rafmacalaba/datause-encoder-data huggingface dataset:
In contrast, official imports from its ECOWAS partners remain very small at around 1 percent. It is important to stress that the official data reported here do not account for the purportedly large amount of unrecorded informal trade (smuggling) between Nigeria and its regional neighbors. This issue will be addressed later in this section.

**Figure 1. Structure of Nigeria's imports, by source**

<!-- Start of picture text --> ECOWAS 1%<br>AFRICA 3%<br>ROW<br>24%<br>Total: EU<br>INDIA $22,653 37%<br>5% mln.<br>CHINA<br>14%<br>USA<br>16%<br><!-- End of picture text --> _Source_ : Computed by authors based on TRIST database Table 1 reports the 20 most important product categories (according to 3-digit ISIC classification) imported by Nigeria from the EU. Nigeria’s imports from the EU tend to be concentrated with the largest 7 ISIC categories (out of 72 traded sectors) accounting for more than half of all imports from the EU. Basic chemicals constitute the largest category. Other important categories include capital intensive sectors such as general and special purpose machinery, motor vehicles, and television and radio equipment. Nigeria also imports substantial amounts of basic iron and steel, as well as food products (dairy and processed foods) from the EU.

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<!-- Start of picture text --> 0.90<br>5000<br>0.75<br>Q<br>4000 U<br>U<br>no}<br>0.60 @<br>x< lo}<br>(5) fet)<br>se} weer TO ZU.<br>£& were . Le a<br>= ee --- Ser 30005<br>rs}oSous) @_. @—_—_ o—-2@:e-_--“\g—_0—_ e°—-0—__e-___0 0ea0-__ io}Z<br>8 e@ weoccT »<br>eo) eorccT7 =<br>7 S<br>2000<br>0.30 _-@— e e e B<br>eee—_-e New —e— e e—_— TM~ea is)<br>1000<br>0.15<br>0.00 0<br>2007 2009 2011 2013 2015 2017 2019 2021<br>Year<br>-@- National -@- Rural Urban<br><!-- End of picture text -->
_T_ 4), and (4) the effect of autonomy in the use of the grant ( _T_ 3 vs _T_ 2). Comparisons (1), (2) and (4) are, to the best of our knowledge, new in the literature and we briefly review their rationale below.

# **Hypothesis 1 (H1): Extension services paired with tailored recommendations increases practice adoption relative to control farmers (** _T_ 4 **vs** _C_ **)**

The extant research on providing farmers with tailored soil-analysis recommendations (e.g Harou et al., 2018) suggests limited adoption. However, such recommendations appear to be difficult to interpret and implement in isolation. We provide detailed soil analyses and recommendations (more detailed for instance than those provided by SoilDoc) and pair them with high quality extension services. The hypothesis is that the joint provision increases adoption.

Since control farmers received the recommendations at the start of the 2016 growing season, by the 2017 end-line survey, _T_ 4 and _C_ differ only in that _T_ 4 received extension services (in 2015) and had tailored recommendations for a year longer than control farmers. We hypothesize that in 2017, extension services paired with tailored recommendations increase adoption more than tailored recommendations alone.

# **Hypothesis 2 (H2): Plot-level recommendations increases practice adoption relative to clusterlevel recommendations (** _T_ 1 **vs** _T_ 2 **)**

It seems reasonable that farmers are more likely to trust and therefore act on recommendations that are based on the soil quality of their own plots, especially if heterogeneity in soil quality is significant. In addition, previous research, albeit in a different setting (e.g. Madajewicz et al., 2007), suggests that individualized information is more effective. Cluster-level recommendations, however, are cheaper to produce and deliver, so if outcome differences between the two are small, then cluster averaged recommendations may be more cost-effective.

Given the substantial soil heterogeneity we document, our hypothesis is that individualized recommendations will induce more take up and adoption of practices.

There is an important caveat to this test. As we discussed above (see e.g. see p.3) the average cost of the plot-level recommendations was higher than that of the cluster-level recommendations (recall both recommendations calibrated for a yield of 4.5 mt/ha). Holding costs constant would have required reducing target yields for the plot-level calibrations. Instead, we chose to keep target yields fixed for the calibrations (and hence had differing average costs).

