rafmacalaba/data-use-mentions-extended huggingface dataset:
# Half a Century of Development Economics, a Review

Based on the _Handbook of Development Economics_

# by Jean Waelbroeck

# Table of Contents

# **1) Introduction**

# **2) Structuring the Presentation: Policy Consensuses and Academic Paradigms**

# **3) The Indian Cong]ress Consensus**

# **4) The Dual Economy Development Paradigm**

# **5) Neo-Radical Ideas, an Aborted... or a Dormant Paradigm?**

- 5.1) Dependency Theory and Exploitation

- 5.2) Latin American Structuralism

- 5.3) Streeten on the Embryo of a Possible World State

- 5.4) An Aborted Paradigm?

# **6) Questioning the Dual Development Paradigm**

- 6.1) Does a Large Labor Market Failure Exist?

- 6.2) That World Markets Cannot Accommodate Southern Exports is Wrong; the "Four Dragons Consensus"

# 7) **Half a Century of Historical Experience and Data**

- 7.1) Experiments by Nature

- 7.2) "Stylized Facts" of Development and the Fashion for Cross-Country Regressions

- 7.3) Survey-Based Studies Data and the New Micro Econometrics

- 7.4) The Emergence of Today's Homo Economicus

- 7.5) Modelling General Equilibrium

# **8) Development Economics and the Rational Agent Paradigm**

# **9) Growth Accounting as a Cumulation of Entrepreneurial Gains**

- 9.1) Population and Labor Force Growth

- 9.2) Rural-Urban Migration and the Job Opportunities of Migrants

- 9.3) The Supply of Human Capital and Its Use

- 9.4) Private Saving and Its Intermediation

- 9.5) Foreign Capital and Its Intermediation

- 9.6) Technological Progress and Its Implementation

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# **3. Data sources**

As described in the methodology section, the 2009 poverty maps are based on two data sets. The first is the 15-percent sample of the Vietnam Population and Housing Census (VPHC). The 2009 VPHC was conducted by the General Statistics Office of Vietnam in April 2009, with (technical) support from the United Nations Population Fund (UNFP), and includes two modules. The first module is used to collect basic demographic and housing data for the whole population. Data include age, gender, race and education of individuals. The second module, which underpins our small area poverty estimates, contains more elaborate data. Individual data include demographics, education, employment, disability and migration. Household data include durable assets and housing conditions. The 15-percent sample is representative at the district level, and is selected on the basis of a cluster sampling technique, covering 3,692,042 households with 14,177,590 individuals (Central Population and Housing Census Steering Committee, 2009).

The second dataset is the 2010 Vietnam Household Living Standard Survey (VHLSS). The 2010 VHLSS is also conducted by GSO with technical support from the World Bank. It includes very detailed data on individuals, households and communes. Individual data consist of information on demographics, education, employment, health and migration. Householdlevel data include information on durables ownership, assets, production, income and expenditure, and participation in government programs. There are 9,402 households with 37,012 individuals covered in this data set. This 2010 VHLSS is representative for rural/urban areas and 6 geographic regions. Only 3 households are sampled from each commune. Thus, 9,402 households are sampled from 3,113 communes which belong to 686 districts. The 2010 VHLSS differs from earlier rounds (2002-2008) in terms of content as well as sampling design. The principal changes include shortening of the questionnaire, reframing of the consumption modules, and drawing of the sample from the 2009 instead of the 1999
than in most other sectors, in particular with respect to ownership limitations (Figure 8). At the same time, there is also a great deal of heterogeneity across world regions, with a tendency of poorer countries to restrict cross-border trade, whereas higher income countries are relatively more restrictive regarding entry via commercial presence.<sup>28</sup>

**Figure 8: STRI in transportation by region and sub-sector (Mode 1 and Mode 3)**

<!-- Start of picture text --> MENA SAR GCC EAP AFR OECD ECA LAC<br> Air Maritime Mar Auxiliary<br> Road Rail<br>Note: 103 countries included.<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Note: STRI of Air and Martime transportation combines mode 1 and mode 3, with weights of 70 and 30 respectively for the two modes. STRI of maritime auxiliary, road freight, and rail freight transportation services covers only mode 3 or commercial presence. The underlying data for mode 1 air transportation come from the WTO‘ Quasar Database.

