rafmacalaba/datause-encoder-data huggingface dataset:
- 35 -

# V. CONCLUSIONS

# Recent Trends

Three significant trends have emerged in the Bank's ID work in the 1980s. First, the extension or coverage of ID in Bank projects has become pervasive. Nearly 90 percent of the projects today have ID components in contrast to 72 percent in 1978. The awareness of ID issues on the part of Bank staff and their exploration of these issues during project preparation have increased steadily.

Second, there nas been a widening of the scope of ID issues and of the elements that have constituted ID components in recent years. There is also evidence that these elements are, in many cases, common across sectors. By and large, this trend can be attributed to the newer policy-based lending instruments. Widening of the scope, however, has occurred through an expansion of the coverage of the institutions in the concerned sectors. Much greater attention is being given in recent projects to the sector or subsector level institutions and their structures and functions while in earlier years the preoccupation was solely with the PIU and its functions. The latter, however, still continues to be a feature of ID work in some projects and sectors.

Third, while these are important and encouraging developments, the achievement of ID objectives in Bank projects has declined significantly in recent years. A recent OED study has found that only 52 percent of Bank projects evaluated from 1986-88 were rated "likely to be sustainable' though 76 percent of the cohort were considered "satisfactorily" completed in terms of investment.31 This implies that though the overall project implementation is satisfactory in nearly three fourths of the cases, project benefits are sustained only in about two thirds of them. The report further stated that most sustainable Bank projects put a major emphasis on the development of the institutional capacity required to sustain benefit flows after their completion.

The decline in ID performance in the 1980s was only in part due to the problems in the macroeconomic environment. Factors internal to the project also seem to have made a difference to ID effectiveness. Weak analysis of institutional issues and overly complex ID designs which failed to consider local capacity constraints have contributed to the declining ID performance. The difference between the projects in high and low ID achievement categories, however, was not in the problems they faced, but rather in their response to their problems. The quality of leadership, attention to personnel development, flexibility in implementation reinforced by careful supervision, and the harmonization of national and external (TA) inputs were among the key factors which separated the high ID achievement 31 OED, Annual Review of Ev-aluation Results, World Bank, 1989.
and declining world prices. This diversion of cotton is evidence<sup>of vertical integration. An</sup> evaluation of changes in value-added through textile processing is<sup>beyond the scope of this study.</sup> Concentration ratios, and the ogive measures of concentration,<sup>are presented in Table 6.</sup> Malawi showed a clear increase in concentration between the two sub-periods<sup>for its largest, two</sup> largest, "Big-5", and "Big-7" agricultural export commodities. The ogive<sup>measure shows that the</sup> share distribution remained roughly the same among the "Big-5" commodities.<sup>Because, however,</sup> the "Big-5" share increased so much (from 75 to 89%), the "Big-7"<sup>and the "Big-7 plus others"</sup> measures showed a less equal distribution. Overall, Malawi's exports<sup>became more concentrated in a</sup> small group of commodities.

During the first period, Malawi's strong concentration in two exports<sup>with very stable</sup> international prices (tobacco and tea) led to a very low level of export earnings<sup>instability. This</sup> stability is reflected by an extremely low export earnings portfolio<sup>variance (PVEARN), compared to</sup> the other two countries (Table 7). Given this low level of instability,<sup>during the second period</sup> Malawi was able to concentrate on maintaining its growth in earnings<sup>rather than attempting to lower</sup> instability. It became more concentrated in a small number of exports, increasing<sup>dramatically the</sup> share of sugar in total exportse, and its earnings (PVEARN) and price<sup>(PVPRICE) instability</sup> consequently increased (Table 5, Table 7).

The changes Malawi underwent from the first to the second period<sup>had an impact on its two</sup> measures of portfolio variance. PVPRICE increased nearly 4-fold<sup>(from 0.98 to 3.72), and Malawi</sup> would have had a lower value (2.54) of PVPRICE had it maintained<sup>its first period shares. PVEARN</sup> shows, however, that the country was able to stabilize its export earnings<sup>by stabilizing export</sup> volumes. Thus, PVEARN increased only 3-fold (from 0.71 to 1.97), and<sup>the new mix was better</sup> than the old in terms of PVEARN (1.97 compared to 3.53). Thus,<sup>although instability increased, the</sup> new mix was better than the old in terms of total export value instability.

