rafmacalaba/datause-encoder-data huggingface dataset:
# **The Structure and Determinants** of

# Inequality and **Poverty Reduction in** Ghana, 1988-92

# **Sudharshan** Canagarajah **Dipak** Mazumdar **Xiao Ye**

This paper presents findings that help us to understand the structure of inequality in the context of poverty reduction, using three rounds of Ghana Living Standard Survey conducted between 1988 and 1992. First, poverty reduction between 1988 and 1992 can be mainly attributed to improvements in both average levels of income and the distribution pattern of it in the informal/nonfarm sectors in Other Cities and in Rural Areas. Second, within a locality, economic changes -- whether positive or negative -- appear to affect all socio-economic groups in the same direction. These findings may explain why structural adjustment, which aimed at cutting back public sector employment and stimulating private sector activities, was successful in raising living standards in the Rural Areas and Other Ci(ties, but not in Accra. The public sector is much larger in Accra than in Other Cities and Rural Areas. The contraction of the public sector in Other Cities and in Rural Areas was apparently compensated by income growth from the informal sectors. In contrast, in Accra, the contraction of its large public sector dominated the local economy -- the living standard of population in both formal and informal sectors decreased. Accra's economy will likely grow as its private and informal sectors grow. Based on these findings, an integrated regional strategy, taking into account the local socio-economic structure, may prove to be effective in achieving economic growth. Further research on informal sector activities will be required for developing economic strategies with a focus on achieving sustainable poverty reduction.

We gratefully acknowledge financial assistance from the Dutch and the Finnish Trust Funds for this work. We are also grateful to the useful comments by Lionel Demery, Francisco Ferreira, and the participants at the World Bank Inequality Thematic Group seminar on September 10, 1998.
a caravan route passing through is approximately 0.02 hours or 1.2 minutes. This was arrived at by assuming the caravan route travel speed at 5km/hr36; equivalent to the human average walking speed on a stable terrain.

# Cost-Distance function: Calculating travel time

The third step was to merge the off-path and the caravan route friction surfaces. ArcGIS Desktop’s tool MERGE was used to combine the two rasters into a single one where the order of the input defines the order of precedence, in this case the caravan routes overlay the off-path walking. Then the friction surface was obtained to model the time that it takes to move around the entirety of DRC around the year 1900, taking into account terrain, land cover type, and transport infrastructure.

To create the final variable, which was used as an instrumental variable in this study, the time that it takes to travel on foot from each pixel in the study area to different selected cities or target destinations, was estimated. ArcGIS Cost distance tools were used to calculate, for each pixel, the least cumulative amount of time it takes to walk to a specified locations (market). The algorithm utilizes the node/link cell representation, whereby the center of each cell is considered a node and each node is connected to its adjacent nodes by multiple links. Every link has an impedance derived from the costs (measured in units of time) associated with the cells from the natural path friction cost surface and from the direction of movement through the cells. See Figure A6 for an example of a raster measuring travel time from each point to Kinshasa.

The creation of a least cumulative cost raster was replicated for each of DRC’s 57 selected cities and then then cell values from the travel time raster at SPAM locations was retrieved.. As a result an origin/destination travel time matrix of 27,500 rows (number of pixels in DRC) and 57 columns (selected cities) is obtained. Finally, this data set was compared with the current travel cost data set, and the appropriate city for each pixel was selected for the econometric model.

> 36 http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Walking.html

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countries explained more than 80% of total investments, reducing their share to 50% in 1995. In 1998 their share was again over 80% but after the Asian crisis it has been falling, reaching just over 30% in 2002. Globally, the developing countries transport sector only attracted about 18% of the total investment flows that accrued to the infrastructure sector. It lags significantly behind the telecommunications (44%) and electricity (28%) sectors.

