rafmacalaba/datause-encoder-data huggingface dataset:
increases monotonically with income. This presumed pattern of non-response to the household survey would also help explain this difference in capital‘s share, since the rich are likely to have more capital than the poor.

For our central model, we took total value added by sector from the National Accounts, but given our desire to preserve ―real households‖ and our focus on poverty, we did not want to alter the HBS factor shares. Thus, we did not alter the HBS data or value-added data by industry from the National Accounts. Rather we adjusted factor shares at the industry level to be consistent with the factor payments implied by the HBS. This reconciliation of the two sets of data significantly decreased the share of capital reported by the input-output table, especially in some of the more capital intensive sectors like ferrous and non-ferrous metals (see table 3.1).<sup>44</sup> We acknowledge that alternate methods of reconciliation are valid. For the purposes of sensitivity analysis, therefore, we have also run the model with the opposite reconciliation. That is, we adjusted factor shares at the household level to be consistent with the factor payments of the national accounts. We report these results in the sensitivity results.

# **4.4 Estimated Impacts of Russian WTO Accession**

# **Aggregate Results in the Full 55,098 Household Model**

In our general WTO scenario, we make the same assumptions regarding the counterfactual changes in parameters as in section 3. Aggregate results are summarized in table 4.1. Aggregate welfare results in table 4.1 are obtained by aggregating the equivalent variation gains (as a percent of consumption) of the 55 thousand consumers, using population weights. We report both the mean and

> 44 Of course as capital‘s share decreases, labor‘s share must increase, but reconciliation of data with the household data, also resulted in an increase in the relative share of skilled labor to unskilled labor. This is because, lacking more specific data at the household level, we defined all labor income as skilled labor income if the worker had any post-secondary education. Russia has a highly educated labor force, so this reconciliation resulted in an increase in the share of labor income going to skilled labor.

68
To measure general pedagogical knowledge, the survey asked teachers to read and extract information from a factual text (general content knowledge) and to prepare a lesson, including learning aims and outcomes based on their reading (pedagogical content knowledge).<sup>15</sup> Both these tasks are consistent with professional tasks normally expected of teachers, and we therefore consider a teacher to have minimum general pedagogy knowledge if he or she scores 80 percent or more on this portion of the test.

To measure teachers’ ability to assess students’ learning and give feedback (which we shorten here to “assessing students”), they were asked to prepare questions that required students to recall what was learned (lower order) and questions that asked students to apply the material to new contexts (higher order) on the basis of their reading of the factual text. In a second task, teachers were asked to use a marking scheme to give feedback on strengths and weaknesses in students’ writing and to distinguish weak and strong learners. In a third task, teachers were provided with a list of students’ grades; they were then asked to turn the raw scores into averages and to comment on the learning progression of individuals and groups of students with the help of a bar chart. We define a teacher as having “minimum knowledge in assessing students” if he or she could answer 80 percent of the items in the three tasks correctly.

To quantify teaching practices, observers used a modified Stallings (1980) classroom observation snapshot module to record which activities, from a pre-determined list, the teacher performed during each minute of a lesson. Across the six countries for which this analysis can be done, only 10 percent of teachers reached the threshold for minimum general pedagogy knowledge (Table 3).<sup>16</sup> In four countries, fewer than 5 percent of teachers met the threshold. While teachers could usually read and understand the factual text, they were typically not able to translate this information into teaching, as they struggled to formulate lesson aims and learning outcomes based on their reading (average score of 22 percent on this task, Table 4).

Poor knowledge of general pedagogy translated into poor skills in the classroom: fewer than 50 percent of the teachers explained the topic of the lesson at the start and summarized what was learned at the end, and almost 40 percent of lessons seemed unplanned to the observers (Table 5).

> 15 The parts of the test assessing pedagogical knowledge were designed to be consistent with what Sub-Saharan African countries might reasonably expect of their teachers and based on a review of policy documents from developing and developed countries on teacher standards that set out minimum requirements for teachers’ professional practice and conduct (Johnson, Cunningham and Dowling 2011).

> 16 The pedagogy test in Senegal and Tanzania (2010) was structured differently, so we omit these two countries from the indicators here. In Tanzania (2014), the pedagogy test was conducted in Swahili.

