rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: The cash crops channel

||Gha<br>198|na<br>6–8|Ma<br>19|li<br>93|Guinea<br>1993|
|---|---|---|---|---|---|
|Distance to city in km:|All|5 +|All|5 +|All|
|Bandwidth in km:|75|75|75|75|75|
||(1a)|(1b)|(2a)|(2b)|(3)|
|Log. Expenditures per capita|+0.62***|+0.52***|+1.52***|+0.86**|-0.22|
||(0.13)|(0.13)|(0.64)|(0.31)|(0.22)|
|Main cash crop<sup>_c_ </sup>output p.c. (kg)|+29|+23|+733***|+664***|+9**|
||(30)|(46)|(211)|(84)|(3)|
|Cash crops<sup>_b_ </sup>income p.c. (USD)<sup>_a_</sup>|+194***<br>(43)|+184***<br>(65)|+237***<br>(68)|+214***<br>(27)|+16***<br>(2)|
|Cash Expenditures per capita (USD)<sup>_a_</sup>|+177***|+120**|+173**|+117***|+2|
||(52)|(48)|(61)|(38)|(37)|
|Cash crops inc. controlled<sup>_d_</sup>|+135|-2|+48|-13|+63|
||(99)|(77)|(56)|(32)|(64)|
|Cash crops & wage inc. controlled<sup>_e_</sup>|+21|-1|+39|-13|-31|
||(53)|(65)|(53)|(39)|(20)|
|Stunted 6-59 months (%)|-26***|-32***|-20**|-24***|+16|
||(6)|(5)|(7)|(7)|(13)|
|Expend. per cap. controlled<sup>_f_</sup>|-10|-8|-5|-8|+14|
||(7)|(5)|(12)|(14)|(13)|
|Electricity connection<sup>_g_ </sup>(%)|+28**|+22*|-2|-6|+0|
||(13)|(11)|(6)|(4)|(4)|
|N|19/40|10/30|13/11|10/9|27/22|

_Source_ : Authors’ analysis based on data described in the text. _Coverage_ : PSUs in the 75 km bandwidth window. _Notes_ : “Matching RD” estimates with 75 km bandwidth. PSUs are matched to the nearest neighbor on the other side of the border within the same urbanization class: 0-5 km or 5+ km from the nearest city (5,000 + inhabitants) as of 1990. Matched differences are then analyzed as in equation (4) (see text). In the case of the Mali border, where only 5 PSUs are in the 0-5 km class, the “All” estimate (col.2a) does not match PSUs according to class, it is therefore exactly the same as in Table 4 col. (4a). Controls for latitude (Ghana and Guinea) or longitude (Mali), rainfalls, elevation, and distance to river are always included. a: 1988 prices and exchange rates in columns (1a-b); 1993 prices and exchange rates in col. (2a-b) and (3). b: Cocoa and coffee sales in (1a-b) and (3); Cotton output valued at official producer price in (2a-b). c: Cocoa output sold in col. (1a-b) and (3); Cotton output in col. (2a-b). Kilograms per capita. d: Cash expenditures per capita border discontinuity, with cash crop income per capita added as a control. e: Idem d, with instead the sum of cash crop and wage income per capita as a control. f: Stunted children discontinuity, with cash expenditures per capita and its square added as controls. g: At the border with Ghana in 1992–3 and for electricity connection: ’All’: +30** (s.e. 13); ’5+’: +37** (16). Positive numbers indicate differences in favor of Cˆote d’Ivoire. Standard errors in parentheses. ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 31
**Table 1. Overview of included surveys**

