rafmacalaba/datause-encoder-data huggingface dataset:
(1999).<sup>9</sup> In our benchmark specification, a country’s quality of institutions _Qi_ is measured by the "rule of law index" from the World Bank Worldwide Governance Indicators (2009).In the robustness checks, we also use the time and the cost to enforce contracts<sup>10</sup> from the World Bank Doing Business Indicators as alternative measures for the quality of institutions.A country’s ability to export timely is measured by the quality of transport infrastructure<sup>_T_</sup> _i_<sup>as captured by the infrastructure component of the World Bank</sup> Logistics Performance Index.<sup>11</sup> Quality of infrastructure matters because it is an important determinant of the length of time to export and of the certainty of delivery, beyond being an important determinant of the financial dimension of trade costs. As additional measures for a country’s ability to deliver on time, we use the time to export from World Bank Doing Business Indicators<sup>12</sup> and an index of the quality of transport infrastructure constructed as in Limão and Venables (2001). The latter is calculated as the average of the deviations from the sample mean of four variables: (i) the percentage of paved road; (ii) the density of the rail network, both taken from the World Development Indicators 2008; (iii) the number of airports with paved runways over 3,047 meters – obtained from the CIA Factbook; and (iv) a port efficiency index (ranging between 1 and 10) taken from the IMD World Competitiveness year book.

Summary statistics of factor endowments and factor intensities are provided in Tables 2 and 3. These statistics show that timeliness and institutional intensity are on average higher for intermediate than for consumption goods. In particular, 22 percent of U.S. intermediate imports are shipped by air compared to 18 percent of consumption goods, and 65 percent of intermediate goods are not reference priced products compared to an average of 78 percent in the case of consumption goods. In addition, table 4 shows the correlation between factor intensities and the share of intermediate goods in industry exports. This correlation is positive and significant for capital intensity, not significant for skilled labor intensity, and negative and significant for both institutional intensity and timeliness.

> 9 The data is accessible at http://www.stanford.edu/%7Echadj/HallJones400.asc and a number of studies have used the same database. These include Romalis (2004), Levchenko (2007) and Bernard, Jensen, Redding and Schott (2010). 10 Indicators on enforcing contracts measure the efficiency of the judicial system in resolving a commercial dispute. Time is recorded in calendar days. Cost is recorded as a percentage of the claim, assumed to be equivalent to 200% of income per capita.

> 11 This index is based on a worldwide survey of operators on the ground (global freight forwarders and express carriers) and it captures the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology).

> 12 Time to export is recorded in calendar days and measures the time required to compile all procedures required for exporting.

8
the interaction between road and internet access, and validates the hypothesis according to which, gaining access to the internet reinforces the positive effect of roads on employment.

The complementary impact on hours worked is strong. Proximity to roads reverses the negative impact of the internet. As indicated by column (3), individuals connected to fast internet work approximately 23 fewer minutes a week than those not connected. This negative sign could be explained by either fewer work opportunities or the increase in the productivity generated by the internet (Rollo and Paunov, 2015; Hjort and Poulsen, 2019). However, when the distance to the closest road decreases by a kilometer, the number of hours of work per week for individuals connected to submarine internet increases to four hours. As for the isolated effect of the road, the number of hours worked per week increases by about 32 minutes as main roads become one kilometer closer to the location of the household/firm. Indeed, with better proximity to a main road, the travel time to work certainly decreases, which could explain the positive effect of improved road access on the number of hours worked per week. The addition of the accessibility to high-speed internet to a location that has better accessibility to main roads boosts the number of hours worked per week by four hours. So, roads and internet complement each other, leading to at least 3.5 extra hours of work.

