rafmacalaba/datause-encoder-data huggingface dataset:
# V. Conclusion

TIETP incentivizes end‐consumers to pay electronically for their purchases, and at the same time, it induces owner‐operated retail businesses to honestly report their incomes to tax authorities. End‐consumers, to claim tax deductions, use electronic payments that are then automatically reported to tax authorities. It is equivalent to business buyers reporting their business sellers’ sales to claim their input VAT credit in B2B transactions. Thus, retail businesses whose sales are reported by third parties (the consumers) file their income tax returns honestly; in fact, they are only required to confirm their electronically traceable sales on prefilled tax return forms provided by NTS if they find no errors. This paper shows TIETP’s effectiveness as a tool for reducing underreporting in cash‐intensive retail businesses by incentivizing end‐consumers to use electronic payments. In Korea, TIETP was successful in turning a predominantly cash economy into one based on cashless transactions, with a consequent considerable reduction of the shadow economy, in a relatively short period of time.

Future implications and challenges for countries pursuing an incentive structure to induce electronic payment are as follows: First, critical to TIETP’s success is a legal framework and tax information system that enables tax authorities to gain access to and to analyze the financial transaction information. Without the tax authorities’ legal and technical competence to collect and process the information, TIETP would not induce retail businesses’ voluntary compliance. Korea’s 2000 Act on the Submission and Management of Taxation Data played a pivotal role in enabling tax authorities to gather electronic financial transaction information and in inducing voluntary tax compliance in cash‐ intensive business sectors. In addition, NTS established its Tax Integrated System (TIS) in 1997 to integrate all transaction data obtained from electronically traceable payments, including credit cards, debit cards, cash receipts in B2C transactions, e‐invoices in B2B transactions, financial income information, property transaction information, and other third‐party information provided by public and private institutions. NTS now make use of TIS to verify taxpayers’ tax returns and to analyze risk assessments and tax audits. NTS, using TIS, also provides improved taxpayer services by providing prefilled tax return forms and other tax payment guide services.

Second, ensuring that the tax rebate process is easy and guarantees assured return of the rebate are important success factors. The incentives given to end‐consumers to encourage electronic payments are relatively small, so if the application process for the tax rebate is inconvenient or cumbersome, or if rebate payments are unreliable, people would be discouraged from using the system. While Korea’s VAT compensation for receipts, introduced in 1977, failed due to its inconvenience and unreliable cash compensation, TIETP was successful because of its convenience, which was enabled by making electronic data processing and application processes 41
**Table 1a: GDP per capita growth volatility and private credit as a share of GDP**

|**Dep. Var:**|OLS|OLS|OLS|FE|2SLS|GMM|
|---|---|---|---|---|---|---|
|**std_gdppc**|(1)|(2)|(3)|(4)|(5)|(6)|
|privatecredit|-0.0110***|-0.0298***|-0.0296***|-0.0126|-0.0944**|-0.0369***|
||(0.000)|(0.000)|(0.000)|(0.116)|(0.047)|(0.000)|
|privatecredit<sup>2</sup>||0.0001***|0.0001***|0.0001*|0.0005*|0.0001***|
|||(0.000)|(0.000)|(0.094)|(0.060)|-0.001|
|financ.open.|0.0000*|0.0000|0.0000|0.0001|-0.0012|0.0000|
||(0.071)|(0.312)|(0.984)|(0.699)|(0.210)|(0.737)|
|tradeopen.|0.0040***|0.0047***|0.0040**|-0.005|0.0080**|0.0051***|
||(0.002)|(0.000)|(0.024)|(0.237)|(0.029)|(0.008)|
|initialgdppc|-0.1228**|-0.0467|-0.0811|-0.3164|0.2069|0.0027|
||(0.020)|(0.405)|(0.221)|(0.250)|(0.410)|(0.977)|
|financialcrisis|||1.0962***||||
||||(0.001)||||
|ECA|||-0.0950||-0.1909||
||||(0.597)||(0.386)||
|Constant|3.0233***|3.3321***|3.8198***|3.8083***|2.9289***|3.4367***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.005)|(0.000)|
|# of Obs.|984|984|894|984|891|984|
|R-squared|0.06|0.074|0.087|0.014|||
|Prob > F|0.000|0.000|0.000|0.036|0.000|0.000|
|# of countries|147|147|133|147|147|147|

