algorithm is to produce for each country the full ownership tree (i.e. direct and indirect subsidiaries) of: (i) “Public Authorities”; (ii) entities identified as SOEs in the National Legal Form; (iii) entities identified as SOEs from the Supplemental database; and (iv) entities identified as SOEs in the GUOs. In each step, the algorithm ensures that firms are classified as either corporatized or noncorporatized based on the national legal form, and that branches and inactive entities are removed. The detailed algorithm runs as follows:

1. Use the clean Links file<sup>33</sup> to identify the set of entities that are labeled as “Public Authority” under the variable “Entity Type” and their respective BvD ID (company ID). ORBIS classifies governments and government institutions, such as ministries or municipalities, under this category.

2. Use the information on BvD IDs, shareholders, and direct percentage to construct the ownership tree for each entity labeled as “Public Authority”.<sup>34</sup> These ownership trees are constructed using the iterative and recursive process described below:

- For each public authority, identify its direct subsidiaries (entities and their BvD IDs that report the public authority’s BvD ID as a direct shareholder).

- Using the direct percentage, keep all direct subsidiaries for which the public authority’s shareholding direct percentage is greater than or equal to 10%. If the shareholding percentage is unknown, and the subsidiaries are not branches or inactive firms, keep the direct subsidiary so as not eliminate potential SOEs.

- Next, for these direct subsidiaries linked by a known shareholding percentage, identify the set of their subsidiaries. To do this, identify the entities (and their BvD IDs) that report a direct subsidiary’s BvD ID as a shareholder, and did not already appear in the first (previous) level of subsidiaries.

- Using the shareholding percentage again, keep all subsidiaries for which the direct subsidiary’s shareholding percentage is greater than or equal to 10%. If the shareholding percentage is unknown, keep the subsidiary.

- Continue this iterative process until there are no further subsidiaries.

3. Combine all the ownership trees of public authorities, in a given country, into one government ownership structure and identify the unique entities (and their BvD IDs) within this ownership structure.

4. Use the ORBIS interface to double check and eliminate all inactive entities or branches.<sup>35</sup> We define the remaining active set of entities as Corporatized SOEs.

> 33 (Cusolito, 2020) developed the script to remove outdated information in the links file and to obtain the file denoted as “clean” links to run our algorithm.

> 34 We omit from Sovereign Wealth Funds (SWF) and institutional investors (e.g., IFC) as either appearing as public authorities (starting nodes) or potential subsidiaries.

> 35 ORBIS provides a status indicator to differentiate active from inactive entities. Similarly, ORBIS also indicates in the entity’s national legal form whether the entity is a branch or not.

21
# 6.4 Expected returns to education vary widely.

Officials were asked about perceived income levels of similar individuals with different amounts of education (primary, secondary, TVET or tertiary). By comparing their responses from one level of education to another, we can infer how officials foresee returns to different amounts of education.

Overall, perceived returns to secondary education look similar in EAP and non-EAP countries. Perceived returns to tertiary education however are lower in the region than outside of it (Figure 19). Expected returns are much larger at the tertiary level but differ significantly for all countries in the region (across primary, secondary and tertiary completion).

_Figure 19: Differences in education premium envisioned by level and country._ <!-- Start of picture text --> Non EAP<br>EAP average<br>VNM<br>PHL<br>MNG<br>LAO<br>IDN<br>0% 20% 40% 60% 80% 100% 120% 140% 160% 180%<br>Tertiary vs. secondary education completed<br>Secondary vs. primary education completed<br><!-- End of picture text --> Source: CDG–World Bank 2022 survey.

# 6.5 Officials have diverse views on education premiums and substantially overestimate returns to tertiary education.

Actual income gains for individuals who complete secondary education versus those who only complete primary education globally were 51 percent on average (Patrinos and Montenegro, 2014), while the anticipated premium estimated by respondents for completing secondary education ranged from 38 to 63 percent, for an average of 48 percent. Given that the available data on actual returns are not updated, the estimates for returns to secondary education completion are quite close on aggregate. However, there are considerable differences between countries. For example, Lao PDR respondents estimate the returns to secondary by 20 percentage points above the global average, while respondents in the Philippines estimate 25 percentage points below the global average.

