rafmacalaba/datause-encoder-data huggingface dataset:
The evolution of unpaid family work has been strongly gender biased. The increase in the share of unpaid family work in employment was due to the increase of women’s employment in this type of work. The share of working women in this type of employment went from 13.8 percent to 22.2 percent of employment between 2007 and 2009, while the share of men’s employment remained around 11.8 percent. Women shifted from own-account work into unpaid family work and to some extent into wage employment and to a small extent into being employers (Table 14).<sup>4</sup>

**Table 14: Structure of employment for working age population (15+) in 2007 and 2009 (%)**

|||**2007**|||**2009**||
|---|---|---|---|---|---|---|
||**All**|**Male**|**Female**|**All**|**Male**|**Female**|
|Employer|3.2|3.8|2.7|4.8|6.3|3.2|
|Self-account worker|53.5|48.55|58.6|44.7|42.6|46.9|
|Unpaid family worker|12.9|11.8|13.95|16.8|11.8|22.2|
|Wage worker|30.0|35.4|25.45|33.4|38.9|27.5|
|Member of cooperative|0.1|0.2|0.1|0.1|0.2|0.1|
|Others|0.2|0.25|0.2|0.2|0.3|0.1|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Source: (LFS, 2007, 2009)_ This gender bias is also translated into the finding that a smaller proportion of women than men are employers (Table 14). Analysis using the VHLSS (Rodgers et al, 2010) confirms and throws light on these results. They suggest that although the nonagricultural household enterprises sector is buoyant and mostly headed by women, female headed businesses tend to be smaller in scale, with fewer employees, smaller revenues streams, lower likelihood of being licensed and higher likelihood of operating within marketplace rather than established shops.

Moreover, a large share of working youth is in unpaid employment. The great majority of young people who are working are mostly working as unpaid family workers or wage workers. Between 2007 and 2009, the proportion of youth working as wage workers has increased while the proportion of unpaid family workers has decreased (Table 15). The same gender differences exist as in the 15+ population, but they are less marked; the structures of employment of young men and women are similar.

> 4 Nguyễn et al (2010) suggest that the shift between own-account work and unpaid family work may be due to misallocation of women between the two, very similar, categories.

17
B2C level in the year 2017, distinguishing by region, as well as the corresponding _t_ through 2047. Applying these to the two cases under consideration, we obtain the forecast for B2C Internet use for the period 2018‐2047. We transform such forecast estimates back to 1‐to‐7 values, to maintain consistency with the original WEF data.

As Figure 8 shows, B2C adoption converges gradually to the maximum value of 7 in Case 1, as expected. Although MENA and SSA countries<sup>14</sup> lag the rest of the world in the extent to which B2C is adopted, they eventually converge to the maximum level. Indeed, for the average MENA country, B2C exceeds 6 by the year 2023 and 6.9 by 2035. In Case 2, in which adoption never reaches its full potential, B2C internet usage converges to 5.8.

<!-- Start of picture text --> Figure 8<br>B2C Internet Adoption (1‐7)<br>7.0<br>6.5<br>6.0<br>5.5<br>5.0<br>4.5<br>4.0<br>3.5<br>MNA (hi) MNA (low) SSA (hi)<br>SSA (low) World (hi) World (low)<br>Source: Author’s calculation.<br><!-- End of picture text --> In turn, we use the above B2C forecasts to project how the average number of travelers per country of origin —that is, not the total number of visitors— will increase as the use of digital platforms grows in the

> 14 As stated in the introduction, we pay particular attention to MENA and SSA countries, as the two regions have only recently been catching up with the rest of the world in terms of the use of digital tools.

Page **22** of **34**
WPS3899

**_<u>________________________________________________________</u>_**

# **The Data Chase:**

# **What's Out There on Trade Costs and Nontariff Barriers?**

Shweta Bagai and John S. Wilson<sup>1</sup>

# The World Bank

## World Bank Policy Research Working Paper 3899, April 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 John S. Wilson, Lead Economist, Development Research Group, World Bank and Shweta Bagai, Confederation of Indian Industry.  Comments of colleagues at the Bank on this document are gratefully acknowledged.  This report is part of broader project on Trade Costs and Facilitation supported through a Trust Fund of the Department for International Development of the United Kingdom.  The views expressed here do not necessarily represent those of DFID.

