rafmacalaba/datause-encoder-data huggingface dataset:
20 Balassa, Bela (1965). "Tariff Protection in Industrial Countries: An Evaluation",<sup>lourmal of oi</sup> & oMz, (December).

Coppock, J. (1962). International EQonomic Instability, (New York: McGraw<sup>Hill).</sup>

- General Agreement on Tariffs and Trade (various issues), Interational (aious eas, (Geneva: GATT).

- Kojima, Kiyoshi (1964). 'The Pattern of International Trade Among Advanced Countries,"<sup>Hitotaubashi</sup> JouXnal of E=oomics, vol. 5, no. 1, (June).

- Lary, Hal (1968). _rts of Ma_ufacturs fror s t)evelopcd Couries, New York: (National Bureau of Economic Research)

- Leith, J.C. (1971). "Export Concentration and Stabilily: The Case of Ghana," onag. .Bulle1inQfahm, vol. 1, pp. 45-55.

- MacBean, A. (1966). Exprt Instability and Eonomic Developmen , (London: Allen and Unwin.

- Massel, B. (1970). "Export Instability and Economic Structure," A a ovol. 60,pp.618. 30. (Sept.)

- Michaely, M. (1967). Concentration in Intematinal lrade, (Amsterdam: North Holand).

- Prebisch, Raul (1950). The Economnic Developmnt of Latin America and its Principal Proble (New York: United Nations Economic Commission on Latin America).

- UNCTAD (various issues). Handbook of International Trade and Development Statistic, (New York:<sup>United</sup> Nations).

- UNIDO (1982). ChanginPatterns of Trade and World _Industy:_ an Empircal Study<sup>on Revealed</sup> Com.Parative Advanta, (New York: United Nations).

- US. Department of Commerce, Bureau of Census, 1982 Census of Manufactures.<sup>Volume l. Summar</sup> d Subjec Stati1i, (Washington: Government Printing Office, 1985).

- Wall, David (1979). "Industrial Processing of Natural Resources," World Bank Comodity<sup>Workng PAe</sup> _NLQ_ 4, (Washington: World Bank, April).

World Bank (1986). Wodd Development Report 1986 (New York: Oxford University Press).

World Bank (1987). World Development Rport 1987, (New York: Oxford Universty,<sup>Press).</sup> l ~~~ ~'
Pritchett (2001), using the first principal component that weights more heavily those assets that vary the most across households. An asset common to all households receives zero weight, as it explains none of the variation across the households. Intuitively, commonly held assets are also likely to be of lower value. Accordingly, the resulting indices would also reflect some underlying monetary valuation. In order to make comparisons over time, I pooled the data for 1998 and 2003 to calculate the first principal component. This created constant weights that explained any variation across time, as well as across households.

The asset indicators common to both KMS rounds can be divided into housing quality and ownership of durable goods. There were 11 indicators for housing quality, encompassing different types of homes (5 separate indicators for various building materials and the number of rooms), cooking methods (4 unique indicators), use of electricity, and presence of a landline telephone. The KMS surveys categorized home types into five categories: (i) luxurious (three or more bedrooms with attached bathrooms, concrete roof, mosaic floor), (ii) very good (two bedrooms with attached bathrooms, concrete roof, mosaic floor), (iii) good (one bedroom, brick or cement walls, concrete or tile roof), (iv) poor (brick walls, cement floor, tin or asbestos roof), and (iv) _kutcha_ (mud walls, mud floor, thatched roof). For ownership of durable goods, there were 6 indicators, covering ownership of a car, taxi or truck, scooter, television, VCR, and refrigerator.

I created a separate index for each of the two categories of asset indicators—broadly, housing quality and durable goods. I then added an overall assets index, in which all 17 indicators are used together. Each of the two specialized indices has a correlation of 0.9 or more with the overall assets index. The first component accounts for 26 percent, 40 percent, and 27 percent, respectively, of the total variation across the indicators used in the housing quality, durable goods, and overall assets indices.

