rafmacalaba/datause-encoder-data huggingface dataset:
for $5.7 billion, Italy with 17 projects for $6.6 billion, New Zealand also with 17 projects for $6.3 billion, and Australia with **15** transactions for $6.2 billion.

Figure I GLOBAL PRIVATLZATIONS, 1988-1992 **By Number of Transactions** <!-- Start of picture text --> 500-<br>450 i Developing Countnes<br>400 S<br>350 - 0 Industnalized Countries<br>300 -<br>250-<br>200 . ......<br>150 - -<br>100- _<br>50<br>0<br>1988 1989 1990 1991 1992<br>US Billion By Amount of Revenue<br>6ri<br>Developing Countries ]<br>50-M Industrialized Countnles|rm<br>40.......<br>30-<br>20 -<br>10 " - ~ " '<br>0<br>1988 1989 1990 1991 1992<br>PvFi2zao h Xu fmw, ESa Owmw by m TmuANl wan exc&ed<br><!-- End of picture text -->

# **ITI.2 Trends in Developing Country** Privatizations

In developing countries, privatizations increased drastically over the last five years, both in terms of the number of transaction'q as well as in revenue. The number of transactions rose from a meager 26 in 1988 to 416 in 1992, and, over the same time period, privatization revenues increased from $2.6 billion to $23.2 billion, implying an average nomninal growth rate of about 73 percent per year While privatization programs are generally becoming more popular and expand rapidly, the developments differ quite substantially across regions. In the region _Middle East and Northern Africa,_ only Tunisia showed any substantial privatization activity, _Sub-Saharan Africa_ has generally not to any major extent participated in the recent wave of privatizations and progress has been very slow. While some countries engaged in not insubstantial privatization programs in terms of number of transactions, the asset value of the SOEs involved is overall only marginal with the exception of two sales in South Africa during 1989. Over the period as a whole, 76 transactions could be recorded for a total volume of about $240 million or only a meager 0.4 percent of total sales revenues in the developing world. 5 For _East Asia and the Pacific,_ no transactions could be found for

**15** Excluding South Africa with two transactions worth US$1.4 billion in 1989.

12
# **Table 4 Questions 1–3: Multinomial Logit Regression**

This table reports the marginal effects of a multinomial logit regression. The dependent variable is the variable _Loani,j_ , where _Loani,j_ = 1 if student _i_ chooses the Debt in question _j_ , _Loani,j_ = _−_ 1 if the HCC is chosen instead, and _Loani,j_ = 0 if the option “indifferent” is chosen. The coefficients should be interpreted as the marginal effect on the probability of choosing the alternative labeled in the column.

||HC|C|Indife|rent|Loa|n|
|---|---|---|---|---|---|---|
||||N=3110||||
||Coef.|Std. Error|Coef.|Std. Error|Coef.|Std. Error|
|**Treatment**|**0.0893*****|**0.0180**|**-0.0419*****|**0.0113**|**-0.0473*****|**0.0177**|
|Loan Better|-0.0478**|0.0221|-0.0275**|0.0124|0.0754***|0.0219|
|HCC Better|0.0563**|0.0221|-0.0374***|0.0123|-0.0188|0.0218|
|Mexico|-0.0079|0.0282|0.0251|0.0185|-0.0172|0.0276|
|Chile|-0.0617**|0.0312|-0.0180|0.0186|0.0797**|0.0315|
|Age|-0.0018|0.0024|0.0013|0.0015|0.0005|0.0024|
|Male|0.0213|0.0183|0.0255**|0.0113|-0.0468***|0.0180|
|Student|-0.0401*|0.0213|-0.0120|0.0133|0.0520**|0.0207|
|Children|0.0649|0.0373|-0.0153|0.0222|-0.0497|0.0356|
|Married|-0.0252|0.0411|-0.0064|0.0236|0.0316|0.0414|
|Mother Education|-0.0056|0.0242|0.0439***|0.0137|-0.0383|0.0238|
|Father Education|0.0499**|0.0238|-0.0217|0.0158|-0.0282|0.0234|

* 10% Significance Level

** 5% Significance Level

*** 1% Significance Level

24
institution participating in the trial, the fifth-largest commercial bank in Peru, piloted the EFL tool starting in March 2012, with the goal of expanding its lending to SMEs. Loan applicants were screened by the EFL tool, and all applicants that achieved a score higher than a threshold set by the bank were offered a loan.

