rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables and Figures**

Table 1: Relationship between lights and economic activity in subnational units of developing countries

||(1)<br>∆ln(GDP)|(2)<br>∆ln(GDP)|(3)<br>∆ln(HH income)|
|---|---|---|---|
|∆ln(light)|0.265**|0.268**|0.171***|
||[0.101]|[0.119]|[0.0573]|
|Observations|204|146|284|
|R-squared|0.054|0.053|0.028|
|unit|city proper|prefecture|magisterial district|
|sample|China|China|South Africa|
|years|1990/92-2005|1990/92-2005|1996-2001|

Each column is a separate OLS long difference regression for the years shown. The independent variable is the log of the lights digital number, summed across all pixels in the unit shown, and averaged across satellite-years within a year when applicable. In columns 1 and 2, the lights are from 1992 but the administrative GDP data are from 1990, the closest year with good data. In column 3, household incomes are aggregated are summed from a 5 percent sample of the census, and topcoded households are assigned a value 1.5 times the topcode value. Robust standard errors are reported in brackets. *, **, *** mean significance at the ten, five, and one percent level, respectively.

29
the Fed and PBoC would record any incoming or outgoing payments from and to their correspondent central bank in the bilateral monetary agreement. In their internal department, central banks would record the result of payments for foreign trade in their local currency and whose counterparties are local depository institutions, whilst the same result would be recorded in USCSCs in their external department, which is connected to the settlement facility through a real-time gross-settlement protocol similar to those existing in any advanced countries for the settlement of (mostly largevalue) domestic transactions. In recording payments for foreign trade in USCSCs, the facility would debit the account of the paying central bank and simultaneously credit the account of the receiving central bank, both central bank accounts being kept within the facility and whose balances are available for central bank payments only.

All entries at the settlement facility would be denominated in USCSCs, the facility acting as monetary catalyst and keeping the record for all those payments that China and the United States carry out for the settlement of their bilateral trade on both products and financial markets. While trade deficits will still be possible – but limited in their level – they will no longer correspond to payment deficits (by selling of internal denominated-debt instrument).

Supranational central bank money issued by an international settlement institution for any two member countries will increase stability in the foreignexchange market, and align changes around productivity changes. The clearing of any settlement balances being the result of an electronic algorithm running on the settlement facility computer, the agreement between the Fed and the PBoC can carry it out bilaterally, implementing it via the so-called Payment-versus-Payment (PvP) protocol: every time the Fed credits the PBoC for any imported items from China, it 22
and Chinitz (1961) and the literature following him have emphasized how varied these local conditions are in terms of their appropriateness for small and young businesses compared to large establishments. The components of our study have identified that small firms in the organized sector have access to similar average input levels as unorganized sector firms. While we observe productivity consequences in both sectors, the impact is steepest among small organized sector firms. This may suggest that weaker input conditions for small organized sector firms limit their ability to outperform unorganized sector firms (or, said differently, that one cost of unorganized sector status is an inability to gain and exploit these distinct material inputs). This divergence is perhaps showing a variation of the Chinitz (1961) theme within India’s manufacturing that depends upon sector. To provide greater traction for future study, we intend to develop a district-industry panel over time for the two sectors that can better separate how these input conditions matter.

# **6. Conclusions**

The patterns that we document in this study are quite intriguing. Counts of distinct material inputs are clearly far from uniform across Indian manufacturing establishments. They are stronger in urban settings than in rural locations, and they are stronger in the organized sector than in the unorganized sector. Looking across districts, higher input counts are most closely associated with wealthier areas and those with higher literacy rates. Local infrastructure and population density are also important in some specifications, but these patterns are less robust. Plants in the organized sector utilizing a greater variety of inputs display a higher productivity, with the effects mostly concentrated among smaller plants with fewer than 50 employees. There is also a link between input usage and productivity in the unorganized sector, but it is more modest in size compared to the organized sector. It is likewise quite difficult to predict higher input counts in the unorganized sector using district-level conditions.