Table 2 shows the weights or scoring factors of the first principal component for each index. Following McKenzie (2005), I rescaled them by their respective standard deviations in order to 16
# **I. Introduction**

We investigate changes in the structure of wages in India over the post‐reform era, the roughly two‐decade period since 1993, paying particular attention to recent trends in the wages of rural workers, especially the unskilled. Poverty reduction, much of it concentrated in rural areas, has accelerated over the last few years, largely due to increased earnings from non‐agricultural wage employment (Balcazar et al. 2015). An exploration of the fine‐grained details of India’s labor market transformation will thus help us to better understand this poverty decline.

Our approach hews closely to the Supply‐Demand‐Institutions (SDI) framework pioneered by Katz and Murphy (1992) and Bound and Johnson (1992). The idea is to divide the workforce into imperfectly substitutable demographic groups; e.g., by gender, education, and age. The twist, in our case, is to also cut the data by rural/urban, recognizing that, to a large extent, rural and urban India constitute distinct labor markets, or at least are far from being perfectly integrated. Thus, our apparatus allows us to investigate, for example, changes in wages of the rural unskilled _relative_ to their urban counterparts.

A second point of departure from conventional SDI analysis is its application at the state level, treating each Indian state (or group of states) as having separate urban and rural labor markets. A state‐level approach provides the requisite degrees of freedom for econometric analysis (see Juhn and Kim, 1999, for a related study of US states). In particular, SDI decomposes wage changes for a group into supply shifts (changing group employment shares), demand shifts (changing industrial composition biased for or against a group), and wage‐premia shifts (essentially, movements into or out of structurally low‐paying jobs). We then take the analysis a step further by investigating the key state‐level drivers of recent relative wage trends; i.e., we ask what types of supply or demand shifts were particularly influential in explaining the changing wage structure in India over the last decade.

There is a modest literature exploring India’s wage structure using data from NSS’s Employment‐Unemployment surveys. Hnatkovska and Lahiri (2013) consider rural‐urban wage convergence in India from 1983‐2009 using a model of long‐run structural transformation, but they do not decompose supply and demand factors behind the more recent wage trends. While Chamarbagwala (2006), like us, uses a supply‐demand decomposition, it is focused on the impact of trade liberalization over the earlier 1983‐99 period (see also Azam, 2010).

The organization of the paper is as follows. We begin in Section II by defining our groups and industries and then documenting how real and relative wages in India have changed over the past two decades. In Section III, we review the SDI framework and apply it to the national level. Next, we turn to the state‐level SDI analysis in Sections IV and V, followed by conclusions in Section VI.

2
Europe Central Asia regions (see Figure 8). With a negative growth per capita of 6 percent, fragile low-income countries would be hit harder than other low-income countries (World Bank, 2020). Economic activity in Sub-Saharan Africa is expected to contact by 2.8 percent, the deepest on record.

The scope of the recession would also vary depending on the depth and length of the COVID-19 pandemic, the strictness of containment measures, and the success of fiscal and monetary policy responses in supporting consumer and investor confidence. In the downside scenario _(more stringent lockdown measures)_ , GDP in low and middle-income countries would shrink by about 5 percent, while in the upside scenario _(prompt recovery)_ , a mere 0.5 percent, i.e. still negative.

## **Figure 8. Growth projections for all regions are negative**

<!-- Start of picture text --> Projected GDP growth in regions with majority low and middle income economies in<br>2020<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>EAP ECA LAC MNA SAR SSA<br>Baseline Downside<br>in percent<br><!-- End of picture text --> Source: World Bank Global Economic Prospects, June 2020.

Note: EMDE = emerging market and developing economies. The projections of growth in real GDP shown in the graph are based on the baseline and downside scenarios.

Albeit at varying levels of stringency, governments in all affected countries are responding to the COVID-19 crisis by taking containment measures and economic responses to dampen human and economic impacts of the crisis. Development Finance Institutions are also using various instruments to help firms amid the crisis. This section focuses on _(i)_ how governments are supporting SMEs during the pandemic and _(ii)_ how suitable are DFI instruments to support SMEs, including helping to protect jobs and limit the downside risks at different phases of the pandemic.

