rafmacalaba/datause-encoder-data huggingface dataset:
|**Appendix Table 6.1: Does Treat**|**ment Chan**<br><br>Have a|**ge Which Firm**<br><br>Worker in Ro|**s Have Wo**<br><br>und 4|**rkers?**<br><br>Have a|Worker in Rou|nd 12|
|---|---|---|---|---|---|---|
||Control|Treatment|p‐value|Control|Treatment|p‐value|
|Number of Paid Workers|0.43|0.27|0.157|0.47|0.30|0.213|
|Education (Years)|10.17|10.33|0.682|10.60|10.63|0.943|
|Raven Test Score|3.19|3.23|0.891|3.08|3.08|0.985|
|Digitspan Recall Score|6.51|6.55|0.858|6.56|6.50|0.816|
|Total Assets|332819|280911|0.483|320187|349938|0.752|
|Total Assets<1500LKR|0.01|0.02|0.750|0.03|0.02|0.633|
|Total Assets>935000LKR|0.09|0.06|0.584|0.10|0.13|0.609|
|Monthly Profits|20500|15473|0.029|17927|17759|0.953|
|Profit Data Missing|0.01|0.02|0.750|0.03|0.02|0.633|
|Monthly Profits<2000LKR|0.03|0.01|0.394|0.07|0.02|0.129|
|Monthly Profits>30000LKR|0.09|0.07|0.778|0.06|0.08|0.600|
|Business Practices Score|8.79|10.19|0.146|9.64|10.30|0.541|
|From booster sample|0.53|0.58|0.523|0.49|0.53|0.602|
|Retail Sector|0.31|0.34|0.762|0.36|0.28|0.324|
|Colombo|0.50|0.33|0.024|0.47|0.30|0.037|
|Kandy|0.46|0.56|0.203|0.44|0.61|0.055|
|Any paid worker at baseline|0.29|0.18|0.105|0.29|0.17|0.102|
|Monthly Sales|57161|67089|0.414|49053|61765|0.276|
|Owner's Age|36.93|35.01|0.046|36.65|35.00|0.122|
|Business is Registered for Tax|0.37|0.26|0.139|0.35|0.27|0.307|
|Weeklyhours worked<br>Notes: characteristics are basel<br>having a paid worker in round<br>for the treatment and control g|59.20<br>ine charact<br>4 (during th<br>roups usin|60.07<br>eristics. P‐val<br>e interventio<br>g a t‐test of eq|0.741<br>ue compare<br>n), and in ro<br>uality of m|56.83<br>s whether c<br>und 12 (last<br>eans.|57.13<br>haracteristics<br>survey) are si|0.916<br>of firms<br>milar|

Appendix Figure 6 explores further how the baseline profitability of those hiring workers during the intervention period compares in the treatment and control groups, and to those who already had workers and those who never hired a worker amongst the treatment group. We see the treatment brings into hiring workers firms with lower profits than those hiring workers in the control group, and than those who already had workers in the treatment group. Those hiring workers in the treatment group have a similar baseline profits distribution to those who never hire a worker during the intervention period.

# **Appendix Figure 6: Treated Firms Hiring Workers During Intervention Were Lower Profit Firms**

54
<!-- Start of picture text --> <Sea 3 eine ae ALO we [once Se:yf XS .Fs aN Ly<br>a pee ‘ ato pEe al<br>(10-10 M820 - 30 M40 - 50 M> 60<br>(10 - 20 M30 - 40 MSO - GOLINA<br>DECCT 2011<br><!-- End of picture text --> Source: Land degradation (FAO 2008)
In the face of such variation, it might seem plausible to posit substantial variation in the rank-ordering of sites across the six model versions. If this were the case, developing a stable priority list for cleanup would require the arbitrary imposition of one parameter set on the model. Fortunately, this is not the case. As Table 2 shows, the rank correlations are quite high across all versions of the model. This fortuitous result owes something to self-canceling random variation across model variables. However, the largest part seems due to the correlation of weighted and un-weighted pesticide indices across sites. Those which have big volumes of hazardous pesticides also have big total volumes, so they rank high whether or not highly-varied hazard ratings are applied.

The ultimate implications of this study are most clearly visible in the sixth column of Table 3 (Top Rank), which displays the highest index value across all six models for each site. It is immediately clear that the results are heavily dominated by the top-5 sites, and the index values tail off very quickly. All things considered, we believe that column 6 of Table 3 provides the best guide for cleanup action, because it highlights sites that achieve a very high priority index value for some combination of the hazard, population vulnerability and risk-decay parameters that drive the model.

