rafmacalaba/datause-encoder-data huggingface dataset:
years 1995-98. Table 6 estimates the same set of models, but the dependent variable now is the maximum US tariff (over 1995-1998) for each of these products in the sample (e.g. 24 industries in 1995). The estimates from the models affirm the earlier findings, implying that the results are robust to whether we model the average US tariff or the maximum US tariff on these products.

**Table 5: Determinants of agricultural tariffs (incl. Specific Tariffs) on products with export subsidies**

Dependent <u>Variable: Average Tariff between 1995 and 1998</u>

|||||<br>||**%PAC**<br>|**RANKPA**<br>||<sup>2</sup>|<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|**year**|**Dep. Var.**||**constant**|**M/Y**<br>**X/Y**|**AvgEU15**|**EXP**|**CEXP**|**_N_**|_R_|Adj._R_|
|1995|AvgUS|est|0.008|−0.009 0.088|0.114|0.905||24|0.513|0.410|
|||_t_|0.279|1.166 1.144|0.366|**4.091**|||||
|1995|AvgUS|est|−0.025|−0.005 0.056|−0.323||0.187|24|0.321|0.178|
|||_t_|0.541|0.620 0.609|0.889||**2.574**||||
|1996|AvgUS|est|0.052|−0.005 0.062|−0.443|1.067||26|0.416|0.304|
|||_t_|**2.126**|0.914 0.837|**2.486**|**3.667**|||||
|1996|AvgUS|est|−0.047|−0.005 0.076|−0.193||0.226|26|0.375|0.256|
|||_t_|1.137|0.990 0.989|1.273||**3.349**||||
|1997|AvgUS|est|0.045|−0.005 0.074|−0.373|1.094||28|0.314|0.194|
|||_t_|**1.760**|0.933 0.875|**2.026**|**3.082**|||||
|1997|AvgUS|est|−0.038|−0.006 0.105|−0.158||0.195|28|0.275|0.149|
|||_t_|0.859|1.188 1.182|0.998||**2.788**||||
|1998|AvgUS|est|0.030|−0.011 0.201|−0.394|1.104||30|0.370|0.270|
|||_t_|1.240|**1.692 1.650**|**2.312**|**3.234**|||||
|1998|AvgUS|est|−0.044|−0.011 0.210|−0.169||0.182|30|0.337|0.231|
|||_t_|1.147|**1.682 1.683**|1.153||**2.949**||||

Interestingly, the 1996, 1997, and 1998 tariff appear to be deterred by EU tariffs on these particular products. The coefficient on Table 5 on AVGEU15 is ─0.443 for the year 1996 in the model with %PACEXP included. That indicates that a higher EU average tariff deterred the average US tariff. While this is a somewhat surprising finding, it is not robust across the two models for each year. With RANKPACEXP included, the coefficient on AVGEU15 becomes statistically insignificant. It is quite possible that the most influential %PACEXP values are correlated with influential AVGEU15 values. When influential values get lesser weight (as happens when RANKPACEXP is included), the correlation between the lobbying variable and AVGEU15 declines, and we get statistically insignificant results on 52
analog services or cash transactions, they could be more accessible for members of underserved groups such as women, low-income adults, rural households, and youth.

Thirteen percent of adults in middle- and high-income countries worldwide have a mobile money account as of 2021, up from 4 percent in 2017 (Global Findex 2021). In Sub-Saharan Africa, 35 percent of adults use a mobile money account and about three in five of those account holders _only_ have a mobile money account. The rest have accounts with both a mobile money provider and a bank or similar financial institution. It is difficult to overstate the significant role these accounts are playing in expanding financial access and use in the region. Since 2017, account ownership at a bank or similar financial institution in Sub-Saharan Africa has been flat, yet overall account ownership has grown by 12 percentage points—all of it attributed to adults opening a mobile money account.

# **3. Digital payments**

A digital payment is one where money is directly sent into or received from a recipient’s mobile money or financial institution account. This includes payments made using a debit or credit card, a mobile phone, or the internet. Digital payments include making in-store or online merchant payments; paying utility bills; and sending or receiving domestic remittances; as well as receiving payments for agricultural products; receiving wages; or receiving government transfers, or a public pension directly from or into an account.

