rafmacalaba/datause-encoder-data huggingface dataset:
index changes by 1 percent, then the trade cost factor changes by β 3 percent and change trade by β 3(1 −σ _k_ ) from equation (3)<sup>7</sup> . The same percentage change in the trade cost factor is brought about by<sup>β3</sup> β 1 percent change in the tariff factor (1 + τ _ij_ ). From the estimate by Helbel, Shepherd and Wilson (2007), the ratio is about - 1.312 (= -1.864 /1.421)8. This gives us the tariff-equivalent (more accurately, tariff-power-equivalent) measures for the transparency index changes. One may consider this ratio to convert from the percentage change in ITI to that in tariff power to bring about the same amount of changes in trading costs.

The rather large value of the estimated coefficient of the ITI should reflect the characteristics of this index as a proxy of institutional capacity and efficiency in trade policy. The ITI is essentially a principal factor, which is mechanically calculated from the eleven variables (see Appendix A). While these variables should commonly indicate the degree of transparency in trade policy, the variables may contain wider information, not limited to transparency. The ITI may reflect not only transparency in a narrow sense, but also institutional efficiency, or even modernization of trade procedures in an economy. An economy with a higher ITI score is likely to have better governance and institutions in trade. In this sense, the index in the gravity regression should be regarded as proxy variable, representing institutional efficiency, openness and fairness in trade – i.e. transparency.

The comparison among the results from the various sector base regressions confirms the plausibility of the magnitude of the conversion factor. HSW undertake the same regression on the sector/commodity breakdowns. The estimated conversion factors and the cases are: -2.61 for all the commodities, -1.22 for only the non-basic manufacture (HS > 83), -2.27 for homogeneous goods (goods traded on an organized exchange, or reference priced goods). As a result, our reference conversion factor, -1.312, locates

> 7 This applies only when the change in the bilateral trade cost factor does not significantly change the multilateral trade resistance terms.

> 8 These figures are the baseline estimate, covering all the commodities except for raw materials (HS 1-27). The coefficient of the tariff variable is significant only at a 15% level in the specific regression. However, the estimated value is around the mid-point among the various specifications.

9
# Table 14: Health - age subgroups

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||Diarrhea|(14 days)|Hemoglobi|n level(g/L)|Weight-|for-age|Height-|for-age|Head circumfe|rence-for-age|
|VARIABLES|AGE 0-2|AGE 3-4|AGE 0-2|AGE 3-4|AGE 0-2|AGE 3-4|AGE 0-2|AGE 3-4|AGE 0-2|AGE 3-4|
|HWWS (β1)|-0.012|-0.014|-0.306|0.951|0.026|-0.010|0.039|0.001|0.281*|0.151|
||(0.354)|(0.161)|(0.689)|(0.349)|(0.641)|(0.849)|(0.598)|(0.990)|(0.053)|(0.317)|
|TSSM ( β2)|-0.008|-0.015|-0.199|-1.674|-0.057|-0.043|0.009|-0.059|0.106|0.060|
||(0.548)|(0.173)|(0.787)|(0.103)|(0.219)|(0.330)|(0.906)|(0.424)|(0.408)|(0.683)|
|HWWS + TSSM (β3)|-0.026*|-0.013|-2.359***|-0.469|-0.094**|-0.060|0.013|-0.056|0.288*|0.120|
||(0.078)|(0.239)|(0.005)|(0.646)|(0.047)|(0.245)|(0.854)|(0.430)|(0.073)|(0.446)|
|Observations|21,337|12,707|3,239|1,964|3,236|1,967|3,241|1,967|3,241|1,967|
|R-squared|0.043|0.021|0.164|0.149|0.072|0.080|0.109|0.081|0.225|0.229|
|Control Mean:|0.204|0.106|109.038|115.594|-1.019|-1.058|-2.004|-1.847|-0.467|-0.586|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects, child gender and age (month) dummies. Columns (1) and (2) use listing data while columns (3) - (10) use household survey data.

50
#;@e * e

# 8. SPECIALIZED SOURCES OF SKILL DEVELOPMENT

# 8.1 The role of specialized sources

Specialized sources of skill development mostly fall into two broad categories. First, there are those which are primarily enterprise-linked and are complementary with other forms of enterprise-based training. Examples are training provided by other enterprises through various types of commercial links (participants in joint ventures, suppliers of materials or capital goods, even customers), training of a similar nature promoted by donor or technical assistance agencies, and training provided by expatriates. Second, there is the training offered by proprietary schools which is a substitute for public provision. Fitting into neither category particularly well is the training in civilian skills provided by the military in some countries.

