rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3** : **Ratio of Actual to Fiscal Gap Formula DAU 2006 Allocations**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>1 205 409<br>Districts<br>Formula<br>Ratio DAU 06 Actual / DAU 06 Pure<br><!-- End of picture text --> Source: Authors’ Calculations. For presentation purposes, ratios of >6 are excluded.

Figure 3 presents the ratio of the actual DAU allocation to the presumed formula ideal.<sup>17</sup> When the ratio is 1 for a given district, the allocation is “ideal” and there is no mis-targeting by this benchmark. Those districts with a ratio of less than one are underresourced, whereas those with over one are relatively over-resourced. It is important to note that this measure refers to relative rather than absolute resourcing. The absolute transfer amounts received by districts ultimately depend on the overall vertical share (i.e., the total amount of fiscal resources transferred to localities). On the other hand, if the absolute total amount of money being distributed to districts is too low, individual districts will also be receiving too little even if the proxies for relative expenditure needs are appropriately used. Municipalities of Kota Dumai, Kota Surabaya and Kota Samarinda received 18, 20 and 34 times (omitted from the graph from presentational purposed) more than what they would have received if pure-formula based allocation

> 17 The districts that received zero DAU transfers are omitted from the graph since their ratios are equal to infinity (X / 0=∞)

16
will not occur without major policy incentives. A large number of governments have decided to increase solar energy development, using a range of fiscal, regulatory, market and other instruments<sup>16</sup> . In fact, the strong growth in solar energy markets, notably those for gridconnected solar PV and solar thermal water heating, has been driven by the sustained implementation of policy instruments in Europe, the United States and some developing countries to induce or require increased use of solar power.

This section briefly presents key characteristics of policy instruments that support solar energy for both electric and direct heating applications. A large number of policy instruments have been implemented to increase power supplies from solar PV and CSP. The key instruments we highlight here include feed-in-tariffs, investment tax credits, direct subsidies, favorable financing, mandatory access and purchase, renewable energy portfolio standards and public investment. Three rationales are commonly offered for utilizing these policies. One is to encourage the use of low-carbon technology in the absence of a more comprehensive policy for greenhouse gas mitigation, like a carbon tax. The disadvantage of this approach for greenhouse gas mitigation is that it does not create incentives for cost-effective mitigation choices. The second rationale is that expanded investments will ultimately help drive down the costs of those technologies through economies of scale and learning-by-doing. There is clear evidence that scaling-up has driven down unit costs for PV, though not yet to the point that it is cost-effective with conventional alternatives in most cases. CSP is still relatively a pioneer technology with only a few medium-scale investments and no larger-scale investments, though some are planned. It remains to be seen how scale economies and learning-by-doing will lower its costs. The third and most unambiguous rationale is that subsidization of small-scale, off-grid PV (and other renewable energy sources) to bring electricity to remote and poor areas lacking access is a powerful force for stimulating economic development.

> 16 A number of recent studies, such as ESMAP 2011a, 2011b and EPIA 2011 present in-depth analysis of various policy instruments designed to promote renewable energy, including solar, at the global level as well as for a particular country, such as India.

24
_Source:_ Original figure for this publication. _Note:_ 1 = fully disagree with the statement, 10 = fully agree with the statement

# End-Line Survey

## **End-Line Data Collection**

The end-line survey was in January of 2019, about seven months after the assessment. The survey firm once again tracked all the respondents for face-to-face interviews. The attrition of the sample was insignificant: only 1.7 percent, as the firm could not survey only 17 of the 1,000 respondents. The most common reason given for attrition was that the participants’ phones were switched off or they did not answer the calls (table 2.2). Four applicants were now abroad, and three declined to interview during the end-line survey. Of these 17 applicants, 10 were in the control group and 7 in the treatment group. The difference in attrition rates between control and treatment groups is not statistically significant.