# 4.1 How are governments supporting SMEs during the crisis?

Governments around the world are taking policy actions in response to COVID-19 to protect SMEs and workers from the economic disruption associated with the pandemic. The World Bank has been recording policy actions taken by countries and registers eight main measures that are being taken by authorities in support of SMEs. These include _(i)_ debt finance, _(ii)_ employment support, 15
In the case of South Asia, the BPS includes data for six countries: Afghanistan, Bangladesh, India, Nepal, Pakistan, and Sri Lanka, over three waves of data collection. The first one was conducted at the onset of the COVID-19 crisis (between May and July 2020), a follow-up round was collected between May and August 2021, and the third and final round was collected between May and July 2022. The last round was not implemented in Sri Lanka and Afghanistan due to fragile political situations in the two countries (see Figure A1 for the timing of the surveys and Table B1 for the observation count in each round).

# **3.2 Measuring Informality**

Starting from the second round, the BPS in South Asia also captures the multidimensional nature of informality by including six questions relating to business license, registration under company law, contributions to employee provident funds, value-added tax (VAT) registration, the firm holding a bank account, and the separation of this account for business use.<sup>6</sup>

# **3.2.1 Informality Composite Index**

To measure the multidimensionality problem of informality, we follow an approach similar to Medvedev and Oviedo (2016) in constructing an informality index that relies on a combination of informality indicators. We take the first component of a Tetrachoric Principal Component Analysis (PCA), given that our informality variables are binary, and build three scores to capture distinct aspects of informality. Such an approach helps reduce the multidimensionality of informality into one index and allows us to build a continuous quantitative measure. The first index, _PCA 5_ , uses all five measures captured in the BPS, except for the bank accounts mix, as this variable is conditional on having a bank account. We build two sub-indices, for robustness checks. _PCA 1_ restricts to operational indicators that measure the extensive margin of informality (Ulyssea, 2018, 2020) accounting for aspects relating to business license, VAT registration, registration under company law.<sup>7</sup> _PCA 2_ focuses on intensive and direct measures of informality (Ulyssea, 2018, 2020) pertaining to a firm’s contributions to social insurance schemes and holding a bank account, which may likely suffer less from misreporting issues.

Table B2 shows the tetrachoric correlations, eigenvalues, and factor loadings for the main index and the two subindices. Of the total variance of the main Tetrachoric PCA composite

> 6As we cannot track transitions into and out of informality, we are implicitly assuming informality is time-invariant in our analysis.

> 7These dimensions are, initially, more sensitive when asked directly and, therefore, more subject to misreporting as they hold a veil of illegality.

7
<!-- Start of picture text --> Number of earning calls Number of firms<br>600<br>10,000 i<br>m Earning calls 500<br>8,000 — Firms (RHS) 400<br>6,000 300<br>2,000 100<br>0 0<br>HV BETSH BEVEO TCOOCON=DOCOCHSOCOLYOUVUVVNREALS SSVCO¥OGTIY<br>O85 5 gtQ=s 35°20 25 ¢B0<br>vue Ome ESL QS $e<br>23 5 2 3 3<br>Sz =<br><!-- End of picture text --> <!-- Start of picture text --> Number of earning calls Firms<br>50,000 ; 1,600<br>@ Earning calls 1.400<br>40,000 — Firms (RHS) 1,200<br>30,000 8001,000<br>10,000 _ 40000<br>0 -<br>DeoSORLPrARZRwTSEELCELCHOCACEETE 2SzsscerDO 0<br>si W 9TBESLEDSER<br>s Ss SFEQe Geo<br>5 ce £ §=8 2*<br>= g 8 ui<br><!-- End of picture text -->
How do job creation, destruction and reallocation compare in transition and developed market economies? Previous such comparisons have been made by Konings et al. (1996), Davis and Haltiwanger (1999), Haltiwanger and Vodopivec (2002), and the papers in the symposium edited by Haltiwanger et al. (2003). These studies typically show that during the socialist period and in the early years of transition, gross job creation rates in state-owned manufacturing did not change hugely and were similar to those in the OECD, while job destruction rates in the state-owned sector following the start of transition increased dramatically and then decline. New private sector firms, by contrast, show high rates of job creation, job destruction, with the former predominant especially in the early phase of transition. It should be noted that such comparisons need to be interpreted with caution as they were hampered by lack of full compatibility of samples; in particular, studies for TEs have typically used firm-level data, whereas studies of JC/JD in Western economies have used establishment-level data. In this respect, the BEEPS data offer a better opportunity for such comparison. The data presented in Table III.3 show that for the later transition period, the job reallocation rate is actually no higher in the TEs than in the cohesion countries – about 20% – and has been very steady in the TEs.