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

10
**Annex II: Cameroon’s 1995/96 Financial Social Accounting Matrix for IMMPA** _(10_<sup>_6_</sup> _CFA Francs. 1 Euro= 655.957 CFA Francs) -_ **_Folio 5/6_**

||EXPENSES|**Capital**<br>Urban Skilled<br>Households|**Accou**<br>Capitalistic Households|**nts of Eco**<br>Non Financial<br>Companies|**n**<br>Commercial Banks|**omic Agent**<br> <br>Central Bank|**s (Continued)**<br>Central Government<br>Rest Of the World|Fiduciary Currencies<br>Deposits|Special Deposits to<br>Central Bank|**C**<br>Bank Reserves|**hanges in**<br>Capital Shares|**Assets and L**<br>Credits<br>Bills issued by<br>Government|**iabilities**<br>Advances of Central<br>Bank to the Treasury<br>State Consolidated<br>Credits of Central Bank|Refinancing<br>External Engagements|External Financial<br>Resources|**Summation**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||RESSOURCES<br>No.|41|42|43|44|<br>45|46<br>47|48<br>49|50|51|52|53<br>54|55<br>56|57<br>58|59||
||Unskilled Rural Labor<br>1|||||||||||||||676 226|
|**Production factor**|Unskilled Urban Labor<br>2<br>|||||||||||||||687 196|
||Skilled Labor<br>3<br>|||||||||||||||430 125|
||Capital<br>4|||||||||||||||2 625 251|
||Rural Non Traders Households<br>5|||||||||||||||803 254|
|**mic**|Rural Traders Households<br>6|||||||||||||||72 083|
|**ono**|Urban Unskilled Informal Households<br>7|||||||||||||||1 615 904|
|**Ec**|Urban Unskilled Formal Households<br>8|||||||||||||||447 306|
|**s of**<br>**ts**|Urban Skilled Households<br>9|||||||||||||||481 711|
|**unt**<br>**gen**|Capitalistic Households<br>10|||||||||||||||452 991|
|**cco**<br>**A**|Non Financial Companies<br>11|||||||||||||||672 482|
|**t A**|Commercial Banks<br>12|||||||||||||||80 884|
|**ren**|Central Bank<br>13|||||||||||||||17 227|
|**Cur**|Central Government<br>14|||||||||||||||836 907|
||Rest Of the World<br>15|||||||||||||||993 631|
||Non Traded Rural Production<br>16|||||||||||||||941 449|
|**tion**<br>**rs**|Traded Rural Production<br>17|||||||||||||||386 853|
|**duc**<br>**cto**|Urban Informal Production<br>18|||||||||||||||2 527 447|
|**Pro**<br>**Se**|Urban Corporate Production<br>19|||||||||||||||3 121 770|
||Public Sector Production<br>20|||||||||||||||543 424|
|**cts**<br>**c**|Non Traded Rural Production<br>21|||||||||||||||938 833|
|**du**<br>**esti**<br>**et**|Traded Rural Production<br>22|||||||||||||||190 876|
|**Pro**<br>**om**<br>**ark**|Urban Informal Production<br>23|||||||||||||||2 544 460|
|**cal**<br>**r D**<br>**M**|Urban Corporate Production<br>24|||||||||||||||2 487 478|
|**Lo**<br>**fo**|<br>Public Sector Production<br>25|||||||||||||||543 424|
|**rts**|Non Traded Rural Production<br>26|||||||||||||||22 235|
|**po**|Traded Rural Production<br>27|||||||||||||||1 564|
|**Im**|Urban Corporate Production<br>28|||||||||||||||815 392|

39
# **Infrastructure, Value Chains, and Economic Upgrades**

Xubei Luo<sup>1</sup> , Xuejiao Xu<sup>2</sup> _1. Poverty Global Practices, The World Bank, Washington D.C., USA,_ _<u>xluo@worldbank.org</u>_ _2. Columbian College of Arts & Sciences, The George Washington University, Washington D.C., USA, xuejiaoxu@gwu.edu_