Digital payments play an essential role in catalyzing the benefits of account ownership, especially for vulnerable populations. To best conceptualize the benefits of digital payments, it is important to understand how people make and receive payments when digital payment services are not available or widely used.

# **3.1 The costs and risks of a cash economy**

In places that lack digital payments infrastructure, adults—even those with accounts—receive their wages in cash and must use cash to purchase their daily necessities. If they want to or need to use an account, they must travel to the nearest bank, either carrying the money they would like to deposit on the way there, or the money they have withdrawn on the way home. For rural, poor, 4
Table C.1: Parameters for Counterfactual Scenarios

|Parameter|Description|Units|Value|
|---|---|---|---|
|Scenario A|(Declining Growth of Agricultural Yield)|||
|_θ_<sup>_g,c_</sup><br>_T_|Food Crop Yield in time T|tons / Ha|8.05|
|Scenario E|(Rising Fossil Fuel Costs)|||
|_κf_|Logistic Oil Price Growth Rate||0.075|
|Scenario T|(Land-Use Emissions Target)|||
|_zτ_|GHG Emissions in 2025|GtCO2e||
|_zT_|GHG Emissions in 2104|GtCO2e|0.4_zτ_|
|_κμ_|Logistic Rate of Decline in Land-Use Emissions||0.04|

# E.1 Model Sensitivity to Parameter Values

This section explores the model sensitivity to the values of several important parameters used in the empirical analysis. These include the conversion cost of natural lands, substitutability between agricultural lands and fertilizers in production of agricultural output, the costs and eciency of biofuels' production, energy eciency, and the demand for energy services. We show model baseline sensitivities with respect to the following changes<sup>11</sup> :

- a 50% decline in short-term adjustment costs of natural land conversion;

- a 50% increase in elasticity of substitution between agricultural land and fertilizers,

- a 50% improvement in second generation biofuels' production technology,

- a 50% increase in xed factor specic technological change,

- a 50% decline in energy eciency growth rate, and

> 11Because of a lack of space, we are unable to show sensitivity results across all parameters and scenarios. In this section we concentrate on the parameters subject to largest uncertainties. The additional results are available from authors upon request.

26
# **Legend**

Coefficients in bold; SD in italics

***, **, * indicate significance at 1%, 5% and 10% level, respectively

- Creation Proxy of formal job creation (% of people moving from OLF, U, SE, I to F out of total OLF, U, SE and I). Pooled (by year) instantaneous transition computed using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in section III. Computations are based on 10.000 Monte Carlo replications.

- Destruction Proxy of formal job destruction (% of people moving from F to U out of total F). Pooled (by year) instantaneous transition computed using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in section III. Computations are based on 10.000 Monte Carlo replications.

- Sector size Share of formal (F) on specific sector workforce Industry Industry informality differentials (betas coming from 1st stage regression in which F is explained with differentials individual characteristics and industry differentials) Effective Effective protection (scaled by 10<sup>-3</sup> ). Sources are: Kume et al. (2003) for 1987-1998; Pinheiro and Bacha Tariff de Almeida (1994) for 1983-1986 Imports Imports penetration (weighted imports/consumption). Sources are: Muendler (2002) for 1987-1999; Penetration Pinheiro and Bacha de Almeida (1994) for 1983-1986; Nassif and Pimentel (2004) for 1999-2002 Tenure Dummy (active since 1989) interacted with tenure (in years) of workers fired in the specific industrial sector (average 1983-1987)

- Overtime Dummy (active since 1989) interacted with the proportion of workers working more than 44 hours in the specific industrial sector (average 1983-1987)

- Union Dummy (active since 1989) interacted with union enrollment - understood as % of unionized workers in the specific industrial sector - (average 1986 and 1988)

43
macroeconomic variables have no heterogeneous association by sector with returns adjusted by PME-MSCI.