There are several directions that we hope to take with this study going forward. A first effort would continue on the current themes with the goal of expanding the range of inputs considered to include energy inputs, which are also separately tabulated on the surveys. This would be attractive for two reasons. First, energy scarcity and instability is an acute problem in India, leading to lost productivity during brown or black outs. As a consequence, many firms 19
**Table 9: Reports of secondary activities by recall period, 1992/92 – 2009/10**<sup>**a**</sup>

||Survey<br>IHS 1992/93<br>|UNHS 2005/06|UNPS 2009/10<sup>16</sup>|
|---|---|---|---|
|Short Recall (7day recall)|25.3<sup>~~b~~</sup>|17.4|21.0|
|Long recall (12 month)|30.7|36.7|33.7|
|Difference|5.4***|19.3***|12.7***|

**Source:** Authors calculations from the IHS 1992/93, UNHS 2005/06, UNPS 2009/10. Note: a) ages 15 – 65, unweighted b) Based on 30 day recall period, c) *** indicates significant at 0.1% level of significance (LOS).

The symmetry of the questions across recall period in the Uganda National Household Panel survey (UNPS), 2009/10 allows a comparison of which secondary jobs are missed in the 7 day recall. Missing jobs were mainly wage work (see Figure 1), both farm and non-farm. In both cases, the missing activity in the 7 day recall could be daily labor, meaning that both demand and supply can vary by week, month or season. Not surprisingly, family farm work (reported either as own account or contributing family member) was also under reported in the 7-day recall.

**Figure 1: Comparison of reported secondary jobs by activity type: 7 day versus 12 month recall, 2009/10**

<!-- Start of picture text --> 12 month recall 7 day recall<br>Family worker-farm 317 533<br>Family worker-nonfarm 119 125<br>Own account-farm 435<br>359<br>Own account-nonfarm 401<br>437<br>Public wage 18 58<br>Private non-farm wage 133 444<br>Private farm wage 246 684<br>0 100 200 300 400 500 600 700 800<br>Number of observations<br><!-- End of picture text -->

**Source:** Authors calculations based on the UNPS 2009/10.

Using only short recall affects other indicators and analyses as well. One common indicator prepared as an alternative to the ILO defined unemployment measure is _underemployment_ . This concept is used because few people in a low-income country can afford to be completely without work, although they may not

> 16The Uganda National Panel Survey (UNPS) 2009/10 is a follow up of a randomly selected sub-sample of the UNHS 2005/06. It is the latest survey with available data that captures multiple activities in different recall periods. However, the national representativeness of this data is compromised by high attrition, which could be non-random from the perspective of employments characteristics of those who attritted.  While the structure of primary employment [weighted with attrition adjusted weights] from the data is similar to that in the UNHS 2009/10, the paper uses unweighted statistics from the survey and only provides a sample interpretation of the statistics to analyze how respondents in the sample varied their responses to questions with different reference periods.

19
training and actual requirements, resulting in unemployment of trained personnel" (Nigam, 1986).

Indirect links may be established via the mediation of placement services which gather and disseminate appropriate labor market information. This is a particular strength of vocational education in Hong Kong, Taiwan and South Korea, and a recent survey of training institutions in the Middle East provides further examples (World Bank, 1986).

# Adaptability

Compared with enterprise-based training, the kind of institutional training provided by national agencies has a greater risk of being geared towards semi-permanent, obsolescence prone courses. The establishment of a new course involves substantial overheads, both material and organizational: instructors have to be recruited and trained, equipment has to be purchased, accommodation has to be found, a curriculum has to be devised, materials have to be developed, knowledge of the course has to be disseminated to its potential clientele, and not least, its credibility has to be gradually established. All of these factors contribute to momentum which tend to make a course supply-driven rather than demand-driven.

In the short run this may actually be beneficial, in that it gives the course some protection against short-run destabilizing influences. In particular, it helps to sustain the volume of training provision when the demand for graduates is temporarily slack. But in the long run the attenuation of the influence of demand can lead to a failure of the course to adapt its volume and content as needs change.

Another factor which impairs the adaptability of institutional training is the difficulty in replacing redundant resources. Specialized instructors are often difficult to dislodge when their courses become. redundant: indeed some types of employment contract, giving tenure and promotion by seniority, seem expressly designed to exacerbate the problem. Likewise it is often difficult to replace obsolete or redundant plant and equipment before the expiration of their planned working life. Even when in economic terms the cost of training equipment is a relatively small component of the total cost, there is an understandable reluctance to incur expenditure on up-to-date technology when existing equipment is still relatively new and working as designed. When the equipment is expensive, the problem is correspondingly magnified, especially when the rate of technical change is fast. Enterprises are less constrained in this respect, both because they may be able to use productive equipment for training and because the cost of dedicated training equipment may be an insignificant part of their total operating costs.