Both investments benefit firms that tend to have longer hours of operation and more full-time employees, but the complementary impacts are small. The impact of internet access on hours of operation is relatively small. From the WBES, we find that, in the absence of internet access, an increase in the proximity to the closest main road increases a firm’s operation hours by 3 hours and 46 minutes. By contrast, bringing a main road one kilometer closer leads connected firms to operate approximately only 6 minutes more than non-connected ones. Similarly, firms connected to the internet operate approximately half an hour more than non-connected ones. On average, the combined impact of bringing a main road 1 kilometer closer and having fast internet access increases a firm’s hours of operations per week by 4.4 hours and leads it to add one more full-time employee 26
microcredit lending was the highest for Grameen Bank (89 percent) and the lowest for BRAC (38 percent). In earlier years, women’s share of BRAC microloans was much higher (e.g., 95 percent in 1998/99); but nowadays, most of the BRAC loans extended for small- and medium-sized enterprises (SMEs) target men more than women.

Another feature of microfinance operations in Bangladesh is mandatory savings of members/borrowers, in the form of weekly savings and deposits of a certain percentage of the loan amounts when disbursed. The more savings MFI members accumulate over the years, the better both they and the MFIs perform. MFIs consider member savings as part of their portfolio for financial transaction; aggregate program-level data shows that, in recent years, member savings have accounted for as much as 60 percent of MFI loans outstanding. However, for members, these savings should be compared to cumulative borrowing. Member savings represented about 8 percent of cumulative borrowing in 1991/92, increasing slightly to 10 percent for both 1998/99 and 2010/11 survey years (Table 2), perhaps suggesting that less than 10 percent of borrowing consists of MFI mandatory deposits, which nonetheless accounts for some 60 percent of MFI loans outstanding in Bangladesh.<sup>7</sup>

# **3. Correlation of Changing Participation Status with Poverty and Other Welfare Measures**

In this study, the outcome of particular interest is poverty dynamics over a long period of time. It is conceivable that, as a result of a higher level of micro-borrowing and savings mobilized over this period, households enjoyed a higher level of income (assuming it was augmented through activities financed under microcredit programs), a higher level of consumption (since the participating households were poor to begin with), and thus a reduced level of poverty. For purposes of

> 7 Unlike other MFIs, Grameen Bank also mobilizes voluntary savings from its members and non-members; thus, Grameen’s savings account for more than 80 percent of loans outstanding in recent years.

8
<!-- Start of picture text --> —— All countries —-- IAB sample —— All countries —-- IAB sample<br>2 8<br>‘<br>@ 15 / 7 Sy 6<br>1 \ 4 ‘<br>0 0<br>20 22 24 26 28 30 2 4 6 8 10<br>Log of GDP Log of Distance<br>—— Non-Zero Group == Zero Group —— Non-Zero Group == Zero Group<br>A 8<br>3 r m 6<br>> 1\ /<br>Dncg 2 ~ A / \<br>5 / \ i \<br>A | _— \<br>Al J \ 2 \<br>-./ / \ --<br>0 ~, =. 0 --<br>20 22 24 26 28 30 4 6 8 10<br>Source: WDI & CEPII<br><!-- End of picture text -->
by Teach in classrooms worldwide will allow us to continue to improve not just Teach, but also our understanding of effective teaching practices.

# **References**

<mark>Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010).</mark> _<mark>How learning works: Seven research-based principles for smart teaching</mark>_ <mark>. John Wiley & Sons.</mark>

- Ames, C. (1992). Classrooms: Goals, structures, and student motivation. _Journal of Educational Psychology_ , _84_ (3), 261.

Aslam, M. and G. Kingdon (2011): What can teachers do to raise pupil achievement? _Economics of Education Review_ , 30, 559-574.

Azigwe, J. B., Kyriakides, L., Panayiotou, A., & Creemers, B. P. M. (2016). The impact of effective teaching characteristics in promoting student achievement in Ghana. _International Journal of Educational Development_ , 51, 51- 61.

- Babad, E. (1993). Teachers' differential behavior. _Educational Psychology Review_ , _5_ (4), 347376.