According to officials, labor market premiums associated with tertiary education (compared with secondary education) are expected to be much larger than actual premiums associated with secondary education (and compared with primary education). The EAP respondents expected premium for completing tertiary education ranged from 77 to 161 percent, versus 41 percent to 122 percent actual 23
of bank size. To the extent that larger banks can take advantage of economies of scale, we would expect market share to be negatively related to spreads.<sup>13</sup> We control for bank orientation using _interest income_ , the ratio of total interest income to operating income, and _intermediation_ , the ratio of net loans and advances to total liabilities. Using bank-level data across countries, Laeven and Levine (2007) demonstrate that specialized loan-making banks have different performance characteristics than specialized investment banks, and that loan-making banks tend to have a higher share of interest income. We expect competitive pressure in the lending market to be better reflected in the banks specialized in that area, and thus we expect a negative association between _interest income_ and spreads. Similarly, we expect those banks that lend a relatively high share of their available liabilities to be most responsive to the same competitive pressures, and thus a negative relation between _intermediation_ and spreads.

Following the literature, in some specifications we include a control for banking sector structure and three macroeconomic control variables. _Herfindahl_ is a standard index of sector concentration, which we calculated based on bank shares of total deposits.<sup>14</sup> If deposits are concentrated in the hands of a few banks, those banks might be able to drive up lending rates, as they control the supply of funds. We would therefore expect a positive relation between concentration and spreads. The macroeconomic controls are the _T-bill rate_ , _inflation,_ and _real growth_ . The _T-bill rate_ is the rate of interest on short-term treasury bills, which is included as a proxy for the marginal cost of funds faced by banks. _Inflation_ is included because price shocks might not be passed through equally to the nominal lending and borrowing rates, and thus these differential effects would be reflected in the spread. _Real growth_ is included to capture business cycle effects that are reflected in spreads. As an economy slumps and growth slows, borrowers become less creditworthy, and thus banks must charge higher lending rates, which are then reflected in higher spreads (all else equal).

> 13 This is consistent with some of the regression results in Beck and Hesse (2009) for Uganda from 2000 to 2004. Martinez Peria and Mody (2004) note, however, that market share could also be equated with market power, and thus the ability to charge higher rates on the loans. The coefficient on this variable will therefore indicate which of these two hypotheses is better supported by the data.

> 14 The index is calculated by summing the squared market shares of all banks.

19
Following the Uruguay Round agreements, the situation regarding the formal commitments of developing countries to maintain a liberal services trade regime parallels the situation with respect to tariffs: in both cases the actual policies are far short of the commitments. Except in the case of services the difference is often greater. Table 14 shows that actual policies in developing countries in all major regional groupings restrictive as they may be, are still far more liberal than their commitments—just like applied tariffs are far lower than bindings. As with tariffs, developing countries argue that this is needed to maintain policy flexibility – and the possibility of pursuing other social objectives. But the cost, as with tariffs, is the increased risk of policy change which often inhibits foreign investment.

**Table 14: Services Trade Liberalization Commitments by Income Group and Region**

|CountryGroup (No. of Co.)|Actual<br>Policies|Overall Offer<br>Improvement<br>(UR Commit -<br>Doha Offer)|As % of UR<br>Commitments|Overall Binding<br>Gap<br>(UR Commit -<br>Actual Policies)|As % of Actual<br>Policies|Total Services<br>Liberalization<br>Commitments<br>at UR|Financial<br>Services<br>(No. of commit<br>as % of max.)|
|---|---|---|---|---|---|---|---|
|**INCOME GROUP**||||||||
|Developed countries (27)|18.0|6.6|18%|18.9|105%|57.8%|..|
|Developing countries (65)|29.5|5.5|8%|42.6|144%|16.5%|24.0%|
|High income Developing (7)|38.7|3.7|5%|30.1|78%|18.9%|23.4%|
|Upper middle income (21)|29.2|8.7|12%|40.5|139%|25.5%|26.4%|
|Lower middle income (18)|27.8|-2.5|-4%|43.4|156%|14.4%|27.8%|
|Low income (19)|28.2|..|..|48.8|173%|14.7%|27.6%|
|Of which: LDCs (13)|27.8|..|..|57.1|206%|3.0%|15.0%|
|World|21.0|6.4|13%|27.2|130%|21.7%|..|
|**REGION**||||||||
|OECD (20)|14.0|9.4|46%|13.1|88%|..|..|
|East Asia (7)|36.8|1.7|3%|26.0|71%|..|..|
|South Asia (3)|36.0|15.5|18%|47.8|133%|..|..|
|Latin America (9)|16.6|6.3|10%|48.2|290%|..|..|
|Other Europe & C. Asia (5)|10.6|5.4|19%|9.8|93%|..|..|
|Middle East & N. Africa (5)|36.9|3.8|7%|21.2|57%|..|..|
|Sub-Saharan Africa(6)|16.9|0.7|1%|53.6|318%|..|..|

