rafmacalaba/datause-encoder-data huggingface dataset:
- where R = Impact risk  (death from floods; affected by floods and droughts)

- L = Total loss (persons killed or affected)

- P = Population

- G = GDP per capita

- E = Female educational enrollment rate

- R = Precipitation

- T = Temperature ε = A random error term

Event-related losses are drawn from the CRED database; data on population, GDP per capita (in constant $US 2000) and education are drawn from the World Bank’s World Development Indicators. We use constant-dollar GDP data to maximize the sample size for years prior to 1980. Data on net female primary and secondary enrollment rates are limited to the period since 1990. To extend the series for panel estimation, we “backcast” them using panel regressions that relate enrollment ratios to income per capita, life expectancy and the total fertility rate (see Section 5). These associational regressions are a transformation of the conventional fertility equation, in which the total fertility rate is a function of income per capita, life expectancy and female schooling.

We specify the education rates as flogs, to ensure that predictions are restricted to the interval 0-100%. For a probability p between 0 and 1, the flog is defined as log [p/(1-p)]. This is an appropriate specification for the regressions in any case, since it is consistent with natural lower and upper bounds for net enrollment rates. These are, in effect, firststage regressions, with the total fertility rate, life expectancy and per capita income playing the role of instruments for second-stage estimation of the climate impact regressions. Life expectancy is not significantly affected by deaths from floods, which are minuscule by comparison with deaths from other causes. Therefore, use of this variable as a first-stage instrument should not lead to biased estimates for schooling in the regression for death from floods.

Table 5 presents the fixed-effects estimation results, which are extremely robust in both regressions for the total fertility rate and life expectancy. Per capita income is not a 10
19 focus on a limited set of relationships<sup>and do not inquire whether the results</sup> are sensitive to other explanatory<sup>variables.</sup> The lack of sensitivity analysus<sup>is particularly troublesome because of</sup> the serious methodological, conceptual,<sup>and statistical problems with cross-</sup> country regressions discussed above.<sup>We need to investigate extensively the</sup> sensitivity of our findings to slight<sup>changes in the list of explanatory</sup> variables, small reweighting of observations, minor alterations in assumptions about the distribution of residuals,<sup>different measurement error processes,</sup> 14 etcetera. Levine and Renelt (1990) present evidence<sup>showing that slight</sup> alterations in the list of explanatory<sup>variables can over turn the results</sup> found in many empirical growth studies.<sup>This finding surely tempers the</sup> confidence we should place in the conclusions<sup>of existing studies.</sup>

**14** See Leamer (1983, 1987) for a general<sup>discussion of sensitivity analyses</sup> and for citations that exemplify different<sup>types of sensitivity analyses.</sup>
budget is given by

# **3 Computation**

The plain algebraic formulation of the model presented above hides a few details that facilitate an efficient computational model. The model is solved as a Mixed Complementarity Problem (MCP) within the General Algebraic Modeling System (GAMS) software using the Mathematical Programming System for General Equilibrium (MPSGE) as a subsystem.<sup>4</sup> The base Armington model, formulated using MPSGE, is augmented with the Krugman and Melitz modules as outlined in the decomposition methods suggested by Balistreri and Rutherford (2013).

An MCP takes advantage of _variational-inequality_ (or sometimes _complementary-slack_ ) equilibrium conditions whereby positive variables (prices and activity indexes) are associate with specific inequality constraints. For example, the market clearance condition (4) is actually computed using the following variational-inequality condition: In words we would say supply is greater than or equal to demand and if the associated price is positive supply equals demand. If supply is greater than demand the associated price

> 4GAMS Development Corporation. General Algebraic Modeling System (GAMS) Release 24.9.1. Washington, DC, USA, 2017. MPSGE is a subsystem written by Thomas F. Rutherford (Rutherford, 1999) that automatically generates and checks consistency of the nonlinear constant-returns system.

7
61 Box 1 The Incidence of International Transport Costs on Exporters and Imnporters.