# Table 4: Correlates of Annual Equity Returns by IFC Sector (1990-2023)

Dependent variable is annualized TVPI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. The base category for the interaction is the FIG sector: collective investment vehicles (Funds) and finance and insurance. MAS: Manufacturing, Agribusiness and Services. INFRA: electric power; information; oil, gas and mining; primary metals; transportation and warehousing; and utilities. Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Qua|ntile Regre|ssion||
||0.1|0.25|0.5|0.75|0.9|
|GDP growth|0.544|0.514|0.311|0.646***|1.098*|
||(0.764)|(0.433)|(0.192)|(0.197)|(0.577)|
|RER|-1.155***|-0.594**|-0.512***|-0.908***|-0.811***|
||(0.413)|(0.234)|(0.104)|(0.106)|(0.312)|
|Ln(Size)|0.011|0.007|0.007**|0.007**|-0.004|
||(0.012)|(0.007)|(0.003)|(0.003)|(0.009)|
|Ln(Duration)|0.112***|0.031*|0.009|-0.027***|-0.097***|
||(0.032)|(0.018)|(0.008)|(0.008)|(0.025)|
|MAS_×_GDP growth|0.754|0.419|0.025|-0.477|-1.546*|
||(1.179)|(0.668)|(0.297)|(0.304)|(0.890)|
|MAS_×_RER|1.272*|0.422|-0.224|0.454**|0.547|
||(0.720)|(0.408)|(0.181)|(0.185)|(0.543)|
|MAS_×_Ln(Size)|0.085***|0.034***|-0.000|-0.005|-0.023|
||(0.021)|(0.012)|(0.005)|(0.005)|(0.016)|
|MAS_×_Ln(Duration)|0.273***|0.155***|-0.008|0.002|-0.044|
||(0.055)|(0.031)|(0.014)|(0.014)|(0.041)|
|INFRA_×_GDP growth|-0.544|-0.286|-0.641|-0.663|-1.271|
||(1.728)|(0.979)|(0.435)|(0.445)|(1.304)|
|INFRA_×_RER|1.621**|-0.449|-1.283***|-0.662***|-0.980|
||(0.796)|(0.451)|(0.200)|(0.205)|(0.601)|
|INFRA_×_Ln(Size)|0.040*|0.033**|0.003|-0.017***|-0.061***|
||(0.023)|(0.013)|(0.006)|(0.006)|(0.018)|
|INFRA_×_Ln(Duration)|0.149**|0.193***|0.025|-0.032*|-0.078|
||(0.065)|(0.037)|(0.016)|(0.017)|(0.049)|
|_R_<sup>2</sup>|0.194|0.082|0.025|0.045|0.118|
|Observations|1668|1668|1668|1668|1668|

20
# STRUCTURAL CHALLENGES FOR SOEs IN BELARUS: A Case Study of the Machine Building Sector<sup>1</sup>

Edgardo Favaro<sup>2</sup> , Karlis Smits<sup>3</sup> and Marina Bakanova<sup>4</sup> JEL Classification: D22; D23; D24; P31 Sector Board: Economic Policy (EPOL)

> 1 This paper has benefited from valuable comments and suggestions from Martin Raiser, Benu Bidani, Gallina A. Vincelette, Pablo Saavedra, Sebastian Eckardt, Jesus Crespo Cuaresma, Harald Oberhofer. Maryna Sidarenka provided valuable research assistance. Questions should be addressed to <u>KSmits@worldbank.org.</u>

> 2 Consultant. Email: emfavaro@gmail.com

> 3 The World Bank. Email: ksmits@worldbank.org

> 4 The World Bank. Email: mbakanova@worldbank.org
-10-

# that rid loans could always be repaid with new ones.

Distribution of credit by type of enterprises, type of loan and economic<sup>sector followed the</sup> State Plan's material product targets closely (see Table 4).5 Within the enterprise<sup>sector most</sup> credit was allocated to socialist enterprises and little to cooperatives<sup>and the household sector. On</sup> average, in 1987-89 the socialist enterprises accounted for 80<sup>percent of total credit provided to</sup> the enterprise sector. The most important tVpe of loans<sup>were working capital and investment loans</sup> accounting for 50 and 40 percent of total credit, respec"<sup>,ely. Mortgages and consumer credit</sup> (household sector loans) were less imporant and accounted for<sup>10 percent of total. Although this</sup> credit distribution reflected the relative importance of each<sup>sector in the economy, it also indicates</sup> the Plan's priority in allocating credit to socialist enterprises.