Over and above these problems, there is a well-documented tendency for an institutionalized training provider or system to develop an identity of its own embodied in bureaucratic routines and norms which leaves its staff with little or no incentive to promote change. Little appears to be known about the determinants of institutional rigidity or methods of minimizing its 61 -

-
TH3E USE _OF_ **K. Y.** COTTON FUTUWRES CONTRACTS TO HEDGE **COTTON PRICE RISK IN DEVELOPING COUNTRIES** by Panos Varangis, Elton Thigpen and Sudhakar Satyanarayan International Trade Division The World Bank Washington, D.C.

New York Cotton futures/options contracts provide an effective way to reduce cotton price volatility, despite the existence of relatively high basis risk.
22 dichotomy in the quality of jobs held by foreigners. Clumping at the extremes of the scale reflects a greater demand for expatriates in unskilled jobs as well as in highly skilled or professional positions.

It is interesting to decompose total household earnings into the various sources of income to examine how these differ between Malaysians and non-Malaysians. Sources include employment income, pensions, gifts in cash and in kind, property income and other transfers. For citizens, the incidence of individuals receiving pensions is generally low and rises across deciles until a peak in the 7th decile, in which almost 7 percent receive a pension. By contrast, few non-citizens earn a pension: incidence is zero for half of the non-citizen population, below one percent for deciles 6 through 9, and jumps only to 2 percent for the richest decile of foreign households. Gifts are relatively more important to the poor, but the impact is similar for poor Malaysians and poor non-Malaysians. In the poorestdecile, for instance, gifts represent 4 and 5 percent of net household income for non-citizens and citi.zens. respectively; this share falls to 3 percent of income in the middle deciles and only I percent for the top income groups.

The employment status of non-Malaysians provides another indication that the labor mrarket responds to immigration policy; non-citizens are much more likely to be employees rather than employers or self-employed, compared to the work status of Malaysian citizens. For example, 90 percent of noncitizens in the sample are employees, versus 68 percent for citizens. Self-employment, herein defined as own-account workers, is much more common among Malaysians, nearly 30 percent of whom are selfemployed, compared to under 10 percent for non-Malaysians.<sup>1</sup> 1 This is not surprising given that foreign workers entering the country legally must be sponsored by a prospective employer; the exception is the employer category (representing a mere 1 percent of non-citizens) which includes foreign investors seeking to set-up business in Malaysia.

The income implications of these employment status results are ambiguous at this stage. Milanovic (1998) finds that self-employment in Malaysia is much more common among the poor, and that the incidence of poverty is 32 percent for those primarily self-employed in agriculture, compared to 10 percent for the population at large. On the other hand, the greater share of own-account workers among Malaysians corroborates earlier assertions by Blau (1986) that workers in Malaysia tend to move

> The 1995 labor force survey yields somewhat different estimates for own-account workers: 19 percent for Malaysians, 9percent for non-Malaysians _(Malaysia: Labor Force Survey Report 1995)._
# **The Role of Firm Dynamics in Aggregate Productivity, Job Flows, and Wage Inequality in Ecuador**<sup>*****</sup>

Fausto Patiño Peña<sup></sup> and Esteban Ferro<sup>++</sup> JEL Codes: D2, J21, J3, L11, L25, O47 Keywords: Firm Performance, Aggregate Productivity, Job Flows, Wage Inequality

> * For helpful comments we thank Besart Avdiu, Elwyn Davies, Christian Gonzalez, Mariana De La Paz Pereira Lopez, and Julio Velasco.

>  fpatinopena@worldbank.org.

> ++ eferro@worldbank.org.
# Table 3. Benchmark sectoral information (in %)

