rafmacalaba/datause-encoder-data huggingface dataset:
We consequently examine the role of real commodity prices, real interest rate and risk in a VAR system.<sup>12</sup> This is similar to the approach adopted by Bernanke et al. (2005) in the sense that they utilize the information from a factor of economic activity variables and incorporate this into a VAR when examining the impact of monetary policy on output and prices.<sup>13</sup> We are primarily interested in the relationship between commonalities in commodity prices, real interest rates and risk. Following the notation of Bernanke et al. (2005), within the FAVAR the transition equation is as follows: Equation (8) consists of the common factor ( _Ft_ ), the matrix _Xt_ which contains the real interest rate and risk term and a lag polynomial (i.e.  ( _L_ ) ) with lag order four. As an identification scheme for the residuals ( _vt_ ) is required, we use Koop et al. (1996) Generalized Impulses responses to examine the impact of risk and real interest rates on real commodity prices; our results are thus not sensitivity to the ordering of the VAR, as would be the case with an identification scheme based on the Cholesky decomposition. Before we implement the FAVAR, we investigate commonalities in prices.

# **4. Commonalities in Commodity Prices**

As discussed above, we aim at investigating the co-movements in commodity prices and how these co-movements relate to determinants of commodity prices discussed in the literature. In order to pursue this objective, we first employ the

> 12 Frankel (2008) examines the impact of risk and demand on inventory levels of oil. We examine the impact on commodity prices. All variables were stationary, see Table A2 in the Appendix. 13 Bernanke et al. (2005) identify a negative relationship between nominal interest rates and NAPM commodity prices. See also Sims (1992) for an examination of monetary policy with a role for commodity prices to ameliorate price puzzles.

12
International Competition, Returns to Skill, and Labor Market Adjustment Rod Falvey David Greenaway Joana Silva<sup>_∗_</sup>

**Keywords:** International trade, Skill acquisition, Labor market adjustment. **JEL Classification:** F11, F16, J31, J62.

> _∗_ Address for correspondence: The World Bank, 1818 H street NW, Washington DC, 20433. Tel + 1-202-473-7911, Fax: + 1-202-477-0036. E-mail: jsilva@worldbak.org. We are grateful to participants at the Festschrift Conference in Honor of Professor Chris Milner, the Midwest International economics Meetings, IZA/World Bank Conference, Canadian Economic Association Annual Meetings, COST Workshop on Firms and Wages: New Research Using Linked Employer-Employee Data, and seminars at the World Bank. Helpful comments on an earlier draft were received from John Abowd, Rita Almeida, Olivier Cadot, Carl Davidson, John Earle, Ana Fernandes, Garth Frazer, Richard Kneller, Steve Matusz, Richard Upward, Pedro Portugal and Eric Verhoogen. Falvey and Greenaway acknowledge financial support from the Leverhulme Trust under Programme Grant F/00/114/AM. Silva acknowledges financial support from Funda ̧c ̃ao para a Ciˆencia e a Tecnologia Grant SFRH/BD/13162/2003 and Economic and Social Research Council Grant PTA-026-27-1258. The finding, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily reflect the views of the World Bank.
<!-- Start of picture text --> Bangladesh:Spousal age difference<br>osn<br>25<br>St<br>cB<br>Sfo<br>po<br>tec<br>iN<br>52<br>£3<br>584<br>25<br>ee<br>us<br>9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 t—— Quintile 3<br>—— Quintile4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal:Spousal age difference<br>os<br>#=<br>ot<br>naySo<br>ec<br>oo%<br>£a<br>cw<br>oY<br>ge<br>us,<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>*— Quintile2 +——— Quintile 3<br>— Quinte 4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India:Spousal age difference<br>N<br>gs<br>at<br>oF<br>§u0<br>oF<br>Ge<br>23%<br>aS!<br>£3<br>S8y<br>25<br>38<br>eS<br>WS wo<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Quintile2 t— Quintile 3<br>—— Quintile4 H—— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan:Spousal age difference<br>on”<br>83<br>3<br>oeo<br>tec<br>a3<br>£a<br>cw<br>oot<br>ge<br>ws ©<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 +—— Quintile 3<br>—— Quintile4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
Wadsworth and Acquisti, 1999)—stopped being a generalized practice during the 2000s (Gimpelson and Kapeliushnikov, 2011).<sup>7</sup> The minimum wage grew by 398 percent in real terms between 1996 and 2012. The two largest hikes were in 2001 and 2009, at increases of 180 and 69 percent, respectively (Figure 7). The real minimum wage markedly declined with the 2008 international crisis—as the average wage did, too. It is worth noting that this type of downward adjustment of the real minimum wage is a common feature of the Russian labor market used to cope with economic hardship (Gimpelson and Kapeliushnikov, 2011).