i ____________________________________________________________________________________________________________
# **Table 4: Distribution of Benefits in Indonesia**

|**Name of case(s)**|**Description and demographics of those**<br>**affected**|**N**<br>**affected**|**% under-**<br>**privileged**|**N**<br>**underprivil**<br>**eged**|
|---|---|---|---|---|
|026/PUU-III/2006, 011/PUU-III/2005, and<br>012/PUU-III/2005: Judicial Review of the 2005 and<br>2006 Budget Laws and the National Education<br>System Law|Increased government funding for<br>education|750,000|36.3%%|272,250|
|28/Pdt.G/2003/PN.JKT.pusat: Indonesian Ctizens v.<br>the Republic of Indonesia|Accusations of neglect of migrant workers<br>deported from Malaysia|25000|40%|10000|
|007/PUU-III/2005:<br>Judicial Review of the National Social Security<br>System Law|Allowed local governments to continue<br>providing social security schemes, impact<br>to formal sector employees making below<br>minimum wage|10000|30%|3000|
|35/PDT.G/1994/PN.JKT.PST:<br>People of Keomas vs. Director of PLN and the<br>Republic of Indonesia|Health complaints due to high tension<br>power wires constructed in residential areas|5000|9.4%|470|
|406.PdtG/2004/PN.Jaksel:<br>The People of Buyat v. the Republic of Indonesia<br>Government|Pollution of Buyat Bay—government<br>promised free medication and relocation of<br>families|200|70.7%|140|
|13/Pdt.G/2005/Pn.Cbd:<br>Opik v. Republic of Indonesia Government|Negligence in production and distribution<br>of polio vaccine to children in Cidahu|100|28%|28|
|**TOTAL**||790,300|36.17%|285,888|

28
use within-country variation in ...scal institutions. This approach, in e¤ect, eliminates the unobservable country speci...c ...xed e¤ects that may in‡uence budget de...cits. The problem of omitted variables is thus alleviated; however it is not eliminated.<sup>24;25</sup> Finally, if some of the variables in the analysis are not measured accurately, there is the potential for errors-in-variables bias, which usually dampens the e¤ect of interest. Although in our empirical model the primary explanatory variable, MTEF status, can be observed with a reasonably high degree of precision, there is still scope for measurement error in the other explanatory variables.

Our empirical strategy exploits within-country variation in ...scal institutions. Annual data allow us to account for the possibility that observed ...scal performance in a given year may not represent long-run equilibrium values, because of incomplete adjustment in other variables. For example, as revenues cannot be perfectly anticipated budget balance in a given year ‡uctuates around the equilibrium balance level. To allow for the possibility of partial adjustment, we use a dynamic speci...cation with a lagged dependent variable: where yi;t is a measure of ...scal performance, L is the number of lags of the dependent variable, MTFFi;t; MTBFi;t; MTPFi;t are indicators of the three MTEF phases, xi;t is a vector of covariates, and "i;t is an error term that contains country and year ...xed e¤ects: with the idiosyncratic error �i;t assumed to be mean zero.

While this dynamic panel formulation allows for a richer model of ...scal adjustment, the presence of a lagged dependent variable introduces new sources of endogeneity. First, without controlling for the ...xed e¤ects, the model in equation (5) has a built-in positive bias in the ...rst lag of the dependent variable.<sup>26</sup> Second, the di¤erenced version of the equation eliminates this bias, but has a built-in negative bias due to the fact that yi;t<sup>�=</sup> �yi;t is negatively correlated with yi;t<sup>�</sup> �1<sup>= �yi;t�1:Thusanunbiasedestimateofthelagged</sup>

> 24Most studies have not been able to use this method because either budget institutions do not change much over time or because changes are di¢ cult to measure. When it has been implemented with U.S. statelevel data (e.g., Knight and Levinson 2000) the results are typically di¤erent, indicating that the problem of omitted variables is relevant.

> 25Additional omitted variables could include political institutions. Evidence from Europe shows that institutional design responds to political factors and events (Hallerberg, Rolf, and von Hagen 2009).

> 26Nickell (1981) has shown that this "dynamic panel bias" disappears only when T approaches in...nity.