Peru has several private credit bureaus that, together, cover 100 percent of the adult population. Thus, all applicants for bank loans have a credit score. But for individuals who have not previously taken out a loan from a formal financial institution, this score is based primarily on demographic information rather than actual credit history. For the purposes of this study, these individuals are referred to as “unbanked.” Applicants with credit scores in an acceptable range (as defined by the implementing institution) were offered a loan even if their EFL score was below the threshold.

This setup allowed the researchers to test two possible uses of the EFL tool: (i) as a secondary screening mechanism for entrepreneurs accepted under the traditional credit-scoring method,<sup>6</sup> to lower the risk of the SME loan portfolio; and (ii) as a skimming mechanism for applicants rejected under the traditional credit-scoring method, to offer more loans without increasing the risk of the portfolio. We also tested whether the EFL tool could increase access to credit for unbanked borrowers whose traditional credit score might not provide sufficient information to banks.

We used monthly data on formal credit usage and repayment behavior patterns, as collected by the Superintendencia de Banca y Seguros (SBS) in Peru from June 2011 to April 2014, as well as data collected by the implementing institution and EFL on the 1,993 potential clients that were part of the pilot exercise.

Our results show that the EFL tool can reduce portfolio risk for “banked” entrepreneurs (i.e., those who have taken out loans from a formal financial institution) when it is used to complement traditional credit scores. Banked applicants accepted under the traditional creditscoring method but rejected based on their EFL score are 8.6 percentage points more likely to have been in arrears for more than 90 days during the 12 months after being screened by the EFL tool, compared to 14.5 percent of entrepreneurs who are accepted using both methods. We did not find

> 6 We use “traditional screening process,” “traditional method,” “traditional model,” and “traditional credit-scoring method” interchangeably to refer to the conventional screening process used by the bank to evaluate credit risk based on the traditional credit score.

3
**_<u>________________________________________________________</u>_**

———. 2003b. _Trade Facilitation and Capacity Building: Global Perspective_ . Unpublished. The World Bank, Washington, D.C.

Wilson, John S., Catherine L. Mann, Yuen Pau Woo, Nizar Assanie, and Inbom Choi. 2002. _Trade Facilitation: A Development Perspective in the Asia-Pacific Region_ . Singapore: APEC Secretariat.

Woo, Yuen Pau. 2002. _Trade Facilitation in the WTO: Singapore to Doha and Beyond_ . In Will Martin and Mari Pangestu, eds., _Options for the Next Trade Round: View from East Asia_ . Cambridge, U.K.: Cambridge University Press.

World Bank. (2000). _The Evolution of Ports in a Competitive World. Port Reform Toolkit_ , Module 2. <u>(http://www.worldbank.org/transport/ports/toolkit.htm), The World Bank, Washington, D.C.</u> World Bank. 2002. _Costs of Doing Business Survey_ . World Bank Country Study, The World Bank, Washington, D.C.

<u>World Bank Contribution to Meeting of Governmental Experts from Landlocked and Transit Developing Countries, New York, July 30 to August 3, 2001. Display or download the Report by the UNCTAD secretariat.</u> 69 ____________________________________________________________________________________________________________
spread by approximately 2.86–3.18 percentage points, other factors being constant.<sup>8</sup> This suggests that strengthening the quality of the regulatory regime for private sector development can have a substantial impact on reducing interest rate spreads in Tajikistan.