**Figure 7: Annual inflation rate and minimum wages, 1995-2013**

<!-- Start of picture text --> 250 6,000<br>5,000<br>200<br>4,000<br>150<br>3,000<br>100<br>2,000<br>50<br>1,000<br>0 0<br>Minimum wage (2011 rubles) (RHS) Annual inflation rate (LHS)<br>Inflation rate (%)<br>Constant 2011 rubles<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> _Source:_ WDI and ILOStats. _Notes:_ Minimum wages were adjusted to 2011 rubles using the WDI consumer price index. Wages for 1996 and 1997 were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). Data on minimum wages for 1995 is not available.

The distribution of real wages also shifted to the right during the 2002 – 2012 period. At the same time, the ratio between the minimum to the average wage increased, from 12 percent in 2002 to 24 percent in 2012; and the share of workers earning less than the minimum wage remained at low levels, averaging 5 percent between 2002 and 2012, and peaking at 11 percent in 2009 (Figure 8). Thus, the increase in the minimum wage during the period of analysis

> 7 See Annex A1 for a more detailed discussion on wage arrears and their implications for the distribution of wages.

9
49 kerosene price increase during 1982-8S is the welfare consequences for households.

Welfare effects of kerosene price changes are often considered more serious for low-income households in urban areas with limited access to non-commercial fuel. Assuming a monthly household expenditure of Rp IS000, with a budget share for kerosene equal to 6 percentTM and price elasticity equal to -1.02, loss in consumer surplus due to the kerosene price increase during 1982-85 for The household was equivalent to 0.65 to 2.23 percent of monthly household expenditure. The largest welfare loss occurred in 1982-43 when prices increased by nearly 50 percent and the lowest occurred in 1984-89 when th priea increase was equal to 14 percent.

Another important policy issue related to an increase in kerosene prices is the subsidy effect Figure 3.3 shows the rlationship between domestic and border prices for kerosene for Indonesia during thel980s. Despite large price changes, domestic prices for kerosene remained below border prices for most of the 1980s.

_Macroeconomic Impacrs_ What were ihe macroeconomic impacts of the domestic energy price changes during the period 1982-95? The crucial fctor determining the macroeconomic impact of an energy price hike is ihe effect on the consumer price index (CPI) and subsequently the nominal wage response to an increase in CPI. Except for 1985, the CPI was relatively stable during energy pnce reforms and thus the wage response is less important in ftis context Table 3.4 summarize **s** elected macroeconomic indicators for the Indonesian economy during the early 1980s.

As regards the impact on CP1, thre is the diect effect according to the weight of ihe energy pices. However, there is also ihe indirect effect via the impact of energy prices on food prices, which does not seem to have been important during the period of energy price increases.' An important factor in this conext is the itenity of commercial energy use in the agriculture sector and the govenmnent policy wilh respec **t** o the pricing of agricultural inputs. In Indonesia, the main commercial energy itensive inputs in the agicue sector are chemical fabriliz, wiich account for around 4.S percent of the production costs for staple crops such as paddy and maize. However, ferilizr were highly subsidized and ihi prces were ' **Aveaep k1roi expuuln dsa** of a **low icome uban** household **in** 1984. **See Wodk Bak** (1990).