|Survey|Rounds|Sample<br>Size|
|---|---|---|
|A. General Household Surveys:|||
|Indonesian Family Life Survey (IFLS)|2000,2007/2008,2014/2<br>015|2,462|
|Mexican Family Life Survey (MxFLS)|2002,2005/2006,2009/2<br>010/2011/2012|723|
|Nigeria General Household Survey (NGGHS)|2010/2011 - 2012/2013<br>(post-planting and post-<br>harvest each)|1,804|
|Townsend Thai Survey (TTHAI)|1997 - 2014 (Annual)|1,122|
|B. Specialized Firm Impact Evaluation Surveys|||
|Cotonou Informal Enterprise Survey (BJINFORMALITY)|2014 - 2016|1,197|
|Egypt Macroinsurance for Microenterprises Survey<br>(EGMACROINSURANCE)|2012-03 - 2012-11|1,441|
|Ghana Microenterprises Survey (GHMS)|2008 - 2010|397|
|Kenya GET Ahead Business Training Program Impact|2013 - 2016|2,365|
|Evaluation Survey (KEGETAHEAD)|||
|Malawi Business Registration Impact Evaluation Survey<br>(MWBRIE)|2011/2012 - 2013/2014|757|
|Nigeria YouWiN! National Business Plan Competition<br>Impact Evaluation Survey (NGYOUWIN)|2011 - 2016|280|
|Sri Lanka Microenterprise Survey (SLMS)|2000 - 2016|269|
|Sri Lanka Impact Evaluation Survey of Business Training for<br>Women (SLFEMBUSTRAINING)|2009 - 2015|260|
|Sri Lanka Informal Enterprises Survey (SLINFORMALITY)|2008 - 2011|105|
|Sri Lankan Longitudinal Survey of Enterprises (SLLSE)|2008 - 2014|284|
|Lomé Informal Enterprise Survey (TGINFORMALITY)|2013/2014 - 2015|499|
|Uganda Women’s Income Generating Support Impact|2009 - 2011|461|
|Evaluation Survey (UGWINGS)|||

Note: Only the control group is used from surveys that are part of randomized experiments.

34
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**Table 11. Trade Partners Likely to See the Largest Decreases in Exports to Kazakhstan**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage of Total 2012 Kazakhstan**<br>**Imports from Partner**|
|---|---|---|
|Russia|-150.55|-0.9|
|Belarus|-15.98|-2.4|
|Ukraine|-14.54|-0.5|
|Kyrgyz Republic|-4.07|-1.2|
|Uzbekistan|-2.94|-0.4|
|Georgia|-1.08|-2.2|
|Moldova|-1.07|-2.6|
|Azerbaijan|-0.61|-1.5|
|Tajikistan|-0.07|-0.1|
|Macedonia, FYR|-0.06|-3.0|
|Mauritania|-0.03|-4.1|
|Tanzania|-0.02|-0.8|
|Mozambique|-0.02|-1.0|
|Armenia|-0.02|-0.6|
|Turkmenistan|-0.01|-0.0|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 38
6 (2004), a number of studies present findings that domestic firms operating in industries that supply inputs to industries with a strong FDI presence experience positive productivity effects (see, for example, Blalock and Gertler 2008; Javorcik and Spatareanu 2008; Jordaan 2008a; Du, Harrison, and Jefferson, 2014). Findings drawn from meta-analyses on sets of estimates of inter-industry spillovers also show that, in comparison to insignificant intra-industry spillovers, inter-industry productivity spillovers are an important phenomenon. When comparing findings on productivity spillovers through forward and backward linkages, the evidence indicates that productivity spillovers among local suppliers are far more prevalent (Havranek and Irsova 2011). This also indicates that backward linkages between MNC affiliates and local firms constitute the main channel transmitting productivity spillovers.