|||% in aggregate|<br>% in aggrega|<br>te<br>Marke|<br>t shares|<br>Regulatory barriers|<br>Weight in private|Business services intermediates|
|---|---|---|---|---|---|---|---|---|
||Tariff|exports|imports|Domestic<br>firms|Foreign<br>firms|All firms<br>Additional barriers<br>for foreign firms|consumption|(% of total costs)|
|**Business Services**||**12.5**<br>|**10.9**<br>||||**4.7**<br>||
|Telecommunication||1.0|1.9|10.0|90.0|3.0<br>10.7|0.9|15.3|
|Insurance||0.2|0.1|70.0|30.0|17.9<br>55.6|0.0|11.8|
|Banking||4.4|2.2|60.0|40.0|14.7<br>50.9|0.8|11.8|
|Professional business services||4.8|2.4|70.0|30.0|6.9<br>24.0|0.9|11.8|
|Air transport||0.2|0.4|60.0|40.0|0.0<br>30.0|0.2|11.6|
|Road transport||1.3|2.7|80.0|20.0|0.0<br>0.0|1.3|11.6|
|Railway transport||0.1|0.1|40.0|60.0|0.0<br>10.7|0.1|11.6|
|Water transport||0.5|1.0|20.0|80.0|0.0<br>42.0|0.5|15.3|
|**Dixit-Stiglitz Goods**|**7.4**|**3.9**|**60.7**||||**38.0**||
|<br>Processed food|11.1|0.5|3.4||||10.6|2.0|
|Beverages & tobacco products|28.4|0.1|0.7||||3.9|4.0|
|Textile & leather products|29.7|1.3|3.3||||10.7|2.6|
|Wood paper printing|11.6|0.4|3.0||||0.8|2.6|
|<br>Manufacture of basic & industrial chemicals|3.6|0.2|5.1||||3.6|4.7|
|Manufacture of fertilizers & pesticides||0.0|0.6||||0.0|0.5|
|Petroleum refineries|3.2|0.0|10.8||||4.4|5.1|
|Rubber plastic & other manufacturing|6.0|0.1|2.9||||1.4|3.8|
|Glass & cement|7.1|0.5|0.3||||0.3|2.9|
|Iron steel & metal products|5.5|0.1|5.8||||1.3|2.2|
|Manufacture of equipment|6.3|0.6|24.9||||1.0|1.9|
|**Agriculture**|**15.1**|**26.0**|**7.0**||||**21.1**||
|Maize|0.2|0.1|1.0||||3.5|1.0|
|Paddy|20.5|0.2|1.2||||0.7|2.0|
|Sorghum or millets|4.6|0.0|0.0||||0.4|3.6|
|Wheat|8.7|0.0|1.1||||0.1|2.4|
|Beans|25.1|0.1|0.0||||2.5|0.6|
|Cassava|25.0|0.0|0.0||||1.0|0.0|
|Other cereals|8.8<br>|0.0<br>|0.0<br>||||0.1<br>|0.5<br>|
|Oil seeds|1.1|0.3|0.0||||1.4|0.6|
|Other roots & tubes|0.5||0.0||||1.3|0.0|
|Cotton|1.2|2.9|0.0||||0.0|4.9|
|Coffee|11.8|6.6|0.0||||0.0|1.7|
|Tobacco|11.1|3.4|0.0||||0.0|9.2|
|Tea|18.9|1.8|0.0||||0.5|3.1|
|Cashew nuts|22.2|6.9|0.0||||0.0|0.8|
|Sisal fiber|||||||0.0|2.4|
|Sugar|22.3|0.9|3.0||||0.2|2.8|
|Fruits & vegetables|6.7|1.9|0.5||||6.8|0.4|
|Other crops|4.3|0.3|0.0||||0.6|0.8|
|Poultry & livestock|4.4|0.5|0.2||||2.2|1.2|
|**Other CRTS**|**3.9**|**14.9**|**4.3**||||**36.1**||
|Fish|22.7|4.9|0.0||||5.5|0.0|
|Hunting & forestry||0.4|0.0||||2.8|2.7|
|Mining & quarrying|3.2|1.5|0.9||||0.0|2.5|
|<br>Meat & dairy products|27.2|0.0|0.2||||5.9|0.3|
|Grain milling|8.6|0.5|0.9||||13.2|0.3|
|Utilities|||||||1.3|6.6|
|Construction<br>Wholesale & retail trade|||0.1||||0.0<br>0.0|3.2<br>11.5|
|Hotels & restaurants|||||||5.6|4.1|
|Postal communication||0.1|0.1||||0.1|15.3|
|Real estate|||||||0.3|2.8|
|Other services||1.8|0.9||||0.3|11.8|
|Public administration health & education||42.7|17.1||||1.1|2.9|
|Tourism||5.6|1.0|||||13.1|

Source: Author's calculations based on Jensen and Tarr (2010) and Thurlow and Wobst (2003).

