rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Household characteristics**

Besides agricultural activities, the household module data of the IMDG shows that durable asset holding also increased from 2007 to 2010. In the table, durable asset holding is adjusted for household size (i.e. real values per household member). We use the provincial level consumer price index (2007=100) available online on the BPS website to calculate real values. Also, we use trimmed data to remove outliers throughout the analysis of this paper. It is defined as the ownership of non-production assets (such as residential house and land; consumer electrical appliances such as TV, radio, satellite antenna, and telephone) and the value has increased by 12.3% from 2007 to 2010.

# **Local food price data**

The wholesale price of agricultural products has started rising since 2004 and the price increased substantially after 2007. Figure 2 disaggregates the price dynamics into five crops based on the monthly price data available from the Indonesian Bureau of Logistics (Bulog) and BPS, which shows that prices of all crops increased quite fast between 2007 and 2010. The prices of rice, maize, and cassava were taken from Bulog statistics, while those of estate crops and horticulture crops are from BPS.

For the monthly raw price data of each crop, the Phillips-Perron test statistic (with time trend) does not reject the null hypothesis of unit root at any critical values, confirming that all price series are non-stationary. Table 1 shows that the first-order autocorrelations and standard deviations using the de-trended version of the monthly price series (which removes linear trends for each province and year). In general, the persistence of food price decreased and the volatility increased from pre- to post-crisis period, which implies that the movements of food price have become more uncertain in the post-crisis regime.

17
7 own right for evaluating or monitoring the impact or outcomes of a certain intervention, the data collected through such a survey cannot be used to generate reliable statistics for any population outside the beneficiaries (or control group if any) of that particular project or program. Examples of project-specific datasets include energy monitoring framework survey, program monitoring beneficiary survey, postdistribution monitoring survey, among others. As we note in the following section, a non-negligible share of the existing FDP datasets turns out to be project specific.

Second, we code the topic coverage of each dataset based on its questionnaire modules. The metadata of each dataset contains information on survey modules or main topics covered in the questionnaires (e.g., health, education, food insecurity). Since there is no pre-determined list of thematic areas commonly included in the corpus of FDP micro-level datasets that are known to us _ex ante_ , we need to create our own taxonomy of topic areas. To this end, we look at keywords that are frequently mentioned in the descriptions of the survey modules in the metadata, rank-order them based on frequency, and then map them into 14 different broad topic areas. For instance, words such as “consumption”, “income”, “expenditure”, “non-food”, and “welfare” are clustered under the topic area of _consumption and welfare_ and if a given dataset mentions at least one of these keywords in the description of the survey modules in its metadata, the dataset is coded as relevant to that topic area.<sup>19</sup> To assess gaps in topic coverage among FDP datasets, we also apply the same methodology to all other micro-level datasets in the WB MDL and compare differences in topic coverage between FDP datasets as identified in our analysis and other non-FDP datasets contained in the WB MDL.

Third, we also evaluate whether a given dataset contains the core set of questions recommended by EGRISS.<sup>20</sup> EGRISS recommends a set of questions that are essential for proper
component is approximately 15.5, while the second and third components have eigenvalues under 2.0. Moreover, the lower curve in the right panel, the contribution in percentage to the total variance, and the upper curve shows the cumulative contribution to the total variance. The largest eigenvalue contributes over 70 per cent of the total variance, and the three principal components account for almost 90% of the total variance. Despite that an elbow at the second eigenvalue is visible, the similar value of the third eigenvalue led to decide that three principal components are required to represent the data.

> 7 Data on the female labor participation rate from 1960 to 1983 was constructed using census data, and the missing observations were fit by a _spline_ curve to the observed values.

7
our control sections 59 percent of confirmed cases were subject to contact tracing, compared to just 22 percent in CM and 28 percent in NFA (Table E.16). Second, we use three measures derived from the VHF data (all measured at the section-level): (1) the probability that a case received laboratory testing to confirm or rule out an infection; (2) the average number of days that passed between a case being reported and lab testing; and (3) the number of unique case workers (logged) that entered information into the VHF. We expect these variables to proxy for top-down surveillance efforts during the crisis. In Table E.17, we find no significant differences for these variables across treatment and control.

