rafmacalaba/data-use-mentions-extended huggingface dataset:
) only 20 countries are the recipients of FDI inflows.

_Figure 1. Country recipient distribution of sector level FDI_ <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>[5,10] [11,20] [21,30] [31,40] [41,50] [51,60] [61,70] [71,80] [81,90] [91,116]<br>Number of countries<br><!-- End of picture text --> Note: the histogram reports the number of countries for each of the 245 sector/activity pairs which received FDI inflows over the 2015-19 period.

The same steps are used with the trade data. The initial data set included information on RCA for 1,245 4-digit sectors. We excluded sectors which have fewer than 5 non-zero observations for the sample countries, leading to a final number of 1,220 4-digit sectors. We transform the data set and construct a dummy variable which takes value 1 if the country _j_ has RCA in sector _i_ above 1 and 0 otherwise. The RCA is calculated based on the sum of export and import flows over the 2015-19 period. Analogously, ii,jj we create a variable YY based on the information on country/sector pair RCA "success" over the 201014 period. Like Figure 1, Figure 2 shows the histogram of the empirical distribution of number of ii,jj ZZ 20 There is the possibility of reverse flows (i.e., an outflow investment is made and then disinvested) during the period, but if any at all, these are likely to be negligible relative to the overall sum of investment or trade flows.

12
utilization of child health care was a key condition to receive cash benefits, the greater exposure to nutrient rich ASFs among children participating in the program would have resulted from either the income effect of the cash transfer (relaxing budget constraints), Pantawid’s family development sessions which emphasized infant and young child feeding and nutrition, or both. In addition research using the 2013 NNS data - which collected detailed data on children’s nutrient intake and food sources through a 24 hour recall module - found that the diets of children aged 6-60 months were concentrated primarily to foods with low nutrient density such as refined rice with little contribution to total energy or nutrients from vegetables, meats, fruits and eggs.<sup>32</sup> A recent observational study looking at the relationship between ASF consumption and stunting globally using DHS data finds that children between the ages of 18 and 23 months who consume 2 or 3 types of ASFs the previous day are around 6 pp less likely to be stunted compared to children who do not consume any ASFs, controlling for other child and household factors.<sup>33</sup> In South, Central and Southeast (SE) Asia, 56.8 percent of children (6-23 months) consumed any ASF the prior day (compared to a high of 83 percent in Latin America & Caribbean and a low of 49.3 percent in Eastern & Southern Africa). Of the children who consumed any ASF, the vast majority (75 percent) only consumed 1 type of ASF - the highest rate across all regions - suggesting relatively low exposure to different kinds of ASFs among children in South, Central and SE Asia. High prices may be a key constraint to relatively low levels of ASF consumption. The prices per-calorie of ASFs relative to the prices per-calorie of the cheapest staple cereal in South, Central and SE Asia, range from a high of 7.8 for fresh cow’s milk to a low of 5.3 for fish; chicken eggs are 6.
running a simple cross-country regression using the pooled sample of countries that participated in the 1999 or 2003 rounds of TIMSS (47 countries) or participated in the 2000 round of PISA (42 countries).<sup>30</sup> The results strongly support the notion that _LAYS_ captures important aspects of both the quantity and quality of schooling. Average years of schooling of the cohort of 25- to 29-year-olds in 2000 is related to subsequent GDP per capita growth (Column 1 of Table 1), as are average test scores (in mathematics; Column 2). The coefficients on both variables fall substantially when both are included in the model (Column 3), and, unsurprisingly, the explanatory power of the model increases (R-squared of 0.68, compared with 0.65 with average years alone and 0.59 with test scores alone).<sup>31</sup> _LAYS_ is also strongly related to subsequent growth (Column 4). Importantly, the explanatory power of the model that includes just the _LAYS_ measure (R-squared of 0.68) is the same as that of the model that includes years of schooling and test scores as separate variables (R-squared of 0.68), and greater than the model that includes just average years of schooling or just test scores. This means that _LAYS,_ as a single variable, captures key dimensions of its constituent parts that are associated with economic growth. The findings are consistent (although point estimates differ because of smaller and different country coverage) when using just the TIMSS or the PISA samples (Columns 5-12): again, _LAYS_ explains about as much of the variation as its constituent parts do when entered together. The findings are likewise consistent under various other robustness tests—notably, if we use GDP per capita growth between 1990 and 2016, or between 1995 and 2016 (see Annex Tables A1 and A2); if we use the average years of schooling for the whole population aged 15 and over, together with
17

