rafmacalaba/datause-encoder-data huggingface dataset:
firms is flat or falling. Second, while Africa’s large manufacturing firms appear to be productive, employment growth in these firms has not been rapid enough to decrease the share of small firm employment in total manufacturing employment, at least not yet. Third, labor productivity growth in Africa’s manufacturing sector is largely accounted for by structural change (or the increase in manufacturing output associated with the increased employment share in manufacturing and the decline in activity in agriculture where labor productivity is lower than in manufacturing); conversely, within-sector labor productivity growth in manufacturing is close to zero.

The relatively nascent stage of manufacturing in Africa presents both opportunities and challenges. Probably the most important opportunity stems from the African Continental Free Trade Area, which took effect on January 1, 2021. Given that the total population of Africa is roughly equal to that of China, the integration of African markets could attract the foreign investment needed to upgrade capabilities in the manufacturing sector and elsewhere. Some evidence suggests that this is already happening (Newman et al. 2016; Abebe et al. 2021). We also discuss opportunities for Africa’s manufacturing in the “in-between sector,” as well as manufacturing in sectors related to Africa’s specific resources, green energy sources, and health needs. As Africa’s manufacturing sector evolves, its biggest challenge is likely to be that global manufacturing around the world is shifting toward rising capital intensity—think “robots”—which reduces the importance of Africa’s comparative advantage in unskilled labor-intensive manufacturing (Diao et al. 2021). We emphasize that the ready-made garment industry remains relatively low-skilled labor intensive. In addition, even if a shift to capital-intensive manufacturing might limit manufacturing’s direct role in job creation, there are large indirect benefits associated with manufacturing--including job creation in different but related sectors.

Industrialization spans a wide array of sectors and the continent of Africa is home to 54 countries. In this essay, we will focus on Sub-Saharan Africa and exclude discussion of the countries of North Africa, in part due to data limitations and in part because neither of the authors of this piece has worked on these countries. We also focus almost exclusively on the 21<sup>st</sup> century, the period during which many African countries experienced rapid labor productivity growth (Diao et al. 2019). Finally, we focus exclusively on manufacturing. For more on the status and potential of industries other than manufacturing, the reader is referred to the excellent work by John Sutton and his co-authors in his series of Enterprise Maps and a more recently published volume, which explores the potential for job creation in Africa’s economies outside traditional manufacturing (Newfarmer, Page, and Tarp 2019).

Patterns of Labor Productivity Growth and Structural Change 3
**Table G5: Relationship between food security measures and weight-for-age z-scores (Pakistan): Limited Controls Version**

|VARIABLES|(1)|(2)|(3)<br>Weight-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Per capita kcal|-0.002<br>(0.033)||||||
|1-SSR||0.026<br>(0.038)|||||
|1 - SSEXR|||0.007<br>(0.036)||||
|DDS||||-0.035<br>(0.036)|||
|PC expenditure|||||-0.006<br>(0.042)||
|Food share||||||0.003<br>(0.037)|
|Rural|0.123|0.132|0.122|0.113|0.123|0.124|
||(0.083)|(0.084)|(0.083)|(0.083)|(0.083)|(0.083)|
|Punjab|-0.242*|-0.234*|-0.242*|-0.234*|-0.242*|-0.241*|
||(0.141)|(0.142)|(0.141)|(0.141)|(0.141)|(0.142)|
|Sindh|-1.370***|-1.351***|-1.367***|-1.360***|-1.367***|-1.366***|
|Khb Pkht|(0.141)|(0.144)|(0.141)|(0.141)|(0.140)|(0.140)|
|yer aoon|||||||
|<br>Khaw|0.128|0.143|0.127|0.154|0.129|0.129|
||(0.166)|(0.167)|(0.165)|(0.169)|(0.167)|(0.168)|
|Constant|-0.999***|-1.017***|-0.999***|-1.002***|-0.998***|-1.000***|
||(0.144)|(0.146)|(0.143)|(0.143)|(0.143)|(0.144)|
|Observations|2,775|2,775|2,775|2,775|2,766|2,766|
|R-squared|0.097|0.097|0.097|0.098|0.096|0.096|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure in total expenditure.