**Table 2.2 Reasons for Attrition**

|**Reasons**|**Frequency**|**Percent**|
|---|---|---|
|Phone switched off or not answered|9|52.9%|
|Abroad|4|23.5%|
|Declined to interview|3|17.6%|
|Sick|1|5.9%|
|**Total**|17|100.0%|

_Source:_ Original table for this publication.

Contamination of the control group was also insignificant. In the end-line interview, the respondents answered whether they participated in the RPL assessments conducted by any organization. Their responses by treatment status are in table 2.3. Of the 408 respondents who had participated in the RPL assessment, only one was from the control group. All others were from the treatment group, who were eligible to participate in the RPL assessment. Therefore, the contamination of the control group was negligible.

**<u>Table 2.3 Actual Participation in RPL, by Treatment Category</u>**

||**Participated**|**Not participated**|**Total**|
|---|---|---|---|
|Treatment|407|86|493|
|Control|1|489|490|
|**Total **|408|575|983|

_Source:_ Original table for this publication.

On the other hand, a sizeable number, 86 (17.4 percent) of 493 applicants in the treatment group did not participate in the RPL assessment. Actual participation in the assessment was entirely voluntary, even if they are in the treatment group. Some seem to have changed their mind between the application and assessment. A probit analysis (data not shown) shows that nonparticipation of the treated does not seem to correlate with age, gender, years of education, or trade in any significant way. However, it is impossible to preclude the possibility that some unobservable characteristics affect the willingness for assessment participation. For instance, those who are less confident about their skills may be more likely to opt out of the assessment for fear of not passing the examination.

Given the low control group contamination and such possibilities of bias, the study focuses on the _intention to treat_ (ITT) analysis. The assignment to the treatment group, not the actual participation, serves as a binary treatment status. One may argue that this might underestimate the impact size due to the treatment 11
**Table 9.** Heterogeneous treatment effects - STI prevalence: Risk preferences

|||Panel A: All||Panel B: R|isk-loving|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Any lottery×Risk-loving|-0.021<sup>*</sup>|-0.019|-0.017|||
||(0.012)|(0.012)|(0.013)|||
|Risk-loving|0.031|0.030<sup>**</sup>|0.028<sup>**</sup>|||
||(0.011)|(0.012)|(0.012)|||
|Any lottery|-0.020<sup>*</sup>|-0.021<sup>**</sup>|0.005|-0.036<sup>***</sup>|-0.037<sup>***</sup>|
||(0.010)|(0.010)|(0.053)|(0.012)|(0.013)|
|Mean control group|0.033|0.033|0.033|0.051|0.051|
|Baseline controls|No|Yes|Yes|No|Yes|
|Full set of control×treatment covariates|No|No|Yes|-|-|
|Observations|1693|1693|1693|642|642|