**JEL classification:** H54; O40, F14, F60

## **Keywords**

_Infrastructure gap; economic growth; structural upgrades; least developed countries; global value chains_
industries. On average a company takes 17 days to process job applications, 50% of them take more than 20 days.The offered monthly salary posted in the job ad is 3,283 MYR (755 US dollars).<sup>9</sup> More than 50% of the jobs offer up to MYR 4,348 (1,000 US dollars) per month. For the degrees of computer science, electrical engineering and mechanical engineering almost 50% of the jobs ads offer more than MYR 6,522 (1,500 US dollars). In our final sample, the Business Administration degree makes up almost 50% of the positions. There were far fewer job ads posted in the Technology and Engineering specializations.

Tables 2, 3 and 4 provide evidence of the balance in our sample for the three dimensions of randomization we conducted: ethnicity, gender and soft skills. For each of these tables we present the means, standard errors across a set of company and job characteristics. In addition, we present the differences between the treatment arms and the standard errors of these differences. The job and company characteristics we observe are: companies located in the Greater Kuala Lumpur area, small firms (with less than 50 workers), offered salary, average days a company takes to process applications, if the job application included a pre-scan questionnaire, the average number of questions for the questionnaire, proportion of applications with a business degree, proportion of job applications with engineering degree, number of applicants for the job position, applicants with more education, applicants asking for a higher salary, and number of foreign applicants. There are minor differences among treatment arms. We only find statistically significant differences for the pre-scan questionnaire in the gender randomization, 53% of male applicants face a pre-scan questionnaire, while this number is 57% for female. In the soft skill randomization, the number of foreign job applicants for the leadership skills is slightly higher than the control group. We believe these differences are spurious. We observe balance across all other job and company characteristics.

The outcome variables we observe represent 5 stages of the hiring process performed by the employer: 1) Application is rejected (declared not suitable), 2) Profile is viewed by employer, 3) number of profile views, 4) candidate is contacted and 5) an interview is offered. These stages reflect actions taken by the employer in the hiring process ranging from candidate screening to job interview offers.

> 9The monthly salary is obtained by taking the average of the salary range advertised in the ad. To convert Malaysian ringgit (MYR) to US dollars we use the April average exchange rate of 0.23 published by the Central Bank of Malaysia https://www.bnm.gov.my/exchange-rates.

13
The first round of phone surveys was conducted in June and July 2020, the second round in August and September 2020, and the third round in October and November 2020. While most survey questions were asked round-by-round, recall of sales and profits was elicited at a more frequent monthly interval with respondents asked to recall values for each month since the previous survey round. Hence, for these business performance outcomes, we have recallbased monthly phone survey data from February to October 2020.

Response rates for the phone surveys were high with 83% of the sample reached in all three rounds and 91% in at least one round. Attrition is uncorrelated with treatment status.

# **3.3. Overlapping Timeline**

The timing of the follow-up phone surveys overlaps with the rollout of BDS components in our sample. By the first survey round in June, the classroom BDS sessions had been completed, counseling sessions had just begun, and digital BDS was not yet available. The latter was introduced in August, coinciding with the start of the second survey round. Both counseling and digital BDS continued to be rolled out through the third survey round in October, with the counseling sessions spanning June-December and the digital BDS repository open from August-November.

The pooled “Any BDS” indicator variable described in section 3.1. is based on eventual treatment assignment. While this identification strategy is independent of treatment rollout timing and is therefore the simplest to implement, it can depress the average treatment coefficient of “Any BDS”, since the full suite of BDS treatments only became available in the later survey rounds.

An alternative strategy is to allow the “Any BDS” indicator to vary by survey round depending on which treatments were in place at the time each survey was conducted.<sup>7</sup> Appendix Table 2 presents the round-by-round analysis following this strategy and the results are discussed in section 5 below.

Regardless of which strategy is used, it is important to note that the results on BDS should be viewed as very short-term effects given the short gestation period between the trainings and surveys.

> 7 Anticipation effects are not a great concern as invitations to the various BDS sessions were sent out only shortly beforehand.

9
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**Polay nning, and Rd"c**

# **Public Economba**

There is a compelling fiscal rationale for encouraging greater reliance on taxing the consumption of electricity and telephone (ET) services.

