rafmacalaba/datause-encoder-data huggingface dataset:
availability tends to drive up food prices, cutting into their limited budgets and forcing painful trade-offs.

For our analysis, we combine the remote sensing satellite-based monthly weather data for 15 years (2006-2020) with the geo-coded 2019-2020 IE-LFS, a nationally representative cross-section of household data that collects detailed information on socioeconomic indicators and household consumption. Our sample comprises 14000 households, both rural and urban, from 1495 clusters<sup>3</sup> covering all 34 provinces of the country. We extract data on precipitation, Normalized Difference Vegetation Index (NDVI),<sup>4</sup> and land surface temperatures.

To identify the effects of the 2018 drought, we construct a standardized measure (zscore) of weather shocks for a cluster in the 2018 growing season based on long-run weather distribution in the country. The z-score for a weather indicator differences the 15-year national growing season average (over 2006-2020) from the cluster’s 2018 growing season realization and normalizes this difference by the country’s long-run growing season standard deviation. This allows us to identify anomalies in cluster weather realizations in 2018 from the _usual_ national weather distribution.

We exploit the spatial variation in the intensity of weather anomalies in the household cross-section to identify the effects of weather shocks. To allow for flexibility in the relationship between weather shock and consumption, we fit a partially linear model where weather anomalies enter non-linearly. To mitigate concerns around potential omitted variables, we include several household and cluster characteristics as controls that may explain consumption and district fixed effects that control for any district characteristics that may confound the effect of weather anomalies. However, there remain potential historical factors that may have affected welfare measures and weather, which we are unable to control for in our cross-sectional specification. In light of such omitted historical factors, our results can be interpreted as the contemporaneous impact of the 2018 drought on consumption and poverty in a conflict-affected state, rather than a measure of the longterm equilibrium effect of weather on welfare arising from such historical processes (Dell et al., 2014).<sup>5</sup> Our results show that consumption decreases with lower NDVI and higher temperature

> 3Clusters are the lowest administrative units in Afghanistan, from which ten households were randomly sampled in the survey.

> 4NDVI is the Normalized Difference Vegetation Index that is an index of vegetation that relies on light reflected by plants and captures many vegetation-related metrics. See Section 3 for details.

> 5This paper is not an attempt to capture the reasons behind the effects we observe, but only to quantify the best estimates of the effects of weathe shocks in Afghanistan, which is highly vulnerable to extreme weather shocks.

2
the regressions. Such shocks could affect firm performance and increase their demand for financing. This would imply caution in interpreting the results. However, there is a second interpretation. Work by Rajan and Zingales points to potentially important differences across sectors in the impact of improved access to finance for growth. Beck et al. 2008 show that sectors with a larger proportion of small firms stand to benefit disproportionately from financial development. Including interactions sector-survey dummies then shuts down this channel, explaining why these two variables, share of sales sold on credit and the share of working capital financed externally lose their significance when all the interactions are included in the regression.

Column (4) also includes alternative measures for the other 3 investment climate measures. They are days of inspections, the size of bribes paid and losses incurred during transportation delays. The measure of inspections has the same effect as that of management time spent dealing with officials, deterring small and medium sized firms from growing. However, the new measures of corruption do not offer new insights, and the use of the transportation related variable provides weaker results. One conclusion is that power, not transportation (at least when measured as percentage of sales lost in transportation), is the more important dimension of infrastructure affecting firm growth.

# _7.3. Variations in Definition of Size Categories_

Table 6 investigates whether results are robust to variations in size classifications. For comparison purposes, column (1) reproduces the results of Table 4, column (1). Column (2), Table 6 reports the results of defining micro firms as firms with 5 to 10 employees, and excluding from the estimation firms with less than 5 employees. The other size categories remain unchanged. The results on access to finance weaken for medium sized firms, suggesting that the differential effects of finance between micro and medium size firms are in part explained by the more negative effect of finance on the smallest firms. This lends additional support to the selection story proposed by Jeong and Townsend, 2007, as the very small firms are the ones that are likely to be less productive, and therefore, more likely to be hurt by increasing costs of inputs driven by a higher supply of credit.