The convergence of the empirical evidence toward identifying backward linkages as the main channel underlying productivity spillovers justifies a new survey of empirical studies that focus especially on this channel. To structure the survey, we separate the process that underlies productivity spillovers through backward linkages into three (related) components, as depicted in figure 1. The first component consists of the level of local sourcing by MNC affiliates. Only when there is a sufficient amount of MNC investments that interact with local suppliers will there be a meaningful potential for productivity spillovers to occur. The second component focuses on the scope for productivity spillovers, which is related to the nature of the linkages that MNC affiliates create with local suppliers. In particular, this component concentrates on the extent and types of technologies that are transmitted by the foreign firms to their suppliers. The third component relates to the degree that the effects of linkages and technology dissemination materialize in the form of productivity spillovers among local suppliers.
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29

**Figure 12.   Global welfare loss from the World’s education challenge**

Loss as % of GDP <!-- Start of picture text --> 14<br>12<br>One year of schooling<br>10 Literacy<br>8<br>6<br>4<br>2<br>1900 1950 2010 2050<br><!-- End of picture text --> Investments in the health, knowledge and skills of the people—human capital—are as important as investments in the more visible, physical capital of the country. Every country that sustained high growth for long periods put substantial effort into schooling its citizens and deepening its human capital. Conversely, considerable evidence suggests that other developing countries are not doing enough (Commission on Growth and Human Development 2008) **.** This is the remaining challenge.
assume imports from different countries are imperfect substitutes, and that the importing country is a small country in world markets. These assumptions ensure that a change in tariff policy, whether it applies to all goods in a particular tariff line or only those from a particular source country, will have no effect on the world prices of any of the imported goods. The domestic production data is assumed to contain data on imports in IM product categories, which are used as intermediate inputs in domestic production and/or as consumption goods. The problem is to aggregate the NxH import values to IM composite imported goods.

The aggregation problem for a particular product category i is illustrated in Figure 1. The nodes at the top (denoted by a I) of the Figure represent the value of imports in industry i, denoted Mi, that are utilized as intermediate inputs or consumption goods in industry i in the domestic data. The entries at the bottom of the Figure correspond to the trade data, which is the value of imports by tariff line and source country. We will develop a quantity index, Z1, and a price index, j, for each sector i such that Zioi = Mi. In addition, we will obtain an index of the average tariff rate, **tj** , such that tariff revenue for each sector i is ti Zi. The effect of tariff policy changes on 4, and **t,** will then be utilized in the general equilibrium model to calculate the effect on domestic output, employment, and welfare.

Nested CES aggregation is used to allow for different degrees of substitution within product categories than between product categories. First, we allow for the possibility that goods in the same industry group have a different degree of substitutability for each other than for goods in another industry group. In the example illustrated in Figure 1, this is done by dividing each of the industry groups into J; subgroups. Each tariff line is then classified into an industry and a sub-industry. This results in each tariff line being identified by a triple {i,j,k}, where i is an indicator of the industry to which it belongs, j is an indicator of the sub-industry to which it belongs, and k is an indicator of the tariff line within the sub-industry. Second, we allow for the possibility of different degrees of 3
32 TABLE 4 BETWEEN ESTIMATES OF LABOR PRODUCTIVITY DISTRIBUTIONa (Absolute values of t-statistics in parentheses)