-Whil t nspott as rd ly pail bycxporeawben goods are sold on a cif. basis, and by;the impnrhnpurchasesa ta&thoe pstypsig the fitight bill doss not necessarily bear dhe trasport cost. In **t,cja** the exprcar bexugard ashearig 1<sup>hrcwaslffth~lvedp</sup> ir the inposiagmarkatis no affected by changes in uanportosuta. Cmen,theimporterbe lbs cost of uasotlfthecmoite pric in the xport marke remains thec same irrespecive afcaet rsb3ipping costs. 't **(a) Incidence ot freighi cosfa when supply (j b) Incidence ol 1reigh' cost when demand Is Is perfectly hIneldslc per@fectly lneam|tlc** I I-- **D** .~~ O ~~~~~~~~~~~~~ ***** cw orafany changer in these , d.idkmfrabindt~zob.S--C is detemed ym dnsd.bJ4m-n6cras pIy Whe yzpl swrdatwyinelsi ( liklsey aoeirstiuaifon <Wt ls **thawiwn n4r1ebndn evl-Ay,n4t** in te latickys of deand ahiMa oaiof:£dthnnpo&bwdcroeth impoter.When decad andiupply aduulul havec eai ates s.hipping costs am shared eqaltbuymund selr demand grows jxgeidbcbdtesae of freight coeborn by importes rises, with the eatictyofdemsad deterraining frumtwtlwh;iquain~tD etatdeddoin h bsence of right41cagLAey pointtonotek that tbeAf :coun±rfrs ar generally _smlls_ fnple ainE s i E rdcs a lulbe quit **dUL** the ahoY iClgZt _|show,thi su_ tharhincidnceofmn costs orha inhesecots) wil falmosthe6ils ou iXdca,rx 'ies'
A country for which a TRIST has been developed and for which the necessary data on sectoral domestic production are available is Mauritius. Table 8 presents selected results illustrating the impact of a duty-free island trade policy scenario on domestic production and employment.<sup>28</sup> The table presents changes in domestic production and employment in the 15 sectors that are projected to be most severely affected (in terms of absolute production losses) by the reform scenario. The sectors most severely affected are the manufacture of furniture, the manufacture of wearing apparel, the manufacture of soap and detergents, the manufacture of bakery products, the manufacture of preserving of fruits and vegetables and the manufacture of wines.

The overall impact of the scenario on domestic production is rather weak (692 million Mauritian rupees, which accounts for about 1% of total industrial production). The reason is that tariff levels are already very low in Mauritius such that substitution effects resulting from any liberalization scenario are not likely to be large.<sup>29</sup> Table 8 also contains figures illustrating the impact of the trade reform on domestic employment where the loss accounts for about 2% of total industrial employment<sup>30</sup> .

**Table 8: Duty‐Free Island in Mauritius: 15 most affected sectors**

|Sector|Change<br>Output (in Mio M|% Change<br>auritian Rupees)|Employment<br>Change|
|---|---|---|---|
|3610 ‐ Manufacture of furniture|‐115.0|‐3.9%|‐427|
|1810 ‐ Manufacture of wearing apparel...|‐111.3|‐2.0%|‐842|
|2424 ‐ Manufacture of soap and detergents...|‐58.0|‐6.6%|‐64|
|1541 ‐ Manufacture of bakery products|‐53.4|‐3.2%|‐92|
|1513 ‐ Processing and preserving of fruit and vegetables|‐31.8|‐3.3%|‐20|
|1552 ‐ Manufacture of wines|‐29.8|‐6.2%|‐20|
|2710 ‐ Manufacture of basic iron and steel|‐25.2|‐1.5%|‐7|
|1554 ‐ Manufacture of soft drinks...|‐23.6|‐1.1%|‐29|
|2520 ‐ Manufacture of plastics products|‐21.3|‐1.4%|‐26|
|1511 ‐ Production, processing and preserving of meat...|‐21.1|‐0.5%|‐7|
|3230 ‐ Manufacture of television and radio receivers...|‐14.9|‐3.4%|‐6|
|2511 ‐ Manufacture of rubber tyres and tubes...|‐14.0|‐9.5%|‐23|
|2109 ‐ Manufacture of other articles of paper...|‐13.6|‐1.9%|‐19|
|1920 ‐ Manufacture of footwear|‐13.3|‐7.9%|‐30|
|1543 ‐ Manufacture of cocoa, chocolate and sugar confectionery|‐12.0|‐6.9%|‐7|

# _Poverty_

Another interesting extension of TRIST is the analysis of the impact of trade policy reforms on poverty. Given that TRIST automatically produces results on sectoral price changes in response to policy reform, it is straightforward to extend the projections to give an indication of how different population groupings will be affected by these price changes. An essential requirement for such an analysis is, of course, the availability of a

> 28 The scenario involves that all tariffs are set to zero. We use an exporter substitution elasticity of 1.5, a demand elasticity of 0.5 and a domestic substitution elasticity of 1. 29 See also World Bank (2006).