A few countries have chosen to close one or more transportation subsectors altogether to foreign presence, among them Ethiopia, India, Jordan or Algeria. In the road trucking sector, most EU member countries have instituted a two-tiered regime such that entry is allowed for firms domiciled within the EU but is closed to entry from outside. Almost one-third of all

> 28 For air passenger transportation, we rely on the WTO‘s Air Liberalization Index (ALI) that quantifies the

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sublet their units at market rents.

Considering the survey data that reflect broader urban area definitions and landlord categories, and contrasting current rent levels with investment return requirements, the picture of high profitability softens considerably. Lithuania may be more characteristic for the rental profitability trend in the region: according to brokers interviewed, rents in Vilnius have been stagnating since about 2001, owing to an increasing supply in both the upscale and the popular rental markets. Apartment prices nevertheless have risen, fuelled by the expanding mortgage market that experienced falling interest rates and generous homeownership subsidies. In combination with rising prices and stagnating rents, private rental yields have dropped.

As mortgage markets in the region have started to swiftly expand, broadening homeownership affordability, filtering up into the owner-occupied market should steadily increase. The combined effect should soften the profitability of renting for some time to come.

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On” crisis. The sample consists of garment-making firms with access to electricity in Hohoe, Ghana, a mid-size district capital in the Volta Region. The study combines daily firm-level data on electricity outages and labor hours with weekly panel data on wages paid, revenues, and profits, to estimate the effects of blackouts on firm outputs and input choices. All specifications include time and firm fixed effects to control for blackout propensity variation across days and across firms, extracting plausibly exogenous variation in daily exposure to blackouts. High-frequency micro data on firm-level exposure to power outages, firm inputs, and firm outputs is rare, which is one reason why research on the impact of blackouts is so limited, despite the ubiquity of rolling blackouts in many developing countries. The panel dataset of small garment-making firms in Ghana analyzed in this paper offers an unusual opportunity to estimate direct Findings reveal that frequent power outages have disproportionately negative impacts on the profitability and productivity of single-person firms. Among single-person firms, _each_ additional blackout day in a workweek is associated with a 10% decrease in weekly revenues and a 13% decrease in weekly profits. Point estimates are nearer to zero for firms that have access to workers during the period of the panel data collection, and the analysis is unable to reject that these firms experience no profit or revenue decreases in response to blackouts.

This heterogeneity in output effects suggests differential success in adapting production to cope with unreliable infrastructure, and that these coping strategies may center around labor inputs. Oneperson firm owners decrease their intensive and extensive labor supply on days with outages, without compensating for those lost hours on days when the power is restored. Aggregated to the weekly level, one-person firm owners reduce their weekly labor supply by 5% for each blackout weekday, utilizing labor as the primary input to contract production in response to changes in productivity. These productivity shifts, however, not only contract hours and outputs, they also have a statistically significant negative effect on profits per owner hour worked, which decrease by 16%. As is
7. Model Fit (Predicted Correctly in Percent of Original Cluster Analysis Classification)**

||Low<br>vulnerability|High<br>vulnerability|
|---|---|---|
|Low growth|**52**|**74**|
|es: IMF’s World<br>ti Th hdd<br>High growth|Economic Outlook<br>ll i th 22 t<br>**56**|database and authors’<br>i flt th f<br>**72**|

Sources: IMF’s World Economic Outlook database and authors’ calculations. The shaded cell in the 2x2 matrix reflects the reference cluster, which is also been defined as the worst of all possible outcomes; that is, lower growth and higher vulnerability.