Finally, using data from Sierra Leone’s National Ebola Response Center (NERC) and the UN Mission for Ebola Emergency Response (UNMEER), we count the number of Ebola-specific treatment facilities in each section (see Section E.1). There were three types of specialized facilities: Ebola Treatment Units

> 46 It is possible that the ratio of confirmed to total cases could stay constant if there was an increase in the number of probable and suspected cases. Figure E.9(b) repeats the bounding exercise but uses the ratio of confirmed to confirmed plus negative cases. This exercise delivers the same conclusion, as the number of probable and suspected cases are small and unaffected by treatment (see Table E.6.

35
0.10|19.4|19.9|196.2|205.7|
|Number of respondents|1,977|1,648|1,977|1,648|||

## <u>Notes:</u>

(1) Sample includes men and women self-reporting in the LSMS+ time diary module, aged 18 and older. All estimates weighted by household sampling weight.

(2) Significant differences in minutes spent across men and women (separately for full sample and among t>0) are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 (3) As seen in Table 1, the time use module also included categories for (a) making goods (furniture, pottery, baskets, clothing), as well as (b) work for other household free of charge as an exchange laborer, but nearly no respondent reported time in these areas.

# 3.2 Labor

Adult individuals are economically active in Cambodia, even women despite having high unpaid work burdens. Table 3 reflects summary statistics as reported in the labor module across agricultural and offfarm activities. The labor module provides employment information as well as the hours worked in the last seven days. Seventy-seven percent of women and 88 percent of men reported being involved in any activity across agricultural and off-farm work, and about 21 percent of women and 27 percent of men reported being involved in more than one activity in the last 7 days (with greater shares in rural areas).<sup>20</sup> Roughly equal shares of men and women (about 45 percent) reported being involved in agriculture; about 16 percent of men and 18 percent of women reported themselves as owners/managers of NFEs; but there were wider gender gaps in wage employment (44 percent of men and 26 percent of women).

> 20 Among those in multiple areas of work, the most common combinations
|0.025|0.041|

Note: All specifications use linear fixed effects estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the species-month-fishing team level, while columns 3 and 6 include fixed effects at the species-week-fishing team level. “Num. groups” indicates the number of unique combinations at each level. Standard levels clustered at the level indicated in the “Clustering” row.

4
br>60 10<br>40<br>5<br>20<br>0 0<br><!-- End of picture text -->

**Graph 1 – Container imports traffic and dwell time in the Port of Douala (2005-2009)** Source: Port Autonome de Douala

Most recent DIT statistics indicate an average dwell time of 19.3 days for the first semester of 2010 while means of 18 days and 20 days respectively have been observed in July and August<sup>10</sup> . This value has been quite stable in the last few years despite strong and consistent growth in traffic.

> 9 Source : Annual statistics, _Port Autonome de Douala_

> 10 Source: DIT

8
|||
|Constant|2,185.355***|714.280|-122.089***|-24.294*|
||(31.172)|(536.167)|(40.467)|(13.028)|
|Observations|222|222|31|32|
|R-squared|0.757|0.768|0.013|0.086|
|State FE|Yes|Yes|No|No|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Standard errors clustered by state in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Columns 1 and 2 use the full panel of state caloric intake and TEE, with state fixed effects. Columns 3 and 4 are the regression equivalents of Figure 2 in the paper, using only the within-state long-difference for two periods.
0.35|0.21|0.93|0.93|
|2001|0.34|0.21|0.93|0.93|
|2002|0.31|0.19|0.93|0.93|
|2003|0.31|0.19|0.93|0.93|
|2004|0.31|0.19|0.93|0.93|

Mean values of policy variables from 1985 to 2004. Final goods and input tariffs variables are fractions, with 1 representing an ad valorem tariff of 100%; FDI Reform is a dummy variable equal to 1 if any products within the industry are liberalized, 0 if not; and Delicensing is a dummy variable equal to 1 if any products within the industry are delicensed, 0 if not. Source: Authors’ calculations based on various publications of the Government of India, as well as the TRAINS database.

