rafmacalaba/datause-encoder-data huggingface dataset:
focus of this paper is on relative outcome differences between the finance and marketing treatment groups, both of which were compared to the same control group.

# **6. Conclusion**

This paper develops and studies four distinct hypotheses on pathways to profits for small businesses exposed to finance and marketing training in South Africa. Through a randomized control study of 852 firms, the analysis finds significant improvements in profitability from both types of business training interventions. However, the pathways to achieve these gains differ substantially between the two groups. The marketing group achieves greater profitability by adopting a growth focus of significantly higher sales, improvements in inventory, and hiring more employees. In contrast, the finance group adopts an efficiency focus of significantly lower costs. To precisely identify pathways, the analysis finds that these profitability gains are achieved by both groups adopting business practices related to their respective training.

To test further hypotheses on benefits of each type of training, the analysis shows that marketing and sales training is significantly more beneficial to firms that ex-ante have less exposure to different business contexts; while entrepreneurs who have been running more established businesses prior to training benefit significantly more from finance and accounting skills.

These results have important implications for practitioners and policy makers. In terms of practice, for multinationals a better understanding of the pathways to profitability and the heterogeneity in managerial capital across small businesses can enhance the success of market expansion and customer segmentation strategies. In addition, our results provide useful benchmarks that multinational firms can use to learn how best to develop business skills among their distributors and suppliers in emerging markets.

This research is also important to policy makers wishing to promote business skills training in emerging markets. The World Bank alone invests almost $1 billion per year on skills training programs (Twose 2015), yet the evidence on their effectiveness lags far behind the policy and funding interest. “Vast armies” (de Mel, McKenzie, and Woodruff 2010) of micro businesses continue to populate the poor parts of the world, and very few appear to grow to a level that allows 32
Policy Research Working Paper 8979

# **Abstract**

Using administrative data for an exhaustive sample of formally registered firms, reveals that the engagement of Rwandan firms in global value chains (GVCs) is remarkably limited. The paper documents several patterns of firm-level exports and compares firm characteristics between exporters and non-exporters. It also illustrates which firm-level characteristics are good predictors for a variety of extensive margins of export and import activities. The analysis includes firms from three goods-producing sectors, agriculture, mining, and manufacturing, but focuses mostly on manufacturing firms. The results indicate large differences between small and large exporters in terms of export market participation, type of products exported, and destinations served. GVC engagement has increased over the 2008– 2016 sample period, especially for manufacturing firms, but this is a slow process with frequent set-backs.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. 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://www.worldbank.org/prwp. The authors may be contacted at jo.vanbiesebroeck@kuleuven.be or garth.frazer@rotman.utoronto.ca.

_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
# **Figure B31: Permanent component—Energy and base metals**

<!-- Start of picture text --> Index, January 1970 = 100<br>150<br>Coal Crude oil Naural gas<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Index, January 1970 = 100<br>105<br>Aluminum Copper Lead Nickel Tin Zinc<br>100<br>95<br>90<br>85<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

**Source** : Authors’ calculations

_— 72 —_
10 countries useu Stern (1991)J **6-** Table 1 reports sinple cross.-country correlations between different moacurec of financial policy; it also report. correlations between indicators of financial policy and financial market development. The data used are period averages for 1965-89. The table show. that the simple correlation between the average real interest rate for the period 1965-89 (denoted by r6589) and the average ratio of total assets of the financial institutions to GDP for the period 1965-89 is positive (denoted by M3Y6589); the correlation is + 0.28. Figure 2 shows the positive association between real interest rate and financial deepening for the sample ef developing countries. Korea and Malaysia are examples of countries t at maintained positive real interest rates between 2 to 3 percent per annum and experienced substantial monetary deepening between 1965 to 1989. In contrast, Somalia and Zaire had negative real interost rates, averaging some -14% per annum, and have shallow financial markets. Bolivia and Peru are examples of Lati-i American countrics that lack financial depth as a result of negative real interest rates in the past.