Table 2 shows the economic significance instead of the statistical coefficients for each variable in all specifications using standardized variables.<sup>9</sup> The advantage of this approach over statistical significance is that we can use comparable coefficients. The results indicate the comparatively large impact of income diversification, loan size, risk aversion, market power, and credit risk on net interest margins in Tajikistan.

|**Variable**|**Coefficient**|**Standard Error**|**z-value**|**p-value**|**Ranking**|
|---|---|---|---|---|---|
|Income diversification|-0.516|0.043|-11.930|0.000|1|
|Size of operations|-0.421|0.057|-7.450|0.000|2|
|Risk aversion|0.404|0.039|10.390|0.000|3|
|Market power|0.333|0.038|8.850|0.000|4|
|Credit risk|0.310|0.036|8.630|0.000|5|
|Operating costs|0.183|0.031|5.840|0.000|6|
|Legal environment|-0.085|0.042|-2.030|0.042|7|
|Interest rate risk|0.082|0.043|1.930|0.053|8|
|Opportunity cost of excess bank reserves|0.055|0.032|1.720|0.085|9|
|Inflation|0.005|0.017|0.290|0.770|10|

Source: Author’s calculations.

# V. CONCLUSIONS AND POLICY RECOMMENDATIONS

This paper provides empirical evidence on the determinants of banks’ interest spreads in Tajikistan using a panel dataset for the period 2011Q1 – 2022Q4. To this end, we specify an empirical model constituting an extension of the model developed by Ho and Sanders (1981), which we estimate using Beck and Katz’s (1995) OLS-based PCSE procedure. As predicted by the empirical model, all the explanatory variables, except for inflation, are significant and have the expected effect on banks’ net interest spreads.

variation of interest rate spreads. Operational efficiency is a significant driver of interest spreads in Tajikistan. Higher operating costs are reflected in higher interest spreads. In addition, we find that higher perceived credit risk translates into higher net interest spreads as banks compensate for the increased risk of loan default. This signals the importance of legal and institutional frameworks for lending and debt resolution. The opportunity cost of holding cash and balances at the central bank is another significant determinant of net interest spreads, reflecting limited interbank market activity and underdeveloped capital markets.

the existence of room for consolidating the banking sector in Tajikistan to remove diseconomies of scale for small banks. Risk aversion is also associated with high spreads, whereby higher capital adequacy ratios are compensated for by higher spreads and margins. We also find that higher market power

> 8 Tajikistan’s estimate of the Regulatory Quality index in 2022 (the latest available data point) was –1.198.

> 9 The economic significance coefficients are obtained by using standardized variables z = (x-μ)/σ where μ is the mean of the population; and σ is the standard deviation of the population.

18
**Evidence from the Delhi Primary Survey** The results on female time use so far are based on the CPHS data which does not separate travel times into purpose of travel (i.e., commuting to work vs non-work related trips). The Delhi Survey, on the other hand, reports the purpose of travel and the average travel distance for each respondent. While this data does not report employment status, the survey answer ‘ _travel for work_ ’ is meant to capture travel _to work_ in the case of employed women and travel _to search_ for work in the case of unemployed women.

We use the Delhi data to compare the purpose of travel and the average travel distance between new and continuous users of bus transit in the post-implementation periods. We acknowledge that the new users are induced into using the buses because they are free and hence are selected relative to incumbent users. The comparison group of continuous users is matched on observable characteristics to the new users as discussed in Section E.2. While this does not address the selection issue completely, this comparison is still revealing and useful. With this disclaimer in mind, we estimate the following regression: where _Yi_ is a dummy variable for travel purposes which is equal to one if it is for work or a dummy variable for average travel distance via buses which is equal to one if the average distance exceeds 10 kilometers. _I_ new users is a dummy variable set to one for new users and zero for continuous users. _X_ is a vector of control variables including an individual’s marital status and education level. The results are reported in Table A8.

In column 1, we find that new bus users are 6.8% more likely to travel for work than continuous users. The estimated coefficient decreases slightly to 6.6% after conditioning on marital status and education. We then restrict the matched sample to users whose travel purpose is work, which includes travel to work or job search depending on unreported employment status. In columns 3 and 4, we observe that, on average, new users are 18.2%-19.8% more likely to travel longer than 10 kilometers compared to continuous users. These results provide suggestive evidence that free passes enable women to travel greater distances, potentially allowing them to work in more remote jobs or facilitating their job search efforts.