|Table 4: Breakdown of Ente<br>(End-Per<br>.........................<br>|rprise<br>Cr<br>iod<br>Stocks<br>|edit<br>by Ty<br>,<br>in Bitlio<br>.........................<br>|pe of Enter<br>n<br>Levs)<br>...............<br>|prise<br>. ...<br>|
|---|---|---|---|---|
|1986<br> <br> <br> <br>|1987<br>|1988<br>|1989 Se<br>|pt. 1990<br>|
|1. Total Bank Credit<br>by Type of Enterprise<br>1/<br>34.9<br>(=A+B*C+D)|37.3|39.8|41.7|43.8|
|A. Socialist<br>Enterprises<br>28.4|30.3<br>|32.5<br>|33.9<br>|n.a.|
|Working Capital<br>15.9<br>|17.3<br>|18.9<br>|19.9<br>|n.a.|
|Investment<br>12.5|13.0|13.5|14.0|n.a.|
|B. Cooperatives<br>2.7<br>|2.8<br>|2.7<br>|2.7<br>|n.a.|
|Working Capital<br>0.9<br>|1.0<br>|1.0<br>|1.0<br>|n.a.|
|Investment<br>1.8|1.8|1.7|1.7|n.e.|
|C. Non-Socialist<br>Entities<br>0.0|0.0|0.0|0.0|n.s.|
|D. Households<br>3.8<br>|4.2<br>|4.6<br>|5.1<br>|n.a.|
|Mortgages<br>2.9<br>|3.6<br>|3.6<br>|3.8<br>|n.a.|
|Other<br>0.9|1.0|1.1|1.2|n.a.|
|Memorandwn Items:|||||
|Investment credit<br>from SCIF 2/<br>-2<br>|--|2.1<br>|4.1<br>|5.7<br>|
|Energygeneration<br>-|-|1.0<br>|2.0<br>|3.3<br>|
|Metallurgy<br>and mining<br>--|--|0.3|0.6<br>|0.8<br>|
|<br>Agricultural<br>construction<br>and irrigation<br>--|*-|0.2<br>|0.3<br>|0.1<br>|
|reavymachine building<br>-|--|0.1<br>|0.3<br>|0.3<br>|
|Housing industrial<br>construction<br>--|--|0.1<br>|0.2<br>|0.3<br>|
|Other 4/<br>--|--|0.4|0.S|0.8|

.................................................................................................

Source: National lank of Bulgaria.

Notes: (1) Differences with the Monetary Survey are explained by differences in data sources used. (2) State Investment Credit Fund.

Loans to enterprises were mostly denominated in Levs. The<sup>share of enterprises' credit</sup> denominated in foreign exchange was about 2 percent<sup>in 1986 and only increased to 10 percent in</sup> 5t Differences in the estimates of this table and Table<sup>3, resulted from the different sources used for"gircompilation.</sup> While Table 3 provides more accurate est6nates<sup>of the volume of credit extended to eaterprises, Tablb 4 illustrates its</sup> distribution.
# 1 Introduction

In their recently launched trade talks, the European Union and the Association of Southeast Asian Nations (ASEAN) agreed to focus not on tari¤s and quotas but on what Pascal Lamy, the EU trade commissioner, called "the real 21st century trade issues": harmonizing standards.<sup>1</sup> This is the latest episode in a process of deep integration that is most advanced within the European Union but also underway in many other regions. Two factors explain the shift in regional negotiating emphasis away from conventional barriers and toward standards. First, multilateral negotiations have achieved remarkable reductions in tari¤s and quotas but done relatively little to reduce the trade restrictive impact of technical barriers. Second, while multilateral trade rules governing regional agreements on tari¤s seek at least in principle to balance the interests of integrating countries and the rights of excluded countries,<sup>2</sup> the rules treat regional agreements on standards as always benign and worthy of encouragement.<sup>3</sup> Are regional agreements on technical barriers indeed an unambiguous blessing for global trade? The voluminous research on regionalism with its almost exclusive focus on tari¤s and quotas provides no adequate answer. This paper is a ...rst step in the theoretical and empirical analysis of regional initiatives on technical barriers to trade. Using a simple analytical model, it asks: How must the conventional analysis of free trade areas and customs unions be modi...ed to apply to agreements on standards? And what elements of international agreements on standards are relevant to predicting their impact on patterns of trade? Then employing a specially constructed panel dataset that directly identi...es di¤erent types of policy initiatives in each manufacturing industry, the paper addresses two empirical questions: Do regional agreements on standards lead to signi...cant increase

> 1 The two groups of countries have ageed to begin by harmonizing sanitary standards in the agricultural and ...sheries sector, as well as technical standards for electronics and wood-based industries. Pascal Lamy, the EU trade commissioner said that the choice was driven by the belief that "the real 21st century trade issues were standards and rules in areas such as safety, health or consumer protection", rather than tarri¤s or quotas ("EU and Asean to pave way for trade pact talks", Financial Times, 7 September 2004.)