# **Hypothesis 3 (H3): The in-kind grant increases practice adoption (** _T_ 1 **–** _T_ 3 **vs** _T_ 4 **)**

The effect of the in-kind grant could work through multiple channels: (a) a direct income effect, (b) an endorsement effect (i.e. signalling that the team’s specific recommendations were not just cheap talk) and (c) directly incentivizing experimentation. Since the tailored recommendations differed dramatically from business-as-usual, focus group discussions with farmers and 14
It is usually believed that for regularly purchased items, accuracy of recall increases when the recall period is shortened (Scott and Armenuvegbe, 1990). For items which are infrequently purchased, it is more meaningful to use a longer reference as well as recall period. The length of the reference period is important for the precision of annual estimates - the customary time period for welfare analysis. The shorter the reference period, the more difficult it becomes to extrapolate the results to an annual basis. In the extreme case, if for all expenditure items a one-day reference period were used, the estimate of mean annual expenditure would have to be obtained by multiplying survey responses by 365. And while, strictly speaking, 'his would still yield an unbiased estimate of mean annual expenditure, the resulting error variance would be so large as to make the results practically meaningless.

The extrapolation to an annual basis of expenditures recorded over shorter reference periods will always yield an estimate of the variance of expenditures which overestimates the true variance (Scott, 1993). The reason is that the correlation of household expenditures over successive time-periods is less than unity. The lower this correlation is in reality, the more the variance of extrapolated expenditures will be biased upward. The problem is compounded when annual expenditure is built-up from components which each were recorded over different reference periods (as is the case in the CILSS).

One solution to all this is to visit the household repeatedly and to obtain multiple observations over short recall periods which in total add up to the year, but this is obviously a very costly solution which would greatly increase the organizational complexity of a survey. An alternative is to obtain estimates of the over-time correlation of expenditures by repeatedly visiting a small sample of households. The results of such exercise could then be usAd to correct the data from the main sample. While some limited research has been undertaken to address these issues (see e.g. Chesher, 1991), coincide. In a multi-visit scheme, a reference period can be covered by multiple recall periods, e.g. if expenditures are to be reported over a one-month reference period, this can be achieved in four visits, each with a recall period of one week.

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# **Figure A4. Gender comparisons by education: Employment, Unemployment ratio, Non-participation**

<!-- Start of picture text --> Employment ratio Unemployment ratio Non-Participation ratio<br>Less educated workers Better educated workers Less educated workers Better educated workers Less educated workers Better educated workers<br>Men vs. Women Men vs. Women Men vs. Women Men vs. Women Men vs. Women Men vs. Women<br>MK LV LT<br>LV<br>LT<br>ID SA EG<br>LT LV<br>RO ME CH BR EG RO TRM ECHMKBR JOPHEGID RO TRBGCHMESABRCRJOAREG PHPLID ROTR BGSAME MK BRCH ARCRJOPL PH ID RO TR BG MEARCHJOPL BRCRPH EG LTLV RO TR BGCHSAMEBRARMKCR P PHL<br>BG CR JO PLPH BG SA ARCR PL MK ID<br>EG JO<br>TR ID<br>SA<br>AR MK<br>LV<br>LT<br>LT<br>LV<br>-20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0<br>GDP slowdown GDP slowdown GDP slowdown<br>lines indicate mean value and running line smooth lines indicate mean value and running line smooth lines indicate mean value and running line smooth<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>Disparity in change in change (pct points) Disparity in change in change (pct points)<br>-4 -4 -4<br>-6 -6 -6<br>-8 -8 -8<br><!-- End of picture text --> See notes for figure 5.