<mark>Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change.</mark> _<mark>Psychological Review</mark>_ <mark>,</mark> _<mark>84</mark>_ <mark>(2), 191.</mark> <mark>Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory.</mark> _<mark>Journal of Social and Clinical Psychology</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>(3), 359-373.</mark> Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. _The Quarterly Journal of Economics_ , _122_ (3), 1235-1264.

<mark>Banks, J. A. (2014). Diversity, group identity, and citizenship education in a global age.</mark> _<mark>Journal of Education</mark>_ <mark>,</mark> _<mark>194</mark>_ <mark>(3), 1-12.</mark> <mark>Banse, H., & Palacios, N. (2018) Supportive classrooms for Latino English language learners: Grit, ELL status, and the classroom context.</mark> _<mark>The Journal of Educational Research, 111</mark>_ <mark>(6), 645-656. doi: 1</mark> <u>0.1080/00220671.2017.1389682</u> Bargh, J. A., Gollwitzer, P. M., Lee Chai, A., Barndollar, K., & Trötschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. _Journal of Personality and Social Psychology_ , 81, 1014-1027.

Bau, N. and Jishnu, D. (2017). The Misallocation of Pay and Productivity in the Public Sector: Evidence from the Labor Market for Teachers. Policy Research Working Paper, 8050. World Bank, Washington, D.C.Behrman, J. R., & Birdsall, N. (1983). The quality of schooling: Quantity alone is misleading. _American Economic Review, American Economic Association_ , _73(5),_ 928-946.

42
<!-- Start of picture text --> g (a) Total Income 2 (b) Migration Income<br>5 30000.00 g nnn<br>=S20000.00 £5 10000.00<br>: :<br>>=<br>2 40000.00 3 000.00 JV \<br>G 2 0.00<br>iS<br>ie~= -10000.000.00 =o -5000.00<br>2 0 2 4 6 8 1 72 0) 2 4 6 8 1<br>Quantile Quantile<br>g (c) Household Borrowing<br>FE 0.00<br>£<br>5 -20000.00<br>Zz<br>g<br>> ~40000.00<br>2<br>an<br>& -60000.00<br>£<br>= -80000.00<br>2 0 2 4 6 8 1<br>Quantile<br><!-- End of picture text -->
my approach and interpretation of the results. My objective here is to set out the critiques, discuss them, and assess their validity.

By way of introduction, I should clearly explain how my analysis was done. For each country and year (using household-level data from surveys available through the Luxembourg Income Study, LIS), individuals were ranked by their (household per capita) market income, from the poorest to the richest, and grouped in deciles. Market income is the sum of wages, property incomes, self-employment income and imputed own consumption. In addition, I defined _marketP_ income (called _factor P income_ in my 2000 paper), which is equal to market income _plus_ state-funded pensions. The reason why state-funded pensions are included as part of market income is that they can be regarded as deferred wage payments through a contractual obligation incurred by the state to its wage-tax payers. Market (or marketP) income can also be called pre-redistribution income because it is the income received before the government intervenes through its direct-tax-and-transfer policy.<sup>2</sup> To assess redistribution directly, I then observed how the share of each market income decile (that is, the _same people_ ) changes in the move from market to gross to disposable income. For simplicity, we can skip gross income, which is equal to market income plus government transfers. Consider the share of the people in the poorest decile (according to market income) in market and disposable income. If a tax-and-transfer system is in their favor, they have a larger share of disposable than market income. The difference between the two shares is called sharegain. The empirical results showed that the sharegain was larger for the (market income) poor deciles and then declined before becoming negative for the rich. In other words, the beneficiaries of the redistribution process were the market-income poor. The results also showed that as the market income share of _any_ decile goes down, its sharegain becomes larger. For the rich, this means that if market income distribution becomes more equal, their sharegain will be less negative.

> 2 Government cash transfers include unemployment benefits, child benefits, social assistance, and alimony payments.