# _Table 3 (contin.): Descriptive statistics, by survey and wave_

|||CH|NS||GS|CF|H8BS|
|---|---|---|---|---|---|---|---|
||1991|1993|1997|2000|2000|2004|1998|
|Per capita income (RMB)|1675.0|1324.5|1534.5|1392.7|1844.29|3351.85|825.61|
|Years of schooling|15.28|15.29|16.78|17.87|13.56|16.77||
|Age|||||||28.96|
|Gender|||||||52.4%|
|Han|||||||81.8%|
|Married|||||||51.9%|
|No. children under 5|||||||0.36|
|No education|||||||23.7%|
|Elementary school|||||||31.2%|
|Primary middle school|||||||36.3%|
|Senior middle school|||||||7.4%|
|Vocational school|||||||1.1%|
|Junior college|||||||0.3%|
|N|14034|13227|13604|14502|7580|7580|42581|

Note: CHNS panel is unbalanced because not all provinces were sampled in each wave (cf. Table 1), some households left the CHNS as it proceeded, but some were replaced by new households.
✓|✓|
|Country FE|||✓|✓|✓|
|Year FE|||✓|✓|✓|
|First Stage F-stats for Price||||54.67|77.00|
|First Stage F-stats for Charging Station|||||36.34|
|Underidentifcation Test||||62.60|77.22|
|Weak Identifcation Test||||31.85|22.47|
|Overidentifcation Test||||55.14|62.14|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is log(sales). _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 27
causing higher demand for leisure), which may reduce family labor supply. Nevertheless, as Table 2 shows, male labor supply increases by 49 percent and female labor by 51 percent in RDP villages because of a road development project. There are no similar statistically significant gains in RRMIMP villages for family labor supply.<sup>19</sup> The results do suggest that gains from both input and output markets due to road investment are substantial for rural households.

The overall economic returns to road development can be measured by summing over the gains through transportation cost savings, higher output and lower input market prices, and higher productivity. While there is no an easy way we can summarize these benefits in one return estimate, such gains

> 15 Jacoby (2000) predicts that land prices can also rise from better transport, using household distance from the market center as an indicator of transport quality in a household survey from Nepal.  More specifically, he finds that land values rise by about 2% if household distance to the nearest market center falls by 10%.

> 16 In RDP, about 68% of the villages were project villages, and about 75% of villages in the RRMIMP sample were targeted by the project.

> 17 Normally, the effects are higher at the household level, and thus our findings are consistent with previous results for road development in comparable areas.

> 18 This is consistent with results from Jacoby (2000) as well, who finds that wages are also lower in remote rural areas of Nepal.  We also found rural electrification to have a significant negative impact on fertilizer price and increase agricultural real wage, aggregate crop price, and productivity.  Interestingly, the returns to rural electrification are higher than those of road investment for some of the outcomes considered.  The extended fixed effects results are available upon request.

> 19 Lokshin and Yemtsov (2005) also find in their study of Georgia that road projects do not seem to significantly impact overall male and female labor supply in treatment as compared
for all industries combined.

In practice in the detailed analysis of chapter III we had to settle for comparisons using the US Census of Manufactures definition of value added and switch to our preferred concept only at the level of manufacturing branches and manufacturing as a whole. In chapter IV which provides a direct bilateral comparison between Mexico and Brazil we were able to use our preferred "former national accounts" concept of value added throughout.

# Input-Output Tables Potentially Useful for Double Deflation

We were not able to make much use at this stage of the input-output tables (Mexico table 2.5, USA table 2.8). This was partly because in the US they were not readily reconcilable with the census information, and in the Brazil case because the statistical office (IBGE) had not published its work on the 1975 table. However, these tables will be most useful for purposes of double deflation (see chapter III and VIII) at a later stage.
adjustments should at least be anchored to inflation rates to uphold the purchasing power of individuals. Our results show that increasing the minimum wage to the level of the consumption basket can generate significant income gains for employees who keep their jobs. However, a significant formal employment reduction may also be recorded. Therefore, the government should implement labor market policies to support the transition of those who lose their job back to employment and minimize job mismatches. This approach ensures that the minimum wage remains attuned to the changing economic landscape while safeguarding the real value of earnings **.**