Table 2 **. Estimates of Transport Infrastructure Projects having reached financial closure in developing and transition economies (1990-2001)**

||**Africa**|**East**<br>**Asia**|**Eastern**<br>**Europe**|**Latin**<br>**America**|**Middle**<br>**East**|**South**<br>**Asia**|**Total**|
|---|---|---|---|---|---|---|---|
|**AIRPORT**||||||||
|Number of transactions|10|17|14|32|7|2|82|
|Value in millions of US$|375|2,625|1,500|7,125|750|125|12,500|
|**PORT**||||||||
|Number of transactions|10|52|16|78|8|13|177|
|Value in millions of US$|180|8,820|720|5,580|900|1,800|18,000|
|**RAIL**||||||||
|Number of transactions|9|11|7|48|1|0|76|
|Value in millions of US$|288|10,080|288|17,856|288|0|28,800|
|**ROAD**||||||||
|Number of transactions|9|149|6|137|0|26|327|
|Value in millions of US$|2,280|34,200|2,770|36,000|0|750|76,000|
|**TOTAL**||||||||
|**_Number of transactions_**|**_38_**|**_229_**|**_43_**|**_295_**|**_16_**|**_41_**|**_662_**|
|**_Value in millions of US$_**|**_3,123_**|**_55,725_**|**_5,278_**|**_66,561_**|**_1,938_**|**_2,675_**|**_135,300_**|

_Source:_ World Bank, Infrastructure Vice-Presidency, PPI Database.

The sectoral distribution of projects (measured in US$) is just as skewed toward road deals as it is in developed countries simply because the increase in demand for passenger mobility is also very high. The distribution of projects in the developing world also shows however the impact of trade liberalization and of globalization and their catching up with regional integration long established within the US and across European countries. Freight needs to be moved and exported. Ports are a key element in that strategy. In Latin America, this concern implies that railways also need to be scaled up because in many countries declining reliability and overall quality was such that except for captive shipments, the rail sector had been losing a large share of its potential market to the trucking industry. In Brazil for instance, the odds of relying on rail rather than trucks were lower, the longer the delivery distance. In general, it is becoming increasingly clear to governments that both efficient logistics and the opportunity for multimodal arrangements are essential to competitiveness. The most perceivable outcome of the need to reduce logistic costs is that the private sector will be responsible for large shares of transport services. It is not too risky to predict that this trend is likely to continue as the demand for transport services continues to grow much faster than the government’s ability to raise the resources to finance it.

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village latrine register and monitoring progress, while continually motivating households to move up the sanitation ladder.

CLTS activities were accompanied by intensive sanitation marketing and promotion campaigns directed towards the head of the household. Marketing approaches included direct consumer contact (DCC) which are large, community-based events, and mass media (print and radio). Promotional activities at the village level were delivered by village leaders, community health workers, as well as the CLTS champions. To maintain focus on aspirations, activities of the sanitation marketing campaign fell under the common slogan _Choo bora chawesekana_ (“a good latrine is possible”).

While the mass media radio campaign was broadcasted across the country, 182 villages in 91 wards were targeted to receive direct sanitation marketing efforts through promotional activities. TSSM demand-side strengthening activities consisted of the following:

1. A mass media campaign including: (i) radio advertisements that feature a short jingle about the importance of sanitation, and (ii) a soap opera during a primetime slot on two radio channels with 15 minute episodes covering promotional messages for both latrine upgrading and handwashing behavior.

2. Wall murals, posters and flyers with the _Choo bora chawesekana_ slogan providing information on pricing, simplicity and the benefits of the “ _sungura_ ”<sup>19</sup> slab constructed by masons trained in the program (see Figure 4 for an illustration).

3. A roadshow (DCC event) promoting improved sanitation which would include skits, competitions, music, dancing, and _sungura_ slab sales promotions.

4. CLTS triggering and the creation of a sanitation committee.

Beyond lack of demand, formative research developed to inform the intervention design identified limited availability of low-cost, durable sanitation options as being prominent challenges to the success of the intervention in rural communities. Furthermore, masons exhibited limited business and marketing skills. To overcome these constraints, demand-side interventions were accompanied by supply-side strengthening to ensure local provision of latrines and sanitation products. The program trained masons, or _fundis,_ in latrine construction and marketing skills. The training lasted a week and taught masons how to build _sungura_ cement sanitation platforms to be sold for approximately $5 per slab. Masons were also taught more general techniques for upgrading latrines together with basic marketing and management skills to guide their operations. The aim of the training was to enable masons to capitalize on the demand for sanitation products generated by the promotion activities, and to spur future demand.