4
# **1. Introduction**

While it is widely appreciated that poverty is an individual deprivation, household aggregate data are almost invariably used to infer individual poverty. It is almost always assumed that each individual within the household has the same level of economic welfare as measured by household aggregate consumption per person (or per equivalent single adult). An array of antipoverty programs, now found almost everywhere, are targeted on this basis, though typically using readily available proxies for household consumption or income per person.<sup>2</sup> Partly in response to concerns about high chronic undernutrition in certain regions, including Africa, there is an expanding effort at social protection in developing countries.<sup>3</sup> This effort is typically focused on transfers targeted to poor families.<sup>4</sup> For its part, the World Bank has made reaching poor families—as often identified by the poorest two quintiles of people based on household consumption per person—the main objective of its social protection operations.

Reaching deprived individuals using antipoverty programs that explicitly target poor households is an attractive option for three reasons. First, there is a data constraint, namely that standard data sources do not allow us to measure individual consumption. Second, interventions at the individual level may be seen to be paternalistic and intrusive (as they require intervention within families) and may well be costly (to the extent that they rely on fine targeting, constrained by the fact that individual deprivations are not comprehensively observed in large populations). Third, a large literature has documented that poorer households in terms of consumption, income or wealth are more likely to include deprived individuals.<sup>5</sup> Aggregate household resources constrain consumption for all household members. For these reasons, it is not surprising that, in practice, many social policies hope to reach deprived individuals by targeting poor households, or (more commonly) households with characteristics known to be associated with poverty.<sup>6</sup> However, the existence of a household wealth effect on individual welfare does not imply that targeting poor households will be very effective in reaching poor individuals. A growing body of

> 2 On these programs in developing countries see Coady et al. (2004), Fiszbein and Schady (2010), Del Ninno and Mills (2015), and Ravallion (2016, Chapter 10).

> 3 For evidence on this point see Ravallion (2016, Chapter 10).

> 4 See, for example, the various case studies in Del Ninno and Mills (2015). Many cash transfers aiming to improve child nutrition are paid directly to women in targeted poor households.

> 5 The evidence is reviewed in Ravallion (2016, Chapter 7). The present paper will return to the literature.

> 6 An overview of the programs found in practice is given in Ravallion (2016, Chapter 10) and Ruel et al. (2013).

2
<!-- Start of picture text --> Policy, Roearch, and External Affairs<br><!-- End of picture text --> <!-- Start of picture text --> _~~~~~ !<br><!-- End of picture text -->

**World Development Report**

This paper, a background paper for the 1988 World Development Report, is a product of the Office of the Vice President, Development Economics. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room S13-060, extension 31393 (20 pages with tables).

Policy recommendations to reduce the growth of public spending are haunted by the inevitability _of:_

* Wagner's law - the hypothesis that with economic development an increasing share of GDP is devoted to public spending.

. Baumol's effect - the hypothesis that as economies develop, public sector prices rise faster than prices in the general economy.

Neither ot these hypotheses has adequateiv been tested, largely because consistcnt public sector prices are unavailable for most developing countries.

Khan proposes that the unavailability of consistent public sector price deflators can be overcome by econometrically estimating these series with the help of data on public spending and the widely available GDP deflator. He uses this method to test both hvpotheses. An analysis of time-series data from 71 countries indicates that: e Although data support WaVgner's law in the majority of developing countries. the degree of support varies with the level of development. In response to rising income, real public sector spending rises most in the low-income economies, less in the middle-income economies, and least in the industrial market economies.

* Similarly, the average income elasticity of public spending drops from 2.2 in the lowincome economies to 1.6 in the middle-income economies to about 1.0 in the industrial economies. In the long run. the size of the public sector tapers _off_ as economies develop.

This is mainlv because of changing price levels in the public sector relative to price levels in the general economy. Although Baumol's effect cannot be observed in a majority of the countries, relative prices tend to fall rarnidl% m the low-;ncome countries. less rapidly in the middle-income countries. and actu 4lv start rising in the industrial economies.