In column (3) we follow the size category cutoffs used by Beck et al (2005), which implies combining most of our micro with all our small into one category and combining our 25
# **Table 6. Data-generating processes and inequality measures, using coefficients resembling 2012 Egypt Labor Force Survey**

||**Ineq**|**uality mea**|**sures**||**R‐squa**|**red by ob**|**served circ**|**umstance**<br>**Father’s**|**Mother’s**|
|---|---|---|---|---|---|---|---|---|---|
|**Error**||**Total**|||**Femal**|||**educatio**|**educatio**|
|**Distributio**|**Gini**|**MLD**|**IO Ratio**|**All**|**e**|**Urban**|**Region**|**n**|**n**|
|**n**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|||||_Panel A._<br>|_Baseline S_<br>|_cenario_<br>||||
|_N(0,0.1)_|0.165|0.012|0.984|0.987|0.041|0.075|0.923|0.060|0.075|
|_N(0,0.3)_|0.174|0.013|0.869|0.890|0.037|0.068|0.833|0.054|0.068|
|_N(0,0.7)_|0.212|0.022|0.543|0.600|0.024|0.045|0.562|0.036|0.045|
|_N(0,1.0)_|0.252|0.033|0.358|0.422|0.018|0.033|0.393|0.026|0.033|
|_N(0,1.2)_|0.282|0.043|0.270|0.336|0.015|0.025|0.314|0.020|0.026|
|_N(0,1.5)_|0.332|0.064|0.183|0.246|0.011|0.019|0.230|0.015|0.018|
||||_Pan_|_el B. Inequ_|_ality Enha_|_nced Scen_|_ario_|||
|_N(0,0.1)_|0.170|0.013|0.973|0.978|0.041|0.074|0.915|0.059|0.075|
|_N(0,0.3)_|0.178|0.014|0.860|0.884|0.037|0.067|0.826|0.053|0.067|
|_N(0,0.7)_|0.217|0.023|0.535|0.594|0.024|0.045|0.558|0.036|0.046|
|_N(0,1.0)_|0.259|0.035|0.353|0.420|0.017|0.032|0.392|0.025|0.032|
|_N(0,1.2)_|0.290|0.046|0.267|0.335|0.014|0.026|0.315|0.021|0.025|
|_N(0,1.5)_|0.338|0.067|0.179|0.243|0.010|0.018|0.230|0.015|0.019|

Notes: Data generating process is presented in equation (4). Error terms listed in the first column are randomly added to equation (4) to induce variation in incomes across individuals. R‐squared is obtained from an OLS regression using ln(income) as dependent variable and circumstances included in equation (4) as explanatory variables. MLD is the mean log deviation, IOO is the ratio of the smoothed distribution MLD and total MLD. All estimates are based on observing the full income distribution (i.e. no truncation).

24
**Table 10: Trade Diversion in response to an EPA**

in millions of Nigerian Naira

|||Trade Diversion|||Trade Correction||
|---|---|---|---|---|---|---|
||win|lose|net|win|lose|net|
|ECOWAS|0|‐11,203,153,841|‐11,203,153,841|0|‐2,791,497,191|‐2,791,497,191|
|AFRICA|0|‐54,188,905|‐54,188,905|0|‐83,572,496|‐83,572,496|
|EU|0|0|0|0|‐8,410,471,744|‐8,410,471,744|
|USA|0|‐1,514,977,744|‐1,514,977,744|0|‐316,497,655|‐316,497,655|
|CHINA|12,772,320,490|0|12,772,320,490|11,602,039,086|0|11,602,039,086|
|TOTAL|12,772,320,490|12,772,320,490|0|11,602,039,086|11,602,039,086|0|

Unsurprisingly, Table 10 shows that China is the only trading partner that is projected to increase its exports to Nigeria with the FTA being implemented. Nigerians now import an additional 24.3 billion naira worth of goods from China. Notice that all other trading partners lose from the agreement in the sense that their exports to Nigeria drop in volume. For all trading partners aside from the EU some of the diverted trade is classified as trade diversion and some is classified as trade correction. The reason is that some but not all Chinese products for which import volumes are projected to increase had initially been subject to significant discrimination from preferences given to other trading partners. The trade diverted away from the EU, on the other hand, is entirely due to trade correction. The simple reason is that imports from China increase at the expense of EU goods only in product categories in which tariffs on Chinese goods had not already been zero from the outset. All product groups in which Chinese imports increase in response to the trade reform are thus such that Chinese exports were initially disadvantaged by preferences given to EU exports as part of the EPA.

