rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A4: Management Practices Scoring**

|**MG1 Problem resolution (r1)**|**Score**|
|---|---|
|**Action when problem in the production/service provision arose**||
|Most structured: We fixed it and took action to make sure that it did not happen again, and had a<br>continuousimprovement process to anticipate problemslike theseinadvance|1|
|Second most structured: We fixed it and took action to make sure it did not happen again|0.667|
|Second least structured: We fixed it but did not take further action|0.333|
|Least structured: No action was taken|0|
|**MG2 Number of performance indicators monitored (r3)**|**Score**|
|**Number of production or service provision performance indicators monitored**||
|10 or more indicators|1|
|3-9 indicators|0.667|
|1-2 indicators|0.333|
|No indicators|0|
|**MG6 Length of focus of production targets**|**Score**|
|**Focus of production targets**||
|Combination of short-term and long-term targets|1|
|long-term only|0.667|
|short-term only|0.333|
|No targets ortargetsnot achieved|0|
|**MG3 Level of ease or difficulty to achieve production or service provision targets (r6)**|**Score**|
|**Level of ease or difficulty to achieve targets**||
|No targets or targets not achieved|0|
|Achieved without much effort|0.2|
|Only achieved with extraordinary effort|0.4|
|Achieved with some effort|0.6|
|Achieved with normal amount of effort|0.8|
|Achieved with more than normal effort|1|

|**MG4 Knowledge of production or service provision targets (r7)**|**Score**|
|---|---|
|**Personnel's knowledge of production or service provision targets**||
|All managers andmost workers|1|
|Most managers and most workers|0.667|
|Most managers and some workers|0.333|
|Only senior managers|0|
|No targets|0|
|**MG5 Basis of bonuses (r9)**|**Score**|
|**What managers' performance bonuses were usually based on**||

31
- Busso, M., Gregory, J., and Kline, P. (2013). Assessing the incidence and efficiency of a prominent place based policy. _American Economic Review_ , 103(2):897–947.

- Callaway, B. and Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. _Journal of econometrics_ , 225(2):200–230.

- Carrillo, B. (2020). Present bias and underinvestment in education? long-run effects of childhood exposure to booms in colombia. _Journal of Labor Economics_ , 38(4):1127–1265.

- Chen, J. and Lin, T.-F. (2008). Class attendance and exam performance: A randomized experiment. _The Journal of Economic Education_ , 39(3):213–227.

- Cling, J.-P., Razafindrakoto, M., and Roubaud, F. (2005). Export processing zones in madagascar: a success story under threat? _World development_ , 33(5):785–803.

- De Chaisemartin, C. and D’Haultfoeuille, X. (2018). Fuzzy differences-in-differences. _The Review of Economic Studies_ , 85(2):999–1028.

- De Chaisemartin, C. and D’Haultfoeuille, X. (2020a). Difference-in-differences estimators of intertemporal treatment effects. Technical report.

- De Chaisemartin, C. and D’Haultfoeuille, X. (2020b). Two-way fixed effects estimators with heterogeneous treatment effects. _American Economic Review_ , 110(9):2964–96.

- Denteh, A., Asare, S., and Senadza, B. (2022). Is four better than three? the effect of the 4-year high school policy on academic performance in ghana. _The Effect of The_ .

- Duflo, E. (2001). Schooling and labor market consequences of school construction in indonesia: Evidence from an unusual policy experiment. _American economic review_ , 91(4):795–813.

- Durden, G. C. and Ellis, L. V. (1995). The effects of attendance on student learning in principles of economics. _The American Economic Review_ , 85(2):343–346.

- Edmonds, E. V., Pavcnik, N., and Topalova, P. (2010). Trade adjustment and human capital investments: Evidence from indian tariff reform. _American Economic Journal: Applied Economics_ , 2(4):42–75.

- Gertler, P., Heckman, J., Pinto, R., Zanolini, A., Vermeersch, C., Walker, S., Chang, S. M., and Grantham-McGregor, S. (2014). Labor market returns to an early childhood stimulation intervention in jamaica. _Science_ , 344(6187):998–1001.

21
proportion of household members that are illiterate, while Pakistan has the highest and Bangladesh has the second highest. In South Asia, Pakistan scores the worst when it comes to education among the international extreme poor. Although Bhutan has the second lowest international extreme poverty rate in the region, its international extreme poor are worse off in many categories when they are compared with the Indian international extreme poor. For example, only 19 percent of the heads of household who are international extreme poor in Bhutan are literate, compared to 50 percent in India and 29 percent in Bangladesh.