> 2 These rules are in Article XXIV of GATT 1994.

> 3 Article 2.7 of the WTO’s Agreement on Technical Barriers to Trade encourages members to "give positive consideration to accepting as equivalent technical regulations of other members, provided they are satis...ed that these regulations adequately ful...ll the objectives of their own regulations." This provision would seem to allow a country to selectively recognize standards of other countries, without violating the fundamental obligation not to discriminate between its trading partners. There is no mention of the rights of, or obligations vis-à-vis, countries that happen not to receive "positive consideration."

2
|ShareHHmen26-65|0.433|1.84+|0.449|1.89+|0.440|1.87+|0.464|2.00*|
|---|---|---|---|---|---|---|---|---|
|NumberofHHmembers|-0.030|-6.16**|-0.025|-4.97**|-0.030|-6.03**|-0.025|-5.13**|
|Head ismale|-0.115|-2.99**|-0.090|-2.35*|-0.117|-3.05**|-0.090|-2.37*|
|Headisindigenous|-0.213|-4.36**|-0.256|-4.63**|-0.203|-4.11**|-0.250|-4.81**|
|Mother'sage|0.062|3.69**|0.055|3.30**|0.063|3.74**|0.056|3.43*|
|Mother'sage squared|-0.001|-2.60**|-0.001|-2.41*|-0.001|-2.63**|-0.001|-2.47*|
|Mother'sheight(cm)|0.054|22.79**|0.051|22.54**|0.054|22.79**|0.052|22.98**|
|Mother'seduc-primary|0.018|0.35|-0.024|-0.49|0.024|0.47|-0.017|-0.36|
|Mother'seduc-secondary|0.077|1.27|0.011|0.20|0.086|1.41|0.025|0.45|
|Mother'seduc-postsec.|0.130|1.91+|0.105|1.63|0.136|2.01*|0.120|1.90+|
|Father'seduc-primary|-0.029|-0.61|0.005|0.11|-0.029|-0.62|0.007|0.14|
|Father's educ-secondary|0.112|2.47*|0.112|2.50*|0.111|2.46*|0.114|2.55*|
|Father'seduc-postsec.|0.084|1.64|0.127|2.50*|0.083|1.62|0.129|2.54*|
|House floordirtdummy|-0.029|-1.02|-0.064|-2.15*|-0.033|-1.15|-0.063|-2.20*|
|Pipeddrinkingwater dummy|-0.155|-4.34**|-0.046|-1.24|-0.154|-4.31**|-0.039|-1.09|
|Flushtoiletdummy|0.031|0.75|0.055|1.36|0.046|1.06|0.050|1.25|
|Urbandummy|0.075|1.83+|Fixedeffect||0.064|1.55|Fixedeffect||
|Constant|-10.122|-20.01**|dummies o|mitted|-9.933|-19.57**|dummiesomi|tted|
|FONCODES index(t=0)inIVequation|||||-0.098|-8.13**|0.21|848.17**|
|FONCODES index(t=l)inIVequation|||||0.239|11.95**|0.10|013.69**|
|FONCODESindexinIVequation|||||||||
|Numberofobservations|19,053||19,053||19,053||19,05|3|
|R|10.300||0.356||0.301||0.34|2|

_Note:_ Instrument in IV models is district-level FONCODES index. All models were also<sup>estimated without householddemographicvariables.</sup> The remaining estimates were not statistically, nor substantively affected.

** indicates significance at 99 level of confidence, ** at 95 level of confidence, and + at 90 level of confidence.

_30_
the time this project commenced, the World Bank Enterprise Survey (2013) indicated that 32 percent of firms expect to give gifts in meetings with tax officials and 37 percent expect to give gifts to any public officials to “get things done.” Opportunities for corruption in this context may be reinforced by the fact that tax officials are responsible for a portfolio of taxpayers, which means that they interact with the same taxpayers frequently.