# **Second, monitoring the labor market is essential, as well as accurately estimating the wage elasticity**

**of labor demand.** The minimum wage can have different effects depending on the country's context at a certain point in time, particularly during different stages of the economic cycle.  Therefore, to set a minimum wage and impose an adjustment formula, a detailed monitoring of the labor market and the estimation of the critical parameters that determine the effect of the minimum wage are needed. In this note, we consider several scenarios based on the estimates of the wage elasticity of labor demand from international evidence, which

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Table 3 quantifies the sensitivity of an array of statistics of interest for welfare analysis. The first four columns refer to inequality measures: the Gini index (column 2), two members of the Generalized Entropy family of indices (Mean Log Deviation and Theil index, in columns 3 and 4), and the Atkinson index (with the inequality aversion parameter set equal to two, column 5). The remaining columns refers to Foster, Greer and Thorbecke (1984) poverty measures: the headcount ratio (H), the poverty gap index (PG), and the poverty gap squared index (PG2). The results show that the exclusion of outliers decreases estimated inequality according to all measures. The Gini index is the least sensitive among the inequality measures shown in Table 3, although before-after differences can be large enough to cast doubt on the validity of estimates obtained from the raw data: the Gini index for Nigeria, Mozambique, Malawi, and Mexico is bumped down by 5 to 6 percentage points, a salient difference by all metrics. The Theil index is the most sensitive measure among those we examined, showing differences as large as 28 percentage points. Overall, these results align with the theoretical analysis in Cowell and Flachaire (2007). Differences observed for three poverty measures – the poverty headcount, poverty gap, and poverty gap squared – are smaller across the board, in line with the theoretical findings available in the literature (Cowell and Victoria-Feser 1996b). However, our illustrative example uses relative poverty lines, which may affect results.

The empirical magnitude of the effects documented in Table 3 is consequential, and can be appreciated focusing on the case of Gini index (the more conservative scenario in our setting). Figure 6 (top panel) shows the point estimates of the Gini index calculated on the RuLIS ‘raw data sets’, that is on data as available from the data provider. Countries are ranked low (Kyrgyzstan) to high (Bolivia) accordingly. The bottom panel of Figure 6
3,000 public primary schools across 19 major Indian states found that over 25 percent of teachers were absent from work on a typical working day in 2003 (Kremer et al., 2005). Although administrative data from the government’s official records<sup>2</sup> suggest that SSA has led to an improvement in various observed measures of school quality, there is little evidence on whether these investments have translated into improvements in education system performance, both with respect to intermediate metrics such as teacher absence and final outcomes such as test scores.

Our study of this nationwide campaign to improve school quality in India uses a new nationally-representative panel dataset of education inputs and outcomes that we collected in 2010. We constructed this data by revisiting a randomly-sampled subset of the villages originally surveyed in 2003 (see Kremer et al. (2005)) and collecting detailed data on school facilities, teachers, community participation, monitoring visits by officials, and teacher absence rates. Thus, in addition to reporting updated estimates of teacher absence, and independently-measured summary statistics on input-based measures of school quality, we are able to correlate changes in input-based measures of school quality with changes in teacher absence. The panel data help mitigate concerns arising from fixed unobserved heterogeneity at the village-level, and our results provide the best available estimates using nationwide data of how sharp increases in public education spending over the last decade have affected school quality.

We find significant improvements in almost all input-based measures of school quality between 2003 and 2010. The fraction of schools with toilets and electricity more than doubled, and the fraction serving mid-day meals nearly quadrupled. There were significant increases in the fraction of schools with drinking water, libraries, and a paved road nearby. The fraction of teachers with college degrees increased by 41 percent, and student-teacher ratios (STR) fell by 16 percent. The fraction of teachers not paid on time fell from 51 to 22 percent, and the fraction of teachers aware of recognition programs increased from 50 to 81 percent. Finally,
Figure 1: Scatter plot and kernel regression: claim payments against yields, for all weather indexed products sold in one state in one year <!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 50% 100% 150% 200%<br>Subdistrict average yield, as percentage of average historical yield<br>1999-2007<br>WBCIS claim payment, as percentage of sum insured<br><!-- End of picture text --> Note: The lines show the point estimate and 95% confidence intervals for an Epanechnikov kernel with a bandwidth of 0.8.

On this basis we estimate that conditional on total crop loss, that is an area average crop yield of zero, there is a 1-in-3 chance of receiving no claim payment from the WBCIS (Figure 2). This may arise because of perils not captured by the weather station data, such as localized weather, pestilence or disease, or weather events not adequately captured by the index used in the WBCIS product. Conversely, there is a high risk of farmers receiving a positive WBCIS claim payment, even in years with good yields. For example, again reading off from the kernel regression point estimate in , conditional on the yield being twice the historical average the WBCIS products included in this analysis paid claims with probability of 45% (Figure 2).