Bangladesh scores much worse in many of the non-monetary dimensions than India. India has the highest proportion of international extreme poor in the region, while Bangladesh had the second highest proportion of international extreme poor. But the Bangladeshi extreme poor have lower human capital and lower levels of physical assets compared to their Indian counterparts. Bangladeshi households that are in international extreme poverty have lower educational attainment, and have lower ownership rates of household assets, even though both Bangladeshi and Indian international extreme poor have the same proportion of households engaged in agriculture.

**Table 14. Summary statistics of indicators of non-monetary well-being for the extreme poor** **<u>identified using the standardized consumption aggregates</u>**

|**Variable**|**Bangladesh**|**Bhutan**|**India**|**Maldives**|**Nepal**|**Pakistan**|**Sri Lanka**|
|---|---|---|---|---|---|---|---|
|_Household Composition_||||||||
|Household size|5.68|8.18|5.60|7.58|7.20|9.68|5.68|
|Dependency Ratio|0.47|0.40|0.95|0.41|0.53|0.57|0.41|
|_Education Attainment in Household_||||||||
|Household head Knows How to<br>Read/Write|0.27|0.21|0.50|1.00|0.34|0.31||
|Proportion of Household Member<br>Illiterate|0.69|0.62|0.45|0.43|0.62|0.76|0.20|
|Household Members with Primary<br>Schooling|0.18|0.33|0.14|0.17|0.33|0.20|0.35|
|Ratio of Female Children in School<br>to All Children in School|0.52|0.49||0.51|0.53|0.37|0.49|
|_Employment_||||||||
|Household<br>Head<br>Engaged<br>in<br>Agriculture|0.54||0.54|0.59||0.41|0.46|
|Household Head Self-Employed|0.37|0.14|0.42|0.43|0.83|0.37|0.31|
|_Household Assets/Facilities_||||||||
|Household has a land phone|0.01||0.006|0.07|0.02|0.02|0.13|
|Household has electricity|0.29|0.65|0.55||0.38|0.82|0.74|
|Household has a radio|0.05|0.35|0.14|0.65|0.43|0.02|0.48|
|Household has a television|0.12|0.22|0.23|0.93|0.13|0.29|0.60|
|Household has a fan|0.19|0.06|0.40|1.00|0.12|0.78|0.22|
|Household has a sewing machine|0.03|0.01|0.05|0.55|0.04|0.25|0.17|
|Household has a bicycle|0.16|0.04|0.59|0.30|0.28|0.23|0.35|
|Drinking Water from Hygienic<br>Source|0.96|0.98|||0.75|0.86|0.87|
|Availability of Proper Sanitation<br>Facilities|0.33|0.61|||0.08|0.34|0.79|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). All reported summary statistics are population-weighted.

20
a household<sup>�</sup> _αk_ . We find evidence that the pattern of land tenure due to land redistribution persists. In the highland regions most affected by the reforms, the number of parcels are in the range of _≈_ (3 _._ 5 _,_ 4 _._ 5), which corresponds neatly with the four categories of land discussed earlier. In other parts of the country, the number of parcels is closer to 2. In these regions land tenancy is characterized by homesteads. The size of parcels varies, but tends towards a quarter or half hectare. Recall that the distribution was done in ‘timads’, approximately a quarter hectare. Finally, the total number of hectares held by households is between .9 and 1.5 hectare, reflecting strict limits on large land tenure and further evidence of the legacy of land redistribution efforts.

In addition to area _α_ , the data-set contains geovariables matched at the plot level using non-scrambled GPS coordinates. These include: Distance from plot to household (in km); slope of the plot (in percentages); plot elevation (in metres), plot potential wetness index.<sup>8</sup> These plot-level characteristics were averaged at the parcel level, weighted by plot area. They are also reported in Table 2b.

# _Welfare measures: Food Insecurity_

LSMS-ISA contains two measures of welfare, _Yi,t_ , well suited for our purposes. Both relate to food insecurity: the number of months a household experiences hunger, and the Coping Strategy Index (CSI).

Months Hungry, also referred to as the food gap, measures the temporal extent of hunger. It is the sum of months in the past year a household experienced hunger for five or more days. This welfare measured is used widely in Ethiopia, including in the evaluation of its flagship social protection program,

> 8. Local up-slope contributing area and slope are combined to determine the potential wetness index: _WI_ = _ln_ ( _A_<sup>_s_</sup> _/tan_ ( _b_ )) where _A_<sup>_s_</sup> is flow accumulation or effective drainage area and b is slope gradient. Data matched from the Africa Soil Information Service by the World Bank.