**_"In_ 1985 the inflatioP** me vs lowes for **the** past decade. During **April and** May 198S **thee ws** a **saig upwad puaro wlm** petrolem **prines wer increased.** Howor, as **th rice crop cams on** to **the nmiket** food **pnie dclied and** so **did** do **CPL**
37 lending to SMEs more costly. In addition to transaction costs, SME lending, more than other lending products, is affected by challenges in managing risks. Compared with large firms, SMEs are commonly less likely to be able to post collateral, have less formal governance structures, and often do not have audited financial statements that allow a better picture of the enterprise and its projected profits. Compared to retail clients, financial institutions can rely less on the law of large numbers to exploit scale economies and diversification benefits in the case of SMEs as there are fewer of them in a given sector and their characteristics are harder to capture in a few quantitative indicators.<sup>14</sup> Lending techniques, government policies and structural characteristics of financial systems and economies affect the extent to which transactions costs and risk reduce SMEs’ access to external funding. We define as the _access possibilities frontier_ the maximum share of SMEs applying for loans that can be served by financial institutions in a commercially viable way (see Figure 10, Point I, _A_ ).<sup>15</sup> This concept implies that, in many economies, a large share of micro-enterprises and even small formal firms might not be bankable from a commercial viewpoint. This frontier—and thus the share of bankable SME loan applicants — is determined by the state variables we have discussed above, including available lending technologies, risk management facilities (such as availability of hedging and securitization instruments), credit registries and the contractual framework.<sup>16</sup> Please note that the shape of this frontier is somewhat different from the previous, more general and aggregate, analysis, as

> 14 See Beck and de la Torre (2007) and de la Torre, Martinez Peria and Schmukler (2010) for a more in-depth discussion and references.

> 15 As discussed in more depth in Beck and de la Torre, (2007), the fact that there is no unique combination of costs, expected return, and risk that maps one-to-one to the interest rate limits our graphical analysis to loan applicants as opposed to all potential borrowers.

> 16 The supply curve underlying this concept is non-linear and can bend backward.  i* denotes the marginal interest rate at the rationed equilibrium rather than the market-clearing equilibrium.  For a detailed technical discussion on the derivation of these curves, we would like to refer the reader to Beck and de la Torre (2007).

37
bins, half of the village clusters were randomly selected to enter a “gender parity lottery”.<sup>5</sup> Among these, half of the village clusters (74 clusters and all 325 villages in those clusters) were selected to be free to choose the gender composition of their management committees. In the other clusters (75 village clusters, 337 villages), the program was implemented as normal; i.e. with an obligatory equal number of men and women in the committee. This design feature allows us to learn about the impact of having women in leadership positions.

The short-term impact of the program was assessed by Humphreys et al. (2019), who collected data between October 2010 and October 2011; after the onset of village-level projects, but well before the end of the village cluster-level projects. Appendix C illustrates the timing of the village and village cluster phases of the program, and the short-run data collection. This study leverages the same design as Humphreys et al. (2019) but returns to the villages in 2015, eight years after program onset and three years after all program activities have concluded.

# **Outcomes, Data and Empirical Strategy**

This study focusses on eight outcome families: service provision, health, education, economic welfare, women’s empowerment, governance, intra-village cohesion, and inter-village cohesion.<sup>6</sup> We collected data between June and December 2015, targeting 781 villages in the provinces of Haut Katanga, South Kivu and Tanganyika.<sup>7</sup> Data were collected from four sources. In each village, surveys were conducted with the village chief and a randomly selected adult from five randomly selected households per village. In addition, as part of the household survey, in each household with children of school-going age (between 6 and 11 years old), one child was randomly selected for a brief exam. Finally, enumerators visited the village’s primary school and health facility.<sup>8</sup> At each facility, enumerators undertook three activities. They conducted a carefully designed audit to assess the quality of the infrastructure and the presence of materials and

> 5 In total, 149 village clusters (661 villages) entered the lottery.

> 6 Note that the initial goal of the Tuungane program was improvements in governance, social cohesion and economic welfare (Humphreys et al., 2012).

> 7 Humphreys et al. (2019) targeted two randomly selected villages in each of the 560 clusters (280 treatment, 280 control). In total, 816 villages out of the targeted 1,120 villages were visited. There are no differences in attrition by treatment condition (Humphreys et al. 2012). In 2015, we targeted the same villages as visited for Humphreys et al. (2012), excluding the Maniema province for logistical reasons. These villages sum to 781.