14
|**Table**|**3: Characteristics**<br>**Poverty Rat**<br>|**of thepoor**<br>**e (%)**<br>|**Population Dis**<br>|**tribution (%)**<br>|
|---|---|---|---|---|
||**2000**|**2005**|**2000**|**2005**|
|**Division**|||||
|Barisal|53.1|52.0|7.1|6.4|
|Chittagong|45.7|34.0|20.1|19.3|
|Dhaka|46.7|32.0|31.4|32.2|
|Khulna|45.1|45.7|11.7|11.7|
|Rajshahi|56.7|51.2|23.4|24.1|
|Sylhet|42.4|33.8|6.4|6.3|
|**Highest Level of Education**|||||
|No Education|63.2|54.7|57.3|53.5|
|Primary|40.3|35.1|15.4|15.5|
|Secondary|30.0|21.4|19.9|22.1|
|Higher Secondary|8.8|8.5|5.9|3.6|
|Graduate and above|3.1|4.3|1.6|5.3|
|**Landownership (acr.)**|||||
|Landless <0.05|63.5|56.8|48.0|45.8|
|Functionally Landless 0.05 to 0.5|59.7|48.8|13.0|15.9|
|Marginal 0.5 to 1.5|47.2|35.1|17.5|18.8|
|Small 1.5 to 2.5|35.4|23.7|9.2|8.8|
|Medium and Large 2.5 or more|20.7|12.8|12.4|10.7|
|**Construction Material of Roof**|||||
|Brick/cement|6.5|5.2|6.9|8.1|
|C.I. Sheet/wood|47.2|40.1|70.6|82.4|
|Tile/wood|38.7|62.1|3.8|1.9|
|Hemp/hay/bamboo|73.3|71.3|18.1|6.8|
|Other|57.1|70.4|0.6|0.8|
|**Electricity Connection**|24.0|20.8|32.6|45.4|
|**Landline or cellphone connection**|1.7|3.7|1.8|14.2|
|**Receiving Domestic Remittances**|42.5|37.5|18.5|21.6|
|**Receiving remittances from abroad**|26.2|17.1|9.7|10.4|
|**Activity of household head**|||||
|Self: agri|40.2|32.9|20.7|22.9|
|Self: non-agri|41.7|32.7|25.7|20.4|
|Salary wage employment|28.9|22.0|13.3|14.9|
|Daily wage: agri|76.5|72.4|18.4|15.7|
|Daily wage: non-ag|66.9|58.5|11.7|13.0|
|None<br>_Source_: HIES 2000,2005|39.6|27.7|10.3|13.2|

25
Table 2: Changes in protection, Syria, 1999–2009<sup>_†_</sup>

|Category||Tari|fs||NTB|s|
|---|---|---|---|---|---|---|
||2002|2009|Change<br>(2002–09)<sup>_‡_</sup>|1999|2001|Change<br>(1999–01)<sup>*</sup>|
|Food and live animals|21.8|16.1|-5.7|29.1|15.4|-13.8|
|Beverages and tobacco|58.0|37.4|-20.6|329.6|307.6|-22.0|
|Crude materials|6.3|5.3|-1.0|4.9|-1.1|-6.0|
|Mineral fuels|7.7|6.9|-0.7|17.7|-7.4|-25.1|
|Organic oils and fats|6.6|3.0|-3.6|31.8|18.2|-9.1|
|Chemicals|5.1|6.9|1.8|35.1|22.7|8.3|
|Manufactured goods|14.3|11.3|-3.0|51.5|43.4|-24.3|
|Machinery and transport|19.4|7.0|-12.4|89.9|82.1|-92.8|
|Miscellaneous articles|20.2|26.0|5.8|6.7|-2.9|46.4|
|**Average**|**17.7**|**13.3**|**-4.4**|**66.3**|**53.1**|**-15.4**|

- Source: Authors’ calculations based on UNCTAD TRAINS and Chemingui & Dessus (2008).

- Change between 2002–09 chosen on the basis of available data. Tariffs are unweighted; import-weighted changes average -5.2.

- Change between 1999–01 chosen on the basis of available data. Nontariff barriers are estimated using a price-differential decomposition; see Chemingui & Dessus (2008) for details.

the balance of payments deficit to 6.6% of GDP, increasing nonoil exports by about 13% annually, and increasing private sector exports by 15% per year.