||Trade EXDos|ure**Measu**|**red**with I<br>Percent|mport and E<br>ile|xgort Shares|
|---|---|---|---|---|---|
||10th|25th|50th|75th|90th|
|ln(M/Q)|.158|.239|.230|.289|.398|
||(.474)|(.898)|(.890)|(1.08)|(1.58)|
|ln(X/Q)|-.271|-.258*|-.089|- .(45|-.186|
||(1.81)|(2.16)|<br>(.764)|<br>(.i72)|<br>(1.64)|
|ln(Q)|.260|.387|.490|.643*|<br>.619*|
||(.775)|<br>(1.44)|<br>(1.88)|<br>(2.38)|<br>(2.44)|
|TUR|8.26|8.81|7.26|9.27|6.46|
||(.754)|(1.01)|(.854)|(1.05)|(.780)|
|TUR*ln(M/Q)|<br>-.389|-.592|-.644|- 667|-.824|
|--<br>_____|(.550)|(1.05)|(1.18)|(1.17)|(1.54)|
|TUR*ln(X/Q)|<br>.910*|.806|.107|-.064|.513|
||(1.40)|(1.55)|<br>(.213)|<br>(.122)|<br>(1.04)|
|TUR*ln(Q)|-.495|-.516|-.547|-.618|-.312|
||(.708)|(.926)|<br>(1.01)|<br>(1.10)|<br>(.589)|
|Chile Dummy|<br>1.91*|1.88*|<br>1.85*|<br>1.72*|1.55*|
||(5.65)|(7.00)|<br>(7.09)|<br>(6.32)|<br>(6.06)|
|R<sup>2</sup>|.963|.978|.981|.980|.983|
||Trade Expos<br>10th|ure Measu<br>25th|red with Ef<br>Percenti<br>50th|fective Pr<br>le<br>75th|otection Rat<br>90th|
|ERP|.392|.296|.161|.143|.204|
||(2.04)|(1.73)|(1.01)|(.851)|(1.21)|
|ln(Q)|.788*|.715*|.493|.479|.552|
||(2.46)|(2.50)|(1.86)|(1.71)|(1.96)|
|TUR|36.08*|28.30|11.44|8.44|12.05|
||(2.31)|(2.03)|(.884)|(.618)|(.879)|
|TUR*ERP|-.872|-.588|-.031|.104|-.147|
||(2.07)|(1.56)|(.089)|(.281)|(.399)|
|TUR*ln(Q)|-2.49*|-1.97*|-.880|-.628|-.868|
||(2.51)|(2.24)|<br>(1.08)|<br>(.726)|(1.00)|
|Chile Dummy|<br>2.51*|2.48*|<br>2.52*|<br>2.56*|2.45*|
||(4.65)|(5.13)|<br>(5.63)|<br>(5.43)|<br>(5.18)|
|_R2_|.967|.976|.981|.980|.979|

# Trade Exposure Measured with Effective Protection Rates Percentile

**a** Industry dummies were included in the regressions but are not reported. ^Significantly different from zero at the .05 level using a two-tail test.
I PoI _Icy_ RESEARCH -WORKING PAPER 2449

# Summary findings

As wvater scarcitx and population pressures increase, more countries are adopting water pricing meclhanisms as their priimarv means of regulating the consumption of irrigation water.

The way to allocate water efficiently is to "get the prices right," but how to accomplish this is open to debate. Water pricing methods are sensitive to the social, plhysical, institutional, and political setting. To assess the costs and benefits of a particular irrigation project, the pricing method must be tailored to local circumstances.

johansson's survey of the resource economics literature on irrigation services and pricing will be useful for developing comprehensive guidelines for water policy practitioners. He synithesizes accumulated knowledge about the implementation and performance of various water pricing methods used over the past two decades: volumetric pricing (marginal cost pricing), output and input pricing, per area pricing, tiered pricing, two-part tariffs, and water niarkets.

Theoretical and practical issues will become increasingly important as demand for food and water increases. Pricing water efficiently will help meet that demand, but what is the best way to make pricing more efficient?

Many argue that water markets offer a solution, but under what circumstances are water markets viable? What effect will decentralization have on farm production and the rest of the economy? What forces are moving toward decentralization or (re)centralization?

The answers to these questions are complex and often site-specific. To help compare them, Johansson lists case studies, data sources, and relevant methodologies in the appendixes.

This paper-a product of the Rural Development Department-is part of a larger effort in the Bank to improve the efficiency of water use. The study was funded by Rural Development, Development Research Group, and the Bank's Research Support Budget under the research project 'Guidelines for Pricing Irrigation Water Based on Efficiency, Implementation, and Equity Concerns." Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Melissa Williams, room MC5-724, telephone 202-458-7297. fax 202-522-3308, email address mwilliams4(@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/ research/workingpapers. The author may be contacted at johaO081C@tc.umn.edu. September 2000. (80 pages) _The Policy Research Working Paper Series disseminates the findings of wovrk in progress to encourage the exchange of ideas_ **_about_** _developmnent issues. An objective of the series is to get thefindingsout quickly, even if the presentations are less than fully polished. The papers carr' the namies of the authors antd should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the 'World Bank, its Exectutive Directors, or the cozPntises thev represeatn_ Produced bv the Policv Research Dissemination Center
from the country and time dummies). However, to test the robustness of our results, we also report estimations combining the most significant variables.