> 8 We are interested in service provision as experienced by villagers. We therefore visited the main primary school and health facility for each village, thus not necessarily the Tuungane-built facilities in treatment areas.

6
# **Firm performance**

Labor productivity and value added per worker increased in the period 2008-2014 (Figure 14). Contribution to value added per worker became increasingly concentrated in the medium and large firms (Figure 15).

Figure 14. Labor productivity and value added per worker <!-- Start of picture text --> 12.1<br>11.3<br>10.7<br>10.0<br>2008 2014<br>Labor productivity Value Added (VA)<br>Source: Authors' calculation based on Enterprise Surveys<br>15<br>10<br>5<br>Labour productivity and VA per worker (Log)<br>0<br><!-- End of picture text --> Figure 15. Contribution to value added by size 13
# **Figure 18: Bottom 20 six‐digit products with lowest EXPRELY of overall GVC Exports, 2014**

<!-- Start of picture text --> 0.03 1<br>0.9<br>0.025<br>0.8<br>0.7<br>0.02<br>0.6<br>0.015 0.5<br>0.4<br>0.01<br>0.3<br>0.2<br>0.005<br>0.1<br>0 0<br>540233 650692 610451 520534 550921 846693 520643 520514 630691 640291 650400 570110 550951 630510 540751 520943 610411 570190 620891 901811 580410<br>Six‐digit products, H0 Nomenclature<br>EXPRELY of GVC Exports HHI_X of GVC Exports<br>HHI of GVC Exports<br>EXPRELY of GVC Exports<br>headgear, of furskin skirts of wool o cotton (excl. ten headgear, plaited or textile floor cov for packing go of wool or textile floor cov dressing gowns, p cardiographs fabrics<br>accessories nes for use o covering the ankle o<br>yarn of polyesters, nprs APPAREL FINAL: Hats and other cabled cotton yarn, with > with >=85% polyester s ELECTRONICS FINAL: Parts and cabled cotton yarn, with <85 single cotton yarn, with > APPAREL FINAL: Hats and other polyester staple fibres, or bleached woven fabric or 4‐thread twill, with ELECTRONICS FINAL: Electro‐<br>APPAREL INTERMEDIATE: Textured APPAREL FINAL: Skirts and divided APPAREL INTERMEDIATE: Uncombed APPAREL INTERMEDIATE: Single yarn, APPAREL INTERMEDIATE: Combed APPAREL INTERMEDIATE: Uncombed TEXTILES FINAL: Camping goods, of FOOTWEAR FINAL: Footwear, nes, TEXTILES FINAL: Carpets and other APPAREL INTERMEDIATE: Yarn, <85% TEXTILES FINAL: Sacks and bags, used APPAREL INTERMEDIATE: Unbleached APPAREL INTERMEDIATE: Coloured 3 APPAREL FINAL: Women's or girls' suits TEXTILES FINAL: Carpets and other APPAREL FINAL: Women's or girls' TEXTILES FINAL: Tulles and other net<br><!-- End of picture text --> <u>Notes:</u> Values expressed in ratios computed from original variables expressed in thousand current USD, and calculated on the total number of 254 countries/geographies left after excluding two small countries (Bermuda and Luxembourg) with normalized 2001 GDP >1 from the original dataset with 256 countries/geographies with available data on at least some years along the 27‐year period spanning 1988‐2014, even though some may have missing data in some years (HS 1988/92 nomenclature, mirror data, six‐digit products). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI. <u>Sources:</u> Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator). World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

33
# **3. Hydrodynamic model set up, selection of mangrove species and estimation of resistance to water flow**

To understand the protective role of mangroves, a hydrodynamic model was set up and updated with location-specific data to represent the coastal as well as estuarine features of the study areas. Cyclonic wind field and pressure field information generated from historic cyclone data were added to the hydrodynamic model for developing the Storm Surge Model **_._** Upon consultation with the local experts and after conducting focus group discussions with local inhabitants, various species of mangroves were short-listed for each study area considering their economic value and survivability in the study sites in the location-specific tidal characteristics and water salinity. Manning’s Numbers (M)<sup>10</sup> were computed for short-listed mangrove species taking into account the physical characteristics of the mangrove trees. These Manning’s Numbers served as estimates of resistance to the flow of water from the mangrove trees while passing through the forest. Further details on the process are described below.