Note: Sample of individuals aged 18-32 at baseline who responded to the hypothetical risk aversion question. Panel A: Full sample. Panel B: Risk-loving individuals. See notes to table 5 for the definition of the dependent variable. Risk-loving is an indicator variable taking the value 1 for respondents who, at baseline, prefered a fixed amount of money equal or above 250 maloti instead of a lottery with 50% chance of winning 500 maloti, and 0 for respondents who prefered a fixed amount of money below 250 maloti instead of a lottery with 50% chance of winning 500 maloti. Any lottery is high and low lottery combined. The control variables are STI status at baseline and all household characteristics listed in panel B, table 2. All regressions include village fixed effects. Robust standard errors in parentheses, clustered at the village level. *** 1% , ** 5% , * 10% significance.
**Table 6: Antitrust, Concentration, and Informality.** The regression left hand side variable is the estimated change in an outcome measured by the SDID estimator, where synthetic control weights match pretreatment trends in the log of sales for comparability with previous results. All specifications use a random effects meta regression. Firms with multiple establishments are treated as a single firm. The Herfindahl-Hirschman Index is the sum of firms’ squared percentage market shares, and ranges from 0 and 10,000. The CR4 ratio is the market share of the four largest firms in a market, expressed as a percentage from 0 to 100. The CR1 ratio is the market share of the largest firm in a market, also expressed as a percentage. The legal informal share of employment is the percentage of workers in establishments with workers but no recorded remuneration, indicating workers are compensated out of profits, as in the case of a family business. The social security share of wage bill is employer contributions to social security as a percentage of the wage bill. Levy Algazi (2010) proposed definitions of legal and illegal informal employment that can be measured in the Economic Census. Legal informal employment is unremunerated labor, illegal informal employment is remunerated labor without employer contributions to social security. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Change in|The log of<br>the number of<br>frms|Herfndahl-<br>Hirschman<br>Index|CR4: Sales share<br>of four<br>largest frms|CR1: Sales share<br>of largest<br>frm|Legal informal<br>share of<br>employment|Social security<br>share of<br>wage bill|
|Financial sanction (=1)|0.056|253.237|-0.121|0.816|-2.405**|1.072|
||(0.130)|(316.398)|(3.351)|(3.356)|(1.045)|(0.908)|
|Constant|-0.140*|-510.553**|-0.038|-1.535|1.445**|-1.504***|
||(0.083)|(198.522)|(2.205)|(2.092)|(0.643)|(0.551)|
|_γ_<sup>2</sup>|0.197|1.686e+06|135.9|147.3|18.76|12.98|
|Observations|90|90|90|90|90|90|
|Meta analysis framework|RE|RE|RE|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|SDID|SDID|SDID|
Klapper (2012). In the regression analysis, we condition on relevant control variables including GNI per capita, growth in aggregate output, the population size, inflation, occurrence of a banking crisis, existence of explicit deposit insurance, a banking sector stability indicator, the banking sector liquidity position, banking sector concentration, the deposits to GDP ratio, the loan to deposit ratio, and capital account openness. We address the problem of potential endogeneity by the timing of the explanatory variables before or by 2008. When using the measure of financial inclusion in deposits of Demirguc-Kunt and Klapper (2012), we estimate the regression by the general method of moments (GMM), instrumenting the measure by the deposit access proxy of Honohan (2008). Moreover, we use _robust_ regressions to gauge the impact of outliers on the estimation results.

**Figure 1: Broader Access to Bank Deposits Can Aid Financial Stability—Especially in Middle-Income Countries**

<!-- Start of picture text --> 20<br>SYR GAB<br>10<br>0<br>LSO PHL<br>y = -0.40*x<br>-10 Marginal R2 = 0.12<br>GEO<br>BWA<br>MDA PRY<br>-20<br>HND<br>LBY URY<br>UKR<br>AZE<br>-30 NGA SLV<br>ZMB BLR PER DOM BOL KAZ<br>IND<br>NIC ARM MNG JOR BGR<br>GHA<br>-40 MKD ROM<br>YEM BIH AGO NAM DZA ALB MAR BRA TUR LKA<br>PAK COG GTM ZAF LTU<br>-50 MUS<br>CMR IDN CHN<br>SWZ<br>TUN PAN<br>MEX EGY THA MYS<br>-60<br>ARG<br>GUY<br>-70<br>RUS<br>-80<br>0 10 20 30 40 50 60 70 80<br>x =access to bank deposits, 2005<br>y =maximum decline in bank deposits, 2007 - 2010<br><!-- End of picture text --> Source: Authors’ calculation based on data from IMF, Honohan (2008), Laeven and Valencia (2012), FinStats, Global Financial Development Database and WDI.

Note: The figure depicts the vulnerability of bank deposit base of 59 middle-income countries conditional on per capita income, growth in aggregate output, bank z-score, loan to deposit ratio, occurrence of banking crisis, and implemented explicit deposit insurance. The Honohan (2008) composite measure of access to financial services is used as a proxy for access to bank deposits.