# **5. Conclusions**

TRIST is a simple, easy to use model that brings substantial advantages in simulating the impact of trade reforms and thus in evaluating different trade policy options. It contributes to a better understanding of trade-related adjustment implications which is crucial to design a reform agenda that maximizes benefits from greater openness while minimizing and adequately cushioning adjustment costs. While TRIST has been developed to simulate the short term implications of trade reform on fiscal revenue, TRIST can be extended to estimate the initial impact of trade reform on domestic production and employment in import competing sectors, as well as on poverty.

The key features of TRIST compared to previous studies and models are that: it uses collected tariff rates at the tariff line level which leads to greater accuracy than using statutory rates; it takes into account all taxes collected levied on trade, not only tariff revenue but also revenue from VAT and excise taxes; it works at a highly disaggregated and policy relevant level breaking down results for detailed products and trading partners; it is transparent, open and flexible and does not require sophisticated software or specific 26
# **The Effects of School-based Management in the Philippines: An Initial Assessment Using Administrative Data**

Nidhi Khattri<sup>1</sup> Cristina Ling Shreyasi Jha

**Keywords:** school-based management, decentralization, evaluation, propensity score matching, administrative data

> 1 Nidhi Khattri, Senior Evaluation Officer, Independent Evaluation Group, The World Bank, Washington D.C. email: nkhattri@worldbank.org; Cristina Ling, Evaluation Officer, World Bank Institute Evaluation Group, The World Bank, Washington D.C. email: <u>cling@worldbank.org; Shreyasi Jha, Adjunct Professor, The Wilf Family</u> Department of Politics, New York University, New York. Email: <u>sj46@nyu.edu.</u> We would like to thank the Department of Education of the Philippines and Professor H. Nuqui, University of the Philippines, for providing the data and information on SBM.  The authors also gratefully acknowledge the support of Dingyong Hou (Senior Operations Officer, EAPHD, and  Task Team Leader for the Third Elementary Education Project), Lynette Dela Cruz Perez (Senior Operations Officer, EASHE), Christopher Thomas (Manager, EAPHD), and Eduardo Velez Bustillo (EASHE).  This work was partially supported by the Education Program Development Fund.  We are indebted our reviewers for providing helpful comments and suggestions on previous drafts.  All remaining errors are ours alone.  The opinions and conclusions expressed in this paper do not necessarily reflect those of the Executive Directors and officials of the World Bank or of its member governments.
TABLE 51: ADVANTAGES AND DISADVANTAGES OF PAPER BASED AND ELECTRONIC PROCUREMENT AS REPORTED BY PE OFFICIALS

|Advantage/Disadvantage for PE|M<br>|anual<br>|Ele<br>|ctronic<br>|
|---|---|---|---|---|
||Advantage|Disadvantage|Advantage|Disadvantage|
|Bidder capacitydevelopment|13%|1%|6%|6%|
|Cost ofprocessinga tender|2%|20%|12%|3%|
|Inappropriate bidding practices (e.g.<br>collusion)|1%|34%|21%|7%|
|No. of tenderers applyingfor a tender|27%|1%|6%|12%|
|Processingdifferent lots at the same time|9%|1%|3%|6%|
|Qualityofproject implementation|10%|1%|9%|1%|
|Technical complexity (internet connectivity,<br>software usability)|6%|2%|3%|76%|
|Tendererqualityand skills|17%|11%|6%|7%|
|The stress related to processing a tender|4%|37%|6%|3%|
|Time taken toprocess a tender|9%|1%|22%|3%|
|Training needed to learn the processing<br>method|4%|11%|5%|29%|
|Translations for standard documents|6%|1%|4%|4%|
|Transparency|0%|0%|58%|1%|
|Trust or reliabilityof the system|4%|4%|21%|8%|