24
- Kumar, Sameer, and Palo Petersen, "Impact of e‐commerce in lowering operational costs and raising customer satisfaction," _Journal of Manufacturing Technology Management_ 17 no. 3 (2006), pp. 283–302, https://doi.org/10.1108/17410380610648263.

- Leamer, Edward E., and Michael Storper, “The Economic Geography of the Internet Age,” _Journal of International Business Studies_ 32, no. 4 (2001): 641–65.

- Levin, Jonathan D., “The Economics of Internet Markets,” National Bureau of Economics Research Working Paper Series, no. 16852, March, 2011: 1–5.

- Lieber, Ethan, and Chad Syverson, “Online vs. Offline Competition,” in M.Peitz and J.

- Waldfogel (eds.), the _Oxford Handbook of the Digital Economy_ , Oxford University Press, 2012.

- McKinsey (2013), _China’s e-tail revolution: Online as a catalyst for growth_ . McKinsey & Company

- PwC's Entertainment and Media Outlook (2008), China's next retail disruption: End-to-end value chain digitalization. https://www.pwccn.com/en/retail-and-consumer/publications/globalconsumer-insights-survey-2018-china-report.pdf.

- Radloff, L. S. (1977). The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. _Applied Psychological Measurement_ 1(3), 385–401, https://doi.org/10.1177/014662167700100306.

- Sinai, Todd, and Joel Waldfogel, “Geography and the Internet: Is the Internet a Substitute or a Complement for Cities?,” _Journal of Urban Economics_ 56, no. 1 (2004): 1–24

- Startz, Meredith, _The Value of Face-to-Face: Search and Contracting Problems in Nigerian Trade_ (November 27, 2016). Available at SSRN: https://ssrn.com/abstract=3096685 or http://dx.doi.org/10.2139/ssrn.3096685.

- Zhang, Xiaobo, and Wu Zhu, “The Spatial Patterns of E-Commerce in China,” working paper, Peking University, 2015, 1–26.

The World Bank Group, _Digital Dividends World Bank Report 2016_ .

·· 27
**Table 3:** Average Gains and Losses

|||(1)<br>Satisfed with<br>overall house<br>quality (1=Yes)|(2)<br>Satisfed with<br>quality of family<br>life (1=Yes)|
|---|---|---|---|
|_Model with Disappointment_||||
|Permanent Gains|_βP_<br>_R_|-1.797|0.678|
|(PG)||12.952|2.33|
|Transitory Gains|_βT_<br>�<br>_e_<sup>_−R_∆</sup><sup>_T_</sup><br>_δT_+_R_ <sup>+ 1</sup><sup>_−e−R_∆</sup><sup>_T_</sup><br>_R_<br>�|7.127|3.326|
|(TG)||12.71|2.188|
|Delay Losses|_βD_<br>_δD_<br>_δD_+_R_<br>�<br>1_−e_<sup>_−_(</sup><sup>_δD_+</sup><sup>_R_)∆</sup><sup>_T_</sup><br>�|0.165**|0.071*|
|(DL)||0.072|0.043|
|Total Gains||5.164***|3.932***|
|(PG+TG-DL)||1.129|0.929|
|_Model without Disappointment_||||
|Permanent Gains|_βP_<br>_R_|0.404|1.589|
|(PG)||2.166|0.427|
|Transitory Gains|_βT_<br>_δT_+_R_|1.415|0.9104|
|(TG)||1.845|1.712|
|Total Gains||1.819***|1.729***|
|(PG+TG)||0.548|0.492|
|_OLS estimate_||||
|Pooled coeffcient||0.095***|0.096***|
|(PG)||(0.026)|(0.026)|

Notes: Gains and losses are computed using the NL estimated coefficients in Table 2 and evaluated at the mean of ∆ _T_ of 6.8 months. Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

19
_Table 5 - The unequal effect of shocks on expenditure and income. The sample includes households in the HEIS data. Shock is measured as the yearon-year difference in log(USD to IRR rate) in each month._