33
1988). In a one period game this would imply that landlords would rank tenants by wealth, choosing to enter into contract only with the wealthiest tenants. Empirical evidence reported by Quibria and Rashid (1986) confirms such behavior. By implication, the efficiency of any particular tenancy contract is increasing in tenant wealth and the overall efficiency of the tenancy market would depend on the initial wealth distribution of potential tenants, generating a direct mapping between the distribution of wealth and economic efficiency (Bardhan et al. 1997). In a repeated game, landlords would allow all but the least wealthy tenants to earn positive profits in equilibrium, thus using the threat of contract termination (or eviction) as a device to elicit effort supply.

An extension of this argument is provided by Mookherjee (1997), who shows that in the context of bargaining on the terms of an interlinked tenancy contract between landlord and tenant the efficiency of

> still reported to be common in Bangladesh (Cain 1981), Java (Morooka and Hayami 1986), and Thailand (Fujimoto 1988). In the Philippines, tenancy transactions emerged as a credit substitute in response to limitations on the transferability of land (Nagarajan and associates, 1991). 1 A third rationale for adoption of the share contract, namely that imperfect information on tenants' unobservable characteristics such as ability, causes landlords to use sharecropping contracts as a screening device where the tenants' acceptance of certain types of contracts provides a signal for their productive ability (Newbery and Stiglitz 1979) does, at least for Indian data, not conform well with the data (Olson-Lanjouw 1995).

23
results to other concerns, including the omission of relevant regressors and alternative definitions of the dependent variable of interest (SAR).<sup>17</sup> Furthermore, because we have multiple waves within a country/year, our results might be influenced by the fact that each country changes over the sample period and in very different ways. We deal with this issue by first including country/wave fixed effects and then clustering the standard errors at the country/wave level, thus allowing for within country-wave intra-cluster correlations.<sup>18</sup> Thus, our results can be viewed as covariates of radicalization within the same country/wave group.

Another potential problem is the item nonresponse bias, which is frequent in household surveys in developing countries. Item nonresponses are characterized by survey noncompliance in which some of the participating individuals refuse to answer some questions. If the process driving these nonresponses is not random, then the parameter estimates from the system (1) and (2) may be biased (see, among others, Van Praag et al., 1983; Korinek et al., 2006). We deal with this problem in three different ways. First, we check whether there is a systematic association between the distribution of item nonresponses on our LHS variable of interest (SAR) and our covariates. Our estimates would be contaminated if this association is significant. Second, we follow the empirical literature on this issue (see, among others, Lillard et al., 1986; Little and Rubin, 1987) and estimate the system (1) and (2) using an extended sample. The extended sample is obtained by imputing the nonresponses using the within country-wave sample median of each variable in the system.

> 17 Due to missing observations and therefore reduced sample sizes, we include these additional controls only in the sensitivity checks. We believe that the resulting potential sampling bias due to limited sample size may be a more serious concern than the omitted variable bias.

> 18 However
**<u>│</u> 33**

level differences, as well as volumes of respective activities, spillovers to domestic firms and local starting conditions. Policy inferences from these indicators must be treated with caution. The exercise does not aim at distinguishing between ‘good’, ‘bad’, ‘better’ or ‘worse’ foreign investment in any universal way, but rather to enhance understanding around the complementarity of multinational firms in specific contexts and to support the alignment of their outcomes with national development objectives, however diverse and evolving the latter may be. The “quality” of FDI, if there is such a notion, is ultimately highly idiosyncratic and to a large extent independent from capital _._ The same type of investment will produce different outcomes in different contexts that are more or less desired from a societal, economic and strategic point of view. The policy and strategy package needs to find ways to unlock these opportunities from _all_ incoming foreign investment, by addressing the market and government failures that may be preventing multinational firms from delivering expected gains.