Sources: World Bank Services Trade Restrictions database and Gootize & Mattoo (2009), Services in Doha: What's on the Table? World Bank PRD Working Paper, no. 4903.

63
Table 3: Main Results—Effects of dry and wet shocks on employment

|||_Depe_|_ndent variabl_|_e:_||
|---|---|---|---|---|---|
|||curre|nt employme|nt||
|cutofs=1.5|(1)|(2)|(3)|(4)|(5)|
|Dry shock (SPEI < -1.5)|-0.0516***|-0.0538***|-0.0230*|-0.0267**|-0.0251*|
||(0.0166)|(0.0138)|(0.0122)|(0.0135)|(0.0135)|
|Wet shock (SPEI > 1.5)|0.0476***|0.0433***|0.0562**|0.0430*|0.0426|
||(0.0166)|(0.0117)|(0.0245)|(0.0260)|(0.0266)|
|Controls|N|Y|Y|Y|Y|
|Gridcell FE|N|N|Y|Y|Y|
|Year FE|N|N|N|Y|Y|
|Month FE|N|N|N|N|Y|
|Observations|580,592|572,907|572,907|572,907|572,907|
|R<sup>2</sup>|0.022|0.133|0.231|0.234|0.234|

**_Note:_** _∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01. This table reports a set of estimates building on the baseline specification expressed in Equation 1. Our main regressor are dummies indicating wet and dry shocks, considering deviation of the SPEI above and below a threshold of 1.5 and -1.5, respectively. Column (5) is our preferred specification in which controls including slope, individual age and years of education and for grid cell, year and month-of-year fixed effect are added. Standard errors clustered at the grid cell-month-year level are reported in parenthesis.

30
the asset index. The asset index was chosen over household expenditure primarily because expenditure, as the product of price and quantity, is subject to price change and therefore becomes endogenous to food price shocks. It is not necessarily a good indicator of economic status during a food price crisis, especially when food is a major component of household expenditure. Further, there is little difference in economic gradient of education and health outcomes between using the asset index or expenditure (Filmer and Scott (2009). In fact, the asset index better captures the permanent income, rather than transient income.

# **3. Data and Methodology**

# **3.1. Data**

The 2008 and 2010 rounds of the Pakistan Standard Living and Measurement Survey (PSLM) are used to study the impact of the food price increase on child education and labor. The 2007– 2008 PSLM survey is a nationally representative survey, covering about 15,000 households, conducted by the Federal Bureau of Statistics (FBS) in Pakistan. The food price crisis started at the beginning of 2008 and peaked in mid-2008. At the beginning of 2010, in order to understand the impact of the crisis, the FBS, in collaboration with the World Bank team, followed half of the sample households interviewed in 2008 for the 2007–08 PSLM survey. The FBS was able to conduct a follow-up survey of 6,252 of the 8,000 households originally surveyed in 2008 across the 82 districts in Pakistan. All households were surveyed in the equivalent quarters for both rounds, thereby eliminating possible seasonality effects.

The PSLM in both rounds follows the Living Standard Measurement Survey (LSMS) questionnaire, which includes detailed modules on household consumption, education, and labor participation. Labor participation information is only available for household members ages 10 or older. In addition to the standard modules, the PSLM 2010 also includes retrospective questions about the food price crisis. It asked whether the household had experienced a food crisis since the last survey in 2008, to what extent it had affected them, and how they dealt with it. Answers to the questions about their experience with the food crisis were used to construct the self-reported food price shocks.