A more detailed picture of regional patterns and convergence in firm growth can be obtained from a decomposition analysis of employment growth.<sup>5</sup> This can help answering such questions as to what extent the differences in the employment growth rates observed across regions are due to the differences in the endowments of the respective economies –specifically, ownership, sectoral distribution, and size of firms – or stem from different relationships between these characteristics and firm growth across the regions. For example, we expect that “traditional” (state-owned and privatized) firms will contribute less to employment growth than the new private sector. The sectoral distribution of employment across traditional and new private firms is different in the EU8 countries and in the CIS countries. How much of the difference in the employment growth rates between the two groups of economies should we attribute to having different

> 5 We used Ben Jann’s (2005) “decompose” addin for Stata for the decompositions in this section.

15
An alternative measure is to establish an agency which can organize start-up training for a prospective investor, taking over in a caLalytical manner the role played by the training services division of a multinational. It assesses the training needs of the investor and helps it to identify, and contract with, existing training institutions to meet these needs during the construction phase of the investment. By making it possible for the training to be done in real time after needs have been assessed, the guessing and waste associated with the train-ahead strategy are avoided.

Many developing countries have established agencies to facilitate direct foreign investment, but often their contribution is limited to the provision of information concerning the existing labor supply and relevant legislation. The best examples of actively-invc,lved start-up agencies are those to be found in some of the southern and mLdwestern states of the United States: Arizona and Kentucky (Langen and Thomas, 1979); Mississippi (Brooks, 1976); Alabama, the Carolinas and Virginia (van Cleve, 1976); and Oklahoma and Texas (Russell, 1980). These operations have typically originated at the grass-roots level with post-secondary institutions broadening their market by adding a flexible, quick-response training mode to their norma] academic programmes. It has then been a normal progression for them to join forces with state agencies responsible for administering regular vocational education or job-training programmes.

# 8.4 Desultory training links with other entergrises

# SupRlier training

The piecemeal introduction of equipment and systems embodying new technology into existing enterprises is less dramatic than the wholesale technological transfer associated with new projects but it too gives rise to a need for skill development which can be satisfied in a similar way. In developed as well as developing countries, suppliers of the equipment are utilized as a source of the associated training, as a recent U.S. survey of small businesses indicates: "Equipment suppliers are important alternative providers of training. Most vendors of industrial equipment either operate factory training schools, send trainers out with the equipment or provide canned print and audiovisual training materials, depending upon the difficulty of the skills involved. Employers rarely find it necessary to train the operators of new equipment though they are generally responsible for training. the replacements of the original cadre.. .Two mining machinery manufacturers and' a power shovel manufacturer maintained training staffs which travelled all over the world with their machines to train the new operators. Manufacturers of specialized computer equipment did the same but on a national level only. Many firms reported relying heavily on vendor training." (Mangum and Mangum, 1984).

Similar findings are reported by a U.S. study of manufacturing firms using programmable automation (United States, Office of Technology Assessment, 1983). Only 22% of the firms undertook or sponsored employee training. They left this task to the suppliers of the equipment, 93% of which provided

- 96 -
# **1. International Trade and Transport Problems in the Maghreb**

1.1 This study seeks to identify the main distortions affecting the transport sector by focusing on intra-Maghreb and Euro-Maghreb trade flows, to assess the potential economic gains that may come from eliminating these barriers, and to make recommendations for facilitating intraMaghreb and Euro-Maghreb transport. The study benefited from a seminar jointly organized by the Economic Development Institute (EDI) and the World Bank, held in March 1994.