3
<!-- Start of picture text --> 4600 _—\__<br>z=_x1400 SF 4<br>=‘a0 Ligh<br>S 1200 \-<br>o ==<br>Fs SA<br>g 1100 a<br>5 1000 soe5s<br>©<br>BOD : y= 1.896xRe= 0,791+ 599.4<br>100 200 300 400 500<br>Exports per cap (const MK 94)<br><!-- End of picture text --> <!-- Start of picture text --> 4é000 _|_a_OS<br>1400<br>1200<br>1000<br>400 Ss).<br>gogo) —————________—<br>400<br>BREESLLELSSSSRRBEBESOAIHBARIBAIBHSAARR<br>GDP pe (constant 4 MK)<br>—_ Merch Exports oc (constant $4 MK)<br><!-- End of picture text -->
**<u>Appendix table 2: Plot Characteristics for Owner-cum-Tenants with Partner Information Only</u>**

||||**Type of arran**|**gement**||
|---|---|---|---|---|---|
||**Own**<br>**plots**|**Leased**<br>**plots**|**Output**<br>**sharing**|**Out-& input**<br>**sharing**|**Fixed**<br>**Rent**|
|**Relation with partner**||||||
|TP relative||0.72|0.73|0.76|0.38|
|TP in-law||0.13|0.13|0.11|0.15|
|TP friend or neighbor||0.10|0.10|0.08|0.23|
|TP unrelated||0.18|0.17|0.16|0.38|
|Female rental partner||0.49|0.52|0.39|0.31|
|<br>**Output and input use:**||||||
|Crop output/ha (birr)|3413.91|3090.97|3115.83|3100.04|2522.06|
|Plot size (ha)|0.28|0.33|0.32|0.39|0.37|
|Used fertilizer|0.41|0.43|0.39|0.64|0.64|
|Fertilized used per ha (kg)|58.67|53.20|49.50|74.81|74.60|
|<br>Manure used|0.23|0.07|0.07|0.07|0.03|
|Manure used per ha (kg)|313.97|80.64|85.62|70.57|6.29|
|Used pesticides|0.08|0.07|0.07|0.13|0.03|
|Pesticides used per ha (kg)|0.33|0.19|0.20|0.12|0.02|
|Pair of oxen days per ha|26.08|20.97|20.65|21.22|25.34|
|<br>Male family labor/ha (days)|123.52|90.22|92.94|75.50|75.19|
|Female family labor/ha (days)|61.22|44.62|46.28|36.60|35.17|
|<br>Total family labor/ha (days)|184.74|134.84|139.22|112.11|110.37|
|Used hired labor|0.84|0.83|0.83|0.83|0.72|
|Hired labor/ha (days)|11.93|5.63|5.92|4.31|3.74|
|**Plot characteristics:**||||||
|Years of possession|19.05|4.94|5.06|4.67|3.21|
|Good soil quality|0.45|0.46|0.48|0.44|0.33|
|Medium soil quality|0.37|0.37|0.37|0.38|0.41|
|Flat land|0.72|0.71|0.72|0.64|0.74|
|Gently sloped land|0.24|0.24|0.24|0.25|0.26|
|Irrigated land|0.06|0.05|0.05|0.02|0.00|
|**Crop choice:**||||||
|Teff|0.29|0.35|0.34|0.44|0.38|
|Wheat/barely|0.21|0.22|0.22|0.24|0.28|
|Sorghum/maize|0.33|0.25|0.25|0.21|0.28|
|Pulses|0.12|0.14|0.15|0.05|0.05|
|Oilseeds|0.05|0.04|0.04|0.05|0.00|
|Number of observations|2630|1135|955|135|39|

_Source:_ Own computation from AU/WB/UG land tenure survey. _Note:_ Only plots with cereals, pulses and oilseeds included.