Our one-step model described above is equivalent to a two-step procedure in which the local returns for each country are regressed on US returns in the first step, and in the second step, the estimated coefficients on US returns from the first step, which measure each market’s comovement with the US, are regressed on the country-level characteristics that proxy for the different transmission channels. The two-step methodology is useful for graphical representation of our results and we show graphs of the comovement against some country characteristics to visually illustrate whether a given factor appears to be explaining the extent of transmission. For inference, however, we use the one-step methodology as it produces more efficient estimates.

# **3. Data**

Local and US returns data for the period July 2007 through April 2009 are calculated from stock market prices obtained from Bloomberg. All country-level variables used to explore the determinants of the degree of comovement between the US and other markets are measured at the end of 2006 or earlier. Table 1 presents a list of these variables along with their definition and data sources. Table 2 shows descriptive statistics for each variable. To deal with outliers, we remove observations that are three standard deviations below and above the mean of each variable.

To measure bilateral trade linkages, we compute the share of exports to the US relative to GDP from data obtained from the IMF Direction of Trade Statistics. We also collect data on broader trade indicators such as exports to GDP, trade openness (defined as the ratio of exports plus imports to GDP), fuel exports to total exports, and agricultural exports to total exports. This data come from the IMF International Financial Statistics and the World Bank World Development Indicators.

To measure financial linkages between the US and other countries we include a number of indicators, collected from multiple sources. First, we construct the ratio of US holdings of foreign equity to local market capitalization and the ratio of foreign holdings of US equity to local market capitalization from data from the Treasury International Capital System database 8
# **Table 5: Distribution of non-farm wage employment and returns by Sector, by poverty status, in Rural Malawi, 2010/2013**

|**Sector**|Rural|Malawi|Non|Poor|Po|or|p-value d|ifference|
|---|---|---|---|---|---|---|---|---|
||2010|2013|2010|2013|2010|2013|2010|2013|
|Participation|Pr|oportion o|f participan|ts by non-|farm wage|employ|ment sector|(%)|
|**Professional, technical, & related workers**|24.3|24.8|26.7|28|15.3|7.1|0.037|0.000|
|**Administration and managerial workers**|1.3|1.7|1.5|2|0.8|0|0.441|0.022|
|**Clerical and related worker**|5.7|4.6|7.2|5.4|0|0|0.000|0.000|
|**Sales workers**|4.8|5|4.9|4.6|4.3|7.4|0.848|0.496|
|**Service workers**|34.4|30.2|30.3|27.6|50.1|44.4|0.002|0.046|
|**Forestry workers, fishermen and hunters**|1.8|3.8|2.2|2.7|0.2|9.6|0.074|0.165|
|**Transport equipment operators and laborers not elsewhere classified**|27.6|30|27.2|29.6|29.3|31.5|0.745|0.785|
|Return||Average|monthlyw|ages ofpa|rticipants b|ySector|(1000 KW|)|
|**Professional, technical, & related workers**|16.3|16.8|16.8|17.3|12.8|6.2|0.281|0.000|
|**Administration and managerial workers**|10.6|2.9|11.5|2.9|4|-|0.012|-|
|**Clerical and related worker**|10.4|10.9|10.4|10.9|-|-|-|-|
|**Sales workers**|5.6|6.8|6.5|7.4|2|4.8|0.000|0.065|
|**Service workers**|4.7|4.9|4.6|5.2|4.9|4.1|0.659|0.131|
|**Forestry workers, fishermen and hunters**|14.6|3.3|14.8|5|4.4|0.7|0.072|-|
|**Transport equipment operators and laborers not elsewhere classified**|7.3|5.7|7.6|6.2|5.9|3|0.239|0.001|
|**Overall**|8.1|7.6|8.8|8.4|5.5|3.7|0.000|0.000|

Source: Malawi IHS3 panel sample (2010) and IHPS (2013).