> 19 Sungura means rabbit in Swahili and refers to the appearance of the slab

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substantially enhanced by including, in their design, measures and structures that use leasing as an intermediation mechanism for microfinance of longer maturities and acquisition of capital equipment. This approach would be preferable to one that is neutral and non-specific as to the varieties of loan maturities and types of financing to be supported my microfinance programs. In this manner, the emphasis is not simply on general access to financial services but better defined access to financial services with maturities and terms appropriate to microenterprise activities.

74. Because of its programs in privatization, deregulation and financial restructuring the Bank would be well-positioned to ensure that a country's legal and regulatory framework is appropriately redesigned to establish a business, fiscal and market environment in which leasing-along with other forms of financial intermediation-can develop and function effectively. In this context, a checklist of considerations pertinent to the use of leasing in MSB development projects is summarized below:

- There must be a clear definition firms and economic activities that belong to the MSB sector, to accurately identify the market segments that are the target of a development and promotion program. Moreover, it is difficult to draw a categorical dividing line between "conventional" microenterprises and smallscale businesses, especially the microenterprises of poor households in the upper layers of the poverty pyramid.

- A MSB finance development program that provides for the use of specialized institutional channels and instruments is more effective than one that is neutral and non-specific as regards maturity and repayment cycles, as well as assets and operations to be financed.

- Leasing can be an effective tool that opens up additional access to the right kind of financial services for MSBs if there exists an enabling macroeconomic, market, financial and fiscal environment.

- Leasing represents a most effective financing technique for reaching those enterprises whose financial needs cannot be satisfied by traditional minimalist microfinance approaches. It has the potential to generate significant developmental impact by transforming marginal enterprises into sustainable businesses.

- Studies on linkages between MSBs vis-a-vis medium-scale and larger finns will be useful and may be necessary in (a) assessing the impact of applications of new technology and use of equipment in employment and production output in small businesses and microenterprises, and (b) quantifying this linkage effect.

- A clearly established legal, regulatory and tax framework for leasing transactions is indispensable for this form of financing to grow and take its appropriate place in the financial system.

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Zealand <u>(www.sendmoneyhomepacific.org), with the latter being the subject of the Gibson,</u> McKenzie, and Zia (2014) remittances financial literacy experiment.

Although remittance costs have fallen following these policy efforts, it is unclear how much of the reduction in costs is due explicitly to the policy actions per se, rather than arising from technological advances and competition that might have lowered costs anyway. Several recent randomized field experiments offer insights on how migrants are likely to react to reductions in remittance costs.

The existing evidence suggests that response to fee reductions can be quite large. Aycinena et al. (2010) implemented a randomized field experiment that estimates the causal impact of remittance transaction fees on remittances. In partnership with a money transfer operator, Salvadoran migrants in Washington D.C. were randomly assigned differently-sized discounts on remittance transaction fees. The remittance fee reductions led to higher transaction frequency by remitters: each $1 fee reduction led to an additional 0.11 transactions per month. There was no change in the dollar amount remitted per transaction. These remittance responses to price reductions are large in magnitude: a $1 reduction in the remittance transaction fee leads to average fee savings per month of only $0.47, but the corresponding increase in average remittances sent per month is an order of magnitude larger, $25.

Given that the impacts found in the Aycinena et al (2010) were surprisingly large, a related study, Ambler et al. (2014b), aimed to confirm these results and to make some improvements in research design. The Ambler et al (2014b) study also examined the impacts of offering migrants in the Washington, DC metro area discounts on remittance fees, this time among migrants from either Guatemala or El Salvador. Migrants were randomly chosen to be offered $3 discounts on remittance fees (off a base price of $8) for remittances sent through the partner organization. The discounts were valid for 10 weeks, for as many transactions as the respondent chose to carry out. In contrast to the previous study, in this case the discount was limited to remittances sent to one specific remittance recipient (the one the migrant had just remitted to at the time of enrollment in the study). This was done to limit the possibility that migrants would allow others to use their discount to send to other recipients, which would lead to a spurious upward bias in the estimated treatment effect on remittances.