This is believed to be due to the differences in technological intensitv between Lhe public and private sectors, the strength of the government in negotiating input prices, and labor market conditions as countnres move through different stages of development Thc PRE W.rking Paper Series dmessminates the fndings of 'work under %av in the Banks Policy, Research. and E\iernai Affairs Cmrilex. An _ohicc:iive_ i _the_ serics Is : oct tniese rndinms out aUICic- . e%en i'nresentations **Ire** lcss than UJ;. **n:hiincd.** The Thdinec. c??rcmalm)ns. **±n.** in nesc _i;c:uslmns_ apiers _Jo_ not receossaru% reprrsent **oi** c **ai** Banki n .

P-'r)OJUtIJ **iIa'l''** iPRE D.scmnnat:oin cnri;Lr
_Table 5: Impact of Electrical Conductivity on Net Primary Productivity, Global_

|Dep Var: Δ log(net||||||||||
|---|---|---|---|---|---|---|---|---|---|
|<br>primary productivity)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Electrical Conductivity >|‐0.0917***|‐0.112***|‐0.112***|‐0.0814***|‐0.114***|‐0.114***|‐0.116***|‐0.135***|‐0.135***|
|100 mS/m|(0.0156)|(0.0131)|(0.0131)|(0.0224)|(0.0197)|(0.0197)|(0.0304)|(0.0247)|‐0.0247|
|Precipitation (m/year)|0.568***|0.552***|0.552***|0.834***|0.777***|0.777***|1.14***|0.992***|0.992***|
||(0.03)|(0.02)|(0.02)|(0.06)|(0.04)|(0.04)|(0.09)|(0.06)|(0.06)|
|Precipitation^2 (m/year)|‐0.117***|‐0.114***|‐0.114***|‐0.221***|‐0.206***|‐0.206***|‐0.345***|‐0.308***|‐0.308***|
||(0.00714)|(0.00501)|(0.00501)|(0.0211)|(0.0147)|(0.0147)|(0.0329)|(0.0200)|‐0.02|
|Temperature (C)|0.121***|0.141***|0.141***|0.219***|0.222***|0.222***|0.359***|0.292***|0.292***|
||(0.0103)|(0.00744)|(0.00744)|(0.0187)|(0.0128)|(0.0128)|(0.0413)|(0.0245)|‐0.0245|
|Temperature^2 (C)|‐0.00337***|‐0.00420***|‐0.00420***|‐0.00570***|‐0.00609***|‐0.00609***|‐0.00866***|‐0.00758***|‐0.00758***|
||(0.000276)|(0.000199)|(0.000199)|(0.000500)|(0.000326)|(0.000326)|(0.000929)|(0.000543)|‐0.000543|
|Year Fixed Effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Gridcell Fixed Effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Geographic Time Trends|State|State|State|State|State|State|State|State|State|
|Distance to nearest station|50km|100km|150km|50km|100km|150km|50km|100km|150km|
|Cropland Threshold|>30%|>30%|>30%|>75%|>75%|>75%|>90%|>90%|>90%|
|N|33236|68984|68984|16550|35070|35070|9110|20002|20002|
|R-sq|0.243|0.237|0.237|0.281|0.275|0.275|0.325|0.314|0.314|

Notes: Table shows results from estimating equation 1 for the global sample using the GEMStat database via ordinary least squares. Each column is from a separate regression. Specifications across regressions remain constant, with only the sample of gridcells included differing. Columns 1‐3 include gridcells where land area is at least 30 percent cropland. Columns 4‐6 restrict the sample to gridcells to those with at least 75 percent cropland, and columns 7‐9 restrict to sample to gridcells with at least 90 percent cropland. Gridcells are also restricted based on the distance to the nearest upstream monitoring station. Columns 1, 4, and 7 include gridcells less than 50km from the nearest upstream station, columns 2, 5, and 8 include gridcells less than 100km from the nearest upstream station, and columns 3, 7, and 9 include gridcells less than 150km from the nearest upstream station. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001 21
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