Future work can build on several directions, namely, to validate and improve the information presented herein, and to extend the findings to other relevant domains that are not covered in the analysis. In terms of validation, benchmarking the sample of multinational firms against a wide array of other sources, business census and registers as well as industrial surveys conducted in developing countries, would be necessary to validate the sample. In addition, part of the variation in development outcomes of foreign MNEs is also explained indirectly through other prevalent characteristics of these firms, such as production scale – i.e., the fact that they are larger than domestic firms – or their sectoral concentration, which may not be explicitly controlled for due to data limitations. Additional analysis to validate a number of hypotheses on drivers of development outcomes presented in this report would be warranted. Assessing the extent of _seasonal_ and _sectoral_ influence, which will be possible in future iterations of World Bank
Kraay A., 2002, « Exports and economic performance: evidence from a panel of Chinese enterprises », in Marie-Françoise Renard (ed.), _China and its Regions : Economic Growth and Reform in Chinese Provinces_ , Edward Elgar, Cheltenham, UK, 2002.

Krugman P.R., 1991a, _Geography and trade_ , Leuwen University Press and the MIT Press, Cambridge, Mass.

Krugman P.R., 1991b, « Increasing returns and economic geography », _Journal of Political Economy,_ 99, 483-499.

Krugman P., Livas Elizondo R., 1996, « Trade policy and the third world metropolis », _Journal of Development Economics_ , 49, 137-150.

Lee J., 2000, « Changes in the source of China’s regional inequality », _China Economic Review,_ 11, 232-245.

Lemoine F., Ünal-Kesenci D., 2002, « Les industries extraverties en Chine : source de dépendance ou rattrapage technologique ? », _La lettre du CEPII,_ 213. Léon A., 1998, « Pôles de croissance régionaux et intégration mondiale : Afrique australe/aire économique chinoise », _Revue Tiers Monde,_ 155, 623-645.

Luo X., 2001, « La mesure de la distance dans le modèle de gravité : une application au commerce des provinces chinoises avec le Japon », _Région et Développement,_ 13, 163-180.

Luo X., 2003, _Impact des investissements en infrastructure sur la croissance régionale : le cas des provinces chinoises,_ Ph.D. dissertation, Université d’Auvergne Clermont 1.

Luo X., 2005, « Growth spillover effects and regional development patterns: the case of Chinese provinces», _Policy Research Working Paper 3652_ , The World Bank.

Mac Kellar F.L., Vining D.R., 1995, « Population concentration in less developed countries: new evidence », _Papers in Regional Science_ , 74, 3, 259-293.

Marti S., 2003, « La Chine, atelier du monde,
The table shows alternative NPV results, based on different combinations of higher relative vocational costs (positive value for c) alternative discount rates and lower value for the m parameter. The overall effect of all these differing combinations, while changing the level of the NPVs, is to leave them positive. Only in the worst assumption case, with high vocational school costs (in excess of those of academic schools by a third), of a 10 per cent discount rate and a lower matched percentage (ten percentage points below those relating to the sample), are the NPVs negative, but only marginally so.26/ We may conclude that vocational schooling in Israel represents a sound investment ir cost-benefit terms. This overall conclusion would be strengthened if a higher secular increase in real incomes was assumed, as seems plausible, with a resultant enhancement in absolute terms of the earnings advantage of matched vocational school completers.

# 4. Vocational education **and** non-formal training alternatives

We noted that the 1983 Census questionnaire included within the category of vocational school completers those individuals that had attended nonformal training institutions rather than vocational schools as such. Our sample of vocational school completers in fact includes a small number of these individuals that had instead completed training in one of these nonformal modes. In this section we shift our focus from a comparison of 26 With this configuration, the NPV "loss" represents about two weeks salary for direct matched workers and only a few days salary for the wider matchings case.