These sources of skill development seldom receive much, if any, attention in national planning or policy documents. One reason is that staff of central government agencies are often unaware of the range and volume of these sources, their familiarity with, and enthusiasm for, one specialized source, donor-agency-sponsored overseas training schemes for government staff, contrasting oddly with their lack of knowledge elsewhere.

The asvmmetry reflects their direct experience with this source and the fact that the other types tend to be overlooked in statist'cal surveys. A second reason is that much specialized training is provided both for and by the private sector and as such is considered almost unworthy of official attention. A third reason is that much of it is provided on a very small scale.

None of these reasons justifies the neglect of specialized training. The fact that it generally takes place without government intervention is itself prima facie evidence (which, of course might be controverted by detailed analysis) that it is cost-effective. Like orthodox enterprise-based training, it may actually be a substitute for the public provision of training. And although it may be provided on a small scale, it is often a key input into the production process, especially when technology is changing. Not least, a knowledge of the contribution of these types of training is essential to the development of an understanding of the interplay between training and employment.

# 8.2 Long-term training links with other enterprises

In industrialized as well as developing countries much training is provided by one enterprise for another with which it has commercial links. In developing countries this type of transfer of skills, like the transfer of technology with which it is usually associated, assumes particular importance when the link involves an overseas firm. In the absence of specialized technical institutes, of competitor firms using the same technology, and of trade journals, it may be the only practical source of new skills.

- 93 -
effects of any feasible reform in a local business environment be swamped by the forces of economic geography and path dependence? We address this issue in the context of regional gaps in manufacturing performance across India’s states. Having analyzed survey data on plants sampled from the 40 main industrial cities in the country, we find that economic geography is always a major source of the variations in productivity observed across the sample.<sup>2</sup> However, the local business environment is probably an even more powerful influence. Some cities such as Bangalore and Chennai show productivity premiums primarily due to their superior geography rather than to a better business climate. At the same time business environment explains a greater share of productivity premiums or shortfalls than geography for most of the other cities. We systematically find that inferior geography appears to be compensated by better business environment in a city’s industrial competitiveness. The more geographically advantaged is a state or a city, it seems, the more it can afford a “poor” business environment while maintaining competitiveness.

A significant source of the effect of geography on plant productivity is agglomeration (localization) economies, where business establishments gain productivity due to proximity to others in the same line of business. An even larger component is the effect of market access, defined as a proximity-weighted average of purchasing power or economic activity in potential trading partners of a location. For example, the city of New Delhi has a total factor productivity premium of 26 % in garments production over the city of Bhopal in the state of Madhya Pradesh on account of its economic geography. However, only 4 percentage points of this premium comes from greater localization economies. The balance of the (geographic) productivity premium reflects better access to markets for garments producers in Delhi. Similarly, the average garment producer in Mumbai has a 22 percentage point productivity advantage over the average producer in New Delhi for geographic reasons. Of this about 3 percentage points are due to

> inevitably mean that their removal would improve social welfare. However, there are circumstances where reducing regional inequality is both feasible and produces welfare gains.

> 2 The forty cities are distributed across 12 of India’s 16 major states, including the most industrially advanced alongside those where the manufacturing sector is the least developed. Each of the 12 states is represented by the top 3 or 4 industrial cities in it.

2
and Wager, 2016; Athey et al., 2019). We include in the causal forest all the individual and household characteristics available at baseline and other school and neighborhood characteristics we collected.

We also follow Athey and Wager (2019) and Carlana et al. (2022) in using the predictions of the expected treatment effect for each student, given the set of covariates, to categorize participants into two groups, the _"Strong"_ and _"Weak"_ groups. This classification is achieved by categorizing the CATEs into three tiers: "High," "Medium," and "Low." The _“Strong"_ group refers to the subgroup whose CATEs fall within the “High" category (that is, above the 66th percentile) of all CATEs when switching from _Clubs_ to the _Mindful_ or _Virtue_ treatment. In contrast, the _"Weak"_ group consists of participants with "Medium" and "Low" CATEs (that is, positioned at or below the 66th percentile). Then, to understand what types of students are more likely to see an increase in the main outcomes due to the interventions, we characterize the groups using a balance test.

# **5 Results**

## **5.1 Main Results**

Table 3 presents the main results of our paper. We show the net impact of the SEL component—beyond the regular protection component—estimated using equation (1) on the primary student outcomes measured using administrative data: an indicator for whether the behavior at school report was above the school median (column (1)), an indicator for whether the students abandoned school during the 2019 academic year (column (2)), and an indicator for whether the math-score report was above the school median (column (3)).