# _<u>Setup of the hydrodynamic water flow model and development of the cyclone model</u>_

The hydrodynamic model for Bay of Bengal was upgraded with conversion of structured computational grid (MIKE 21Classic) to a flexible mesh system (MIKE 21FM) to enhance the resolution of the grids around islands, along coastline and other areas of interest.<sup>11</sup> See Figure 3 for the updated Bay of Bengal model. The area covered by the model starts from Baruria on the Padma River down to 16<sup>0</sup> north latitudes in the Bay of Bengal. Three open boundaries were defined in the model, two in the north defined by the daily discharge time series of river water: one in the Padma River at Baruria, and another one in the Upper Meghna River at Bhairab; and

> 10 Inverse of Manning’s roughness coefficient, n.

> 11 For example, quadrangular/rectangular mashes were generated in the straight stretch of a channel and triangular mashes were generated where reaches were meandering. Both quadrangular and triangular mashes were generated to assess the effect of inter tidal area.

8
Risks, Ex-ante Actions and Public Assistance: Impacts of Natural Disasters on Child Schooling in Bangladesh, Ethiopia and Malawi<sup>1</sup> Futoshi Yamauchi, Yisehac Yohannes and Agnes Quisumbing<sup>2</sup>

> 1 This paper was prepared as a background paper to the joint World Bank - UN Assessment of the Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and Recovery is gratefully acknowledged. We would like to thank Harold Alderman, Alejandro De la Fuente, John Hoddinott, Apurva Sanghi and seminar participants at the World Bank for their useful comments. Special thanks to Wahid Quabili¥ for excellent assistance on Bangladesh data. We are responsible for any remaining errors.

> 2 Yamauchi (corresponding author), Yohannes, Quisumbing: International Food Policy Research Institute, 2033 K Street, NW, Washington DC 20006. Email: f.yamauchi@cgiar.org, Phone: 202-862-5665.
all farmers in the same location— would suggest.

The above formulation of transaction cost also indicates that the e ff ect of market access on farmer’s place of sale may depend on the quantity sold except for the case where the transaction cost is proportional to the quantity sold ( δ = 0). According to the parametric forms speci fi ed above, an increase in the quantity sold will dampen the e ff ect of market access on choice of market as a place of sale implying a negative coe ffi cient for the interaction term. Having more wealth can also in fl uence a farmer’s response to quantity sold. For instance, wealth farmers are more likely to sell at the market, if quantity sold is larger and opportunity cost of labor is higher than the time (and cost) saving in marketing due to higher wealth.<sup>6</sup>

# 3 The Data

The data for the study come from the India agricultural marketing survey which was conducted in four Indian states (Uttar Pradesh, Orissa, Maharashtra and Tamil Nadu) during 2005.<sup>7</sup> The survey collected information on traders, entrepreneurs and farmers involved in trading, processing and producing fi ve crops— mango, tomato, potato, turmeric and maize. In each state, 20 wholesale markets and 40 villages were selected in order to construct a sample of 400 traders and 400 farmers. The survey utilized an innovative sampling design to link farmers and traders to the markets. First, a market was selected for a given crop and all the traders operating in the market were listed. During the listing process, traders of the crop in the selected market were asked to list fi ve villages from which they sourced most of their supply, or which were known to produce a signi fi cant amount of the selected crops. From

> 6 This e ff ect may arise because poorer farmers may face cash constraint in transporting larger quantities to market, and hence would be forced to sell at farmgate. Wealthier and unconstrained farmers, on the other hand will sell at the market if quantity sold increases. This is particularly true in the presence of public transportation.

> 7 The survey was conducted as a part of the Indian agricultural marketing study (World Bank, 2007).

11