**Panel A: Regression with all HEIS households**

||**Total exp**|**enditure**|**Food ex**|**penditure**|**Total i**|**ncome**|**Earned**|**income**|**Wag **|**e rate**|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Afghan dummy**|-0.217***|0.032|-0.191***|-0.082***|-0.007|-0.048**|0.164***|0.181***|0.006|0.131***|
||(0.023)|(0.022)|(0.019)|(0.019)|(0.025)|(0.022)|(0.030)|(0.029)|(0.023)|(0.023)|
|**Shock**|-0.024***|-0.027***|-0.081***|-0.083***|0.059***|0.054***|0.026***|0.024***|0.035***|0.035***|
||(0.005)|(0.005)|(0.004)|(0.004)|(0.006)|(0.005)|(0.008)|(0.007)|(0.008)|(0.007)|
|**Afghan * Shock**|-0.076|-0.073*|0.013|0.013|-0.074|-0.035|-0.008|0.019|-0.096**|-0.082*|
||(0.047)|(0.044)|(0.039)|(0.038)|(0.051)|(0.044)|(0.061)|(0.059)|(0.047)|(0.046)|
|**Male head**||0.084***||0.055***||0.205***||0.282***||0.244***|
|||(0.005)||(0.005)||(0.005)||(0.011)||(0.015)|
|**Literate head**||0.503***||0.211***||0.558***||0.431***||0.346***|
|||(0.003)||(0.003)||(0.003)||(0.005)||(0.005)|
|**Married head**||0.073***||0.127***||0.114***||0.009||0.033***|
|||(0.005)||(0.005)||(0.005)||(0.010)||(0.011)|
|**Size**||-0.075***||-0.032***||0.122***||0.085***||0.007***|
|||(0.001)||(0.001)||(0.001)||(0.001)||(0.001)|
|**Age of head**||0.004***||0.003***||0.006***||0.001***||0.006***|
|||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)|
|**Constant**|15.293***|14.911***|14.173***|13.842***|18.416***|16.966***|17.849***|16.864***|10.175***|9.324***|
||(0.005)|(0.008)|(0.004)|(0.007)|(0.006)|(0.008)|(0.008)|(0.013)|(0.008)|(0.017)|
|**Observations**|341527|341527|341457|341457|342133|342133|265126|265126|121938|121938|
|**R-squared**|0.024|0.141|0.112|0.147|0.017|0.262|0.007|0.084|0.016|0.057|
|**Time FE**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Occupation FE**|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

26
Two issues raise concern about the estimates generated in the empirical analysis. First, in the ORG supplements of the CPS, missing earnings data is imputed by the Census Bureau using a hot deck procedure. However, the CPS also provides flags to identify the instances where wage information has been allocated. For the sake of consistency, all results in the paper are based on respondents who have valid, i.e. non-allocated, wage variables. Second, values at the top end of the wage distribution are top-coded in both the surveys. This is not a significant concern for hourly wage earners, as they seldom earn enough to have their income variables censored. However, to further mitigate the threat of top-coding from biasing our estimates, we follow Autor et al. (2005) and restrict our attention to respondents with income below the 90th percentile.

Data on household level time allocation comes from the 2003 - 2018 waves of the American Time Use Survey (ATUS). The ATUS is conducted by the U.S. Bureau of Labor Statistics (BLS) with individuals drawn from the existing CPS sample. Each wave of the ATUS survey is based on 24 hour diaries where the respondents detail their activities from the previous day. These activities are then assigned to one of the 400 ATUS’s pre-specified classification schemes. Following Aguiar et al. (2013), we segment the total time use into four broad categories. These four categories, described in detail below, are mutually exclusive and partition the total individual time endowment.

Labor time includes all the time spent working in primary and secondary jobs, and overtime, and includes time spent in commuting to and from work. However, we exclude from this the time spent actively searching for jobs.<sup>9</sup> We also exclude from our labor time the total time spent in non-market or informal work which may also generate income for the household.

Shopping time consists of the total time spent in obtaining goods and services. This comprises time spent in shopping for groceries and other household items, including browsing, shopping, paying for, or returning items, as well as the time spent waiting in line and the time spent in commuting.<sup>10</sup> Leisure includes the total time spent on sleeping, relaxing, exercising, sports, reading,

> 9Thus, we do not include time spent in activities such as filling and sending out resumes, commuting to and appearing for job interviews, researching details pertaining to a new job, or searching for jobs in the newspaper or the internet.