Table 1 shows that the correlation between average real interest rate and financial intermediation to GDP ratio (FIY6589) is also positive, +0.31. Figure 3 shows the relationship between these two variables; it shows that a majority of countries maintained low and negative real Interest rates (3 to 4% per annum, resulting in limited financial intermediation (10 to 15% as a ratio of GDP). With regard to the distribution of credit, evidence shows that the real interest rate is positively correlated with credit to the private sector (CPY6589), +0.48, and negatively associated with credit to the public sector (CGY6589),

-0.24. This is consistent with the finding that artificially low anid negative real interest rates reduce the extent of capital allocation

6 It has been argued, though, that it can be difficult to draw the line between the developed and developing countries.
|1968–2008,|**F10**|1.457<br>(0.37)_∗∗∗_<br>-0.168<br>(0.13)<br>-0.012<br>(0.04)<br>-0.094<br>(0.16)<br>0.126<br>(0.27)<br>0.089<br>(0.12)<br>2.273_∗∗_<br>64.921<br>-1.410<br>S-GMM<br>124<br>344<br> and is hence<br>e in log form.<br> (GMM only)<br> percent level,|
|---|---|---|
|03 and|**F9**|2.679<br><br>(4.75)<br>-0.365<br>(0.27)<br>-0.690<br>(0.96)<br>2.992<br>(2.73)<br>0.729<br>(0.83)<br>0.535<br>(1.28)<br>1.281<br>0.445<br>IV-2<br>5<br>33<br>mple size,<br>riables ar<br>t variable<br>nce at 10|
|, 1999–20|**F8**|1.454<br>(0.50)_∗∗∗_<br>-0.190<br>(0.14)<br>-0.032<br>(0.07)<br>-0.134<br>(0.23)<br>0.045<br>(0.39)<br>0.334<br>(0.62)<br>1.676<br>33.795<br>-1.555<br>S-GMM<br>98<br>204<br>aximize sa<br>nel). All va<br>d dependen<br>tes signifca|
|h polarity|**F7**|6.193<br><br>(2.86)_∗∗_<br>-0.739<br>(0.34)_∗∗_<br>0.092<br>(0.27)<br>-1.387<br>(1.04)<br>1.598<br>(1.07)<br>-2.971<br>(2.12)<br>3.245<br>0.027<br>IV-2<br>4<br>40<br>elected to m<br>68–2008 (pa<br>ses. A lagge<br>ted. _∗_indica<br>level.|
|s of growt|**F6**|1.597<br>(0.47)_∗∗∗_<br>-0.195<br><br>(0.14)<br>-0.013<br>(0.08)<br>-0.157<br>(0.26)<br>0.099<br>(0.37)<br>1.850_∗_<br>40.754<br>-1.398<br>S-GMM<br>98<br>204<br>ling frame s<br>ion) and 19<br>d in parenthe<br>ut not repor<br>t 1 percent|
|erminant|**F5**|5.584<br><br>(2.92)_∗_<br>-0.889<br>(0.41)_∗∗_<br>-0.295<br>(0.23)<br>-0.563<br>(0.46)<br>1.037<br>(0.74)<br>3.446<br>0.034<br>IV-2<br>4<br>69<br>lars. Samp<br> (cross-sect<br>are reporte<br>gressions, b<br>gnifcance a|
|mark regressions for fundamental det<br> growth rates*|**F1**<br>**F2**<br>**F3**<br>**F4**|2.374<br>3.627<br>1.71<br>2.430<br>(0.85)_∗∗∗_<br>(1.67)_∗∗_<br>(0.39)_∗∗∗_<br>(0.80)_∗∗∗_<br>-0.555<br>-0.667<br>-0.144<br>-0.510<br>(0.23)_∗∗_<br>(0.27)_∗∗_<br>(0.17)<br>(0.25)_∗∗_<br>-0.126<br>-0.216<br>-0.050<br>-0.118<br>(0.09)<br>(0.17)<br>(0.04)<br>(0.11)<br>-0.058<br>(0.32)<br>0.701<br>(0.39)_∗_<br>3.778_∗∗∗_<br>9.826_∗∗∗_<br>5.750_∗_<br>9.388_∗∗∗_<br>1.687<br>74.473<br>-1.473<br>IV-1<br>IV-2<br>S-GMM<br>IV-1<br>2<br>3<br>82<br>3<br>42<br>75<br>352<br>37<br>lculated from GDP data in real 2000 U.S. dol<br>ta periodicity fve-year averages for 1999–2003<br>ty and autocorrelation-robust standard errors <br>erm (all specifcations) were included in the re<br>ifcance at 5 percent level, and _∗∗∗_indicates si|
|Table 5: Bench<br>with real GDP||Institutions<br>Integration<br>Geography<br>Democracy<br>Fractionalization<br>Social capital<br>Human capital<br>_F_<br>Anderson LR<br>Hansen _J_<br>AR(2)<br>Estimator<br>Instruments<br>N<br>*Growth rates ca<br>unbalanced. Da<br>Heteroskedastici<br>and a constant t<br>_∗∗_indicates sign|