There are reasons to believe that the policy decision to diversify the production and export base was primarily a political decision that—while influenced by historical structural features of the economy—was not directly influenced by contemporaneous economic developments. While the government explicitly acknowledged the structural imbalances across industrial sectors caused by the centralized system of the past, it is also clear that the existing changes in the structure of the economy were hitherto unremarkable and hence did not play a role in its policy choice (State Planning Commission 2005, p. 48):

[The pattern of Syrian] trade has been marked for a long time by a stereotyped nature. . . predominant in most developing economies. . . economic development has not achieved, over the last 50 years, its goals of adjusting the production structure, but. . . increased the dependency on importing local product inputs.

In part due to these changes, Syria has seen a surge in trade flows since 2001. Total merchandise trade increased from 44.1 percent of GDP in 1999 to 64.6 percent in 2007; 6
there is substantial variation in rainfall across countries. However, all models predict summer rainfall to decrease while winter rainfall to increase.

Looking at the trajectories of temperature and precipitation for the coming century, we find that temperatures are predicted to increase steadily until 2100 for both models. Precipitation predictions, however, vary across time for Africa: CCC predicts a declining trend and PCM predicts an initial increase, and then decrease, and increase again.

We predict net revenues based on the estimated parameters in Table 2 and future climates in Table 4. Climate change impacts are measured as the net revenues in the future at 2020, 2060, and 2100 minus the net revenues in the base year. Impact estimates for each AEZ are calculated at the mean of a climate variable at that AEZ. In predicting impacts, we assume that it is only the corresponding climate variable that changes in the future.

We present impact estimates from Model 3 with country fixed effects and Model 2 without country fixed effects in Tables 5a and 5b. Table 5a presents the results from model 3, country fixed effects model, in Table 2. Impacts are presented in both absolute magnitude and percentage change for both Africa as a whole and by each AEZ. African farmers earn $630 per year for a hectare of land based on the agricultural activities during July 2002 to June 2003. With the parameter estimates from Model 3, they are expected to lose 10% of their income under CCC, but gain 24% more income under PCM by 2020. Over time the estimates do not change much. This result indicates that African farmers are more resilient to climate change than earlier studies predicted (Rosenzweig and Parry 1994; Kurukulasuriya and Mendelsohn 2008). These results differ from past findings because farm income includes both crop and livestock income. Reductions in crop income are being partially offset by increases in livestock income. By not only adjusting their methods of growing crops but also switching back and forth between crops and livestock, farmers can adapt to future changes in climate. Farmers are therefore predicted to tolerate and even take advantage of climate change unless a large increase in temperature materializes along with a substantial drying. Table 5a shows how climate change affects farm net revenues in each AEZ. Except for the mid elevation savannahs under the CCC scenario, all the AEZs are predicted to get benefits from global warming.

However, the estimates from Model 2 without country fixed effects tell a slightly different story. Under the CCC scenario, farmers are increasingly vulnerable to climate change. Damage 11
institutional capacity. It requires establishing a credible competition agency, for which the institutional requirements are quite high. Furthermore, even in well-developed countries, questions arise on how to deal with the many network properties in financial services (access to the payments system, credit bureau, distribution networks, etc). Answers here are not obvious. One does not want to undermine the incentives for accurate information provision, for example, by opening up a credit bureau to any new party as that can undermine the incentive structure for entities to provide accurate information. Nor does one want to have financial institutions disclose all type of information as that can undermine their willingness to enter relationships with their clients out of fear that competitors take away the business.

In addition to the general view that competition can help with access, there is specific evidence that allowing greater entry by foreign banks can further enhance access. (General evidence on the effects of foreign bank entry is reviewed in Clarke et al. 2003). A study on borrowers’ perceptions across 36 countries found that reported financing obstacles were lower in countries with high levels of foreign bank penetration (Clarke et al. 2004). The same study found strong evidence that even small enterprises benefited and no evidence that they were harmed by the presence of foreign banks. The channels appear to be both competitive pressures of foreign banks on the domestic banking system, forcing local banks to go downscale, as well as direct provision of financial services by foreign banks. A specific Latin America study found that foreign banks with small local presence do not appear to lend much to small businesses, but that large foreign banks in many cases surpass large domestic banks (Sanchez et al. 2002).