ET taxes are easy to administer. Enforcement and collection of the tax is relatively inexpensive, since the tax can be added to comrnercial charges, and the services quickly turned off for nonpayment. It is not difficult to distinguish in most cases between business and personal use of these services. ET taxes avoid the problems of smuggling and evasion commonly associated with taxing the production or use of commodities that can be imported.

On equity grounds, in COte d'lvoire at least, ET taxes are clearly the most desirable excise taxes. Ranking alternative commodity taxes with high income elasticity, telephone services - clearly dominate and electricity consumption nearly dominates - the taxation of alcoholic beverages and public transportation.

These conclusions on the distributive impact of altemative indirect tax measures are reached through the application of the relatively new concept of marginal conditional welfare dominance. A commodity tax dominates others on social welfare grounds when a marginal shift in the balance of commodity taxation toward that particular comnodity enhances social welfare.

Using household budget data, such dominance can be established statistically and shown graphically without resort to normative considerations. T'is approach suggeats that ET services may be an underexploited tax base in many developing countries.

ET taxes may also meet the test of relatively high efficiency if they can be implemented thomugh a two-part pricing schedule that charges a flat fee for access to service and an additional escalating fee for marginal use.

This paper is a product of the Public Economics Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ann Bhalla, room N10077, extension 60359.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than filly polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright i 1988 by the International Bank for Reconstruction and Developmnent/lhe World Bank
# III. THE HOUSING MARKET OF KUMASI

# A. An Introduction to Kumasi

Kumasi is the second city of Ghana, capital of Ashanti Region, and the center of the forest belt which stretches across southern Ghana. The city is 180 miles from the capital, Accra, by road and occupies a series of low hills separated by sluggish streams. It is now an industrial center with a formal timber processing industry and large informal woodworking, light engineering and vehicle repairing activities. It is also the commercial center for a large area of Ghana including the main cocoa producing region. The city's central market at Kejetia vies with Onitsha in south eastern Nigeria for the claim to be the largest market in West Africa.

The city was founded around the turn of the eighteenth century by Osei Tutu who, through subjugating the local people, established the Asante Empire under himself as Asantehene; both the king and the high priest in one entity. The symbol of power and the soul of the nation, a wooden stool coated with gold, remains the apex of the cultural, religious and political structures of the Asante people even within modern Ghana. As both Asantehen~z and the Golden Stool are to be found in Kumasi, the city retains a very special place in the world-view of its inhabitants and the residents of an area covering several hundred square miles. More detailed studies of the history and significance of Kumasi can be found in Aidoo (1977), Hagan (1971), Lewin (1974), McCaskie (1980, 1981, 1983 and 1986), and Wilks (1966, 1967, 1975 and 1979).

Kumasi was an imposing town in 1817 (Bowdich, 1819) but suffered destruction in the protracted wars with Britain in the last quarter of the nineteenth century. Having been razed by fire, it was reconstructed as the colonial administrative and commercial center for the northern part of modern Ghana, beginning in 1901, eventually gaining a reputation as the "Garden City of West Africa." Its population grew to 45,133 by 1944 (Fry and Drew, 1945), 180,642 by the 1960 Census, 260,000 by the 1970 Census, and an estimated 590,000 in 1980 (Tipple, 1984a).!/

- 1.! The 1980 estimate, made from extensive household data, has been called into question by the recently published 1984 Census preliminary results (Ghana, 1985) which show only 488,991 people in Kumasi. Discussions in Kumasi with Prof. P. Austin Tetteh, a respected Ghanaian demographer, suggest that the 1984 Census may be dogged by under- enumeration. Furthermore, the housing stock estimates and occupancy rate means from the 1986 survey would generate a total population of approximately 690,000 for 1986.
“full” cost function e(p, w, u) of the type discussed by Deaton and Muellbauer (1981). This specifies the minimum cost required by the household to reach utility level _u_ at given a vector of commodity prices, p, and prices for the factors that it sells, w **.** Conventional practice in this type of modeling (see, for example, the studies in Hertel and Winters 2006) is to consider the impacts of changes in prices on the factor returns accruing to households, and to compare these impacts with changes in the cost of living as measured using the expenditure function. Given our focus on the effects of changes in prices of the particular— and sometimes quite finely-specified—foods, we adopt a different approach more frequently seen in microeconomic studies of the impact of changing food prices on poverty (see, for example, Deaton 1989). Given the close links—and the substantial evidence of non-separability between firm and household decisions—between the small farm households of most concern to us and their farm businesses, we focus on the impact of price changes on the profits that farming households derive from their own farm firms and the impact of changes in food prices on the wage rates received by household members for sales of labor off-farm. While our national models allow for sales and purchases of both skilled and unskilled labor, we focus on changes in the wage rate for unskilled labor w and its quantity l.