20
# _Regime shift of type B: Marked increases in real interest volatility_

In order to identify shifts of this type, we estimated an econometric model of each country's _ex ante_ real interest rate and looked for large forecast errors. Specifically, we fitted a simple error-correction model for each country's real interest rate, assuming that it could be modeled as a function of changes in the world interest rate,'<sup>8</sup> of the gap between the world and the local interest rate, and by some autoregressive dynamics.'<sup>9</sup> The estimates were by recursive least squares, and we tested the fitted equations for break-points indicated by systematic failure of one-month ahead recursive forecasts.<sup>20</sup> Examples of the procedure are shown in Figures 7 (a) and (b) for India and Kenya. The recursive residuals from the dynamic interest rate model are shown, bracketed by _5_ per cent confidence intervals. In the lower panel is plotted the probability level at which the hypothesis of no structural change can be rejected in each period. For the Indian data, March 1990 is identified as the break point, and for Kenya, March 1993. Plots for all the countries, together with details of the estimated regressions on the full period, and on the before- and after-liberalization sub-periods are provided in Appendix 2. Note that this method cannot detect a gradual increase in volatility.

As the method identifies regime change events with short-term increases in volatility, the subsequent finding that volatility remained high after the change is not an inevitable and tautological consequence of the identification method, but represents an independent observation. (Indeed, to verify this, we also computed post-event volatility removing a six-month window after the event.)

> 1 We used the first principal component of the 8 large industrial country real TB rates as a proxy for the world rate. '9 The model employed is equivalent to that of equation (1') in the next section below, with k1.

> 20 The criterion for a break was three forecast errors in four consecutive months each statistically significant at least the 1 per cent level. (A single data outlier in the level of interest rates could have triggered a break if the criterion had required only two significant forecast errors).

13
priority bracket is Project 12, Palugaswewa Water Supply, which also has a low safe water coverage rate and the highest prevalence of water‐borne diseases and chronic kidney disease among all the proposed project sites.

Considering the project rankings for both SEI and FEI (Figure 7) under the assumed budget limits, some projects are designated as "high priority" for being relatively high on both indicators (i.e. projects 11, 12, 21, 25 and 27 that have been marked as green points). Given the budget constraints, several projects (such as projects 14, 15, 17, 18, 22, 26, etc. designated by red points) are of lower priority for implementation, scoring lower according to both SEI and FEI.

_Figure 7: IPF Matrix: Mapping of projects by SEI and FEI_ <!-- Start of picture text --> 100<br>P21<br>90<br>80<br>P25<br>70<br>P12<br>P1<br>60<br>P28 P27<br>P11<br>50 P13 P19 P23<br>P16 P4<br>P8 P22<br>40 P10 P5<br>P20<br>P17 P26 P7 P6<br>30<br>P18<br>P3 P24<br>20<br>P15 P2<br>10<br>P14<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>FEI<br>SEI (PCA, positive, min req 5%)<br><!-- End of picture text --> ● High Priority Projects