77
|Water SupplyProject|Abel Mejia|
|---|---|
|Water SupplyProject|Abel Mejia|
|Water SupplyProject|Abel Mejia|
|Water SupplyProject|Abel Mejia|
|Water SupplyProject|Abel Mejia|
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Gas Utilization and Technical Assistance Project||
|Agricultural Sector Loan Project||
|Agricultural Sector Loan Project||
|Agricultural Sector Loan Project||
|Agricultural Sector Loan Project||
|Public Sector Management Technical Assistance Project||
|Public Sector Management Technical Assistance Project||
|Public Sector Management Technical Assistance Project||
|Public Sector Management Technical Assistance Project||
|Public Sector Management Technical Assistance Project||
|Power EngineeringProject||
|Power EngineeringProject||
|Power EngineeringProject||
|Power EngineeringProject||
|Power EngineeringProject||
|Power EngineeringProject||
|Power EngineeringProject<br>||
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Municipal Development Project|Miguel Mercado-Diaz|
|Small and Medium Scale IndustryCredit Project||
|Small and Medium Scale IndustryCredit Project||
|Small and Medium Scale IndustryCredit Project||
|Small and Medium Scale IndustryCredit Project||
|Small and Medium Scale IndustryCredit Project||
|Small and Medium Scale IndustryCredit Project||
|SEGBA V|Ernesto Sanchez-Triana|
|SEGBA V|Ernesto Sanchez-Triana|
|SEGBA V|Ernesto Sanchez-Triana|
<!-- Start of picture text --> 2<br>-2<br>-4<br>1985 1990 1995 2000 2005 2010 2015<br>20 Ap ~A. sonA AM,ly * . TMs 4We tome :<br>-2 Vs<br>-4<br>-6<br>1985 1990 1995 2000 2005 2010 2015<br>Year<br><!-- End of picture text -->
figure is 58% for wealth. Similarly, while 51% of households would move downward from the top income quintile in rural Uganda, this figure is 45% for wealth. The scenario for urban Uganda is very similar to the rural scenario but with higher persistence in both income and wealth.

To summarize, the fact that the rich remain rich and the poor remain poor is more a feature of the United States than of SSA; there is larger economic mobility in SSA for both income and wealth. These results suggest that the fact that top income earners in SSA are not able to transform their saving into wealth at the rate that US households do can be explained, at least partially, by a relatively lower persistence at the top of the income distribution.

# **7 Conclusion**

We provide a comprehensive study of the cross-sectional joint distributions of consumption, income, and wealth using new and unique nationally representative ISA data for three of the poorest countries in the world: Malawi, Tanzania, and Uganda. First, we find that the transmission from income to wealth is low, which we relate to a large inability to save and accumulate wealth in SSA compared with other world regions. Second, we provide evidence of a low transmission — from income to consumption, which suggests that despite being unable to persistently save — and hence, self-insure SSA households are able to insure their consumption relatively well, a phenomenon that requires the presence of powerful informal insurance arrangements. Combining these two findings suggests a trade-off between growth and insurance in SSA. This raises the question of whether the process of accumulation (i.e., saving) and growth potentially requires the disruption of consumption insurance for SSA, a phenomenon already experienced by China (see Santaeul`alia-Llopis and Zheng (2015)).

Looking ahead, our analysis opens the road to potentially more empirical and theoretical assessments of how the joint distributions of consumption, income, and wealth are determined across aggregate stages of economic development. This implies expanding our analysis not only to other SSA countries for which ISA data are becoming available (Burkina Faso, Ethiopia, Mali, Niger, and Nigeria) but also to other developing countries worldwide. We are currently exploring this type of analysis using LSMS cross-sections beyond those under the ISA umbrella.

# **References**

Attanasio, O., Banks, J., Meghir, C., and Weber, G. (1999). Humps and Bumps in Lifetime Consumption. _Journal of Business and Economic Statistics_ , 17:22–35.