14
**Figure 3: Effective tax rates of a hypothetical presumptive tax (2.5% of turnover and exemption for businesses earning less than 50,000 birr), by turnover quartile**

<!-- Start of picture text --> Panel 1: hypothetical, period 1 Panel 2: hypothetical, period 2<br>14.0 14.0<br>12.0 12.0<br>10.0 10.0<br>8.0 8.0<br>6.0 6.0<br>4.0 4.0<br>2.5 2.5<br>1.8 1.4<br>2.0 2.0<br>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.09 0.02<br>0.0 0.0<br>1 2 3 4 ** 1 2 3*** 4<br>Turnover quartile Turnover quartile<br>Male-owned Female-owned Male-owned Female-owned<br><!-- End of picture text --> Source: Author’s calculations using ESS 2018/2019 and 2021/2022.

Notes: The figure shows the effective tax rates (tax as a percentage of turnover) by quartile of turnover for the hypothetical presumptive tax. The exemption threshold of 50,000 birr is placed within the fourth quartile so some businesses would be exempt even in this quartile. The difference in means between male- and female-owned enterprises is significant at ** p<0.05, *** <0.01.

The hypothetical presumptive tax, with an exemption threshold and a flat percentage on turnover in taxes, has several advantages over the current system. First, it is less complicated than the current system which determines tax liabilities based on 1,881 activity-bands. A simpler system would be more transparent and easier for tax assessors and taxpayers to understand. Second, it would eliminate the policy notches that exist within each turnover band in the current system where tax liabilities are lump sum payments according to the turnover bands and activity. Third, the exemption for microbusinesses could facilitate the formalization of informal businesses and bring them into the tax system as the businesses grow. Fourth, it would reduce the activity-specific horizontal inequity the current system has (see Appendix 1 for examples of the tax rates). Fifth, the administration costs of tax collection could be lower because the tax authorities can focus on higher revenue firms instead of collecting taxes door-to-door from micro-subsistence businesses.

The possible disadvantages of the hypothetical tax system include: 1) it assumes a fixed profit margin for all firms regardless of sector and profitability; 2) if women operate businesses with lower profit margins than men, a flat tax rate could disproportionately affect women (among those who earn above the tax-free threshold); and 3) with a tax increase for higher revenue businesses, some may choose to stay just below the exemption threshold to avoid paying tax, although it is not clear whether this would occur in Ethiopia. For example, there is lack of evidence of bunching of formal incorporated firms around the VAT threshold 20
- 21 -

# **IV. Consequences of the Liberalization of the Capital Account and the Financial Sector**

The liberalization of the capital account and the financial sector resulted in rapid build-up of vulnerability, a vulnerability with both macro and micro manifestations. On the macro side, the liberalization program resulted in a surge in private capital inilows and rapid credit growth. The increased foreign borrowing and rapid credit growth resulted in high leverage at the economy wide level as well as an asset price bubble.33 In turn, this led to a rapid increase in foreign exchange exposure and a shortening **cif** the maturity structure, rendering the economy vulnerable to reversals in capital inflows and downturns in economic activity. One micro manifestation of the economy wide borrowing binge is the rapid build-up of leverage and the increased foreign exchange exposure of the corporate sector. Similarly, as a result of the lending boom and coupled with the practice of collateral based lending, banks and finance companies became more vulnerable to economic shocks in the 1990s by: (i) lending excessively to sectors or firms whose debt service capacity was particularly susceptible to shocks; and (ii) reducing; their own capacity to absorb negative shocks, especially by exacerbating currency and maturity mismatches, by mispricing loans, and by underprovisioning for future potential losses. This build-up of vulnerability is analyzed in greater detail below.

## **1. Surge in Capital Inflows, Increased Reliance on Foreign Capital, and the Shortening of the Maturity Structure**

**_Surge in Capital Inflows._** Together with Malaysia, Thailand is one of the cotmtries that received the largest capital inflows in the East Asia region, indeed in the world, relative to GDP. Between 1988-96, according to data from the Bank of Thailand, Thailand received a staggering cumulative amount of US$ 100.3 billion, about 55 percent of 1996 GDP, or 9.4 percent of GDP on average p.a. (excluding errors and omissions). As can be seen from Figure 7, private capital flows to Thailand surged in 1988, when there is a clear structural break in the data. Between 1987 and 1990, inflows increased to some US$1 1.1 billion, where they stabilized until 1993. In 1994 and especially 1995, inflows surged once again, surpassing US$21 billion in 1995, but declining sharply in 1996. As a ratio to GDP the story is somewhat different (see Figure 2 below).