TABLE 52: ADVANTAGES AND DISADVANTAGES OF PAPER BASED AND ELECTRONIC PROCUREMENT AS REPORTED BY BIDDERS

|Advantage/Disadvantage for Bidders|M<br>|anual<br>|Ele<br>|ctronic<br>|
|---|---|---|---|---|
||Advantage|Disadvantage|Advantage|Disadvantage|
|Time taken toprepare a tender.|7%|56%|29%|5%|
|Cost ofpreparinga tender|5%|22%|10%|13%|
|Technical knowledge requirements|45%|0%|1%|25%|
|Level of competition/ number of competing<br>firms|17%|13%|3%|24%|
|Inappropriate bidding practices (e.g.<br>collusion)|23%|0%|10%|23%|
|Translations for standard documents|3%|5%|0%|4%|
|Labour requirements|31%|10%|2%|26%|
|Technical complexities (internet<br>connectivity, software glitches)|48%|0%|0%|60%|
|Transparencyin theprocurementprocess|0%|0%|9%|0%|
|Ease of submission of tenders|0%|6%|72%|1%|
|Coercivepractices/use of musclepower|0%|84%|64%|0%|

The perceptions of each indicator have been given below:

1. Efficiency Indicators

83
as well as their family’s quality of life in current day and five years hence. These outcomes are measured both for female (primary grantees) and male (grantees’ partner) respondents.

Second, to measure bargaining power, we create an index, which encapsulates women’s role in intrahousehold decision making. Specifically, we ask questions on decision making across 13 domains (such as decisions on large household purchases, using contraception, taking loan, etc.)<sup>18</sup> and evaluate the following outcomes: (i) fraction of domains for which respondent makes the decision (independent/joint); (ii) fraction of domains in which respondent's opinion is heard; (iii) fraction of domains in which respondent cares about making the decision; (iv) number of domains for which respondent cares AND makes decision; (v) absolute difference in number of domains for which respondent cares about making the decision and actually makes the decision.

Third, women’s empowerment is measured using two outcomes. The first outcome measures self-efficacy among respondents (construction closely follows Schwarzer and Jerusalem (1995)) and the second outcome - relative autonomy index (RAI)- measures the extent to which a respondent has autonomy over her motivation in undertaking actions (construction closely follows Seymour and Peterman (2018)).

Fourth, to examine the pathway on social networks, we record information on whether the respondent receives information - on topics such as family planning, child health, education, government services and income earning opportunities - from a community worker/representative of the family development sessions (commissioned under the 4P program) or through some other source.

Finally, to encapsulate men’s response to plausible increase in women’s empowerment and bargaining power, we evaluate changes in men’s personal attitudes towards gender equity. To measure these attitudes, we seek answers on respondent’s personal attitude on 9 statements that relate to gender equality and assumed gender roles in the society (Dhar, Jain and Jayachandran 2018).

_Table 2: Composition of Mechanisms Indices_

|**Mechanism**|**Summary Index**|**Outcomes Variables**<br>**(Index components)**|
|---|---|---|
|Enhancement of<br>emotional and<br>mental well-<br>being|Women and men’s<br>well-being|Patient health questionnaire index; respondents’ perception of<br>their own as well as their families’ quality of life in current day, and<br>5 years ahead|
|Improvement in<br>women’s<br>bargaining<br>power|Bargaining Power|Fraction of domains for which respondent makes the decision<br>(independent/joint); Fraction of domains in which respondent's<br>opinion is heard; Fraction of domains in which respondent cares<br>about making the decision; Number of domains for which<br>respondent cares AND makes decision; Abs difference in number of|

> 18 These 13 domains are akin to those included in the Demographic and Health Survey.

11
**4**

decline in exports to the rest of the world of 14% between 1991 and 1994; and a drop of 87% in exports to other FSU countries (Table 1). Russia is by far the largest trading country in absolute terms with its trade accounting for over 50% of the total, with Ukraine at 19% a distant second. There are many problems with these estimates: First, they clearly overstate the actual decline in 1992 because the exchange rate used to convert rubles to dollars in 1991 was substantially overvalued. On the other hand, in subsequent years the ruble and some of the other new currencies were substantially undervalued using purchasing power parity and wages-in-dollars comparisons [Michalopoulos and Tarr 1994a]. Second, there is evidence that the actual exchange rates used in the conduct of intra-FSU trade in the period 1992-1994 involved a much smaller ruble devaluation vis-a-vis the dollar. Third, throughout the period there were significant amounts of trade conducted through barter. While the statistical agencies tried to adjust for it, it is doubtful that the adjustments made captured all the amounts involved.