# **J Conclusion**

In the face of a literature that costs often re- large documenting increasing commuting duce women’s work hours and labor force participation, there is an increased policy 19
## **3.4 Dismantling of coercive institutions**

The advent of democracy in South Africa was followed by numerous interventions aimed at leveling the historically uneven bargaining field. Labor market outcomes were affected via two channels. First, changes in legislation governing union activity directly caused an increase in the bargaining power of unionized labor, _β_ . Second, the abolishment of mobility restrictions for workers in the former homelands, along with the elimination of the restrictions on the type of jobs they would gain access to, led to an increase in the reservation wage of unionized workers, _<u>w</u>_ <u>.</u> Straightforward computations show that, all else being equal, both these forces contribute to an unambiguous increases in the negotiated wage and union utility, while lowering domestic profits and employment levels.

## **3.5 International price shocks**

Consider now the effects of movements in international prices, which we assume are driven by exogenous shocks to global demand, _a_ , and/or foreign marginal costs, _w_<sup>_∗_</sup> .<sup>14</sup> From (8) we observe that: An increase in global demand or a positive shock to foreign marginal costs leads to a higher international price. This leads to an increase in domestic production and quasirents, and hence to a higher negotiated wage. From (9) and (10), we further observe that these positive effects on union wages are strictly increasing in _β_ . The more powerful the union, the stronger its ability to benefit from these resource windfalls.

# **4 Data**

For the main analysis in this paper, we build a community-level panel data set using the community profiles from the 1996 and 2011 population censuses run by Statistics South Africa. In their original format, the community profiles (at the sub-place level) provide aggregated category counts for each variable in the census. South Africa is divided into 9 provinces (equivalent to states), 266 municipalities and over 21,000 communities (subplaces). Each community has a population of 2,000 individuals on average, though there is

> 14During the period of analysis, the emergence of China and the resulting increase in global demand for natural resources is perhaps the clearest empirical counterpart to an exogenous shock to global demand (see, e.g., Autor et al, 2013).

12
# **Table A.12. The effect of pensioners, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.657***|0.409***|
|g|_(0.011)_|_(0.079)_|
|L f hhld i|-0.368***|-0.144***|
|og o ouseo sze|_(0.029)_|_(0.043)_|
|Children*Lo of household size|0.042***|0.019*|
|g|_(0.008)_|_(0.011)_|
|Pi*L f hhld i|-0.022|-0.044|
|ensonersog o ouseo sze|_(0.023)_|_(0.051)_|
|Nb f i|-0.040|0.079|
|umer o pensoners|_(0.027)_|_(0.060)_|
|_Number of observations_|231,972|692,336|
|_Log pseudolikelihood_|-394,096|-256,312|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

47
# **Appendix B: Additional tables**

Table A.1: Summary statistics, estimation sample, 1995-2013

|Sector|TFPR|Employment|Revenues|Exporters|Foreign|N (obs.)|N (frms)|
|---|---|---|---|---|---|---|---|
|Food and beverages|3.865|151.866|64.439|0.466|0.105|25,841|3,592|
||(0.847)|(287.204)|(205.281)|(0.499)|(0.307)|||
|Tobacco|2.743<br>(0.958)|829.116<br>(871.017)|1174.163<br>(1391.239)|0.833<br>(0.374)|0.5<br>(0.502)|138|14|
|Textile, wearing apparel, leather|3.974|157.637|16.212|0.764|0.188|13,782|1,867|
||(0.922)|(235.768)|(42.777)|(0.424)|(0.391)|||
|Wood|3.626|127.085|29.272|0.785|0.152|7,150|1,034|
||(0.691)|(195.2)|(110.173)|(0.411)|(0.359)|||
|Pulp, paper and printing|5.313|116.178|42.833|0.613|0.171|6,686|916|
||(0.884)|(206.713)|(147.089)|(0.487)|(0.376)|||
|Chemicals|4.684<br>(0.886)|258.429<br>(559.629)|119.735<br>(316.885)|0.776<br>(0.417)|0.244<br>(0.43)|5,472|687|
|Rubber and plastic|4.066<br>(0.708)|122.512<br>(266.831)|38.165<br>(130.317)|0.778<br>(0.416)|0.231<br>(0.421)|11,774|1,622|
|Non-metallic mineral products|4.456<br>(0.856)|172.164<br>(298.299)|48.617<br>(108.157)|0.549<br>(0.498)|0.197<br>(0.398)|8,300|1,114|
|Basics and fabricated metals|4.64|141.681|43.103|0.717|0.187|21,419|3,000|
||(0.78)|(374.529)|(264.849)|(0.45)|(0.39)|||
|Machinery and equipment|5.009|152.354|43.198|0.668|0.143|12,704|1,707|
||(0.791)|(326.021)|(256.078)|(0.471)|(0.35)|||
|Electrical, comm., medical|5.227<br>(0.949)|202.276<br>(424.024)|71.201<br>(379.659)|0.709<br>(0.454)|0.231<br>(0.421)|9,619|1,262|
|Motor vehicles, other transport|5.419<br>(0.989)|354<br>(806.788)|152.26<br>(728.611)|0.783<br>(0.412)|0.288<br>(0.453)|8,348|1,056|
|Other|4.612<br>(0.874)|202.387<br>(442.729)|39.12<br>(153.547)|0.83<br>(0.376)|0.196<br>(0.397)|8,004|1,150|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. The table reports means and standard deviations (in parentheses) for the estimation sample of manufacturing firms with more than 10 employees over the period 1995-2013 (except 2000-2002). A firm is foreign-owned if foreign investors hold at least 50% of capital. TFPR is estimated using the method in Levinsohn and Petrin (2003). Monetary variables are in 2010 prices. The Tobacco industry has a small number of firms. To preserve their confidentiality, summary statistics for this industry are not reported.