> 30 These are computed under the assumption that productivity does not change.

23
gender as provided by the United Nations.<sup>17</sup> Data are missing for a few countries and these are instead obtained from the CIA World Factbook.<sup>18</sup> These data are then split across skill (i.e. education) groups using international indicators of educational attainment. Here, we follow Docquier and Marfouk (2006) or Docquier, Lowell and Marfouk (2009) and combine different data sets documenting the proportion of post-secondary educated workers in the population aged 25 and over (i.e. Barro and Lee, 2001, De La Fuente and Domenech, 2006 and Cohen and Soto, 2007). The post-secondary concept corresponds to a broad definition of high-skill labor as it includes workers with at least one year of college or university. This definition is relevant for developing countries, where the share of college graduates in the labor force is sometimes less than one percent. Given the construction of _L_<sup>_i_</sup> _g,s,t_<sup>,Equation</sup> (7) is then used to identify the size and structure of the natural labor force, _Ng,s,t_<sup>_i_,foreach</sup> labor type, country and period.

With all the constituent components in hand we now define gross emigration rates ( _e_<sup>_i_</sup> _k,s,t_<sup>)</sup> and net emigration rates ( _b_<sup>_i_</sup> _k,s,t_<sup>)foragivencountry</sup><sup>_i_aredefinedasfollows:</sup> In comparison to the existing literature, the current paper contributes three major improvements to the measurement of international human capital mobility:

> 17Population data by age and gender are provided by the United Nations Population Division and can be found at http://esa.un.org/unpd/wpp/Excel-Data/population.htm.

> 18See https://www.cia.gov/library/publications/the-world-factbook/index.html.

28
# **1. Introduction**

Accurate poverty measurement is the prerequisite for policies aiming at reducing poverty. Yet, development practitioners face the typical challenges that the available household survey data underlying poverty estimates are either inadequate (e.g., do not offer nationally representative estimates) or outdated (e.g., do not offer timely estimates of poverty trends). Worse still, in the few countries where survey capacity is well established, data were known to turn out to exhibit varying degrees of incompatibilities over time due to changes in survey design (Deaton and Kozel, 2005). These data challenges could likely hinder effective policy implementation, especially for poorer countries with low statistical capacity (Devarajan, 2013; Jerven, 2019).<sup>1</sup> To address these challenges, alternative methods to obtain poverty estimates that rely on data imputation (instead of direct data collection through surveys) have become increasingly common (World Bank, 2021; Dang and Lanjouw, 2023).<sup>2</sup> Building on the seminal technique that imputes from a household consumption survey into a census to generate poverty maps (Elbers _et al.,_ 2003), recent studies have imputed from a household consumption survey into another survey to provide poverty estimates.<sup>3</sup> The central idea is to build an imputation model using appropriate predictor variables from an existing older consumption survey, which can be subsequently applied to the same variables in a more recent survey (that does not collect consumption data) to provide poverty estimates for the latter survey.

> 1 Serajuddin _et al._ (2015) show that over the period 2002-2011, of the 155 countries for which the World Bank monitors poverty data using the World Development Indicators (WDI) database, almost one-fifth (i.e., 28) have only one poverty data point and as many as 29 countries do not have any poverty data point in the same period. Furthermore, poorer countries have fewer surveys: a 10-percent increase in a country’s household consumption level is associated with almost one-third (i.e., 0.3) more surveys (Dang, Jolliffe, and Carletto, 2019). The ongoing Covid-19 pandemic could increase poverty and further exacerbate these data deprivations and digital divides for poor countries (Naude and Vinuesa, 2020).

> 2 Imputation techniques are regularly used by international organizations and national statistical agencies to fill in missing data gaps such as education statistics (UOE, 2020) and income data (US Census Bureau, 2017).

> 3 The poverty-mapping technique combines a household consumption survey and a non-consumption census, which allows us to provide poverty estimates at a more disaggregated level than available in the household survey.