There are also plenty of case examples on the effects of foreign banks’ entry. In Mongolia, a country with an income per capita of less than $500 and a very rural based economy, after many years of operating deficits, loan losses, and a failed attempt at privatization, the government-owned Agricultural Bank of Mongolia (“Khan” Bank) was placed in receivership in 1999. In March 2003 HS Securities of Japan bought Khan Bank from the Government of Mongolia for $6.85 million. Khan Bank now operates a network with 379 points of service throughout Mongolia, much greater than any of the other 16 20
Specific monthly grant amounts range in 2003 from $US9.50 ($105 pesos) in the third grade of primary to about $US53 ($580 pesos) for boys and $US60 ($660 pesos) for girls in the third year of senior high school (grades 10-12). By the senior year of high school, the grant amount represents about two-thirds of Mexico’s minimum wage. All monetary grants are given to the mother of the family, with the exception of scholarships for senior high school, which the youth can receive themselves subject to their mothers’ authorization. The program is means tested, with both geographic and household-level targeting. The geographic targeting uses aggregate census indicators to select poor rural communities based on the marginality index.

Table 1: Monthly Amount of Schooling Grants, 1997 and 2003

||2<sup>nd</sup>semester|1997|2<sup>nd</sup>semester|2003|
|---|---|---|---|---|
||Boys|Girls|Boys|Girls|
|Primary school|||||
|3<sup>rd</sup>year|60|60|105|105|
|4<sup>th</sup>year|70|70|120|120|
|5<sup>th</sup>year|90|90|155|155|
|6<sup>th</sup>year|120|120|210|210|
|Secondary school|||||
|1<sup>st</sup>year|175|185|305|320|
|2<sup>nd</sup>year|185|205|320|355|
|3<sup>rd</sup>year|195|225|335|390|
|High school|||||
|1<sup>st</sup>year|||510|585|
|2<sup>nd</sup>year|||545|625|
|3<sup>rd</sup>year|||580|660|
|Max HH amount without high-schooler|550||950||
|Max HH amount with high-schooler|||1635||

Note: Amounts in nominal pesos. The peso-to-dollar exchange rate was exchange rate was roughly 8 in 1997 and 11 in 2003. Source: www.prospera.gob.mx.

How might PROSPERA affect enrollment, school attended and achievement? In particular, conditional transfers should increase enrollment through both a price and an income effect. The price effect occurs because the subsidy reduces the shadow wage (or relative value) of children’s time in activities other than school. There is also an income effect, because the subsidy increases a family’s total potential income. Thus, both the price and income effect of the transfers work to increase a child’s enrollment in school.

In addition to increasing the probability of enrolling in school, the program might also change the school in which a child enrolls. Part of the transfers might be spent on costs necessary to attend a better school, for instance in paying additional transportation costs for a school that is located further away from residence. The extent to which this is possible, of course, depends on school options within a reasonable distance. For example, if there is only one high school within 50 kilometers, it is unlikely that the transfers would affect the school attended. In this paper, we 5
# **6. Structural Reforms and Growth: Serbia**

By using equation 1 and the results from the baseline model (Section 3) on the estimated elasticities of efficiency, capital, and labor with respect to changes in structural variables, it is possible to compute the impact on GDP growth of the most significant changes in structural policy. In particular, it is possible to derive the additional annual GDP growth if Serbia were to close major structural gaps within a specific time horizon (the methodology is explained in Annex 5).

The selected benchmark is Germany, a highly developed country whose position is close to what would be predicted by the production frontier, suggesting that it employs the available inputs with great efficiency (Figure 4). The figure shows the relation between capital per worker in 2016 and the actual versus the potential output predicted by the estimated production frontier.<sup>13</sup> Unlike Germany, Serbia’s performance is relatively low and it underperforms the predicted output per worker, suggesting inefficiencies in the economy. In fact, Serbia and other Western Balkans countries have large productivity gaps, and, as of 2016, Western Balkans countries had relatively little capital per worker: for a given level of capital per worker, actual output is below what could be achieved, and the efficiency gaps in Western Balkans countries are large and comparable in size throughout the region.