12
WPS3450

# **Discovery and Development: An Empirical Exploration of ‘New’ Products***

Bailey Klinger Daniel Lederman Office of the Chief Economist Latin America and the Caribbean World Bank

**Abstract** : _This paper uses disaggregated export data to explore the relationship between economic discovery and economic development. We find that discoveries, or episodes when countries begin exporting a new product, are not limited to so-called ’dynamic’ industries, rather they also occur in traditional sectors such as agriculture. In addition, the data suggest discovery is a component of the stages of  productive diversification that occur with development, following a consistent pattern: discovery activity peaks at the lower-middle income level and then declines. Based on this pattern, we show that discovery in the 1990s occurred with a higher than expected frequency in Eastern Europe and Central Asia, and lower than expected frequency in Sub-Saharan Africa. Discovery is not found to be a product of structural transformation based on changing factor endowments across income levels. Beyond export growth, population, and development, there are no significant and positive relationships between the expected drivers of entrepreneurship and the frequency of discovery. Combined with the finding that higher absorptive capacity and lower barriers to entry are associated with a reduction in discovery, this suggests that market failures arising from imitation and free-riding may be inhibiting the emergence of new export products in developing countries._

## World Bank Policy Research Working Paper 3450, November 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We are grateful for the valuable input and suggestions provided by Andrés Rodríguez-Clare, Guillermo Perry, William F. Maloney, Ricardo Hausmann, Ana María Ovideo and Maral Shamloo.

Comments: bailey_klinger@ksg05.harvard.edu or dlederman@worldbank.org.
**Figure 14. Daily average and median wages among the employed and share of employed earning wages below the US$1.90-poverty line by** **<u>gender, quintile, locality, and year</u>**

<!-- Start of picture text --> (a) Average and median daily wages<br>(b) Share of employed earning daily<br>of employed population (ratio of<br>wages below US$1.90<br>US$1.90 poverty line)<br>10 15%<br>8<br>10%<br>6<br>4<br>5%<br>2<br>0 0%<br>2009 2014<br>2009 2014 Median<br>All employed Women Men Bottom 20% Bottom 40% Top 20% Rural Urban All employed Women Men Bottom 20% Bottom 40% Top 20% Rural Urban<br><!-- End of picture text --> _Source:_ World Bank 2019b.

Differences in characteristics between male and female wage workers in terms of education, employment sector, location, and age explain about 96 percent of the gender gap in monthly earnings. The OaxacaBlinder decomposition splits the wage gap between men and women into three effects: (a) endowment effect (reflecting the mean increase in women’s wages if they had the same characteristics as men), (b) coefficient effect (reflecting the change in women’s wages when applying the men’s coefficients to the women’s characteristics), and (c) interaction effect (measures the simultaneous effect of differences in endowments and coefficients). Determinants of monthly earnings used in the analysis are education, sector of employment, location, and age. The results show that the mean log wage is 6.75 for men and 6.20 for women, yielding a wage gap of 0.55. The endowment effect explains about 5.4 percent of this difference, while 95.5 percent is explained by the coefficient effect and negative 0.9 percent by the interaction effect. The large coefficient effect is mainly captured in the regression intercept, indicating possible gender-based discrimination in the labor market. In other words, men generally benefit from more favorable return to characteristics. In addition, male wage workers are more advantaged due to their overrepresentation in industries which are linked with higher wage premiums (that is, endowment effect). Women’s overrepresentation in agriculture and other low productivity jobs contributes to the gender wage gap. Annex 2 provides detailed results of the Oaxaca-Blinder decomposition.

These findings are consistent with the FGDs where wages were a frequent topic of conversation, especially among women. A female youth from Khartoum who was working said, “My friend dropped out her work because her salary was too low in return of the efforts she was making.” A female youth from Madani who was not working concurred, “The salary maybe not enough. They prefer to exert effort in house rather than exerting efforts at work in return of low salary.” Some of the focus group participants took the conversation to the next level, noting that some managers used women’s wages as a bargaining chip 21
considerably larger for the better-off. Access to satisfactory sanitation appears to have _fallen_ in the period 1993-98 among the poorest quartile. Vaccination coverage and antenatal visit coverage have increased for both consurmnption groups. By contrast, the proportions of newborns delivered by medically trained persons and in medical facilities have _fallen_ among the poorest quartile, while they have increased among the top three quartiles.

What caused the faster reduction in mortality between 1993 and 1998 among the better-off? Table 6 and 7 show the results of the decompositions of mortality change for each poverty group based eqns. (4) and (5), respectively. Our interest lies more, however, with the _differential_ decline in mortality between the poor and nonpoor. Table 8, which presents the results of the decomposition in eqn. (6), shows that the more rapid decline in mortality among the nonpoor was roughly equally due to: (i) the poor experiencing less advantageous changes in the deterninants of child survival _(DI);_ (ii) these changes mattering less for the poor since they faced a worse sel of coefficients linking survival determinants to survival outcomes _(D2);_ and (iii) changes in coefficients mattering less for the poor since they had worse determinants to start with (D4 ).