Data on intra-FSU trade (called hereafter "interstate trade") published by individual countries are incomplete and contradictory. The constant price series presented in Table 2 is based in part on World Bank and IMF estimates. They show a somewhat smaller decline for intra-FSU trade than the series in US dollars, which however, remains substantial. In most countries exports fell by 70% to 90%. Even Russia and Turkmenistan, which suffered the smallest declines over the proiod, still experienced drops of 67% and 52%, respectively. The fact that intra-FSU trade declined substantially is also corroborated by firm level surveys [Bull,1994].
Chile does indeed suffer from a true innovation shortfall: its R&D investment rate is significantly below what would be expected given its stocks of human and physical capital. Still, our goal here is not so much to reach a solid conclusion for any particular country, as to illustrate how this kind of analysis might be undertaken. We believe that even for the case of Chile, a more careful analysis is called for.

Finally, Section 4 briefly discusses possible explanations of true innovation shortfalls that have been presented in the literature, again focusing on the case of Chile. We end by calling for more research both to improve the methodology to identify innovation shortfalls, and for finding the most important determinants of such shortfalls in specific countries.

# **2.  R&D and the Pattern of Specialization**

As mentioned in the introduction, there is enormous variation in R&D investment rates across sectors. This is a reflection of the fact that, just as with physical and human capital, some sectors are intensive in knowledge capital relative to others. This has important implications for comparisons of R&D investment rates across countries. In a multi-sector economy with international trade, one may have different countries specializing in sectors with differing possibilities for technological change, so that one would observe significant gaps in R&D investment rates that are consistent with factor price equalization and hence similar wage levels (see Grossman and Helpman, 1991). Although income levels would be higher in the economy with higher R&D rates (since it would enjoy a higher stock of knowledge capital per worker), under reasonable conditions it would not be appropriate for countries to encourage the growth of highR&D sectors. We discuss this point by briefly reviewing the relevant theory in the next subsection. The following subsection looks at the R&D data at the sector level to gauge the extent to which differences in specialization patterns may explain a significant part of the R&D insufficiency one observes in LDCs such as Chile.

4
# **Coordination costs within the household**

Finally, we examine the possibility that the purchase of financially inefficient small quantities could be driven by the costs associated with coordinating purchases between household members. In Table 5 we see that losses are increasing in household size, hinting at the possibility of coordination difficulties. Coordination failures would manifest as multiple household members buying the same item in small quantities, rather than aggregating into a single purchase so as to pay a lower unit price. Studying this phenomenon requires data on individual- rather than household-level purchases, so we restrict this analysis to the 500 households that were randomly assigned to complete personal rather than household diaries. The modal household in this group has 2 personal diary keepers (54% of observations); the mean number of diary keepers is 2.1, and the maximum is 7.

To proxy for uncoordinated shopping, we calculate _dmult_ , the number of days on which multiple household members purchase the same item. We then regress _Lh_ or _L_<sup> ̃</sup> _h_ on _dmult_ and an extensive set of household control variables. We also control for the total number of itemdays on which any purchase is made. This focuses on the behavior of interest: controlling for the household’s tendency to make purchases at the item-day level, do we see that losses are increasing in the number of times that multiple household members buy the same item on the same day? The hypothesized link between coordination costs and bulking losses would show up as a positive and statistically significant coefficient on _dmult_ .

Results with _Lh_ as the dependent variable are in columns 1 and 2 of Table 12. For both samples, the estimated coefficients on _dmult_ (“Number of item-days with 2+ purchasers”) are negative and statistically significant. This runs directly contrary to the coordination-cost hypothesis. Not surprisingly, the coefficient on the count of item-days with a purchase is positive and statistically significant. In columns 3 and 4 we repeat the analysis using percentage losses ( _L_<sup> ̃</sup> _h_ ) as the dependent variable. The signs and pattern of statistical significance are the same as for _Lh_ .

These findings do not support the hypothesis that coordination costs are responsible for the forgone bulk discounts in the SHWALITA data.

32