Given that farm firms are price takers and operate subject to constant returns to scale, the revenues obtained from sales of output (including “sales” to the household for its own consumption) must equal the returns to the factors employed by the firm. We also consider the impacts of changes in factor prices on the returns that farm households obtain from their net sales of factors outside the farm firm.

A Dixit-Norman (1980) style money measure of household welfare W at given utility level, u _,_ is given by: _(1)_ W= −e(p, w, u) + π(p, w) _,_ where e(p, w, u) is the full cost function of the household at any given vector of commodity prices, **_p,_** and factor prices, **_w._** ; and π(p, w) is a profit function representing the profits generated by the farm firm. While this measure does not allow for risk aversion, risk preferences could be introduced using a concave, cardinal utility function to evaluate the welfare effects of changes in this real income measure.

6
**Table 2:** Parameter Estimates

|||(1)<br>Satisfed with overall<br>house quality (1=Yes)|(2)<br>Satisfed with quality<br>of family life (1=Yes)|
|---|---|---|---|
|_Model with Disappointment_||||
|Permanent Effect|_βP_|-0.109|0.041|
|||(0.786)|(0.141)|
|Transitory Effect|_βT_|0.543<br>(0.757)|0.2982**<br>(0.131)|
|Hedonic adaptation rate|_δT_|0.026|0.054|
|||(0.056)|(0.057)|
|Delay Effect|_βD_|0.276***<br>(0.066)|0.51<br>(1.301)|
|Delay adaptation rate|_δD_|0.19<br>(0.132)|0.029<br>(0.097)|
|_Model without Disappointment_||||
|Permanent Effect|_βP_|0.0245<br>(0.131)|0.049<br>(0.117)|
|Transitory Effect|_βT_|0.172*|0.109|
|||(0.099)|(0.098)|
|Hedonic adaptation rate|_δT_|0.061<br>(0.104)|0.059<br>(0.136)|

Notes: The specifications include baseline values as covariates when available. The first five rows under the heading “Model with Disappointment” report the structural parameter estimates of the NLS regression: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βT e_<sup>_−δT_(</sup><sup>_t−Tic_)</sup> + _βD_ ( _e_<sup>_−δD_(</sup><sup>_Tic−Te_)</sup> _−_ 1)� + _Xic_<sup>_′θ_+</sup><sup>_εict_.The model without disappoint-</sup> ment losses estimates the NLS regression: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βT e_<sup>_−δT_(</sup><sup>_t−Tic_)�</sup> + _Xic_<sup>_′θ_+</sup><sup>_εict_.Coefficients</sup> with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

18
Choice experiment for stove adoption <!-- Start of picture text --> VerNo:_l_ QNa:_1l_<br>Attributes Alternative 1 Alternative 2 Status Quo<br>Durability of<br>stowe 1-5 years 6-10 years No improved stove<br>Amount of<br>cooking time _» 40 . 2 " -<br>"| |MBO | |MOOG || QOOG<br>50% reduction 25% reduction No reduction<br>Reduction in<br>amountfuel woodof —Oe:== Oe= —te==SS aS= Petf =}Se = tf =<br>50% reduction 25% reduction No reduction<br>Reduction of<br>smoke ; . " ask, ask, ast,<br>A, A, A,<br>50% reduction 25% reduction No reduction<br>Cast of = Ea!<br>improved mul‘ie a<br>stove Ea<br>= Ea “a No payment but no<br>i rel improved stove<br>100 birr 300 birr<br>Please<br>tick/mark (v)<br>only one<br><!-- End of picture text -->