Second, the results indicate that increased financial openness is associated with increased vulnerability. While capital inflows can bring many benefits, including financing for productive investments and allowing diversification of investment risks, they can also complicate macroeconomic management and heighten vulnerabilities. In particular, they can lead to upward pressures on the exchange rate and contribute to overheating, with widening current account deficits and rapid credit growth. Moreover, often, when the flows are intermediated through the banking sector, the banking sector can play an important role in amplifying the macroeconomic challenges. Furthermore, Inflows into the banking sector that are extended as credit to the private sector can lead to the validation of demand that would otherwise be liquidity constrained and can fuel asset prices increases. In turn, the 14
values of the IPC ratings as predictors.<sup>5</sup> This allows us to generate predictions that can be updated monthly based on available data, independently of whether recent IPC ratings are available. We distinguish four key groups of covariates:

# **Structural factors**

Structural factors capture time-invariant vulnerability to food insecurity. We proxy these at the district level with spatial trends, population counts, land area, terrain ruggedness and land use shares (cropland and pasture). In addition, we include fixed effects to capture basic country-specific seasonal variation. Nonlinear prediction models like the Random Forest we deploy can exploit interactions between any (combination) of structural factors and other time-varying covariates to capture heterogeneity.

# **Environmental factors**

We rely on remote sensing data to track environmental factors relating to food production. Food production depends on soil moisture content, which can be monitored through the inputs (rainfall) and outputs (evapo-transpiration) of the water balance equation. We also proxy for food plant health directly using the normalized difference vegetation index (NDVI), which is a commonly-used satellite-imagery-based measure of vegetation coverage (Ross et al., 2009; Brown, 2010).

# **Violent**

Violent conflict is an important factor that can impact food insecurity, by disrupting social systems and blocking access, or may signal other factors that contribute to food insecurity. For example, riots and protests may be the sign of an ailing economy, while violent attacks on civilians by non-state actors can cause displacement of people and disrupt food production and distribution (Br ̈uck and D’Errico, 2019; Maxwell et al., 2020). We use the count of conflict events and the number of fatalities in order to capture events of differing intensity, ranging from protests and riots to lethal attacks.

# **Food price inflation**

Food price inflation can directly cause food insecurity by raising the cost of living for vulnerable households (Baffes et al., 2008; Concei ̧c ̃
cantons and the high density group contains 31 cantons. In terms of working-age population, the low, medium and high groups account for 33%, 53% and 14%, respectively.

# **6.2 Estimation Labor-Market**

Our main analysis is based on a difference-in-difference estimation approach, applied to canton-level labor market indicators defined for several groups of workers differing in their gender, age and educational attainment.

In all cases, our main specification is as follows:

> 22As seen in Table 3, the Venezuelan share in the population ranges from 0% to 78.3%. The latter figure corresponds to one of the _cantons_ with very low population. Since the ENEMDU is not representative for these _cantons_ , we exclude the canton-year cells with the lowest population from our estimation sample. For most years, our sample contains 200 of the total of 221 cantons in Ecuador.

> 23It is worth noting that the two _cantons_ with the largest numbers of Venezuelan migrants (Quito and Guayaquil) are in the medium-density group. We estimate the density of Venezuelan migrants in the working-age population of these two cantons at 4.3% (Table 1).

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# Box 4. **Additional Principal Criteria For a Tax Benchmark**

(an example for a middle income country case) The normative tax benchmark should

- Represent a consistent tax treatment of similar activities or classes of taxpayers and neither favor nor disadvantage similarly placed activities or classes of taxpayers.

- Include certain tax provisions (such as exemptions, deductions, tax credits, and other tax preferences) to adjust taxable income in order to:

- comply with the ability-to-pay principle;

- enhance the economic and collection efficiency of taxation;

- simplify or make feasible tax administration with respect to a class of taxpayers or type of activity.

- Ensure that tax expenditure report provides sufficient information for policy formulation.

# **Building block 2. Estimating and projecting tax expenditures**

The estimating/accounting tax expenditures gives a quantitative measure of the size of tax expenditures and their impacts on revenue loss, direct budget expenditures, and budget balance, in addition to providing data for cost-effective analysis in tax expenditures. Projections of tax expenditures provide similar information for the medium term budget framework (MTBF).

Methods of estimating and projecting tax expenditures used include revenue foregone, revenue gain, and outlay equivalent methods. However, In the OECD and most countries’ experience, the revenue foregone method is usually recommended for estimating tax expenditures.