2
# **DOES MANAGEMENT MATTER? EVIDENCE FROM INDIA**

**Nicholas Bloom**<sup>**a**</sup> **, Benn Eifert**<sup>**b**</sup> **, Aprajit Mahajan**<sup>**c**</sup> **, David McKenzie**<sup>**d**</sup> **and John Roberts**<sup>**e**</sup>

**December 21, 2010**

<u>Abstract: A long-standing question in social science is to what extent differences in management</u> cause differences in firm performance. To investigate this question the authors ran a management field experiment on large Indian textile firms. They provided free consulting on modern management practices to a randomly chosen set of treatment plants and compared their performance to the control plants. The authors found that adopting these management practices had three main effects. First, it raised average productivity by 11% through improved quality and efficiency and reduced inventory. Second, it increased decentralization of decision making, as better information flow enabled owners to delegate more decisions to middle managers. Third, it increased the use of computers, necessitated by the data collection and analysis involved in modern management. Since these practices were profitable this raises the question of why firms had not adopted these before. The authors’ results suggest that informational barriers were a primary factor in explaining this lack of adoption. Modern management is a technology that diffuses slowly between firms, with many Indian firms initially unaware of its existence or impact. Since competition was limited by constraints on firm entry and growth, badly managed firms were not rapidly driven from the market.

## <u>JEL No.  L2, M2, O14, O32, O33.</u>

<u>Keywords: management, organization, IT, productivity and India.</u> <u>Acknowledgements:</u> Financial support was provided by the Alfred Sloan Foundation; the Freeman Spogli Institute, the International Initiative and the Graduate School of Business at Stanford; the International Growth Centre; IRISS; the Kauffman Foundation; the Murthy Family; the Knowledge for Change Trust Fund; the National Science Foundation; the Toulouse Network for Information Technology; and the World Bank. This research would not have been possible without our partnership with Kay Adams, James Benton and Breck Marshall, the dedicated work of the consulting team of Asif Abbas, Saurabh Bhatnagar, Shaleen Chavda, Karl Gheewalla, Kusha Goyal, Shruti Rangarajan, Jitendra Satpute, Shreyan Sarkar, and Ashutosh Tyagi, and the research support of Troy Smith. We thank our formal discussants Susantu Basu, Ray Fisman, Naushad Forbes, Vojislov Maksimovic, Ramada Nada, Paul Romer, and Steve Tadelis, as well as seminar audiences at the AEA, Barcelona GSE, Berkeley, BREAD, Boston University, Chicago, Columbia, Cornell, the EBRD, Harvard Business School, IESE, Katholieke Universiteit Leuven, Kellogg, the LSE, Maryland, the NBER, NYU, PACDEV, Stanford, TNIT, Toronto, UBC, UCL, UCLA, UCSC, Wharton, and the World Bank for comments.

a Stanford Economics, SCID, CEP and NBER; b Overland Advisors LLC, b Stanford Economics and SCID;<sup>d</sup> The World Bank, IZA and BREAD;<sup>e</sup> Stanford GSB 0
# **1. Introduction**

School-based management (SBM) aims to introduce autonomous school governance to reflect local needs with direct monitoring of school activities by their stakeholders, that is, parents and the community. Such a decentralization effort in the context of school education includes some of the following features – decentralized revenue generation, school administration, management, teacher hiring, and curriculum design.<sup>3</sup> These functions are devolved to local governments or schools. In this paper I assess impacts of SBM on students’ achievement, measured by their national achievement test scores, using school-level data available from the second round of SBM scale-up in the Philippines.

In the Philippines where our empirical study is drawn, there were two stages of SBM implementation. First, SBM was initially introduced in 23 poor provinces (divisions, interchangeably) as part of a large school intervention package, called the Third Elementary Education Project (TEEP), after 2001. Those provinces were identified as the most socially depressed in the Social Reform Agenda (see more details in Section 3). Second, once TEEP was completed in 2006, SBM was scaled up to other non-TEEP divisions with the support of neighboring TEEP divisions. In this period the introduction of SBM was the only major change experienced at non-TEEP schools, in contrast to the situation where TEEP schools, when implementing SBM, received other investments such as building construction and renovation, textbooks, teachers’ training, and other facility support.

I use data from the second stage of SBM implementation to avoid contamination effects from the other intervention components. More specifically, I compare schools that received SBM grants in 2006 and 2009 (thus, with a three-year interval) under the assumption that both the School Improvement Plan and the Annual Improvement Plan were proposed before receiving the grant (therefore, a three-year gap in the

> 3 When this policy includes financial decentralization, it may lead to poorer areas having much less funding for their schools.

