rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 6: Summary Statistics for Job Candidates in Second Exper**|**iment**<br>**N**|**Mean**|**SD**|**Male**<br>**Mean**|**Female**<br>**Mean**|**Candidates**<br>**willing to**<br>**take low**<br>**prestige jobs**<br>**Mean**|<br>**Candidates**<br>**only willing**<br>**to take high**<br>**prestige jobs**<br>**Mean**|
|---|---|---|---|---|---|---|---|
|Male|2002|0.53|0.50|1.00|0.00|0.55|0.51|
|Age|1506|22.49|1.46|22.73|22.13|22.51|22.47|
|Years since Graduation|2002|0.74|1.85|0.47|1.04|1.02|0.47|
|University|2002|0.94|0.24|0.96|0.91|0.95|0.93|
|Ever Worked|1506|0.59|0.49|0.77|0.33|0.64|0.56|
|Accounting or Business|2002|0.44|0.50|0.49|0.37|0.53|0.36|
|Engineering|2002|0.12|0.32|0.18|0.05|0.09|0.15|
|Computing or IT|2002|0.09|0.28|0.09|0.08|0.09|0.08|
|Other Majors|2002|0.36|0.48|0.24|0.49|0.29|0.42|
|Prestige score*|2002|2.60|2.24|2.77|2.40|4.58|0.79|
|Lived in Amman for the past five years or longer|1506|0.73|0.44|0.73|0.74|0.74|0.73|
|Interested in being matched to jobs in their field of study|1506|0.96|0.20|0.97|0.94|0.99|0.94|
|Interested in being matched to general jobs|1506|0.97|0.17|0.97|0.97|0.98|0.96|
|Bachelor's degree|1506|0.68|0.47|0.66|0.71|0.66|0.70|
|Mean expected number of months to find jobs|1506|5.17|4.52|4.87|5.61|4.82|5.42|
|Mean self-reported reservation wage(JDper month)<br>* How many low prestige jobs are you willing to work in ? (l|2002<br>ist jobs we|318<br>considered lo|95<br>w prestige)|346|287|312|323|

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countryside. Migrants who work in factories or perform manual labor in construction or household work can get status as legal migrants, entitling them to some public services, but they are not entitled to the full range of social benefits available to permanent residents. This migrant population is referred to in Chinese as “floating population,” since the initial thinking was that such migrants would stay temporarily in cities and then return to the countryside. In practice, however, a growing number of migrants are permanently relocating to cities.

China’s _hukou_ system has had some effect on the pace of this migration, but has not prevented migration from occurring on a large scale. According to _hukou_ registrations, China’s rural population has continued to increase, reaching over 900 million by 2004. Data based on where people actually live and work, however, show a rural population 200 million smaller (Figure 7). Data on the labor force by sector show a similar trend: agriculture’s share of the labor force has declined from 80% at the beginning of reform to 60% today. Industry and services have each expanded from about 10% of the labor force to 20%. This migration has nicely complemented what China has done to develop agriculture. The reforms spurred agricultural production, while the outmigration means that the income is spread over a smaller population.

Some of this rural-urban migration occurs within provinces. But there has also been a clear tendency for population to shift from interior China to coastal locations. According to official data, in 2005 coastal Guangdong province had 75 million permanent residents plus 16 million legal migrants from other provinces. Since all of those migrants are working, they make up about one-third of the labor force. Given that Guangdong has a good location and has built up excellent infrastructure, this migration 16
the 1 percent confidence interval (see stability test graphs 9 and 10). This provides additional confidence to the predictive power of the model and robustness of related findings and analysis.

# **_Key Economic Findings of the Short Run Parsimonious Model—Closed Economy Formulation_**

The short run equation for the real money balances in the parsimonious indicates that short run movements of money demand varies positively with the lagged change in money and negatively with the error correction term. Accordingly, the demand for money in the current period influences the demand in the next period. The stability of the short run money demand requires a pulse dummy for the year 2008. This pulse dummy captures a 12 percent decline in the change in demand for real balances. As earlier discussed, this marks the year of the global financial crises. It is evident that the public lost trust in national currency during this year, influenced by the potential impact of the crises on the domestic economy.