**Table 5. 1993 and 1998 means of determinants of child survival, by poverty group**

||_Po_|_orestquart_|_ile_|_Riche_|_stthree qu_|_artiles_|
|---|---|---|---|---|---|---|
|_Variable_|_Mean_<br>_1993_|_Mean_<br>_1998_|_Amean_|_Mean_<br>_1993_|_Mean_<br>_1998_|_Amean_|
|Northern Uplands|0.23|0.34|(0.11|0.17|0.17|0.00|
|RedRiverDelta|0.26|0.13|-0.14|0.22|0.18|-0.03|
|NorthCentral Coast|0.21|0.18|-0.02|0.13|0.16|0.03|
|CentralCoast|0.08|0.13|_0.05_|0.11|0.10|-0.01|
|Central Highlands|0.05|0.08|0.03|0.04|0.05|0.01|
|SouthEast|0.05|0.02|-0.04|0.12|0.13|0.01|
|Child=male|0.52|0.51|-0.01|0.51|0.51|0.00|
|Child'sage|4.52|5.26|0.73|5.31|5.83|0.52|
|LogequivalentHH<br>|7.52|7.76|0.24|8.31|8.59|0.27|
|consumption|||||||
|Years schoolingofmother|5.79|5.38|-0.41|6.67|7.12|0.45|
|Safewater|0.09|0.14|0.05|0.24|0.37|0.13|
|OKsanitation|0.05|0.02|-0.02|0.16|0.25|0.08|
|Vaccinationcoverage|40.92|55.53|14.61|47.08|64.31|17.22|
|Antenatalvisitcoverage|30.24|39.57|9.32|42.51|53.46|10.94|
|Medically traineddelivery|62.73|57.27|--5.46|72.61|74.71|2.09|
|coverage|||||||
|Facilitydeliverycoverage|43.08|33.35|--9.73|53.54|60.79|7.25|

DI captures the fact that the poor experienced less advantageous changes in the x's. The weights applied to these changes are the nonpoor coefficients in 1998. In _DI_ it is 21
# **Table A 5 Differences on Key Individual, Household and Village Characteristics (Unmatched Sample)**<sup>**25**</sup>

|**Variables**|**Treatment**|**Control**|**T-Test**|
|---|---|---|---|
|Age of Head of Household|45.916|48.038|5.706***|
|Education Level of Head of Household|3.822|4.010|1.456|
|Proportion of Educated Women to Total Members in<br>Household|0.257|0.244|2.089**|
|Proportion of Married Members to Household Members|0.480|0.506|3.428***|
|LogTimefor Round-Trip toFetch DrinkingWater(Minutes)|2.653|2.690|1.582|
|Number of Household Members|4.805|4.421|6.725***|
|Crowding Index: Ratio Number of Members to Dwelling<br>Rooms|2.250|2.248|0.054|
|Dependency Ratio Household|0.649|0.620|1.436|
|Total Numberof Livestock Assets Owned2012|4.510|1.896|2.130**|
|Total Number of Productive Assets Owned 2012|1.235|1.342|1.912*|
|Total Number of Consumptive Assets Owned 2012|2.416|2.235|2.293**|
|SC orST Household|0.791|0.628|12.021***|
|Female Headed Households|0.115|0.091|2.669***|
|Household Type (Kutcha)-SECC 2013|0.567|0.526|2.654***|
|Own TelephoneMobilePhone-SECC2013|0.402|0.420|1.239|
|Own Any Land-SECC 2013|0.459|0.564|6.984***|
|Own Irrigation Equipment-SECC 2013|0.062|0.082|2.581***|
|AnyMember has a Salaried Job-SECC2013|0.054|0.021|5.735***|
|Main source of income of Household-cultivation-SECC 2013|0.397|0.394|0.204|
|Mainsource of income of Household-enterprises-SECC2013|0.006|0.003|1.490|
|Total Households-Census 2011|192.233|231.374|5.932***|
|Total Population of Village-Census 2011|947.469|1,080.050|4.193***|
|Percentage of FemalePopulation -Census2011|49.810|49.500|4.657***|
|Worker force participation rate of all persons in the Village -<br>Census2011|0.522|0.526|1.643|
|Worker force participation rate of females in the Village -<br>Census2011|0.489|0.497|1.854*|
|Worker force participation rate of females in the Village -<br>Census2011|0.489|0.497|1.854*|
|Total Number of Micro-Enterprises in Village 2012|14.035|14.433|0.304|
|Access to Pucca Road-Census 2011|0.788|0.700|6.667***|
|Status of AgricultureMarketing Society-Census2011|0.429|0.345|5.676***|
|Percentage of Irrigated Land from Total Land-SECC 2013|5.886|10.140|7.270***|
|Percentage of Households having Source of Light: Electricity -<br>Census 2011|42.462|45.503|2.830***|
|Percentage of Households having Bicycle-Census 2011|39.483|38.829|0.761|
|Density of Primary Health Centers (per 100 Households) -<br>Census2011|0.013|0.007|2.899***|
|Density of Primary Schools (per 100 Households) - Census<br>2011|0.153|0.134|3.151***|
|Log of Total Geographical Area (Hectares)-Census 2011|6.005|6.089|3.281***|
|<br>Log Distance from Village of Nearest Bank (Kms.)|2.107|2.245|6.737***|
|LogDistrictHead Quarter(Distancein km)|3.915|3.807|4.595***|
|Population Density ofthe Village-Census2011|2.664|2.217|5.103***|