> 10We also include time spent in comparison shopping, coupon clipping, and researching products on the internet. However, we exclude time spent in ordering or acquiring take-out, fast-food or restaurant meals.

20
housring or labor market, the marginal immigrant to participate in the labor force should be more qualified than the marginal native. However, this should lead to _lower_ commuting rates among immigrants as they have an advantage over native competitors in the same market which should reduce their inclination to search in suburbs.

Can job seekers target suburbs with lower discrimination? To shed light on this question, I look at the subset of 1,073 commuters and measure the difference in discrimination between the home and target suburb. I find that on average levels of discrimination in the home suburb are only marginally higher (difference: 0.005 or 0.07 standard deviations, not significant). Taken at face value, this suggests that people have information on discrimination in their place of residence but that this is more difficult to assess for suburbs where one is searching for jobs.

# **7 Concluding Remarks**

This study uses a unique data set of job seekers using a free job advertisement website in South Africa’s Western Cape province. This is to my knowledge the first study of this kind in Africa, a continent in which the rapidly increasing internet access offers new opportunities to create and facilitate markets. In particular, internet fora like gumtree offer an opportunity to match employers with job seekers in spatially segregated urban areas and reduce the reliance of firms to hire through social networks, currently the most common form of hiring in most developed countries (Beaman and Magruder (2012)).

One may wonder why such a large share of job seekers reveal that they are immigrants on their profile page if they get penalized by employers. One explanation is that employers can easily verify job seekers’ nationality (as they typically require to see the national ID card). It is therefore beneficial for immigrants to reveal their nationality upfront to avoid going through the hiring process with discriminating employers.

One limitation of the study is that we know relatively little about the identity of potential employers using the website. From the survey we know that 96% were employed by a private household. In the Western Cape, these employers are predominantely White (63%) compared to Blacks (27%) and Coloured (10%). Whether different race groups have differential preferences over hiring immigrants 27
Policy Research Working Paper 5656

# **Abstract**

In recent years, the number of studies looking at the effect of politics on economic outcomes has flourished. For developing economies, these studies are useful to better understand why long overdue reforms are not implemented. The studies analyze the overall context within which reforms are being implemented and the underlying incentive framework. However, it seems difficult to make such studies actionable, especially in sectors where donors have a heavy presence that can sometimes distort incentives in addition to the reluctance from some governments to amend the existing systems in place. This paper focuses on some conclusions emerging from the political economy diagnostics carried out in Zambia in various sectors in recent years. Based on interviews of World Bank task managers, the paper attempts to assess the relevance of these studies for the implementation of projects and the policy dialogue and draws lessons on how they have influenced the implementation of the World Bank’s support to programs in various sectors in Zambia as well as the main challenges for this type of exercise.

This paper is a product of the Public Sector Reform and Capacity Building Unit, Africa Region. 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://econ.worldbank.org. The author may be contacted at graballand@worldbank.org.

_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
# **Kenyan Exports Values and Shares of Agricultural and Other CRTS Products in 2007**