It worth noting that the government launched a stimulus package that reached almost 5 percent of GDP to revitalize the economy; this, coupled with a hike in public spending and inflation rates. The money adjustment to a 1 percent deviation from disequilibrium is around 1.6 percent. This is a bit higher than the CBE’s 2010 study that read 1.3 percent for monthly data covering the years 1990–2010. Yet the later was under an open economy formulation.

The growth in GDP varies positively with the change in real money two periods ago, and negatively with the previous period’s interest rate. If the change in inflation rate is 5 percent, the change in output would drop by around 0.001. In case the interest rate is 10 percent, the change in real income will drop by 0.002. The change in real income would be 0.45 if the money grows by 1 percent.

The change in the current period’s’ inflation varies positively with the growth in real money balances two periods ago and the previous period’s income. Yet inflation changes negatively with the change in the previous period’s inflation. The change in inflation by 1 percent in the previous period lowers the change in inflation by 0.03 percent in the current period.

30
India's LPI Component Scores <!-- Start of picture text --> 2014 54 3.08 pat: /<br>CUS 35 3.42 2.9 . fp ———<br>2.7<br>2.5<br>2006 2008 2010 2012 2014 2016<br>—*(Customs —*|nfrastructure<br><!-- End of picture text -->
Figure 4.3: _Markets in ECA tend to be overregulated which is likely to hamper job creation_ <!-- Start of picture text --> Market regulation index<br>9 8.2<br>8<br>6.9 7.0 7.1<br>7 5.7 5.8 5.9 5.9 5.9 6.0 6.1 6.1 6.3 6.4<br>6 5.1 5.2 5.2 5.2 5.3 5.6<br>5 4.6<br>4<br>3<br>2<br>1<br>0<br>Russia CIS Turkey Albania Slovenia Slovak Rep. Lithuania Ukraine ECA Romania Poland Bulgaria CEE Czech Rep. SEE Latvia Europe Hungary Croatia Estonia US<br><!-- End of picture text --> Source: Economic Freedom of the World 2003; Bank staff calculations.

Figure 4.4: _Corruption is high in ECA raising the costs of doing business and inhibiting_ <!-- Start of picture text --> job<br>creati<br>Perceived corruption index (TI)<br>on<br>12<br>10.010.0<br>10<br>8<br>6.3 7.0<br>6 4.94.9 5.1<br>4<br>2<br>0.0 0.20.50.70.70.91.11.21.21.62.02.12.63.03.03.03.23.53.54.0<br>0<br>Azerbaijan Moldova Kazakhstan Ukraine Georgia Albania Romania CIS Russia Uzbekistan SEE Turkey ECA Czech Rep. Slovak Rep. Latvia Croatia Poland Bulgaria CEE Belarus Lithuania Hungary Estonia Slovenia Europe US Kyrgyz Rep. Serbia and Mont. Bosnia i Herz. Macedonia, FYR Armenia<br><!-- End of picture text --> Source: Transparency International (2002) 37
Pfaff, A.S.P., Robalino, J., Reis, E.J., Walker, R., Perz, S., Laurance, W., Bohrer, C., Aldrich, S., Arima, E., Caldas, M., et al. (2018). Roads & SDGs, tradeoffs and synergies: Learning from Brazil’s Amazon in distinguishing frontiers. Economics: The Open-Access, Open-Assessment E-Journal _12_ , 1–26.

Raballand, G., Macchi, P., and Petracco, C. (2010). Rural road investment efficiency : lessons from Burkina Faso, Cameroon, and Uganda (The World Bank).

Raptopoulos, A. (2013). No roads? There’s a drone for that.

Roberts, P., KC, S., and Rastogi, C. (2006). Rural Access Index: A Key Development Indicator (Washington DC, USA: World Bank).

Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS ONE _10_ , e0107042.

USAID Global Health Supply Chain Program-Procurement and Supply Management (2017). Unmanned Aerial Vehicles Landscape Analysis : Applications in the development context (Washington DC, USA: Chemonics International Inc).

World Bank (2016). Measuring rural access : using new technologies (The World Bank).

World Bank (2018). Road Costs Knowledge System (ROCKS) – Doing Business Update (Washington DC: World Bank).