27
We use this method to calculate net inflows to mutual funds. For pension funds, we compute this measure by aggregating daily data on net inflows into each fund, directly collected by the Chilean Superintendency of Pensions.<sup>25</sup> The results are displayed in Figure 9. Panel A shows the cumulative distribution of net inflows _k_<sup>_I_</sup> _t_<sup>relative to fixed-income assets for Chilean mutual funds and PFAs. As</sup> a benchmark, we also report those of US mutual funds. Negative (positive) values are outflows (inflows). The figure shows that Chilean mutual funds face significant outflows. For example, the historical probability of experiencing a net outflow of 3% of the portfolio or more is 33%. To complement this evidence, Panel B shows the fraction of fixed-term assets held in short-term assets (up to one and three months) and the probability of outflows of that magnitude.<sup>26</sup> Chilean mutual funds hold 9.3% of their fixed-term assets in instruments with maturity of less than one month, and the probability of an outflow of that magnitude occurring is almost 22%. US multi-sector bond funds are subject to fewer outflows. For example, the historical probability of experiencing a net outflow of 3% of the portfolio or more is 9% (instead of 33%). Therefore, the shorttermism of Chilean mutual funds might be partly explained by the relatively large outflows they face.

Chilean pension funds, on the contrary, are not exposed to significant outflows. The distribution of net inflows of Chilean PFAs is significantly tilted to the right. So redemption risk does not seem to be an important factor explaining pension funds’ short-

> 25 Though not reported, we also compute the monthly inflows using the values and returns derived from our monthly database and obtain qualitatively similar results.

> 26 The values reported correspond to the probability that would be required to have a value at risk (VAR) equal to the fraction of fixed-term assets held by funds at maturities of up to 30 and 90 days. For US funds, we do not have information on the maturity structure at less than three years, so we use the extreme assumption that within the zero to three year interval, the maturity structure of US funds is proportional to that of Chilean mutual funds.

31
15 devaluation came in March 1983, and with it a reshuffling of the cabinet and a fundamental shift in economic policies. Citing _Le Point,_ Sachs and Wyplosz (1986) say that when Mitterrand assigned new tasks in 1983 to Mauroy's new government, he asked him to 'bring down inflation to a rate compatible with our competitors. **.** restore within two years our external trade balance...and respect the financial equilibrium of Social Security while maintaining the public budget deficit within its current levels'. OECD figures confirm the restrictive stance of fiscal policy after 1983: the structural deficit was reduced 0.5 per cent of GDP. This was achieved by freezing budget expenses and by raising taxes. **'3** Monetary policy also remained contractionary although the squeeze, according to Sachs and Wyplosz, came through high interest rates instead of through credit ceilings. The role of the exchange rate is more difficult to assess. Unlike the Danish and Irish plans, there was to our knowledge no official announcement that the exchange rate of the French franc versus the German mark would henceforth be fixed.

# **2.3. Outcome of the Three Programs**

The reason for including Denmark, France and Ireland in our sample is not only that they were the three most clear cut cases of use of the exchange rate as nominal anchor. We were also motivated by the striking differences that we observed in the behavior of real variables among them and when we contrast them with the Latin American cases.

13 The fiscal package included a forced savings plan equivalent to an extra 10 per cent on income tax, a 1 per cent levy on taxable income and rises in the tax on alcohol and tobacco and higher prices for gas, telephone, electricity and rail transport, as well as higher wealth taxes. The main cuts in expenditure were earmarked to come from a reduction in subsidies to state owned energy and transport firms.
Policy Research Working Paper 8825