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**Table 10: Sub-Saharan Road Network Length by Region**

|Region|Network<br>Length<br>(km)|
|---|---|
|West|32,248|
|Central|29,274|
|East|24,146|
|Southern|13,350|
|South|9,802|
|Total|108,820|

# **5.1  Cost Function Estimation:  Specification and Data**

To assess inter-country variability in upgrading and maintenance costs, we fit the following unit cost function to the ROCKS data for Sub-Saharan Africa: where Cij = Unit cost of project-type j in country i

- Ri = Rainfall intensity around the road network of country i

- Pi = Percent of roads in country i that are paved

- Yi = Income per capita in country i

- Gi = Governance quality in country i

- Uj = Upgrading Widening and Reconstruction

Reconstruction New 2-Lane Highway Strengthening Partial Widening

- Asphalt Mix Resurfacing

- Mk = Gravel Resurfacing

- Surface Treatment Resurfacing Bituminous Pavement Preventive Treatment

- Unsealed Preventive Treatment Routine Maintenance Grading

Roads are more expensive to build and maintain in high-rainfall areas, ceteris paribus. Many countries in Sub-Saharan Africa are large and climatically diverse, so national average rainfall provides a very crude approximation to conditions in specific areas. We believe that rainfall in the areas traversed by our network links provides a 28
This “stylized” growth process shed light on the distribution patterns of employment growth in Figure 10. Employment generated by rural enterprises is rather low and occurs mostly for a minority of small and young enterprises. It is thus the youngest along with the smallest firms at startup that are more likely to create jobs - a powerful finding for those concerned with job creation in rural Tanzania. However, after a certain period small enterprises appear to never grow substantially - unless other growth obstacles are considered. The following section analyzes to what extent the rural investment climate aligns with this growth process.

# **Constraints to enterprise operation and growth: investment climate perceptions**

One of the main goals of the rural investment climate survey is to identify the leading factors that constrain enterprise operations and growth. The survey asked entrepreneurs whether they perceived various problems as an obstacle. Although these subjective rankings are not a definitive priority-setting tool, they can be a useful starting point. Additional and more objective data from the community and household survey and quantitative analysis will add weight to the survey results, followed by an extension of the regression analysis.

In rural Tanzania, nonfarm enterprises are most concerned about access and costs of rural finance (figures 11 and 12). About 61% of rural entrepreneurs rate financing as a major or severe constraint to business operations. A comparison between rural areas and small market towns reveals that constraints related to governance and taxation increase with the level of urbanization and market access -the level of perceived business constraints is generally higher in rural areas than in small rural market towns. Not surprisingly, governance constraints score higher in market towns than in rural areas where government presence is limited. However, the level of perceived tax constraints does not differ between rural areas and market towns. Independent by the type of location, the accessibility and cost of rural finance remains the main constraints. This finding echoes a large body of similar analyses for countries in Sub-Saharan Africa (Liedholm, 2002; Bigsten and Söderbom, 2006).

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for years when both observations were available. We then used the predicted IPUW values to replace missing values where needed.

# Other data

Real GDP per Capita and the Growth rate of Real GDP per Capita were obtained from the United Nations Statistics Divisions’ National Accounts.

Real Foreign Aid per Capita was generated by using the World Development Indicators Foreign Aid Series divided by country population obtained from the United Nations Statistics Divisions’ National Accounts. The resulting series was then converted into constant U.S. dollars using the U.S. Department of Commerce GDP deflator.

Appendix Table A1: Sample Statistics

|Variable|Mean|Std. DEV|
|---|---|---|
|Returns to Education|||
|Total|8.09|3.51|
|Male|7.28|3.32|
|Female|9.74|3.79|
|Urban|8.29|3.68|
|Rural|7.50|4.04|
|Returns to Experience|||
|Total|4.64|2.73|
|Male|4.60|2.95|
|Female|4.52|4.16|
|Urban|5.09|3.17|
|Rural|3.99|3.13|
|Economic Freedom Index|55.25|10.81|
|Globalization Index|51.79|12.72|
|Empowerment rights index|6.14|2.76|
|Democracy (Freedom House/Imputed|||
|Polity)|6.26|2.74|
|Women in the Legislature|11.79|8.02|
|log(Life Expectancy at Birth)|4.15|0.17|
|log(GDP per Capita)|6.86|1.03|
|Growth Rate: GDP per Capita (%)|3.18|5.00|
|log(Aidper Capita)|-1.43|1.37|

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give the patient aspirin. Das et al. (2012) show that the average interaction for this standardized patient lasts 3.5 minutes, with doctors asking 3 questions and completing 1.5 examinations. Das et al. (2012) suggest that a minimally correct treatment would require the doctor to complete at least one of these actions but would not penalize the doctor for additional unnecessary tests and medicines, even if they are contraindicated or harmful. Standardized patients in their study receive such a minimally correct treatment in 31.2 percent of interactions and unnecessary or harmful treatment in 55.2 percent.