The impact estimates show positive and significant effects on students’ behavior at school. Compared to students in _Clubs_ , participants in the psychology-based interventions ( _Virtue_ and _Mindful_ ) are 23 pp more likely to be better behaved at school. This improvement is equivalent to an increase of 36% relative to the share of students in the ASP’s protection component with an above-the-median behavior score. We find no statistically significant effects of the SEL component on school dropout and math grades. These re28
At the individual commodity level, there is somewhat more diversity in the effect of including wage rate impacts. In a few cases wage rate impacts are sufficient to change the direction of the effect on national poverty rates. This is the case for beef in Madagascar, where the poverty impact changes from 0.4 to -0.1 percentage points. But these effects are very small and increases in the prices of all of these staple goods continue to raise national poverty rates.

The results in Table 3 reveal a very similar picture for the poverty gap. In most cases, increases in prices increase the poverty gap. A 10 percent increase in the prices of all goods would reduce the poverty gap under our assumptions only in Peru and in Vietnam. For the sample of countries as a whole, the effect of increasing the prices of all considered goods by 10 percent with the inclusion of wage impacts would raise the poverty gap by an average of 0.3 percentage points in rural areas, by 0.2 in urban areas, and by 0.2 percentage points overall.

# **_5.2 Impact of the 2005-7 global food price increases on poverty_**

In the second experiment, we consider the developments in global food prices in 2005-2007 and apply these changes to our sample of households in order to estimate the impact on poverty in the absence of policy responses. Based on the FAO data on price developments, we implement the scenario shown in Table 4. Following Mundlak and Larson (1992), we assume that price transmission between world and domestic markets is approximately complete under normal market circumstances and specify a unitary elasticity of price transmission.

We first consider a scenario with wage impacts ignored. The main poverty rate change results of this scenario are shown in Table 5. As the table suggests, the average impact of the past global food price increase results in a rise in poverty of 3.0 percentage 15
PPPs to investments in capacity expansion may be expected to be relatively limited(see Harris:2003 and Annez: 2007) Expanding electricity capacity in cities cannot rely much on user charges in most developing country utilities.

**Table 3.2   Overview of Cost Recovery in Electricity Utilities World Wide**

|Country Grouping|% of utilities that do not<br>recover full O&M|% of utilities that<br>recover most of O&M|% of utilities that<br>recover O&M plus<br>partial capital costs|
|---|---|---|---|
|World|15|44|41|
|High Income|0|17|83|
|Upper Middle Income|0|71|29|
|Lower Middle Income|27|50|23|
|Low Income|31|44|25|
|OECD|0|17|83|
|Latin<br>America<br>and<br>Caribbean|0|47|53|
|Europe and Central Asia|31|38|31|
|East Asia and Pacific|29|65|6|
|Sub-Saharan Africa|29|71|0|
|South Asia|33|67|0|

Source: Adapted from Komives et alia( 2005) p.26 Regions follow World Bank classifications. See original for discussion of the sample of utilities on which these data are based.

# **_There are economic reasons that make it appealing to subsidize capital costs for both WSS and electricity._**

While it is tempting to blame such widespread subsidization of WSS and electricity on the political process alone, the technology and economics of these two sectors makes them easy targets for subsidization. Most important among these features is the long lives of infrastructure assets in both sectors. As Table 3.3 shows, the lives of both electricity and WSS are relatively long. Accordingly, the costs of running down the assets are relatively low, and slow to be noticed. It is interesting to note that in electricity supply, the overall quality of service is much more sensitive to maintenance spending than for WSS. Unplanned service interruptions result fairly quickly when maintenance is deferred in electricity 15
promoting SHS adoption. That means the poor households who may prefer lower-capacity solar panel units (less than 40 Wp) may not even be able to pay the market price (without subsidy) of such a unit. That is, even if the subsidy is less than 10 percent of the current price, it seems IDCOL’s subsidized operation of marketing SHS may not be discontinued for some time.