The revenue foregone method estimates the cost of a tax expenditure as the deviation between the tax paid by taxpayers affected by a specific tax provision and tax paid by similar taxpayers not affected by that provision. The estimated value of tax expenditure is the economic value on an accrual basis and is consistent with the principal of the Government Finance Statistics (GFS) and harmonized with statistics of the 1993 System National Accounts (SNA) for revenue estimates (on accrual basis) in GFS. However, that method does not take into account changes in taxpayers’ behavior.

The revenue foregone method estimates tax expenditures one at a time. It does not account for the change from the removal of one provision that may result in
5 between their nominal rates of assistance (NRAs) for import-competing and exporting agricultural industries, as well as a continuing large gap (albeit smaller than in the 1980s) between the relative rates of assistance in the two groups of countries. In the light of that evidence, the above question to be addressed here can be expressed more specifically, for any developing country of interest, as: how important are its own policies compared with those of the rest of the world in affecting the welfare of the poor in that country, and what do agricultural policies in particular contribute to those outcomes? Clear answers to this question can guide countries in their national policymaking and as they negotiate bilateral and multilateral trade agreements.

Now is an appropriate time to address this multi-faceted question for at least two policy reasons. One is that the World Trade Organization (WTO) is struggling to conclude the Doha round of multilateral trade negotiations, and agricultural policy reform is once again one of the most contentious issues in those talks. The other is that poorer countries are striving to achieve their United Nations-encouraged Millennium Development Goals by 2015, the prime ones being the alleviation of hunger and poverty.

There are also several analytical reasons as to why now is the time to focus more thoroughly on this issue. One is that methodologies to address it have advanced at a rapid pace recently, involving micro-simulation modeling based on household survey data in conjunction with economy-wide computable general equilibrium (CGE) modeling. Prominent examples include the studies in Hertel and Winters (2005, 2006) and in Bourguignon, Bussolo and da Silva (2008). Household income information is increasingly important for poverty and inequality analysis because farm households and rural areas of developing countries are rapidly diversifying their sources of income beyond what agricultural land and farm labor can generate, including from part-time off-farm work and remittances (Otsuka and Yamano 2006, Otsuka, Estudillo and Sawada 2009). Hence the earlier close correspondence
|50|South Kazakhstan|
|GBAO||20|Central Kazakhstan|
|Soghd||150|Western Kazakhstan|
|Khatlon||170|Eastern Kazakhstan|
|RSS||110|Northern Kazakhstan|
|**Total**||**500**|**Total**<br>**500**|

15. . The climate change module included many questions from a special Eurobarometer survey on attitudes towards climate change that was canvassed in all EU27 countries in 2008,<sup>13</sup> along with a few additional questions of special interest and relevance to Tajikistan. As a result, the data collected for Tajikistan can be compared not just with Kazakhstan, but also with EU27 countries in selected subject areas. The climate change module is presented in Appendix 1.

# **Climate Change: Awareness of the Main Causes and Consequences**

16. Respondents in P-LITS 2 were asked if they thought they were well informed about the different consequences of climate change, with answers recorded on a 4-point scale: (i) very well-informed, (ii) fairly well-informed, (iii) not very well-informed, and (iv) not at all informed. Public awareness of the different consequences of climate change in Tajikistan is quite high, and in fact comparable to prevailing levels in Kazakhstan and EU27 countries (Figure 3a): a majority of respondents 57 percent) in Tajikistan (felt they were either very well-informed (9 percent), or fairly well-informed (48 percent) about the different consequences of climate change. Within the country, the level of public awareness varied considerably across different regions (Figure 3b): overall awareness levels were highest in Dushanbe (81 percent very/fairy well informed), followed by Soghd (60 percent) and

> 12 Albania, Armenia, Azerbaijan, Belarus, Bosnia & Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Former Yugoslav Republic
_E_ [ _zjt_ ( _εjt − ε_ ˆ _jt_ ) _|It_ ] = 0. This problem is less of a concern if _zjt_ is included in the first stage of ACF.