28
Since 2005, the Sakernas survey has been conducted biannually, in February and August.<sup>12</sup> The biannual nature of the survey helps to capture seasonality in labor market dynamics across the two semesters. The two rounds of Sakernas differ primarily in terms of their representativeness; since 2007, sampling for the August round has been designed in such a way as to ensure representativeness up to the district level.<sup>13</sup> Meanwhile, sampling for the February round has been designed to ensure representativeness only at the provincial level. As such, we only include the provincial regional identifiers as fixed effects, as well as for clustering the standard errors, to facilitate a proper comparison between the rounds. Sakernas is not generally designed to be a longitudinal survey.<sup>14</sup> Instead, different Sakernas rounds contain different individual samples that are randomly drawn within each of the 30,000 census blocks spread across Indonesia.

# **_4.2 Adjustments to Sakernas Data Collection during the Pandemic_**

Amid the pandemic, Statistics Indonesia implemented a few adjustments to the August 2020 data collection process. For example, supervisor and enumerator trainings were held virtually, instead of the typical face-to-face format in previous rounds. However, we anticipate the switch to virtual training to pose minimal threat to our identification strategy.

The most important adjustment (given our empirical strategy) lies in the enumeration process. Enumeration areas are categorized into two groups based on their COVID risk status: green zone (low risk) areas and red zone (high risk) areas. For targeted households in green zone areas, the survey process was done _per normal_ : (i) verification and updating of household location were carried out by field officers via door-to-door method and (ii) interviews were conducted face-

> 12 Since then, Sakernas has not always been conducted biannually. In 2011–14, the survey was conducted on a quarterly basis, i.e., in February, May, August, and November. However, since 2015, Sakernas has reimplemented its biannual data collection design.

> 13 Except for 2016, where the August round is only representative up to the province level.

> 14 Although, since 2006, Sakernas allows one to develop a partial panel data through the partial rotation of household sample packages.

13
- 24 -

Table 7 (Con't). Central Budget Infrastructure Regressions with Time Dummies

|Variables|Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|GASTIL|-2.61w|-2.47-|-2.42-|-2.48w|-2.50|
||(4.08)|(-3.89)|(-3.81)|(-3.98)|(-4.05)|
|PCOM|7.57<br>|7.72-<br>|7.27w<br>|6.21-|8.31-|
||(2.22)|(2.29)|(2.15)|(1.85)|(2.42)|
|PCOMSQ|-1.03-|-1.07<br>~|-1.00,|-8.1lE-I|-I. 19|
||(-1.98)|(-2.07)|(-1.93)|(-1.57)|(-2.24)|
|NFFCAPIK|-|-1.72E-2<br>(2.04)|1.96E-2<br>(2.28)|1. 17E-2<br>(1.38)|1.73E-2<br>(2.05)|
|NOFGDP1|-|-|-4.72E-1<br>(-1.21)|-||
|NCBGDP||-|-|1.2r<br>(2.49)|-|
|DFIGDP||-|||-3.23w<br>(-2.14)|
|DFIGDPSQ|-|-|-|-|_I.15-_<br>(3.08)|
|Y81|-9.31E-1<br>(-0.39)|-1.28<br>(-0.54)|-1.01<br>(-0.42)|-1.42<br>(-0.61)|-2.11<br>(-0.91)|
|Y82|1.03|7.93E-1|1.02|2.63E-2|-5.18E-1|
||(0.42)|(0.32)|(0.42)|(0.01)|(-0.21)|
|Y83|-2.97E-1|-7.17E-1|-5.74E-1|-6. 1OE-I|-2.10|
||(-0.11)|(-0.28)|(-0.22)|(-0.24)|(-0.81)|
|Y84|-2.39|-2.13|-2.16|-2.27|-2.72|
||(-0.96)|(-0.86)|(-0.87)|(-0.94)|(-1.13)|
|Y85|-4.33|-3.33|-3.70|-2.93|-4.42*|
||(-1.65)|(-1.26)|(-1.39)|(-1.13)|(-1.70)|
|Y86|-2.88|-1.48|-2.17|-1.39|-2.89|
||(-1.07)|(-0.54)|(-0.78)|(-0.51)|(-1.07)|
||0.8181|0.8240|0.8260|0.8324|0.8373|
|Adjusted R<sup>2</sup><br>|0.7849<br>|0.7902|0.7910|0.7986|0.8029|
|F-Value|24.637|24.380|23.553|24.627|24.342|
|N|150|150|150|150|150|

Significant at .01 level or better Significant at .05 level or better Significant at .10 level or better