2
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**Table 6: Comparison of variable means between face-to-face and telephone interview modes**

|||Ur|ban|||R|ural||
|---|---|---|---|---|---|---|---|---|
|Variables|FTF|Phone|diff|P-|FTF|Phone|diff|P-|
|**Balanced variables:**|||||||||
|Household size|2.85|2.83|0.03|0.83|3.43|3.56|-0.14|0.341|
|Highest HH education<sup>#</sup>|||||||||
|Primary or less|0.07|0.07|0.00|0.888|0.10|0.15|-0.05|0.072|
|Vocation school|0.09|0.08|0.01|0.550|0.24|0.15|0.09|**0.008**|
|High school|0.40|0.34|0.05|0.266|0.47|0.45|0.02|0.681|
|Universityor higher|0.44|0.50|-0.06|0.147|0.19|0.25|-0.06|0.073|
|**Labor and income variables:**|||||||||
|Share of unemployed members|0.19|0.20|-0.01|0.743|0.23|0.24|-0.01|0.772|
|Month worked/12 for all worked|0.50|0.51|-0.02|0.667|0.54|0.53|0.00|0.958|
|Month worked by HHH 12 month|4.91|5.09|-0.18|0.721|5.35|4.03|1.32|**0.010**|
|Main income source ispensions|0.42|0.41|0.01|0.765|0.36|0.35|0.01|0.717|
|**Demographic & economic vars:**|||||||||
|Dependency ratio|1.21|1.05|0.16|0.149|1.01|0.89|0.12|0.198|
|Bought clothes last 3 month|0.58|0.67|-0.08|0.076|0.57|0.62|-0.04|0.319|
|Bought shoes last 3 month|0.51|0.53|-0.02|0.726|0.54|0.46|0.08|0.092|
|**Dwelling and durables:**|||||||||
|Has water installations|0.98|1.00|-0.01|**0.038**|0.94|0.95|-0.01|0.783|
|Has sewerage|0.95|0.95|0.00|0.907|0.85|0.77|0.09|0.158|
|Has electricity|1.00|1.00|0.00|0.951|1.00|0.99|0.00|0.528|
|Has district or local heating|0.64|0.80|-0.17|**0.000**|0.47|0.54|-0.08|0.116|
|Has fixed telephone|0.91|0.93|-0.02|0.418|0.88|0.92|-0.04|0.227|
|Number of rooms|2.36|2.31|0.06|0.620|3.08|3.16|-0.08|0.564|
|# microwave|0.33|0.33|0.00|0.946|0.26|0.27|-0.02|0.652|
|# freezer|0.80|0.89|-0.09|**0.018**|1.01|1.02|-0.01|0.800|
|# washing machine|0.94|0.98|-0.04|**0.029**|0.99|0.99|0.00|0.901|
|# dish washers|0.30|0.29|0.01|0.879|0.12|0.20|-0.08|**0.025**|
|# air conditioner|0.63|0.63|0.00|0.987|0.33|0.31|0.02|0.620|
|# TV|1.34|1.32|0.02|0.775|1.49|1.49|0.00|0.976|
|# satellite dish|0.20|0.06|0.14|**0.001**|0.16|0.15|0.00|0.879|
|# computer|0.75|0.79|-0.05|0.440|0.66|0.69|-0.04|0.530|
|# camera|0.15|0.11|0.03|0.264|0.04|0.07|-0.03|0.116|
|# car|0.58|0.52|0.06|0.221|0.68|0.69|-0.02|0.727|
|# DVD player|0.46|0.40|0.06|0.230|0.44|0.41|0.03|0.565|
|# Double axis tractor|0.04|0.04|0.00|0.992|0.27|0.18|0.09|**0.020**|
|**Subjective Variables:**|||||||||
|<br>Much better and a little better (%)|||||||||
|Better|6.8|4.8||0.789|3.4|4.0||**0.005**|
|The same|34.6|34.9|||36.5|33.7|||
|A little worse|28.8|30.5|||36.5|26.0|||
|Much worse|29.8|29.7|||23.7|36.3|||
|Monthly income satisfy needs (%)|||||||||
|Completely and mostly|33.9|36.1||0.865|23.3|27.1||0.355|
|Mostly not|34.2|33.1|||39.9|41.4|||
|It doesn't satisfy|31.9|30.9|||36.8|31.5|||

_Note: Statistically significant differences at 5 % level are in bold; *T-tests with cluster-robust standard errors are used,_ 16