**3) In the absence of price data (a problem in many firm-level surveys)** , the output elasticity is inconsistent and tends to become the revenue elasticity. This implies that the estimated markup of Equation 13 converges towards one (see Bond et al. (2020) for an in-depth discussion). Considering the revenue elasticity instead of output, Equation 6 becomes

where _ρ_<sup>_R_</sup> _Xjt_<sup>standsforrevenueelasticity.Thisestimatoriswidelyappliedintheliterature</sup> (see for example Diez, Leigh and Tambunlertchai (2018)). Notice, however, that markups are either increasing when the revenue elasticity is constant, but labor shares are falling, or when labor shares are held constant, but the revenue elasticity increases.<sup>2</sup> The revenue elasticity can be expressed as follows where _ρ_<sup>_P_</sup> _Xjt_<sup>=</sup> _∂Q_<sup>_∂Pj_</sup> _jt_<sup>_t_</sup> _<u>QPjtjt</u>_<sup>represents the price elasticity of demand, and</sup><sup>_ρXjt_is output elasticity</sup> as described in Equation 7. To illustrate the problem raised in Bond et al. (2020), consider the following firm profit maximization problem: where _C_ ( _._ ) is the cost function and the marginal cost is expressed as _MCjt_ =<sup>_∂C_</sup> _∂Q_<sup><u>(</u></sup><sup>_<u>Q</u>_</sup> _jt_<sup>_<u>jt</u>_</sup><sup>
greater in some places because of the presence of an illegal economy; in another because of the presence of a dominant political clan with the networks to support vote buying practices; in another because of the _lack_ of a dominant clan, which spurs contenders to emerge on the basis of vote buying; in another because of a persistent history of vote buying as the electoral strategy selected by contenders. The empirical methodology used below relies on this apparently idiosyncratic variation in vote buying to identify its equilibrium policy consequences.

# **4. Empirical specification to correlate vote buying with service delivery**

The following type of equation is estimated at the village-level, alternating different measures of health services on the left hand side, different measures of the main explanatory variable—vote buying—on the right hand side, and different combinations of other control variables:

# HealthServiceijk = β0 + β1VoteBuyingijk + WijkBb + ZjkBm + ak + εijk  (2)

Three different measures of health services in village _i_ , in municipality _j_ , of congressional district _k_ are used: proportion of children under 6 with weight well-below normal, the number of village health workers assigned by the municipality, and the number of health projects undertaken in the village. The vote buying measures are as discussed in the previous section. Fixed effects for the four congressional districts, ak , in the province are included because of their importance in explaining variation in vote buying across municipalities (as shown in the previous section).

Data on health service delivery was collected from health facility records at the villagelevel. Key informants—the village health workers and council members—provided the data on health and other sector projects undertaken by the municipal government in the village in recent years. A nationally mandated program to monitor the health of young children, Operation Timbang (OPT), requires villages, or barangays, to measure and record the weight of young children in their areas. The survey team gathered data from barangay health officers on the number of children participating in OPT weighing, and the number of
the part of the results driven by various assumptions may vastly outstrip the part based on actual data. The best recent examples of such approaches, which are often thinly disguised Concept 2 inequality calculations, are Bhalla (2002) and Sala-i-Martin (2002). In Bhalla’s (2001) calculations of global inequality only one out of every 24 distributions is based on actual data and 23 are “derived” through assumptions; in Sala-i-Martin’s (2002) paper, the ratio is 1 actual to 4 “derived” distributions.<sup>10</sup> The quantum leap compared to this method is to use directly household surveys from as many (ideally all) countries of the world. This was done by Milanovic (2002; 2005) and World Bank (2005). Here in principle global inequality is calculated the same way as one calculates within-country inequality, using not National accounts data, but surveys. Another quantum leap in this line of research will occur when these, so far disparate, national surveys are standardized or a single world-wide household survey is conducted. We shall come back to this in Section 3. Now we have to cover briefly some methodological issues that are indeed very important in this type of research although they seldom receive the attention they deserve.

> 10 For a critique, see Milanovic (2002a and 2005, pp. 119-127).

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