# **Annex 1: OSM roads classification**

Below is a list of OSM road labels and classification in primary, secondary, tertiary, and track. All other types of roads are in “other”.

|‘highway’ tagin OSM|Meta‐Classification|
|---|---|
|unsurfaced<br>services<br>corridor<br>escape<br>emergency_access_point<br>emergency_bay<br>construction<br>bridleway<br>path<br>track|**track**|
|living_street<br>residential<br>service<br>tertiary<br>tertiary_link<br>unclassified|**tertiary**|

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The first order condition for _rL_ and _rAL_ gives the following equations: where _l_ ( _s_ ) _≡ s_ + _h_ ( _s_ ) and _l_<sup>_′_</sup> _>_ 0. As long as _vAL_<sup>_m≥wS_,theoptimalstrategyofthefirmis</sup> to charge _rAL_<sup>_m_forthebundleand</sup><sup>_r_</sup> _L_<sup>_∗_=</sup><sup>_−h_(</sup><sup>_τ ∗_)for</sup><sup>_BL_.</sup>

# **M.3 Pass-through**

Now suppose the valuations _uL_ and _uS_ for product _BL_ and _BS_ increase due to an exogenous increase in SNAP benefits. Again assume that marginal costs are unchanged. Then the price and quantity responses can be written as follows: Therefore, based on this model, there are at least two reasons for the large pass-through elasticity for SNAP-ineligible products. For exposition, assume that _uS − uL_ remains unchanged. First, because SNAP benefits lead to a rise in disposable income for SNAP consumers, demand for SNAP-ineligible products could increase, i.e.<sup>_<u>du</u>_</sup> _db_<sup>_<u>A</u>>_0, since</sup><sup>_h_(</sup><sup>_·_) and</sup> _l_<sup>_′_</sup> ( _·_ ) are increasing functions.

Second, because of the multi-product nature of all grocery stores in our data, food and non-food products can become complementary due to shopping costs. Multi-product retailers with asymmetric market power over food and non-food can leverage this complementarity to raise markups on non-food products when SNAP benefits increase. Even if _uA_ remains unchanged with benefit increases, _pA_ still increases since _uL_ increases, i.e. _<u>dudbL</u>_<sup>_>_0.</sup><sup>_qA_is</sup> negatively correlated with the price response of _A_ , consistent with our empirical results. How 68
on decision making) at the national level of executive power – although without being explicitly discriminated against.

- _discriminated_ : Group members are subjected to active, intentional, and targeted discrimination by the state, with the intent of excluding them from political power. Such active discrimination can be either formal or informal, but always refers to the domain of public politics (excluding discrimination in the socioeconomic sphere).

- _self-exclusion_ : The special category of self-exclusion applies to groups that have excluded themselves from central state power, in the sense that they control a particular territory of the state which they have declared independent from the central government.

From this perspective it should be clear that policy changes are possible on this dimension. Discriminating policies in particular appear to be part of a political situation that can escalate into violence. Amending this could be easier than formal institutional changes. Discrimination by the ethnic group in power could, of course, be endogenously determined. One part of this is inter-ethnic trust which has received a lot of attention in recent research.

# **8.3.4 Trust and**

Figure 19 shows the share of people of people who report that "Most people can be trusted" in the World Value Survey (the alternative is "You can’t be too careful."). The blue line shows the distribution in countries that have not experienced civil conflict in the sample. The red line shows the distribution of trust in countries that have. Countries with a conflict history show drastically lower levels of trust.

These findings are taken up by Rohner et al. (2013b) who argue that this pattern could be explained by a three-way relationship between interethnic trust, trade and conflict. Low levels of trust lead to low levels of economic interactions which, in turn, makes conflict more likely. Conflict, on the other hand, destroys trust. The incidence of conflict can be reduced by policies abating cultural barriers, fostering inter-ethnic trade and human capital, and shifting beliefs. In this view peacekeeping forces by themselves or externally imposed regime changes, have no enduring effects.

Rohner et al. (2013a) bring this idea to data on trust and ethnic identity in Uganda. Using individual and county-level data, they document large causal effects on trust and ethnic identity of an outburst of ethnic conflicts in 2002–2005. Using data from the Afrobarometer and ACLED they find that more intense fighting decreases generalized trust and increases ethnic identity. Controlling for the intensity of violence during the conflict, they also document that post-conflict economic recovery was slower in ethnically fractionalized counties. Again, the interpretation of this fact is that trust is needed for economic activity and that this trust breaks down across ethnic groups.

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in getting CFs motivated to demonstrate to others in their community.