|||Export|value($'00|0)||ex|port shares||||
|---|---|---|---|---|---|---|---|---|---|---|
|Product<br>C|OMESA15|EAC5|EU27|ROW|WLD|COMESA15|EAC5|EU27|ROW|WLD|
|**AGRICULT**|**URE**||||||||||
|Maize|671|2,694|7|9,096|12,468|0.054|0.216|0.001|0.730|1.000|
|Wheat|2|43|0|119|164|0.013|0.264|0.000|0.723|1.000|
|Rice|203|318|5|86|613|0.332|0.519|0.009|0.140|1.000|
|Barley|0|654|0|0|654|0.000|1.000|0.000|0.000|1.000|
|Other cerea|453|107|8|309|877|0.517|0.122|0.009|0.352|1.000|
|Cotton|4|0|18|126|148|0.025|0.000|0.120|0.855|1.000|
|Sugar|10,573|8,616|19|336|19,545|0.541|0.441|0.001|0.017|1.000|
|Coffee|1,093|780|98,647|65,708|166,228|0.007|0.005|0.593|0.395|1.000|
|Tea|170,298|238|131,530|396,147|698,213|0.244|0.000|0.188|0.567|1.000|
|Roots and|1|24|7|0|32|0.022|0.739|0.229|0.010|1.000|
|Oil seeds a|14|157|4,831|3,007|8,009|0.002|0.020|0.603|0.375|1.000|
|Fruits|2,335|4,878|85,188|20,397|112,797|0.021|0.043|0.755|0.181|1.000|
|Vegetables|987|4,610|256,893|26,590|289,080|0.003|0.016|0.889|0.092|1.000|
|Cut flowers|22,982|8|316,343|50,929|390,262|0.059|0.000|0.811|0.130|1.000|
|Other crops|737|3,739|1,233|2,733|8,442|0.087|0.443|0.146|0.324|1.000|
|Beef|287|528|0|484|1,299|0.221|0.406|0.000|0.372|1.000|
|Dairy produ|3,002|10,337|25|3,340|16,704|0.180|0.619|0.001|0.200|1.000|
|Poultry|101|8|0|9|118|0.856|0.067|0.000|0.077|1.000|
|Meats of sh|101|283|0|86|469|0.214|0.603|0.000|0.183|1.000|
|Other livest|150|1,876|69|1,013|3,108|0.048|0.604|0.022|0.326|1.000|
|**OTHER CR**|**TS GOODS**||||||||||
|Fishing|411|162|34,837|25,757|61,167|0.007|0.003|0.570|0.421|1.000|
|Forestry|412|483|4|169|1,068|0.386|0.452|0.004|0.159|1.000|
|Mining|2,305|29,358|21,162|21,545|74,369|0.031|0.395|0.285|0.290|1.000|
|Meats and|3,821|14,847|131|6,576|25,375|0.151|0.585|0.005|0.259|1.000|
|Grain millin|415|538|49|59|1,062|0.391|0.507|0.046|0.056|1.000|
|Sugar & ba|14,420|33,297|1,912|16,008|65,637|0.220|0.507|0.029|0.244|1.000|
|Textiles an|22,415|32,212|3,996|238,463|297,087|0.075|0.108|0.013|0.803|1.000|
|Leather and|14,512|28,989|15,930|31,441|90,872|0.160|0.319|0.175|0.346|1.000|
|Wood and|16,394|47,045|2,587|7,287|73,314|0.224|0.642|0.035|0.099|1.000|

Source: Based on UN COMTRADE Statistics.

84
and the analysis of administrative data shows that T1 members had 4.8 percent higher savings deposits than the control group. Neither of these outcomes are significant for T2 and the coefficients on deposit amounts are significantly different between T1 and T2, with an F-test p- value of 0.077.

The TOT estimates in Appendix Table 6 confirm a similar scaling effect as the previous outcomes, with T1 attendees 17.7 percentage points more likely than the control group to save regularly per their financial plan – a sizable 45% improvement; and hold 47% higher savings deposits in their SACCO accounts as per the administrative data.

Overall, these findings show clear beneficial effects of the financial education program in T1 and fairly muted effects in T2. The crucial difference between T1 and T2, as detailed previously, was in the autonomy given to SACCOs to select the trainers who attended the _training-of-trainers_ workshop. In T1, the selection was left up to the SACCO whereas in T2, the selection was imposed by the program.

Table 10 explores differences in trainer profiles between the two groups to help trace the mechanism behind these differential treatment effects. While the number of trainers per SACCO and the gender of trainers are similar across the two groups, we detect significant differences in the role the trainers played in their communities. Specifically, we find that unlike the fixed selection group, SACCOs in T1 were significantly more likely to send additional SACCO community members to the _training-of-trainers_ workshop and significantly fewer loan officers. Moreover, our qualitative findings suggest that more experienced educators and those with relevant experience conducting trainings were selected as trainers in T1.

These differences in the trainer selection process are quite consequential. Qualitative interviews with community members and SACCO administrative staff reveal that most loan officers faced considerable time constraints in delivering financial education. Loan officers typically have a full set of work requirements and goals; assigning them the further task of teaching financial education in T2 was likely quite burdensome. Qualitative interviews also suggest that the larger pool of 15