1.2 Excess costs -costs that arise because of trade barriers - are calculated using an approach that quantifies nontariff barriers into their tariff equivalents. The analysis considers three types of distortions: distortions connected with transport, distortions connected with international trade ("trade harmnonization"), and distortions connected with technology. Any action aimed at making trade more competitive must attempt to eliminate these distortions.

1.3 In this study we measure only direct costs. As a result the estimate of the trade and economic benefits of removing distortions is only partial. A full estimate would also take into account the impacts of effective protection and restrictive practices on value added, employment, and the environment. But even with our conservative approach, the estimated impact of eliminating excess costs on the balance of payments is significant: Tunisia would cut its Current Account deficit by 23%, Morocco would reduce its deficit by 21 %, and Algeria would raise its surplus by 8% (for the reference year).

## **A. Measuring Transport Costs in the Maghreb**

1.4 Transport costs in general and sea shipping costs in particular form natural trade barriers between the Maghreb countries and their partners. These costs protect domestic producers against competition from imports, as do artificial barriers, and significantly inhibit both the volume and the types of goods exported by Maghreb countries.

_1.5_ For some Maghreb countries, particularly Morocco, the economic cost of artificial barriers and their effect on trade balances have already been scrutinized and documented. I The barriers that transport costs impose, on the other hand, have not been measured. Transport costs are usually thought to be fixed by the market, and thus immune to policy actions, or thought to be only a minor contributor to total protection.

1.6 International transport costs can be analyzed using the international trade data found in the balance of payments tables and Current Account calculations of each country. Difficulty arises in comparing international data. The main source of comparable data is balance of payments statistics, which are compiled by the International Monetary Fund. Balance of

I See: "Morocco: The Impact of Liberalization on **Trade** and Induslrh1 Adjustnent", EMENA, March 15, 1988.
On the policy front, the evidence suggests that reforms that reduce migrant remittance fees can have larger impacts on remittance flows than might have been expected. Such reforms include increases in competition in money transmission markets or improvements in information for migrants on the relative costs of different money transmission services. These results also suggest policies which improve information flows between migrants and their families back home (e.g., improved telecommunications) may promote greater remittance sending.

# **3.4 Policies to channel remittances toward purposes with broader development impacts**

The recent experimental studies on facilitating savings access for migrants, Ashraf et al (forthcoming) and Chin et al (2011) reveal that migrants value control over savings, both in the origin country and in the host country. These findings raise the possibility that migrants may have preferences for other uses of remittances in their origin countries, aside from savings. Such preferences have potential development implications if migrants have stronger desires to see remittances used for purposes that have longer-term and broader development impacts. For example, migrants may have stronger desires that remittances be used for productive investment, housing, education, or other purposes that enhance households’ longer-run income prospects and/or generate positive spillovers to other households. Migrants may also prefer that some of their remitted funds be used to directly fund public or investment goods for the broader community. It is also possible that migrants might send more remittances in total if a reliable way could be found to channel their funds to these various purposes.

A large variety of initiatives are ongoing to channel migrant remittances toward longer-run household investments (enterprises, housing, human capital investments) as well as public goods at the community level. One example is an initiative in collaboration with a Brazilian stateowned bank, Caixa Economica Federal, and the Serviço Brasileiro de Apoio à Micro e Pequena Empresas [Brazilian Service of Support to the Micro and Small Enterprise] (SEBRAE) to catalyze small enterprise investment in Brazil by Brazilians in the U.S. The project provided web-based entrepreneurship training for migrants, and a suite of financial products (remittance, credit, and savings) to support establishment and development of the new businesses in Brazil. This and other projects supported by FOMIN are surveyed in Hall (2010). Programs that are 21