Banerjee, A., Meng, X., Porzio, T., and Qian, N. (2014). Aggregate Fertility and Household 41
**Table 2:** Financial globalization trends

||(1)<br>FGI|(2)<br>UFGI|(3)<br>FGI|(4)<br>UFGI|
|---|---|---|---|---|
|Trend|0.005***|0.019***|||
||(0.001)|(0.001)|||
|Trend×D|||0.002|0.023***|
||||(0.002)|(0.002)|
|Trend×E|||0.009***|0.019***|
||||(0.002)|(0.002)|
|Trend×F|||0.004***|0.011***|
||||(0.001)|(0.002)|
|AC×Trend|-0.001|-0.038***|||
||(0.002)|(0.003)|||
|AC×Trend×D|||0.009***|-0.045***|
||||(0.003)|(0.004)|
|AC×Trend×E|||0.006|-0.038***|
||||(0.004)|(0.006)|
|AC×Trend×F|||-0.006*|-0.027***|
||||(0.003)|(0.004)|
|AC|0.026|0.697***|||
||(0.040)|(0.055)|||
|AC×D|||-0.15***|0.821***|
||||(0.059)|(0.076)|
|AC×E|||-0.075|0.734***|
||||(0.082)|(0.107)|
|AC×F|||0.148**|0.510***|
||||(0.052)|(0.085)|
|Constant|0.214***|0.036**|0.219***|0.062***|
||(0.016)|(0.017)|(0.014)|(0.015)|
||0.214***|0.036**|0.219***|0.062***|
|Obs.|1,760|1,760|1,760|1,760|
|R-squared|0.098|0.444|0.125|0.475|
|Countries|84|84|84|84|
|CountryFE|Yes|Yes|Yes|Yes|
|Clustered standard e|rrors at countryl|evel inparenthes|es. ***p<0.01,*|*p<0.05,*p<0.1.|

# **4.** **<u>Financial globalization and financial volatility</u>**

As we mentioned in the introduction, the question of whether financial globalization is a stabilizing force, allowing for more efficient risk sharing, or a destabilizing one, exposing countries to financial contagion, is an open one. While there are different ways of measuring financial market volatility, in the context of our analysis, the most natural is to look at the volatility of daily stock market returns. Hence, in what follows, we regress the annualized monthly volatility<sup>13</sup> of stock market returns ( _σR_ ) on

> 13 The volatility is computed for each month from the daily returns of the local stock markets. The same approach is used to estimate the volatility of the GF at monthly frequency.

7
<!-- Start of picture text --> 3000 35'000<br>30'000<br>2500<br>Production<br>25'000<br>2000 (tons, left scale)<br>20'000 Yield (kg/ha, left<br>1500 scale)<br>15'000<br>Area harvested<br>1000 10'000 (ha, right scale)<br>500<br>5'000<br>0 -<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Source: FAOstat.

Overall, the data in Figure 6 suggest is that the improvement in Madagascar’s market share observable in Figure 5 is likely to reflect more the slackening of the competitive pressure from Indonesia than a positive domestic supply response to the improved price signals received by Malagasy vanilla farmers. This interpretation should be taken cautiously. First, FAO data on planted area and yields is very imperfectly measured. Second, the regression results reported in Table 3 give a somewhat different picture. The coefficients on year effects (all relative to 1993) are suggestive of entry and output increases in 1997 and 1999, but not in 2001, the year immediately after the hurricane.<sup>22</sup>

# **3.1.2 Regional Income inequality and poverty**

At less than 100,000 (1% of the total), the number of vanilla farmers is too small for vanilla policy to affect nationwide measures of inequality and poverty. However, as vanilla farming is concentrated in just two provinces (Antsiranana and, to a lesser extent, Toamasina) it makes sense to explore the evolution of inequality and poverty indices for those regions in order to track any possible aggregate effect of the 1995 change in Madagascar’s vanilla policy. Table 6 shows changes in selected income and price variables for the two vanilla-producing region, by income-distribution decile.

> 22 The “hurricane” variable is the statistical frequency of hurricanes in the region.

25
|platform|branch plants|and networking|recruit and retain<br>branch plants|
|---|---|---|---|
|State<br>anchored|Large public or nonprofit<br>entity related supplier|Restricted to purchase-<br>sale relationships|Dependent on region’s<br>ability to expand|
||and service firms|between public entity|political support for|
|||and suppliers|public facility|

Source: Markusen 1996.