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# **4.3 Are the effects driven by outliers?**

One way of assessing the robustness of the estimates effects is to examine whether they are driven by outliers, i.e. specific industries only. Figure 5 plots the change in the number of employees within an industry, calculated as the log of employees in November 2013, the last month in the data, minus the log of the average number of employees in the pre‐2009 period. The figure shows the distribution of these calculated changes, separately for eligible and ineligible industries. One of the ineligible industries (manufacture of asbestos‐based products) experienced a particularly large drop in employment of 130%. However, overall the distribution of employment changes in eligible industries lies to the right of the distribution of employment changes in ineligible industries, suggesting that the positive effect of the program on employment was not concentrated in a few industries only. A Kolmogorov‐Smirnov test of equality of distributions rejects equality with a p‐value of 0.017 (0.025 if manufacture of asbestos‐based products is excluded from the group of ineligible industries). Also, there are no outliers in terms of increases in the number of employees for eligible industries.

I test to see how the estimated program effects would change when weakening the influence of the negative outlier (manufacture of asbestos‐based products) by winsorizing this outlier. For all months from January 2009 on, I replace the log number of employees in this industry with the log average pre‐ 2009 value plus the drop in employees for the industry that experienced the second largest drop in employment in that month, calculated as log employment in that month minus average log employment over the pre‐2009 period. Columns 5 in table 3 and table 4 shows the results in the winsorized data. The estimated effects are somewhat smaller in magnitude, but they are still statistically significant.

# **4.4 Falsification exercise**

Although the analysis uses eligible and ineligible industries that displayed the same employment trends in the pre‐program period, a potential concern is that employment across these industries would have behaved differently in the post‐program period even in the absence of the wage subsidy program. For example, the concern may be that the industries show similar employment trends in good economic times, but respond differently to downturns. I conduct the following falsification exercise to address this concern. I estimate equations 3 and 4 for the time period January 2000 to December 2004, which coincided with a recession in Mexico (from the second quarter of 2001 to the first quarter of 2002), where the variables are defined as in equations 1 and 2 and RecessionAndAftert is equal to one for April 2001 through December 2003 and zero for all other months.

The results in table 6 show that employment in eligible and ineligible industries did not recover differently after the 2001 recession. This finding lends support to the assumption that eligible and 10
Table 4: Recovery Growth After Conflict: Ethnic Group Level

||(1)<br>ln(light per capita)|(2)<br>ln(light per capita)|(3)<br>ln(light per capita)|
|---|---|---|---|
|Confict Incidence|-0.311***|-0.312***|-0.312***|
||(0.054)|(0.054)|(0.054)|
|First 5 Years|-0.202***<br>(0.064)|||
|Remaining 5 Years|-0.130**<br>(0.060)|||
|First 10 Years||-0.200***|-0.136**|
|||(0.051)|(0.061)|
|Remaining 10 Years||-0.052|-0.054|
|||(0.087)|(0.087)|
|Fragile Peace|||-0.190**<br>(0.095)|
|Country/Year FE|YES|YES|YES|
|Group FE|NO|NO|NO|
|Observations|8,517|8,517|8,517|
|R-squared|0.847|0.847|0.847|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Unit of observation are ethnic groups. The dependent variable is the natural logarithm of night light per capita. Night light data are gathered form the National Oceanic and Atmospheric Administration (NOAA). “Conflict incidence” is a dummy defined through more than 25 fatalities in ethnic conflicts. Dummies “First 5 years” and “First 10 years” take a value of 1 if the ethnic group is experiencing respectively one of the first five or ten years after the conflict episode it was involved in. Dummies “Remaining 5 years” and “Remaining 10 years” control the remaining years with no war. “Fragile peace” is a dummy that equals one if is experiencing one of the first five year of recovery but will relapse into conflict within the next five years.

selection problem built into the idea of studying long periods of recovery which we turn to next.

# **3.2 Unstable Peace as a Central Selection Problem**

In the analysis of recovery provided above we focus on groups, countries or regions that did not face the resurgence of violence. This is obvious in the existing literature which is focused on countries like Japan, Germany and Vietnam. It is also true for the data we use as the dummies in our regressions condition on the length of the recovery period. In column (1) of Table 4 this implies, for example, that we focus on the recoveries that have lasted longer than 5 years. Economic performance in the other cases cannot be studied because they have entered renewed conflict. This selection problem means we are drawing conclusions from countries that have managed to stay peaceful.

This is a serious concern as most groups do not stay in peace. In Figure 5 we show the distribution of recovery lengths at the group level. The figure shows that 151 20
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