> 33 At the microlevel, rapid credit growth strained financial institutions credit assessments' and monitoring capacity. See Section IV2b.
- 9 -

the other hand, the cumulative probability distribution of joint bids is highly concentrated on about one.<sup>6</sup> Figure 3 reflects different presentations of the relationship between normalized bids and joint bidding practices.

<!-- Start of picture text --> Figure 2. Cumulative Probability Distribution Function of Normalized Bids<br>(Roads) (Water) (Electricity)<br>1.0 1.0<br>1.0<br>0.8 0.8<br>0.8<br>0.6 0.6<br>0.6<br>0.4 0.4 0.4<br>0.2 Solo bid 0.2 Solo bid 0.2 Solo bid<br>Joing bid Joing bid Joing bid<br>0.0 0.0 0.0<br>0 0.5 1 1.5 2 0 0.5 1 1.5 2 0 0.5 1 1.5 2<br>Bid / Engineering cost estimate Bid / Engineering cost estimate Bid / Engineering cost estimate<br>Source: Author’s calculation.<br>Figure 3. Probability Distribution of Normalized Bid by Solo and Joint Bid<br>(Roads; solo bids) (Water; solo bids) (Electricity; solo bids)<br>0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3<br>Bid / Engineering cost estiamte Bid / Engineering cost estiamte Bid / Engineering cost estiamte<br>(Roads; joint bids) (Water; joint bids) (Electricity; joint bids)<br>0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3<br>Bid / Engineering cost estiamte Bid / Engineering cost estiamte Bid / Engineering cost estiamte<br>Source: Author’s calculation.<br>Cumulative probability Cumulative probability Cumulative probability<br>.3 .3 .3<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>Fraction Fraction Fraction<br>.1 .1 .1<br>.05 .05 .05<br>0 0 0<br>.3 .3 .3<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>Fraction Fraction Fraction<br>.1 .1 .1<br>.05 .05 .05<br>0 0 0<br><!-- End of picture text --> With relation to contract awards, joint bidding might increase the average probability of winning a contract in the water and electricity projects. Figure 4 reflects the statistics of joint bidding practices—but for winning and losing bidders separately. In the water sector 33 percent of winners are joint bidders; this is much higher than the share of joint bidders among 6 The figure discards observations if normalized bids are above two.
# Policy Research Working Paper Series

|TlUe|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1411 Income Inequallty, Welfare, and<br>Poverty: An Illustration Using<br>Ukrainian Data|Nanak Kakwani|January 1995|G. Evans<br>85783|
|WPi1 412 Foreign Technology<br>Imports and<br>Economic Growth In Developing<br>Countries|Xiaomlng<br>Zhang<br>Heng-fu Zou|Januaty 1995|C. Jones<br>37754|
|WPS1413 Endogenous<br>Distortons in Product<br>and Labor Markets|Martin Rama<br>Guido Tabelilni|January 1995|S. Fallon<br>38009|
|WPS1414 The World Bank and Legal Technical<br>Assistance: Initial Lessons|The Wold Bank<br>Legal Department|January 1995|K. Mathemovh<br>82782|
- 10 -

In a very simplified framework, where the Central Bank assets are only net foreign assets and the credit to government, the definition of net worth yields: NW - E(NFA) + Cg - M (4) where M is the monetary base.

Therefore, (N W) - E(NFA) + E(NFA) + Cg - M (5) Equation (5), which is obtained from the balance sheet of the Central Bank, is the counterpart to equation (1), which was obtained from the profitand-loss account.

To incorporate the Central Bank's accounts into equation (3), the change in the net worth of the Bank must be subtracted from both the sources of financing and from the deficit.