Because the formation of clusters takes time and needs an ecosystem based on market forces, the purely top-down approach to cluster creation should be exercised with caution, especially in low-capacity countries, where many such efforts have failed. The challenges, however, should not necessarily prevent governments from facilitating the formation, growth, or scale-up of emerging clusters, especially through improving the business environment and making appropriate interventions in the public-goods or quasi–public-goods areas of clusters. Inevitably, it is easier to devise policies for a functioning cluster and devilishly hard to call a cluster into existence, especially when the essential industrial nuclei are difficult to identify (Yusuf, Nabeshima, and Yamashita 2008). In this sense, a mixture of bottom-up and top-down approaches to cluster development are possible, but initially clusters in developing countries are formed mainly through market forces or for “accidental reasons” (Krugman and Venables 1996). (An exception is those that “naturally” or “accidentally” derive from policy-induced SEZs or industrial parks, along with a few special cases, such as specialized industrial parks in certain countries.) Such a “mixed” approach applies perfectly to the case of China as discussed in this paper.

Despite the fact that government can have more control over SEZ development than over that of industrial clusters, an SEZ is not necessarily easier to develop, and many SEZ initiatives have failed. The success of SEZs requires a very capable government and a well-functioning market system, at least inside the zone or park. To design an SEZ using a purely cluster approach might be possible but can also increase the risk of failure unless the market signals are clear and the government has a perfect understanding of the domestic comparative advantages and market situations (both domestic and international), which is often beyond the government’s capacity.

In China, while market forces are usually responsible for initially producing industrial clusters, the government supports or facilitates them in various ways, including setting up an industrial park on the basis of an existing cluster (a process discussed in later sections). Meanwhile, after decades of development, some clusters have begun to grow out of certain SEZs, such as the information and communication technology clusters in Zhongguancun (Beijing) and Shenzhen, the electronics and biotech clusters in Pudong (Shanghai), the software cluster in Dalian, and the opto-electronics cluster in Wuhan. The emergence of these clusters actually 7
TABLE 37: SAVINGS CATEGORIES AND DEFINITIONS

|Component|Description|CalculationStrategy|
|---|---|---|
|1. Government Ec<br>|onomic Cost Savings<br>||
|a. Higher rebate<br>Savings|Savings due to lower<br>barriers for entry, higher<br>competition<br>reduced<br>collusive, coercive and<br>corrupt practices|=<br>∑<br>(rebateeGP<br>eGP Tenders<br>−predicted rebatemanual)<br>× Official Est. Cost)|
|2. GovernmentAd|ministrative Cost Savings||
|a. Advertisement<br>Cost Savings|Savings arising due to<br>lower<br>number<br>of<br>newspaper advertisements<br>required for eGP tenders|=<br>∑<br>#predicted adsmanual× ad costmanual<br>eGP Tenders<br>−#adseGP× ad costeGP|
|b. Tender<br>Preparation<br>Cost Savings|Savings<br>due<br>to<br>not<br>requiring printing, paper,<br>mail, transportation|=<br>∑<br>tender preparation costmanual<br>eGP Tenders|
|c. Security Cost<br>Savings|Savings<br>due<br>to<br>not<br>requiring security support<br>while accepting bids|=<br>∑<br>security costmanual<br>eGP Tenders<br>× %tenders requiring security|
|d. Staff-time<br>savings|Staff-time savings due to<br>increased efficiency<br>of<br>Procuring Entity officials|=<br>∑<br>(tprep<br>manual−tprep<br>eGP)<br>eGP Tenders<br>×PE t l|
|||sa saary|
|3. Bidder Transact|ion Cost Savings||
|a. Staff-time<br>savings|Staff-time savings due to<br>increased efficiency<br>of<br>bidders|=<br>∑<br>(tprep<br>manual× #predicted bidsmanual<br>eGP Tenders<br>−tprep<br>eGP× #bidseGP)<br>× Employee salary|
|b. Tender<br>Preparation<br>Cost Savings|Savings due to lower<br>tender preparation costs|=<br>∑<br>(Costprep<br>manual× #predicted bidsmanual<br>eGP Tenders<br>−Costprep<br>eGP× #bidseGP)|
|c. Security Cost<br>Savings|Savings<br>due<br>to<br>not<br>requiring security support<br>while submitting bids|=<br>∑<br>CostSecurity<br>manual× #predicted bidsmanual<br>eGP Tenders|
|d. Bribe Savings|Savings due to reduced<br>bribe payments|=<br>∑<br>(BribePE+other bidders<br>manual<br>eGP Tenders<br>× #predicted bidsmanual<br>−BribePE+other bidders<br>eGP<br>× #bidseGP)|

1. Government Economic Cost Savings: The economic cost savings are a result of multiple mechanisms as discussed in the paper. The savings were estimated using the models obtained after two matching methods, as done for the Winning Rebate models in the results. The savings for the 69240 tenders was

70
this by banks’ likely incentives to increase their liquidity and equity before the new law on capital requirements took effect on January 1<sup>st</sup> , 2006. Thus, banks that were looking for lending and investment outlets probably charged lower spreads.