# **Abstract**

This paper characterizes the trade-off between the income gains and the inequality costs of trade using survey data for 54 developing countries. Tariff data on agricultural and manufacturing goods are combined with household survey data on detailed income and expenditure patterns to estimate the first-order effects of the elimination of import tariffs on household welfare. The paper assesses how these welfare effects vary across the distribution by estimating impacts on the consumption of traded goods, wage income, farm and non-farm family enterprise income, and government transfers. For each country, the income gains and the inequality costs of trade liberalization are quantified and the trade-offs between them are assessed using an Atkinson social welfare index. The analysis finds average income gains from import tariff liberalization in 45 countries and average income losses in nine countries. Across countries in the sample, the gains from trade are 1.9 percent of real household expenditure on average. We find overwhelming evidence of a trade-off between the income gains (losses) and the inequality costs (gains), which arise because trade tends to exacerbate income inequality: 45 countries face a trade-off, while only nine do not. The income gains typically more than offset the increase in inequality. In the majority of developing countries, the prevailing tariff structure thus induces sizable welfare losses.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at brijkers@worldbank.org, eartuc@worldbank.org, and guido.porto@depeco.unlp.edu.ar.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table <u>8 Dynamic effects of Bangladesh flood on schooling progression: Pre-flood assets and ex-post public assistance</u>

|Dependent: Change in grad<br>|es from 1998 to 2004<br>|||
|---|---|---|---|
|Flood variable:|Depth Days Repair cost Outhome|Depth Days|Repair cost Outhome|
|Flood<br>|-0.2347 -0.0035 -0.00002 -0.0318<br>|-0.4001 -0.0323<br>|-0.0005 0.0209<br>|
||(2.170) (0.660) (0.240) (5.670)|(4.440) (4.480)|(2.760) (0.650)|
|Flood * GR<br>|0.00005 -7.05E-06 -4.24E-08 0.00003<br>|0.0001 9.95E-06<br>|2.44E-07 0.00003<br>|
||(0.370) (0.930) (0.230) (2.990)|(1.000) (1.160)|(0.680) (1.390)|
|Flood * GFV<br>|0.0001 8.04E-06 1.13E-07 0.00002<br>|0.0002 7.98E-06<br>|4.46E-08 0.00002<br>|
||(1.800) (2.150) (1.540) (4.090)|(1.780) (2.130)|(0.710) (4.370)|
|Flood * NGO<br>|0.0001 -2.72E-06 -2.50E-07 0.00002<br>|0.0002 -1.78E-06<br>|-5.98E-08 0.00006<br>|
||(1.330) (0.440) (1.450) (2.830)|(1.160) (0.210)|(0.280) (3.090)|
|Flood * asset<br>|1.77E-06 4.34E-08 -5.64E-10 -2.33E-07<br>|||
||(3.550) (1.970) (1.100) (1.510)|||
|Flood * max educ<br>|<br>|0.0284 0.0026<br>|0.00004 0.0007<br>|
|||(2.480) (4.310)|(2.520) (0.330)|
|Flood * land<br>|<br>|0.0006 0.00001<br>|-2.55E-07 -0.00008<br>|
|||(1.950) (0.890)|(1.070) (0.810)|
|Flood * household size||0.015 5 0.0015|0.00003 -0.0121|
|||(0.730) (1.780)|(1.040) (2.630)|
|Flood * livestock<br>|<br>|-1.50E-06 3.46E-0<br>|7 1.61E-08 -6.93E-07<br>|
|||(0.490) (1.040)|(1.320) (0.590)|
|Flood * female<br>|0.0831 0.0067 0.0001 0.0173<br>|0.0320 0.0089<br>|0.0001 0.0165<br>|
||(1.310) (1.400) (3.160) (2.320)|(0.560) (1.910)|(1.970) (2.790)|
|Union FE|yes yes yes yes|yes yes|yes yes|
|Number of observations|489 489 489 489|458 458|458 458|
|Number of unions<br>|21 21 21 21<br>|21 21<br>|21 21<br>|
|R squared (within)|0.0675 0.0618 0.0533 0.0865|0.1084 0.1191|0.0840 0.1069|

Numbers in parentheses are absolute t values using robust standard errors with union clusters. Age and female dummies are included in all specification. GR, GFV and NGO are the sum of transfers received in 1998-1999 from respective sources.

30