Note: Wage is the estimated monthly salary using the last payment reported by the participant member and the period of time covered by that payment. Kwachas (MWK), local currency used in Malawi.

30
Table 7: Private Sector: Innovation Premium per skill level, 2014-2017

||||Dep|endent variab|le: _ln_(_wijkft_)||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>All|(3)<br>Low|(4)<br>Low|(5)<br>Medium|(6)<br>Medium|(7)<br>High|(8)<br>High|
|Innovation|0.154***<br>(0.0107)|0.0964***<br>(0.0244)|0.379***<br>(0.0289)|-0.00315<br>(0.0224)|0.397***<br>(0.0561)|-0.000373<br>(0.0549)|0.115***<br>(0.0106)|0.0827***<br>(0.0257)|
|_X Low-Skill_|0.0856***<br>(0.0233)|-0.336***<br>(0.0241)|||||||
|_X Medium-Skill_|0.226***<br>(0.0467)|-0.0949***<br>(0.0266)|||||||
|Age Squared|0.233***<br>(0.00404)|0.220***<br>(0.00380)|0.0365***<br>(0.00745)|0.0275***<br>(0.00683)|0.169***<br>(0.00418)|0.143***<br>(0.00466)|0.393***<br>(0.00724)|0.384***<br>(0.00723)|
|Tenure|0.0287***<br>(0.000444)|0.0281***<br>(0.000539)|0.0171***<br>(0.000283)|0.0154***<br>(0.000311)|0.0259***<br>(0.000440)|0.0258***<br>(0.000520)|0.0270***<br>(0.000969)|0.0259***<br>(0.00126)|
|Temporary|-0.0231***<br>(0.00511)|0.0519***<br>(0.00987)|-0.000112<br>(0.00410)|0.0654***<br>(0.0114)|-0.0487***<br>(0.00691)|0.0186<br>(0.0126)|-0.0269***<br>(0.00673)|0.0417***<br>(0.0102)|
|Firm size|0.0264***<br>(0.000810)|0.0309***<br>(0.00112)|0.00989***<br>(0.000817)|0.0182***<br>(0.00114)|0.0276***<br>(0.00123)|0.0304***<br>(0.00142)|0.0363***<br>(0.000746)|0.0352***<br>(0.00178)|
|Low-Skill dummy|-0.0641***<br>(0.00150)|-0.0756***<br>(0.00132)|||||||
|Medium-Skill dummy|-0.0239***<br>(0.00218)|-0.0138***<br>(0.00206)|||||||
|Individual-Year|Yes||Yes||Yes||Yes||
|Individual-Year-Firm||Yes||Yes||Yes||Yes|
|Observations|122814877|122340110|21667647|21481418|40800393|40492276|52325435|52029478|
|R-squared|0.928|0.938|0.874|0.893|0.891|0.909|0.945|0.953|

_Note:_ Innovative firms is measured by the share of PoTec workers in total employment for firm _f_ in year ** _t_ . Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

# **5 Impact of Becoming an Innovator on Wages**

The analysis above shows robust evidence of an innovation wage premium in Brazil. The mechanics, however, about how this premium is generated and what happens to wages when firms become innovators is the focus of this section. First, we analyze whether firms pay higher wages before becoming innovators - analogous to the self-selection in the export literature (see Bernard and Bradford Jensen (1999)). Second, we estimate what is the impact of becoming an innovator on wages and how lasting over time is this effect. In what follows, we explore each of the questions separately.

22