25 Significance levels: *:10%, **:5%, ***:1% 31
**Figure 1: Bound Tariffs and Tariff Dispersion in the case of APEC**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>AUS BRN CAN CHL CHN HKG IDN JPN KOR MEX MYS NZL PER PHL PNG RUS SGP THA TWN USA VNM<br>Percentage of Bound Tariffs Tariff Dispersion<br><!-- End of picture text --> Managing the tariff schedule is not the only trade policy instrument where predictability becomes important. Governments can apply a number of trade policy measures others than tariffs. They are often summarized as non-tariff barriers (NTBs) and encompass all measures that have potential trade effects, such as technical standards, trade remedies, or quotas. As multilateral, regional and bilateral trade liberalization efforts have pushed the overall tariff level down, NTBs are gaining more and more significance in the international trading system. Comparative analyses of NTBs are relatively rare, mainly because many NTBs are not easily quantifiable.<sup>7</sup> However, we are not primarily interested in the presence of NTB, but in the transparency of NTBs. The Global Competitiveness Report (GCR) published by the World Economic Forum (WEF) provides useful information in this respect, based on trade barriers as identified by the business community.<sup>8</sup> In the 2004 GCR, survey participants were asked to assign a score from 7 (strongly agree) to 1 (strongly disagree) to each of the following questions:

> 7 Ching et al. (2004) provide an insightful analysis of the presence of NTB is the Pacific Rim region using a small firm-level survey. According to their study, NTBs are frequently encountered in this region and they have a significant impact on firms’ production costs, revenue, and expansion plans. Recently, the World Bank Development Research Group (see Kee et al., 2006) developed an index of trade restrictiveness that covers a large number of developing and developed economies. Trade restrictiveness is measured taking into account the tariff level, but also NTBs. Among the NTBs considered are price and quantity measures, monopolistic measures as well as technical regulations. The exact data sources and methodologies are described in detail in Kee et al. (2006). As for tariffs, we control for the presence of NTBs in our gravity equation, using the available data from Kee et. al. (2006).

> 8 The WEF conducts each year an international survey assessing the competitiveness of a large number of developed and emerging economies.

11
|**Table A1: Compliers, Evaders, Avoiders and Outsiders under the Factori**<br>**assumed to be zero.**|**es Act, 1948, in 2009-10, wh**<br>**Firm Categ**|**ere missing electricity expe**<br>**ories**|**nditure is**|
|---|---|---|---|
|**A**|**B**|**C**|**D**|
|**ASI Firms**<br>**Firm Characteristics**|<br>**NSSO Firms with 10 or**<br>**more employees using**<br>**power and 20 or more not**<br>**using power**|**NSSO Firms with 9**<br>**employees using power**<br>**and 19 employees not**<br>**using power**|**NSSO Rest**|
|Number of Firms<br>143,452|234,524|61,575|16,900,000|
|Share of Firms<br>0.8%|1.4%|0.4%|97.5%|
|Total Employment*<br>11,500,000|4,252,672|561,756|30,100,000|
|Share of Employment<br>24.8%|9.2%|1.2%|64.9%|
|Mean Employment<br>79.9|18.1|9|1.8|
|Median Employment<br>21|13|9|1|
|Median Labor Productivtiy (Rupees)**<br>135626|63915|76400|23400|
|,<br>*Includes unpaid family membes/helpers working in the firm<br>**Laborproductivityis totalgross value added divided bytotal employment|,|,|,|

20