- Lower Priority Projects

● Medium Priority Projects ‐‐‐‐ Budget constraint of US$ 0.35b Source: Authors' calculation High priority projects are in the upper right quadrant (those that fall within the budget constraint imposed along both the SEI and FEI axes), while the lower priority projects are in the lower left quadrant. Projects located in the upper left and lower right quadrants scored relatively high on either the SEI or FEI, but not both. For example, Project 5, Thambuththegama Water Supply project, scored higher on FEI than SEI, whereas Projects 1, 27
The trajectories of the unweighted and weighted series show significant differences. Africa’s larger economies stagnated or declined in the 1970s and 1980s, causing the U shape. South Africa, which represents, on average, 42 percent of the regions’ GDP, grew by an average of only 0.12 percent a year; and Nigeria, the secondlargest economy, representing 13.50 percent of the region’s GDP, grew by 0.28 percent, while the regional unweighted average growth was 0.71 percent. Both the unweighted and weighted series show a strong positive trend from the mid-1990s on. In the period 1995–2005, the unweighted average GDP per capita growth was 1.81 percent, more than twice the long-term average.

Figure 1: GDP per capita <!-- Start of picture text --> Figure 1a: Unweighted data<br>2800<br>2400<br>100<br>2000<br>0<br>-100<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text --> <!-- Start of picture text --> Figure 1b: Weighted data<br>1900<br>1800<br>1700<br>80<br>1600<br>40<br>1500<br>0<br>-40<br>-80<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text --> Turning to growth rates, figure 2 presents the paths of unweighted and weighted data. The mean and SD of unweighted GDP per capita growth are 0.71 percent and 6.32 percent, respectively. Figure 2a shows that growth declined until the mid-1980s, but it has had a positive trend since then. The mean and SD of weighted data are –0.17 percent 4
**TABLE 3 EGYPT 1975-80 THE DIRECT IMPACT OF NEXT PREGNANCY ON**<sup>**CHILD MORTALITY (before age 60 months)**</sup>

% per cent pregnant (mother became pregnant<sup>prior to child death)</sup> RR relative risk of child death (pregnant /<sup>not pregnant)</sup> AR attributable risk of child death

-------------FERTILITY CONTROL--------------ALL (N-7375 children) NO (N-4714 children) YES (N-2661 children) EST. (95 % CI) EST. (95 % CI) EST. (95 % CI) PREGNANT at anY S % 41.30 (40.18 - 42.43) 38.27 (36.88 - 39.66) 46.67 (44.78 - 48.57) RR 1.88 ( 1.46 - 2.43) 2.08 ( 1.52 - 2.85) 1.74 ( 1.11 - 2.74) AR 26.72 (15.30 - 36.61) 29.29 (15.52 - 40.82) 25.74 ( 2.37 - 43.52) PREGNANT before 12 months % 13.45 (12.67 - 14.23) 13.47 (12.50 - 14.45) 13.42 (12.12 - 14.71) RR 3.12 ( 2.40 - 4.06) 3.34 ( 2.40 - 4.66) 2.67 ( 1.70 - 4.19) AR 22.20 (15.14 - 28.67) 23.99 (14.70 - 32.27) 18.31 ( 6.71 - 28.46) PREGNANT at 12 months or later % 41.26 (39.89 - 42.63) 40.63 (38.84 - 42.43) 42.12 (40.01 - 44.23) RR * * * AR * * *

based on Weibull proportional hazards model controlling for: weaning, parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to<sup>married women</sup> last two children only no multiple births no marital disruption prior to child death Muslim only note: some mothers do not become pregnant by survey *-not statistically distinguishable from 0.0 for per cent (%) and attributable risk (AR) and from 1.0 for relative risk (RR) 35
# **Chapter 3. Risks and Benefits of Wastewater Use in Agriculture**

Wastewater use in agriculture has substantial benefits, substantial benefits, but can also pose substantial risks to public health—especially when untreated wastewater is used for crop irrigation. Farmers often have no alternative but to use untreated wastewater because there is no wastewater treatment and freshwater is either unavailable or too expensive. The major risks to public health are microbial and chemical. Wastewater use in agriculture can also create environmental risks in the form of soil and groundwater pollution. However, if properly planned, implemented and managed, wastewater irrigation can have several benefits for the environment, as well as for agriculture and water resources management. Given these risks and benefits, countries seeking to improve wastewater use in agriculture must reduce the risks, in particular to public health, and maximize the benefits.