Figure 1 shows that the significant deficits in care uncovered in Das et al. (2012) hold for multiple conditions and study sites. For each study, the top bar shows the share of patients receiving the minimally correct treatment and the bottom bar the share who received an antibiotic, which was inappropriate for all the conditions represented in the figure and is therefore a measure of an unnecessary and potentially harmful treatment. Across these studies, 40-90 percent of standardized patients are incorrectly treated, which means that they are treated for entirely the _wrong thing_ —for instance, asthma or pneumonia instead of a heart attack. More stringent definitions that penalize the use of unnecessary medicines or require providers to administer _all_ the required components of the treatment reduce the fraction correctly treated to less than 5 percent (and for many conditions 0-1 percent). Banerjee et al. (2023) use data from five standardized patient studies to show that one consequence of these deficits in care is that 70-85 percent of all out-of-pocket expenditures can be attributed to incorrect care or overtreatment. Interestingly, 52-78 percent of this avoidable medical expenditure is due to misdiagnosis and incorrect care rather than over-treatment based on a correct diagnosis—a conclusion that holds equally for health care providers in the salaried public sector and in the fee-for-service private sector.

# Capacity Constraints

Three types of capacity constraints have been evoked to explain these deficits-- _overcrowding, lack of equipment_ and _lack of adequate medical training_ . We consider each in turn.

**Overcrowding.** The World Health Organization (2016) raises frequent alarms about doctor shortages resulting in an excessive workload for health care providers in LMICs. We are sympathetic to this explanation for certain conditions and contexts. For instance, Andrew and Vera-Hernandez (2022) show that in areas with low capacity in India, demand-side incentives for women to deliver in health facilities _increased_ infant mortality, because the resulting congestion worsened outcomes for women with highrisk pregnancies.  But taken as a whole, the utilization and capacity numbers simply do not add up to a picture of massive overcrowding.

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Based on the LSMS data of the provinces of Hebei and Liaoning, our analysis suggests that the poor households gain more from non-farm activity than the rich households. One of the important reasons is that households that suffer stronger constraints in farm activity are more likely to participate in non-farm activity, and earn relatively higher income compared to those with better resources. Whether a household participates in non-farm activity depends on its incentive and capability. Households are motivated to undertake rural non-farm activity by either “pull” or “push” factors. If the non-farm sector has high returns, the “pull factors” will be strong; if farm activity cannot provide enough income for households (for example, if farm output is inadequate due to drought, flood, or insufficiency of land) or households need to diversify their income sources, the “push factors” may kick in. Poor households are less capable of weathering negative shocks, and are more risk averse. In order to have additional income as well as to diversify and undertake activities with returns that may have a low or negative correlation with those of farming, poor households may have stronger incentives to participate in non-farm activity; while rich households may have better capacity to do so thanks to their better endowments in physical and human capital (FAO, 1998). In rural China, the credit and insurance markets are underdeveloped. Households have strong incentives to diversify their income sources. However, because of their limited capacity and liquidity constraints, poor households tend to participate in non-farm activity with a high labor to capital ratio and low entry barriers.

The high participation in non-farm activity among low-income rural households may result in a more equal distribution of total income. For example, Adams and He (1995) and Adams (1999) argue that non-farm income reduces overall inequality in Pakistan and in Egypt, respectively. They suggest that households with low farm income (because of unequal access to land, etc.) are more likely to engage in non-farm activity, and the pro-poor distribution of nonfarm income across the income scale of the population mitigates inequality.

In the following sections, we will examine the determinants of participation in non-farm activity and those of non-farm income, and study the impacts of non-farm income on rural inequality in the case of two Chinese provinces, Hebei and Liaoning.