5
# **Table 4. (...), continued**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Whole s<br>|ample<br>|Large<br>|banks<br>|
||RWR|LR|RWR|LR|
|_Crisis period:_|||||
|Capital*Crisis|0.114*|0.124|0.207|0.553***|
||[0.063]|[0.096]|[0.143]|[0.194]|
||(0.079)|(0.597)|(0.004)|(0.002)|
|Liquidity*Crisis|-0.037**|-0.037|0.098*|0.094|
||[0.017]|[0.022]|[0.056]|[0.066]|
||(0.002)|(0.015)|(0.308)|(0.334)|
|Deposits*Crisis|0.036**|0.038*|0.074***|0.102***|
||[0.016]|[0.020]|[0.022]|[0.030]|
||(0.125)|(0.180)|(0.022)|(0.011)|
|Net Loans*Crisis|-0.030*|-0.031*|-0.032|-0.073**|
||[0.016]|[0.018]|[0.028]|[0.028]|
||(0.020)|(0.080)|(0.694)|(0.043)|
|Provisions*Crisis|-3.014***|-3.644***|-2.947|-2.927|
||[0.995]|[1.076]|[2.373]|[3.246]|
||(0.068)|(0.017)|(0.497)|(0.637)|
|Size*Crisis|-0.038|0.043|-1.265|-0.73|
||[0.088]|[0.090]|[0.830]|[0.730]|
||(0.180)|(0.691)|(0.005)|(0.010)|
|PB*Crisis|0.027|0.006|0.043|0.024|
||[0.064]|[0.062]|[0.070]|[0.042]|
||(0.885)|(0.893)|(0.582)|(0.465)|
|PE*Crisis|-0.001***|-0.001***|-0.001***|-0.001***|
||[0.000]|[0.000]|[0.000]|[0.000]|
||(0.267)|(0.837)|(0.015)|(0.008)|
|Beta*Crisis|-0.594*|-0.754**|-0.105|0.014|
||[0.350]|[0.358]|[0.506]|[0.658]|
||(0.333)|(0.084)|(0.800)|(0.585)|
|Country*Year FE|x|x|x|x|
|Nber. Obser.|4254|3779|887|741|
|R squared|0.23|0.23|0.31|0.32|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors clustered by bank reported in brackets. In parentheses we report the pvalue for the test of equality of effects during crisis and pre-crisis.

21
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# **5. Empirical Evidence**

In the following subsection we analyze whether a macro wage curve exists in Zimbabwe using aggregate data from 1964 to 1994. The statistical model in use is an autoregressive distributed lag model for wages consisting of: (1) pressure of demand that we proxy by the unemployment rate/informal sector and GDP; (2) expectational influences that we proxy by prices; (3) productivity, captured by a deterministic, linear trend; and, (4) important policy changes captured by including dummy variables.

The following three subsections present findings. Subsection 5.1 gives statistical information and subsections 5.2 and 5.3 analyze wage determination in the total economy and the manufacturing sector.

# **5.1 Statistical information**

Visual inspections indicate that wages and unemployment have strong positive trends of a similar magnitude, so that they may be modeled as stationary deviations from a linear trend or alternatively as variables with stochastic trends within a co-integrated system (see details in Banerjee, _et al._ (1993)). The unemployment or informal sector series also exhibits deviations from the trend in form of cyclical movements that will be important to capture in the modeling of wages. These properties, possibly resulting from changes in economic policies and "autonomous" events such as droughts and changes in commodity prices, e.g., crude oil, may be important to take into account when modeling wages.

The inspection of correlograms for the variables w, p, w-p, and u reveals high first order serial correlation coefficients (around 0.9) with the higher order coefficients declining extremely slowly, and correspondingly the spectra have peaks at the zero frequency. This information is consistent with each of the series having a stochastic trend and, therefore, being non-stationary and presumably integrated of order one, I(1). The result of the Augmented Dickey-Fuller test supports these hypotheses (see Appendix A). Hence, in modeling wages it will be important to choose models that can represent the 15