Thus, for adopters in rural areas, it is important to find out the socioeconomic benefits of SHS. The objective is to estimate benefits to compare and contrast with the costs of acquiring a solar unit. Using a large household survey data from Bangladesh, this paper estimates the effects of SHS on a variety of household and intra-household behavioral outcomes. We find that SHS adoption increases evening study hours of both boys and girls; reduces fuel collection time of women; helps adopt b/w TVs which in turn helps promote women’s decision-making power and information sharing; and also reduces women’s and children’s morbidity from respiratory diseases by reducing kerosene consumption. SHS adoption also promotes household welfare by increasing per capita expenditure. Given the high cost of acquiring a unit plus the grant subsidy attached to each unit, we find nonetheless that the benefits accrued to households outweigh the cost of buying a solar unit. This means SHS is cost-effective for a rural household. Whether the social benefits generated through higher access to electricity via solar power also exceed the social cost (in terms of providing grant and subsidy support) of marketing such units is something worth exploring. Indeed, this issue is addressed in another paper that shows that the social benefits exceed social cost (Haque et al., 2013).

28
Note: The mobility index in an average of the percentage change in mobility to retail and recreation locations, transit stations, parks, and workplaces. The 7-day rolling average is shown. The change is calculated relative to a pre-pandemic baseline. Grey areas indicate when each round of the HFPS was fielded in the specified country. Indonesia had five rounds, but only the first, third, and fifth included information on employment and income. Source: Google Mobility Reports; HFPS

# 2. Work stoppages: Employment impacts of the pandemic in developing EAP countries

The HFPS provides insight into the employment impacts of the pandemic at varying points during 20202021 in each country. In particular, we look at employment impacts at the extensive margin – namely, work stoppages<sup>14</sup> – rather than at the intensive margin due to limitations in the data. The HFPS suggests that the initial impact of the pandemic on work stoppages was significant in most countries<sup>15</sup> but particularly in

> 14 As described in the previous section, work stoppages are defined as the share of respondents working pre-pandemic who are not working in the week preceding the survey. A worker may be not working even though she is still employed.

> 15 The initial impact of the pandemic on work stoppages may even be underestimated, given that most round one surveys occurred after the steep drop in mobility between March and May 2020 (see Figure 1).

10
Susained **lnaadon** - 2 The countries of the former Soviet Union have experienced sustained inflation in the period since independence. Although a burst of inflation was widely anticipated as a response to the "ruble overhang" buDt up in the final years of the Soviet Union, the subsequent price inflation has been more rapid and sustained than expected.

There are a number of causes for this sustained inflation: continuing budget deficits in these countries financed through the inflation tax, increases in prices of energy and other imported intermediate inputs, and more recently the establishment of inflationary expectations. In this paper I demonstrate that removal of the "ruble overhang is not an instantaneous outcome in a forwardlooking economy, but rather will generate a- sustained inflation independent of changes in these other factors. Sustained inflation is thus a predictable market outcome in response to an initial price liberalization. The reason lies in the interaction of private saving responses to inflation and the government's resulting altered reliance upon the inflation tax to finance its expenditures.

In the first section I present the history of the "ruble overhang" and subsequent ruble shortages in the former Soviet economies. I also illustrate the problem of sustained inflation in these economies through examination of historical data for a number of republics. In secion II I construct a simple dynamic model of private wealth accumulation and inflation. I demonstrate that the model has a steady state equilibrium, but exhibits saddlepath stability. Adjustment along the saddlepath from ar position of "ruble overhang" is characterized by an initial price jump followed by susained inflation. A numerical example is examined to illustrate the logic of the adjustment process. Section MII includes conclusions and possible extensions to the analysis. The logic of the adjustment process is examined as well to consider the possibility of an explosive dynamic.
such as access to market and services, infrastructure, and so forth. The 2001 _Population Census_ is used to obtain sub-district level variables measuring access to infrastructure. Finally, data on microfinance coverage at sub-district ( _thana_ ) level was obtained from the _Palli Karma Sahayak Foundation (PKSF_ ), the apex body for microfinance in Bangladesh.

# **II. Trends in standard of living measures and a profile of the poor**

Poverty in Bangladesh fell dramatically between 2000 and 2005 – the percentage of population with per capita consumption below the poverty line declined by 18 percent. The fall in poverty headcount rates was significantly more than population growth during 2000-2005 leading to a decline in the number of poor people by nearly 6 million. The levels and distribution of consumption among the poor improved as well, as evident from reductions in poverty gap and squared poverty gap measures by 30 and 37 percent respectively (World Bank 2008).

Household welfare is influenced by a range of characteristics which are typically correlated with consumption. A number of non-consumption indicators of welfare show significant improvements between 2000 and 2005, for the general population and the poor alike (Table 1).

[insert Table 1 here] Earlier work on poverty in Bangladesh shows that poverty and _quality of housing_ is closely correlated. For example, households who live in houses with straw roofs are typically extremely poor (Hossain, 1995). It is therefore significant that housing 4