5
# <u>1) Introduction</u>

Since the early 1980s, Turkey has been going through a rapid urbanization process at a pace beyond the world average. A recent World Bank study qualified this process as “the strongest socio-economic force that has changed peoples’ lives since the foundation of the Turkish Republic in 1923” (World Bank 2004). More than 20% of the country’s urban population lives in Istanbul’s various district municipalities, and half of the urban population lives in the seven largest urban settlements (each of which includes several municipalities). About 75% of the total urban population lives in the 352 largest municipalities with an average of 104,000 residents per municipality. The remaining 2,848 municipalities have an average population size of about 4,000 (World Bank 2004).

Figure 1: Turkey vs World urbanization rate <!-- Start of picture text --> %<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>World Turkey<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> Sources: WDI 2006.

Given this backdrop, Turkey appears to be a good laboratory for assessing the impact of agglomeration economies on productivity. However, we first need to clearly define what we mean by agglomeration economies. Indeed, a contrasted point in the agglomeration literature is whether agglomeration economies are related to the concentration of an industry or to the size of a location itself. Rosenthal and Strange (2004) nicely summarize this debate by revisiting the seminal contributions of Marshall (1920) and Jacobs (1969) on this topic.

According to Marshall and the Marshallian-externality-based papers (Henderson 1974 and 1988, Carlino 1978, Stelting and al. 1994 among others), the micro-foundations of agglomeration stem from urban specialization through localized spillovers induced by firms operating in the same sector, while Jacobs and Jacobian-externality-based papers (Shefer 1973, Sveikaukas 1975, Segal 1976, Fogarty and Garofalo 1978, Moomaw 1981, and Tabuchi 1986 among others) emphasize on urban diversity fostering crossfertilization of ideas from various sectors.

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low-growth “poverty traps” that generally focuses on low income countries,<sup>7</sup> as mentioned above.

# **III. Relative “Trap”: Some Unpleasant MIC History**

The preceding section (and Figure 1a) already alluded to the obvious failures of countries or entire regions to ever successfully catch up with the rich countries’ income per capita—even when adjusted by purchasing power parity. There have also been some successes, but these have been painfully rare, as documented extensively elsewhere (Felipe et al, 2012; World Bank and Development Research Center (PRC), 2012; Gill and Kharas, 2007; Lin and Rosenblatt, 2012). Only a limited number of countries have made substantial progress in relative terms, and those countries are concentrated in a handful of East Asian economies, the European periphery, and special cases of substantial (per capita) natural resource discoveries.<sup>8</sup> Systematic growth slowdowns among MICs are another way to view the inability to converge (Eichengreen et al (2011) and Aiyar et al (2013)).

By definition, if a country grows faster (in per capita terms) than the rich countries, it will eventually catch up with the high-income countries’ GDP per capita. A relevant benchmark is the long-run per capita growth of high-income countries over the last century, which is on the order of 1.5-2.0 percent per annum. Over the last 50 years, it was 2 percent – using WDI data. If one looks at the average per capita growth rates for 141 developing countries<sup>9</sup> over the last 50 years (1961-2011), 80 grew by at least 1.5 percent – of which, 64 grew by at least 2 percent (WDI data). While these countries are, in fact, converging, this is painfully slow convergence. Thirty-one countries grew by 3 percent or more (in per capita terms), while only 9 reached 5 percent.

More generally, the time it will take a developing country to reach the GDP per capita of a high income reference is given by the following equation: where _R_ is the initial ratio of the high GDP per capita reference to the MIC’s GDP per capita, is the MIC’s compound rate of growth of GDP per capita, and is the high income country’s compound rate of growth of GDP per capita.

A MIC on the border between LMIC and UMIC has a GDP per capita that is on the order of 1/10 the United States’ level or about 1/8 of the high income OECD average (in PPP dollars). The relevant “ _R_ ” ratio is on the order of 8 to 10 then. One can read from Table 1 below that if this MIC grows by only 3 to 4 percent in per capita terms, it will take a century or two to catch up with the rich countries – assuming that the rich countries grow at 1.8 percent in per capita

> 7 There is also a parallel microeconomic literature on poverty traps at the household level. See, for example, Antman and McKenzie, 2007.

> 8 The classic example of the latter is Equatorial Guinea.

> 9 This is using current year classification of developing economies.  This excludes the “graduates” from MIC status then.

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