The underlying assumption of the CF model is that, through peer learning, a change in CF demonstration eort should aect the process of diusion to other farmers in the community. By exogenously aecting CFs' adoption of a new technology, our experiment directly tests whether the CF model is suited to promoting technology adoption on a large scale. Allowing the ITT estimates to vary by CF characteristics provides qualitative evidence of existing barriers to knowledge transfer.

# 3 Experiment and Data

We run a large eld experiment to test for eective knowledge diusion under the CF model of extension, and isolate the additional impact of directly training CFs. A new technology, SLM, was disseminated through the extension network for the rst time in 2010. Our study started in October 2010 and ended after the main 2013 cropping season, thus spanning three main agricultural seasons (Figure 2). We collected three rounds of data: a rapid CF baseline and two CF and household-level follow-ups, respectively, 15 and 27 months after the rst SLM demonstration season. By baseline, we refer to September 2010 and earlier. The initial demonstration season in our study was 2011. Our surveys captured the 2012 and 2013 adoption seasons. This section details the experiment and data sources.

## 3.1 Sustainable Land Management

Sustainable land management (SLM, or conservation farming) is a yield-enhancing farming technology that consists of a bundle of techniques adapted to local crops and agro-ecological conditions (Haggblade and Tembo, 2003; Thierfelder et al., 2015).<sup>5</sup> In the Zambezi valley, the recommended SLM technology package encompasses seven SLM techniques: Mulching, Crop Rotation,

> 5A direct implication is that, while positive yield eects of SLM are relatively well documented for Southern Africa (Haggblade and Tembo, 2003; Thierfelder et al., 2015), there is little evidence on the returns of individual SLM techniques.

7
has two effects—the price of the output is lowered (limiting expansion in the sector); but the additional varieties also lower the costs of the sector (inducing expansion in the sector). Entry into the sector continues as long as price exceeds average costs, but costs may fall faster than price, inducing explosive expansion. A solution to this problem was to exclude the Dixit-Stiglitz productivity gain from varieties in the same sector.

Since we have sector specific factors of production, as the sector expands, the costs of the sector specific factor increases and costs do not fall proportionately with varieties. In practice, we have always had an equilibrium in these models. But in the case of the Russia model, the costs of non-ferrous metals inputs in the total costs of the non-ferrous metals sector were very high—about 40 percent. In the developmental stage of the model in this section, we observed dramatic expansion of the non-ferrous metals sector in our WTO accession counterfactual, while virtually all other sectors contracted. We assessed this to be due to an unrealistically large cost reduction due to the variety externality. Since businessmen, policy-makers and political leaders had been calling for an assessment of the consequences of WTO accession at the sector level, it was crucial that we have results that could be explained with clear economic intuition. We modified the Dixit-Stiglitz formulation such that there was no productivity gain from additional intermediate varieties in the same sector. Subsequently, the sector results were very reasonable and easily interpreted. Moreover, surveys have revealed that policy-makers and political leaders throughout Russia came to adopt the assessments of this model as their expected outcomes at the sector level of the impacts of Russian WTO accession.

# **Elasticity of substitution differences or preference parameters in the demand**

**function.** It is possible to avoid highly specialized solutions (or bang bang solutions in the dynamic version of the model in section 2) by using nested CES functions with elasticities of

42
29 organised on familial lines, and In its physical effects on women's health and nutritional status, it contributes to the maternal depletion syndrome (Mosley (1985) pg 124, Bantje (1980)). Education, at varying levels, can enable women to enter alternative spheres of work, which may involve different consequences for maternal and child health.

Female employment, education and family welfare are inter-related in a complex fashion, with a variety of outcomes seen consequent to an increase in femal wage earning. Female education has ncreased (Awker and Hein (1985) p 73) and this has commonly been associated with women entering formal labor markets and skilled positions (Oshima (1983) pp 590-91); see also Akadiri (1984) on female white collar employment in Africa). It is frequently observed that the contribution of working women's incomes to family household groups are crucial for the survivial of those groups. However, the impact of such earning power on the welfare possibility set of the the mother-child dyad (or the individual woman if not yet a mother) depends on the form of that earning. For example, Sen ((1985b) p 33) compares studies of women in the beedi industry in India, who gain greater status within the family through their earning, with lacemakers, who do not because their home working is regarded as a spare-time activity.

In order to understand in which context wage employment succeeds or fails to transform domestic relations and family welfare, and how female education is related to these factors, it is useful to consider the main trends in female labor makets.