32
In rural China, where the happiness score ranges from 0 to 4 with a mean of 2.7, those who report being “much below” the mean income of the village (five answers are possible) have a happiness score that is 1.06 below those who report being “much above” the mean (Knight and Gunatilaka 2010). Respondents were asked with whom they compared themselves; in rural China, two-thirds of respondents claimed to compare themselves with others living within the village. This finding indicates that the reference groups are narrow. In rural China, the “relevant others” are households in the same village, and in urban China, the “relevant others” are households in the same city. In the cities, where a doubling of income raises the happiness score by 0.10 units, the happiness of respondents whose households fall into the lowest quarter of city income per capita is 0.81 points below those in the highest quarter (Knight and Gunatilaka 2010).<sup>5</sup> Thus, income inequality matters, but it is inequality at the local and not the national or regional level that matters to people.

The finding that subjective well-being in China is sensitive to relative income is in line with the evidence for many countries (surveyed by Clark et al. 2008, and by Graham and Felton 2006). The coefficient on group income is generally negative, but there are cases in which it is positive. For instance, Senik (2004) found a positive coefficient for Russia (on income in the region), and Kingdon and Knight found a positive coefficient (2007) for South Africa (on income in the close neighborhood). The (usual) negative coefficient is normally interpreted to indicate feelings of relative deprivation, and the (unusual) positive coefficient is normally interpreted 15
Figure 12: Although preferential liberalization has targeted highly protected sectors (MFN tariffs greater than 15%), agricultural products, textiles and footwear remain pockets of protection <!-- Start of picture text --> 8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>MFN range<br>Total liberalization Partial Liberalization No Liberalization<br>Share of sector tariff lines<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database How big is the preferential advantage?

The most common way to measure the advantage given by preferential access is through preference margins. Preference margins are traditionally calculated as the difference between the MFN applied rate and the preferential tariff.<sup>11</sup> While the average preferential margin in PTAs is low, more than a quarter of world trade is subject to an average preference margin of 7.4 percent. The average preferential margin is low, because one‐fifth of world trade under preferential agreements is already duty free and a further 2 percent of world trade is not at all liberalized. However, significant margins are applied to the trade that is liberalized under PTAs: the average preference is 7.4 percent for the 28 percent of world trade that is completely liberalized, and 6.4 percent for the remaining 3 percent that is partially liberalized (see Table 3).<sup>12</sup>

> 11 Traditional preference margin = T�,���� �T�,�� , where ��,���� is the MFN rate applied by country k on product i and � ��,� is the preferential rate applied to country j.

> 12 The preferential margin is significantly larger if MFN bound rates instead of applied rates are used as a point of reference. The average preferences are on average 17.4 percent for the 28 percent of world trade that is completely liberalized, and 13.6 percent for the 3 percent that is partially liberalized (see Table 3). 13