Figure 2: Kernel regression: probability of positive claim payment against yields, for all weather indexed products sold in one state in one year <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>0% 50% 100% 150% 200%<br>Subdistrict average yield, as percentage of average historical yield...<br>positive<br>claim
platforms or  Prepaid, credit or debit card processes with the support of or written<br>media, specialized  surveys apps digital<br>Apps or digital<br>Computers with  platforms Online or  technologies<br>specialized installed  Costumer  Online sales using  electronic<br>Resource Planning Enterprise software for demand planning, Specialized software demand forecast Supplier Relation Management  Management Relationship software(CRM)  platforms (e.g., external digital Amazon, eBay, Alibaba) Online through payment Statistical process control<br>(ERP) or equivalent  (SRM) not  platform<br>other back-office integrated with functionssoftware  Enterprise Resource equivalent software Planning (ERP) or integrated with  integrated with production planning analytics / Artificial Big data  commerce) using its own websiteOnline sales (e- Virtual or  Automated systems for inspection<br>other back-office  intelligence cryptocurrency<br>functions Electronic orders<br>Supplier Relation  integrated to<br>Management  specialized supply<br>(SRM) integrated  chain management<br>with production  systems<br>planning<br><!-- End of picture text -->

Figure A1: General Business Functions (GBF) Technologies in the FAT. Digital technologies are highlighted in yellow. Light yellow boxes refers to technologies that can rely directly or indirectly on digital (e.g., consumer surveys conducted online, email for sales, or use of credit card through digital sales’ platforms).

> 19A full description of the FAT survey and the technology sophistication index we use can be found in ( _17_ ).

23
. of countries|33|33|33|
|RMSE|5.07|4.50|4.38|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel >25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

29
retail investor base, the obvious metric to monitor is growth in the number of new retail accounts and investors. Given the approach being taken with the Registry of Scripless securities—where securities are registered at the investor level—monitoring this growth will be feasible.

Another issue is the program’s indirect impact on the financial services industry. The program’s indirect objectives include forcing dealers of government securities to compete more in primary auctions and increasing competition among savings products by offering small investors an attractive instrument.

By distributing its securities directly, the Treasury will increase demand for its securities by competing with other savings products for small investors. Savings instruments such as passbook savings accounts yield about 2-3 percent a year, while the Expanded Small Investors Program will offer a yield closer to 6 percent, though not the same liquidity.<sup>10</sup> The yields offered by banks’ savings 10 Under the Expanded Small Investors Program, investors cannot sell their securities before they mature. But it is unlikely that the liquidity premium and associated administration costs of banks amount to more than 300 basis points (3 percent).

28
In the remaining sections of this paper, we will empirically test our hypotheses. This requires a measure of search costs from the perspective of the buyer for each transaction. As adeptly summarized in the quote above, this information is often missing, as is the case for our case study. To get around this constraint, the approach we adopt is to use a proxy, namely the distance of maize sellers to the marketplace. The further away a seller is from a market point where buyers and sellers congregate, the more difficult he or she is to find, causing buyers to incur higher search costs in transacting with them. Hence, the more remote a maize growing household is, the higher the search costs associated with their transactions and the lower the prices they receive for their maize.

The use of proxies to estimate search costs is common in the literature on this topic. In their study of small-scale farming households in South Africa Randela, Alemu & Groenewald (2008) use distance to markets as a proxy for search costs (amongst other variables) to identify factors that determine the extent of commercialization or market participation. Kuksov (2002) considers online marketplaces and the share of online sales as proxies for buyer and seller search costs and Benjamin & Lusht (1993) proxy the search costs associated with finding a rental apartment using the characteristics of the managing agent (e.g. whether they have a well-located rental office). Most studies (including this one) use proxies because of the dearth of data measuring specific transaction costs. Household budget surveys and agricultural censuses in particular do not tend to include a module directly measuring transaction costs, possibly because the large amount of detail that this would entail could make the survey unwieldy.

Given such data constraints, we have selected distance to market as a proxy for search cost because of its relevance to the marketing structure of Tanzania‟s maize market. The procurement of maize is a largely physical exercise that requires buyers to travel around villages visiting households, literally knocking on doors