Hence, we subtract equation (1) from the left-hand side of equation (3) and equation (5) from the right-hand side: G - T + nB + iEB - iE(NFA) - E(NFA) = - E(NFA) - E(NFA) + Cg - - Cg + A + B + EB (6) Collecting and rearranging terms we finally arrive at:

* * G - T+nB +iE(B - NFA)H-M+ B+ E(B - NFA) (7)
Table 4. EU and CEE countries: Main Banking<sup>Prudential Regulations</sup>

|EU or country|Maximum single<br>exposure|Connected<br>exposure limit|Aggregate large<br>exposure limit|Open foreign<br>exchange<br>exposure limit<br>|Investment in<br>enterprises limit<br>|Capital adequacy<br>|
|---|---|---|---|---|---|---|
|EU|25% of capital|20% of capital for<br>exposures to parent<br>company and<br>subsidiaries|800% of capital|No standard|15% of capital in<br>single firm; 60% for<br>firms in aggregate|Minimum of 8% of<br>risk-weighted<br>exposures<br>|
|Czech Republic|25% of capital to<br>nonbanks; 125% of<br>capital for banks in<br>country or OECD|20% of capital|800% of capital|15% of capital|20% of capital|Minimum of 8% of<br>risk-weighted<br>exposures|
|Hungary|25% of capital to<br>one party or group<br>of related parties|15% of adjusted<br>capital|800% of adjusted<br>capital|Net foreign<br>exchange liability<br>limited to 30% of<br>capital|15% of adjusted<br>capital for single<br>nonbank enterprise<br>(except financial<br>institutions); 100%<br>for all enterprises<br>combined|Minimum of 8% of<br>risk-weighted<br>exposures|
|Slovak Republic|25% of capital for<br>nonbanks; 80% for<br>banks|25% of capital|800% of capital|Total net of foreign<br>exchange liability of<br>25% of capital; 7-<br>10% limits on<br>individual<br>|25% of capital for<br>non-banks|Minimum of 8% of<br>risk-weighted<br>exposures|
|||||currencies|||
|Poland|10% of capital for a<br>single entity and<br>15% for a<br>connected group|15% of capital|Informally expected<br>not to exceed 800%<br>of capital|Aggregate oneway<br>exposure of 40% of<br>capital; net<br>exposure of 30% of<br>capital; maximum<br>open exposure in<br>one currency of<br>15% capital|25% of capital in<br>single group of non-<br>financial entitites,<br>but 50% with<br>approval of<br>supervisor|Minimum of 8% of<br>risk-weighted<br>exposures; higher<br>for new banks|
|Slovenia|25% of capital; an<br>exposure of 10%<br>requires approval by<br>Supervisory<br>Council|5% of capital, and<br>exposures require<br>consent of all<br>members of<br>Supervisory<br>Council|800% of capital|As prescribed by<br>supervisor|100% of capital for<br>land, buildings,<br>business equity, and<br>shares of non-<br>financial<br>organizations|Minimum of 8% of<br>risk-weighted<br>exposures|

_Source:_ EU Banking Directives; national central banks.
# Information is Power: Experimental Evidence on the Long-Run Impact of Community Based Monitoring<sup>*∗</sup>

Martina Björkman Nyqvist<sup>†</sup> Damien de Walque<sup>‡</sup> Jakob Svensson<sup>§</sup> August 2014 _Keywords:_ Community Driven Development, Health, Information, Participation JEL classification: I11, I15, O15

* We are deeply indebted to Frances Nsonzi for her contributions at all stages of the project and acknowledge the important contributions of R. Reinikka, G. Kajubi, A. Ojoo, A. Wasswa, J. Kanyesigye, P. Okwero, R. Chase, C. Winter, I. Njosa, M. Bitekerezo, and the field and data staff with whom we have worked over the years. We thank Andrea Guariso for excellent research assistance. We thank the Uganda Ministry of Health, the World Bank's Country Office in Uganda, and the Social Development Department, the World Bank, for their cooperation. We are grateful for comments by Adam Wagstaff and seminar and conference participants at Dartmouth, University of Michigan, Monash University, the World Bank, DIAL conference in Paris, and the NEUDC conference at Harvard. Financial support from the BNPP, the World Bank Research Committee, the Gender Action Plan (GAP) and the Strategic Impact Evaluation Fund (SIEF) at the World Bank, the Swedish Research Council (421-2009-2209) and the Program for Development Research, SIDA is gratefully acknowledged.

> † Stockholm School of Economics and CEPR. Email: martina.bjorkman.nyqvist@hhs.se

> ‡ The World Bank, Development Research Group. Email: ddewalque@worldbank.org

§ IIES, Stockholm University and CEPR. Email: jakob.svensson@iies.su.se