**Figure 4. Production Frontier in 2016 Compared**

<!-- Start of picture text --> 120<br>Austria<br>100 FranceBelgium<br>Sweden Netherlands<br>GermanyDenmarkFinland Italy<br>Malta Spain<br>80 United Kingdom<br>Greece<br>60 PolandTurkeyLithuaniaSlovak RepublicHungary SloveniaCroatia Portugal Predicted<br>EstoniaLatvia<br>KazakhstanRussiaBulgariaBosnia and Romania Serbia Actual<br>40 Belarus AlbaniaHerzegovina<br>20 Ukraine<br>ArmeniaMoldova<br>Kyrgyz Republic<br>0<br>0 100 200 300 400 500 600<br>Capital per worker<br>Output per worker<br><!-- End of picture text --> Looking at the structural indicators that turned out to be most significant in the empirical analysis, Table 10 compares the values for Serbia and for Germany. Serbia trails in most competitiveness areas, except for the top income and payroll income marginal tax rate and the cost of starting a business.

Except in those areas where it performs relatively better than Germany, such as cost to start a business and tax rates, Serbia’s potential growth would benefit greatly from closing structural gaps by introducing measures to improve, e.g. government effectiveness, regulatory quality, financial development, and worker skills.<sup>14</sup>

> 13 See also World Bank (2019b) for an application of the production frontier for the Western Balkans countries.

> 14 Serbia also performs better than Germany in terms of trade policy, if proxied by the tariff indicator developed by Fraser (2016), which considers not only mean tariff rates but also standard deviation of tariffs and trade revenues (% trade).

23
We also test the robustness of our findings by re-running our estimations using two other measures of disbursements, in addition to net disbursements. The first alternative is gross disbursements, which measures only total disbursements and does not net out repayments of loan principal. The second alternative measure of aid is Net Aid Transfers (NAT), using data made available by the Center for Global Development and described in Roodman (2006 and 2009). NAT subtracts out not only repayments of principal but also interest payments and cancellation of non-ODA loans (aka “debt relief”). Thus NAT more closely approximates the current budgetary outlays associated with ODA.

Our sample covers 24 donor countries during the 1977-2007 period. (For a list, see Appendix Table 1.) For 19 of these countries, aid data are available for the entire 31-year period. For the other five, which became donors after 1977, the period of coverage is between 17 and 28 years. We omitted several new donors that began reporting aid data to the DAC only in the late 1990s.<sup>5</sup> Most of these are transition economies also dropped by Demirguc-Kunt and Detragiache from their recent banking-crisis analysis (2005: 71) because “[banking] problems in these countries were of a special nature.”

# _Data on banking crises_

Our primary source on banking crises is the comprehensive new database assembled by Laeven and Valencia (2008) covering 1970-2007. This dataset includes only systemic crises, which Laeven and Valencia define as those in which “a country’s corporate and financial sectors experience a large number of defaults and financial institutions and corporations face great difficulties repaying contracts on time.” The authors note that they thereby “exclude banking system distress events that affected isolated banks but were not systemic in nature”. Between 1977 and 2007, according to this dataset, banking crises struck 9 of the 24 donor countries in our dataset: Finland, Japan, Korea, Norway, Spain, Sweden, Turkey, the United Kingdom, and the United States. Because Turkey and the U.S. each had two banking crises, there were a total of 11 systemic crises among our DAC donors over that period. Laeven and Valencia’s dataset (hereafter “LV”) provides the year in which

> 5 These recent donors reporting only low levels of aid include Czech Republic, Greece, Hungary, Poland, Slovak Republic, and Thailand.

7
Table S2. Model Without Interaction Between Inequality and Initial Income (Period t, t-1, and t-2 Inequality; Instrumental Variables Estimation vs. Least Squares Estimation)

|Dependent Variable is:|ln(yit)||
|---|---|---|
||(1)|(2)|
||IV Estimation|LS Estimation|
|Inequalityit|1.14**<br>(0.55)|-0.45<br>(0.42)|
|Inequalityit-1|0.55<br>(0.54)|-0.74**<br>(0.40)|
|Inequalityit -2|0.97**<br>(0.37)|0.44<br>(0.33)|
|Endogenous Variables|Inequalityit;Inequalityit-1;Inequalityit-2|.|
|Instruments|Residual Inequalityit;Residual Inequalityit-1<br>Residual Inequalityit-2|.|
|Kleibergen Paap F-Statistic|936|.|
|Country FE|Yes|Yes|
|Time FE|Yes|Yes|
|Observations|320|320|

Note: The method of estimation in column (1) is two-stage least squares; column (2) least squares. Bootstrapped standard errors are shown in parentheses. _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . _Inequality_ is the Gini coefficient; the Gini data are from Brueckner et al. (2015). *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

46