These two concerns—improving service delivery for taxpayers by eliminating long wait times for submission of declarations and curtailing corruption by reducing the frequency of interactions with tax officials—were among the primary reasons the tax authority in Tajikistan introduced e-filing. By making it easier for people to file and pay taxes, and by closing off opportunities for corruption, the government expected e-filing would ultimately lead to increased voluntary compliance and thus increased tax revenues. Other motives for introducing e-filing are to improve the quality of tax records by reducing arithmetic mistakes by firms<sup>7</sup> and data entry mistakes by clerks and to improve the efficiency of tax administration by releasing officials from routine work to focus on higher-value activities.

In order to file taxes online, firms must first register for e-filing by submitting application documents to the tax authority. After processing and approval of the application, the firm receives an e-token containing its digital signature which it must use to verify its online declarations. Once a firm registers for e-filing, it can file all taxes online using the e-filing software either on its own computer or at public e-filing terminals located in tax offices.<sup>8</sup> However, firms are still required to visit the tax office quarterly to reconcile their accounts.

Adoption of e-filing by firms was slower than expected following its introduction in 2012. Initially, firms had to purchase e-tokens for $40 plus an additional $40 registration fee. Even after the elimination of these fees, only about 30 percent of firms registered

> 7 The online system has in-built formulas to perform calculations automatically based on inputted entries.

> 8 Online payment of taxes through e-banking is also available to firms but is separate from the e-filing system: firms that e-file do not have to pay online and vice versa. Although this paper does not study the impact of electronic tax payment, we observe that 66 percent of firms that e-file also pay online.

8
?7 Table I Charges **and Paymente** to Contraceptlve Acceptors. PubiLc *ector

||**Charge**<br>**acceptor**<br>**fees**|Neeithbe ch|arge nor pay|Pay ac|ceptors|
|---|---|---|---|---|---|
|||_aceoto|rs|_||
|Region|Methods<br>Location<br>providedl'|Locatlon|Methods<br>provided&I|Locatlon|Methods<br>providdL'|
|Africa|Botsvana<br>All|Burkina Pas|o<br>St|||
||Burkina Paso<br>I,O,C,S|Chad|All|||
|||Ethiopia|Ailk'|||
|||Liberia|All|||
|||NLger|All|||
|||NLgerLa|All|||
|||Sudan|ALL|||
|||Togo|All|||
|||Zimbabve|All|||
|Asia|Hong Kong<br>ALL|Bangladesh|O,In,C,S.D|Bangladesh|<br>I,St|
||Korea, Rep. of<br>I,O,C|China|All|Indxa|I,St|
||Pakistan<br>O,C,S|Indla|Ab|Korea, Rep.|of£'<br>St|
||Sri Lanka<br>O,C|IndonesLa|I,O,C,In|_Nepal_|St|
||Taiwan, China I,O,C,St|Korea, Rep.|of<br>Ab#'|Pakistan|St|
||ThaLlandS. I,O,C,In,St|Nepal<br>|I,O,C,S,D,In|Sri Lanka|St|
||Singapore<br>ALL|Philippines|<br>All|||
|||Srl Lanka|I.In|||
|Latin America|Peiut'<br>All|Colombia|Ail|||
|||Costa Rica|All|||
|||Jamaica|All|||
|||Mexico|All|||
|||Paraguay|All|||
|Middle East|Jordan<br>I|Egypt|I,O,C,D,S|||
||Turkey<br>St|Jordan|0|||
|||Morocco|I,O,C,St|||
|||Tunlsl-|I,O,C,S,St|||
|||Turkey|I,O,C,D|||

- a. Codes for methods **are:** I - IUD; 0 - oralss C - condoms; S - spermicides; D - diaphragms In - injwctables; St - sterilization (male and female); Ab a abortion.

b. Governme-t program.

- c. The ThLland program officially provides contraceptives free except lnjectables; hovever, in practice, many acceptors **are** charged.

- d. Only menstrual regulation for lov-income vomen.

- *. Payment made only to lov-income acceptors: US$330 so acceptors vith one child,

US$110 to acceptors with two children, and US$45 to acceptors vith three or more children.

f. MLnistry of Health. No charges Ln the Social Security system.