- 41 -
# **<mark>Table 2: Input-Output Tables, 2004 and 2013</mark>**

|**a. 2**|**004 (in bil**|**lion 200**<br>|**4 cedis)**<br>|||||||
|---|---|---|---|---|---|---|---|---|---|
|||AGR|MIN|O-IND|WRT|O-SER|Intermediate<br>share of<br>output|Final<br>demand|Output|
|2004|AGR|0.077|0.000|0.117|0.016|0.000|0.211|32,513|41,195|
|<br>i|MIN|0.000|0.000|0.066|0.000|0.000|0.066|4,472|4,790|
|(n<br>2004|O-IND|0.031|0.036|0.201|0.025|0.070|0.362|14,141|22,174|
|<br>rices)|WRT|0.272|0.010|0.194|0.092|0.000|0.567|5,433|12,562|
|p|O-SER|0.070|0.011|0.017|0.129|0.252|0.479|20,915|40,135|
|**b. 2**|**013 (in bil**|**lion 201**<br>|**3 cedis)**<br>|||||<br>|<
29 index of the subjectively perceived quality of each public facility (school or health clinic) can be constructed27.

The HRDS asked a series of questions about the level of parental and community involvement in the schools which allow us to construct an index of parental participation<sup>2</sup> '. The HRDS also asked individuals about their attendance at 'meetings where issues important to the community, such as health and education, could be discussed.' Table 7 shows that social capital in the SCPS survey is associated with higher reported levels of parental participation in schools and attendance at community meetings in the HRDS data (which surveyed different individuals than those used to measure social capital). Moreover, higher social capital was associated with higher levels of school quality. These fmdings trace out a possible chain of causation from greater social capital to more parental and community involvement in schools to better quality schools. There is, however, no link at all between health facility quality and social capital. This is perhaps not surprising as the major factors for health clinics were drug

**27** Arithmetically, quality index in the **ji** village based on the ik household's assessed importance of the characteristic a. and household's ranking of that characteristic R is defined

as: _Q'i_ Nx (¢l I _c *R6 ) .The_ principal difficulty with this measure at the cluster as:J _NJ_ level is that there is little coherence among household's rankings of the same facility. That is, on each of the five rankings of school quality, within cluster differences account for more than 85 percent of the total variation, which raises some questions about reliability or interpretation of the rankings.

2' The questions asked about the closest government primary school were: "Are parents asked to participate in decisions affecting the school [NAME]?" "Does the school have an active parents/teacher committee?" "Does the school [NAME] have open days for parents to
# **3.3 Public remote sensing data**

The analysis uses publicly available global remote sensing data from numerous sources and summarizes predictors derived from them at the administrative level (i.e. TA level) for Malawi. For most of the variables, we take advantage of the geographically comprehensive raster data structure, which uses pixels to form a grid that covers all land areas. From the raster structure we extract geo-referenced information at the administrative level. Using Geographic Information Systems (GIS) and Google Earth Engine, we overlay the administrative boundaries with other geo-referenced gridded data, (e.g. temperature, precipitation, elevation, etc.). When an administrative area consists of multiple pixels (which is typically the case), the pixel observations are collapsed (i.e. aggregated) to obtain summary statistics (e.g. mean and sum) at the administrative area level.<sup>7</sup> This process of extraction provides a nearly exhaustive correspondence. For a select few cases, we make two minor adjustments:

- Administrative areas near boundaries with water or small islands do not always overlay with the raster data. In this case, we first extract data by center point if the results from the polygon extract are missing. Second, we make a small adjustment by filling in the missing cells from the original raster with the average value of data from contiguous neighbors.

- When the polygon representing an administrative area is much smaller than the raster pixel size, we disaggregate the raster data. If the center point of the polygon is located in an area of “no data” (e.g. a lagoon or a lake), then we move the center point to the nearest land pixel. We also calculate the area at the pixel size to ensure consistency between the sum of pixel values and the corresponding land area.<sup>8</sup>

For the remaining data, we use vector data to calculate distances. Table 3 lists the variables