As already observed, the majority of women in Asia, Africa and
# m. **THE** EVIDENCE: MAJOR ISSUES

3.01 This section examines the treatment in the literature of major factors--capacity, coverage, quality, and equity--that relate to education supply and demand. Table III.1 summarizes the ways sources present education lata, the major issues the data cover, and whether sources link trends or outcomes to adjustment measures. On the whole, appropriate methodologies have not been developed to analyze the impacts of adjustment programs on education. Likewise, the data are too crude to permit any meaningful inference regarding causality. Even when correlation is evident, the literature does not delve deeper to determine what happened, why did it happen, was the outcome intended, and is the direction of change positive or negative. Existing methodologies (annexes 3 and 4) associate declines in public education expenditures and gross primary enrollment rates with adjustment programs, but do not examine quality and equity indicators. The impacts of changes in financing and coverage on quality and equity are by no means obvious. Available case studies provide quality and equity indicators from the "adjustment period," but do not analyze how adjustment programs (distinct from recession or external shocks) affect educationY Annex 1 contains nine case studies: each compiles available education sector data for a specific country and examines possible links to adjustment processes. Boxes throughout the chapter illustrate relevant issues and evidence from the case studies.

A.apaiy 3.02 During the 1960s and early 1970s, public education spending (by central and local governments) in virtually all countries rose considerably faster than either government revenue or GNP (Cornia, et.al., 1987). A UNESCO study describes adjustment as an acute phase of a longer crisis, and the expansion of the education system during the last decades as an "education explosion" (Duvieusart, 1990). Was this expansion sustainable? In many cases downward trends in public education financing began in .he late 1970s.

3.03 Sahn (1989) examines public education financing in real terms and as a share of the government budget from before and after the first World Bank adjustment loan (box 111.1). Gallagher (1990) compares average allocations of total and discretionary public budgets to education among fiscally duressed (adjusting) and other (nonadjusting) countries (annex 3). These simple analyses do not find any strong links between adjustment and public education financing. Gallagher concludes that reductions in social spending are associated with the general decline in total government spending that results from slower economic growth. However, two recent World Bank studies (Kakwani, et.al., 1990; and World Bank, 1990a) associate adjustment directly with declining public resources allocated to education: education's share of the public budget and GDP increased in all country groups except intensely adjusting countries after 1980 (annex 4).

/For example, Aedo-Richmond and Noguera (1989); Cornia, et.al. (1987); Fuller (1989); Hinchliffe (1989), Hoppers (1989); and Kydd (1988).

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nonperforming loans, rising from 0.31 percent in the first two quarters to a peak of 0.43 percent in 2020:Q4. However, in all cases, nonperforming loans for the G-SIBs are below the industry averages, suggesting no concentration of loan risk for the systemically important banks. For the most adverse values charge-offs, recoveries, and nonperforming loans, the maximum 2020 chargeoff ratios are always below the 2019:Q4 maximum, and the maximum nonperforming loans for the large banks is in all cases below the P90 values for the industry.

We next check if loss allowances show any insufficiencies for the G-SIBs. We find that the G-SIBs actually increased their allowances significantly. The mean ratio is 0.36 percent in 2019:Q4, and ranges between 0.54 and 0.72 percent in the quarters of 2020, suggesting the G-SIBs are prepared for credit losses. The minimum values of the allowance also increase in 2020, but are low throughout the sample period, between 0.03 and 0.05 percent. This may reflect a risky G-SIB bank, but it is not an increase in risk during the COVID-19 crisis.

# **4.1.3. Bank Portfolio Allocations**

We finally check bank portfolio allocations to investigate the possibility that there are some significant increases or decreases in major portfolio categories that may suggest banking industry problems. We specifically look at loans, unused commitments, and deposits.

The industry mean loan ratios declined significantly from 64.48 to 60.01 percent over the sample period, with a more modest decline for the P90 statistic from 82.45 to 79.15 percent. A decline in loans generally suggests a reduction in credit risk, which would make the banks safer, although we caution that we do not know the risk characteristics of the loans that are retained. As discussed further in Section 6 below, the lending decline likely detracted from the “economic surprise” of the short recession, but it likely contributed to the “financial surprise” of a relatively healthy banking industry. It is not possible to discern the extent to which these declines may reflect 23