## **3.1  Risks to public health**

## _3.1.1     Microbial risks_<sup>_10_</sup>

### _Wastewater pathogens_

At any point in time wastewater contains all the pathogens (disease-causing organisms) being excreted by members of the community from which the wastewater is derived. If no-one in a community is suffering from any excreta-related disease, then the community’s wastewater does not contain any excreta-related pathogens. However, even in high-income countries, this hardly ever occurs, and wastewaters always contain at least some pathogens. Pathogen numbers in a community’s wastewater reflect the amount of excreta-related disease in the community; in general, the number of pathogens is higher in wastewater in low-income countries than it is in wastewater in high-income countries.

The pathogens present in wastewaters are the agents of excreta-related diseases and so comprise the viruses, bacteria, protozoa and helminths that cause these diseases (Box 3.1). The diseases in the community caused by these pathogens may be endemic—i.e., the diseases are maintained within the community by continuous transmission between community members—or they may occur as epidemics—i.e., they are introduced to susceptible communities by persons from outside the community.

Many excreta-related diseases<sup>11</sup> can be spread by wastewater use in agriculture to those working in wastewater-irrigated fields and/or those consuming wastewater-irrigated foods, especially when eaten uncooked (e.g., salad crops and some vegetables) (Table 3.1). However the consumption of wastewater-irrigated foods is only one possible route of transmission, and this route may or may not be of local public health importance.

10 Microbes (or micro-organisms) are the life forms in the domains Bacteria and Archaea. Helminths (worms) are in the domain Eukarya and so, strictly speaking, are not microbes. However, in the context of wastewater use in agriculture, the term ‘microbial health risks’ includes the health risks due to helminths (see Box 3.1).

> 11 Details of all excreta-related diseases are given in Feachem et al. (1983) and in the CDC _A-Z Index_ available at http://www.cdc.gov/az/a.html.

22
interviews were always administered in private and were attempted to be administered simultaneously and with a gender match-up between the enumerator and respondent.<sup>12</sup> Regarding agricultural land, following the creation of a roster of all owned and/or cultivated agricultural parcels and the identification of those that are “owned” by at least one household member, this common list of owned parcels that is generated as part of the household interview was fed forward to each individual interview in that household.<sup>13,14</sup> Furthermore, the focus on “personal” ownership of and rights to land in the IHPS meant that the phrasing of the questions with respect to the IHS4 was different for a range of questions on:

- (i) **reported** ownership (i.e. _Who in this household owns this [PARCEL]?_ in the IHS4 versus _Are you among the owners of this [PARCEL]?_ in the IHPS);

- (ii) **economic** ownership (i.e. _If this [PARCEL] were to be sold/rented out today, who would decide how the money is used?_ in the IHS4 versus _If this [PARCEL] were to be sold/rented out today, would you be among the individuals to decide how the money is used?_ in the IHPS);

- (iii) **documented** ownership (i.e. Who is listed on the title or ownership document as owner of this [GARDEN]? in the IHS4 versus _Is your name among the names listed on the ownership document for this [PARCEL]?_ in the IHPS), and

- (iv) right to **sell** (i.e. _Does anyone in the household have the right to sell this [PARCEL]?,_ followed by _Who can decide whether to sell this [PARCEL]?_ in the IHS4 versus _With regard to this [PARCEL], are you among the individuals who have the right to sell it, even if you need to obtain consent or permission from someone else?_ in the IHPS); and

- (v) right to **bequeath** (i.e. _Does anyone in the household have the right to bequeath this [PARCEL],_ followed by _Who can decide whether to sell this [PARCEL]?_ in the IHS4 versus _With regard to this [PARCEL], are you among the individuals who_

> 12 For more information on the organization and implementation of the individual-disaggregated data collection as part of the IHPS, please consult the survey’s basic information document, which can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2939/download/47216.</u>

> 13 Parcel is defined as a continuous piece of land which can have more than one plot and is referred to as “Garden” in the questionnaires for the IHS4 and the IHPS.

> 14 In this process, the enumerator for each individual interview in each household copied the garden roster from the tablet of the primary enumerator assigned to the household into his/her tablet that generated a new questionnaire (under Survey Solutions census mode) for each interview target. To better facilitate the process, the enumerators also had paper booklets of household, garden and plot rosters to ensure unique identification of household members and parcels across the individual interviews in the same household.

8