We also find that an increased banking concentration, as measured by the Herfindahl index in the deposit and lending market segments, has not led to higher spreads. Another key finding in this paper is the importance of overhead costs as well as the macroeconomic environment in explaining spreads. High overhead costs are positively related to high spreads in all model specifications. Also, low inflation and real treasury bill rates as well as a stable exchange rate can be conducive to lower spreads and therefore cause a more efficient channeling of savings to productive investments. Overall, we bear in mind that 2005 can be seen as a transition year for the Nigerian banks so besides financial intermediation, banks were also in merger negotiations as well as raised substantial money on capital markets.

What might be the likely effect of the new capital requirements and banking environment on the financial intermediation capacity of the banking sector in the near future? On the one hand, banks are flush with excess liquidity and are highly overcapitalized so there will be some aggressive competition for profitable lending opportunities, potentially leading to further decreasing spreads. With the merger and acquisition activity during the banking consolidation, synergies and cost savings and therefore lower overhead costs could also lower spreads in the future.

On the other hand, there are still some major obstacles in permanently increasing financial intermediation especially to the poor. An uncertain macroeconomic environment could potentially be detrimental for efficient financial intermediation. Also, from a micro perspective, there is no credit information bureau or adequate identification scheme that allows banks to screen their borrowers. Furthermore, the absence of a central land registry makes it hard for banks to verify a borrower’s claim of a particular property ownership that is intended for loan collateral. The registration of mortgages is also very difficult. These legal problems could undermine an efficient degree of financial intermediation and maintain a high wedge between lending and deposit rates.

21
in the integrity of the electronic platform. The Development Bank’s neutrality puts it in the best position to take on this role.

_A business model that provides incentives for all stakeholders_ The original Small Investors Program was suspended primarily because of discrepancies in business arrangements and inadequate backup plans for certain functions. For such a program to work, its business model must create a win-win situation for all parties involved, public and private. The program’s main stakeholders are the Treasury, retail investors, participating banks, the Development Bank, and the software provider.

The question that should be asked is, why should these parties participate in such a program? The stakeholders that will incur initial costs are the Treasury, participating banks, the Development Bank, and the software provider. The break-even point for these parties (that is, the number of transactions required to generate profits) is fairly low: only about 40 transactions a day. Aside from potential profits, the program provides other incentives for each party to participate.

_Use of the Registry of Scripless Securities to register ownership for individual investors_ Philippine government securities have been dematerialized since 1996. The Treasury registers ownership of these securities through a secure trading platform connected to the Registry of Scripless Securities. When investors buy securities through dealers, they are registered at the dealer level— exposing investors to risk of default by dealers. But under the Expanded Small Investors Program, securities are registered under each investor's name, eliminating this risk. In many emerging markets this issue is not always clear. Hence such regulations must be reviewed as part of efforts to establish such programs.

# **Preconditions for a viable program**

Four preconditions must be met for this kind of securities distribution system to work.

_An effective legal framework_ The Philippine government passed the E-Commerce Act in 2000, enabling the use of digital signatures and ensuring the legal enforceability of electronic documents. Although government securities were dematerialized in 1996, the Securities Regulation Code was only recently revised to include provisions on dematerialization. Finally, the Supreme Court has established rules for accepting electronic documents as evidence in court.

Experience with electronic documents is limited, however, and protection of stakeholder rights has not been tested in court. Trust in the Expanded Small Investors Program will increase as it is tested and tried. But at this stage it is important that its policies, procedures, and processes be clearly documented, and that employees be trained in them. Moreover, the framework should be technology neutral to provide flexibility in applying the best and most affordable practices, especially for electronic security.

18