# **5.  Summary and Conclusions**

Virtually every developing country or economy in transition has stockpiles of obsolete pesticides that have accumulated over the past several decades.<sup>12</sup> Public health and environmental authorities strongly recommend removal of stockpiles and site decontamination, but there are many sites, cleanup can be costly, and public resources are scarce. Under these conditions, it seems sensible to develop a methodology for prioritizing sites and treating them sequentially, as budgetary resources permit.

> 12 The FAO has completed data gathering for Africa and the Near East. A total of 51,794 tons of obsolete pesticides have been identified in 53 countries in this region (FAO, 1995b). The FAO program on data gathering was expanded to Latin America in 1998 and to Asia in 2001. In total, it is estimated that global obsolete pesticides stockpiles in developing countries and economies in transition amount to approximately 440,800 – 551,000 tons.

13
had 61 provinces with the total population of about 80 million in 2002. The provinces differed in their exposure to manufacturing industries that benefited from U.S. tariff cuts following the BTA implementation in 2001. Following McCaig (2011), we exploit the variation in “provincial tariff cuts”. The measure is calculated as follows: It is the weighted sum of tariff cuts to all goods. For each good _i,_ it is a product of province � <u>�</u><sup>�</sup> _p_ ’s share of workers that worked in industry _i_ in 1999 ( )<sup>4</sup> and the change in the tariff of industry � � _i_ thanks to the BTA (∆�� ��������� ���������. Since a negative ∆�� implies tariff declines, _a more negative_ �� _reflects a larger average tariff reduction._<sup>_5_</sup>

**Table 4.1: Summary statistics for change in provincial tariffs**

|No of provinces|Mean|SD|Min|10 percentile|Median|90 percentile|Max|
|---|---|---|---|---|---|---|---|
|61|-0.086|0.013|-0.151|-0.095|-0.083|-0.076|-0.066|

Table 4.1 provides summary statistics for change in provincial tariffs. The mean value for change in provincial tariff was -0.086, which implies that the average tariff cuts for a Vietnamese province was 8.6%. The province with the largest average tariff cuts is Ho Chi Minh City (��= - 0.151). The province with the smallest average tariff cuts is Quang Ninh (��= -0.066).

Figure 4.1 shows the province’s average tariff reductions. The darker provinces are more exposed to tariff cuts. The darker areas are concentrated around Hanoi in the North, and Ho Chi Minh City in the South.

> 4Data for detailed employment shares are from the 1999 Population and Housing Census.

> 5 We are grateful to Brian McCaig for providing us with the data on provincial tariff declines ��.

11
Page 77 Schumacher, I., J. Sebstad, and M. Buvinic. 1980. "Limits to Productivity: Improving Women's Access to Technology and Credit." Washington, D.C., International Center for Research on Women.

- Seager, J. and A. Olson. 1986. "Women in the World: An International Atlas." New York: Simon and Schuster.

- Sen, A.K. 1983. "Indian Women: Well-Being and Survival." Cambridge Journal of EcgnoMics, 1983.

- Sen, A.K. 1987. "Notes for the Seminar on Women's Issues in Development Policy." International Center for Research on Women, 27 March.

- Sivard, R.L. 1985. Women -- A World Survey. Washington, D.C.: World Priorities.

- Spencer, D. and D. Byerlee. "Technical Change, Labor Use, and Small Farmer Development: Evidence from Sierra Leone." African Rural Economy Program, Working Paper, No. 15, Michigan State University.

- Spurr, G. "Physical Activity, Nutritional Status, and Physical Work Capacity in Relation to Agricultural Productivity." In Energy Intake and Activity. New York: Alan R. Liss, Co.

- Stiglitz, J. 1974. "Alternative Theories of Wage Determination and Unemployment in LDC's." Quarterly Journal of Economics. 88.

- Stinson, W. 1986. "Women and Health: Information for Action." Washington, D.C., American Health Association, processed.

- Tinker, I. 1981. "New Technologies for Food-Related Activities: An Equity Strategy." In Women and Technological Change, R. Dauber and M. Cain, eds.

- Thomson, A. and others. 1966. "Body Weight Changes during Pregnancy and Lactation in Rural African (Gambian) Women." Journal of Obstetrics and Gynaecology of the British Commonwealth. 73.

- United Nations. 1975. Economic Commission for Africa Women's Programme. "Women of Africa -- Today and Tomorrow." Addis Ababa.

- United Nations. 1987. "Training Users and Producers in Compiling Statistics and Indicators on Women in Development." UN ECOSOC, ECA, and INSTRAW. Studies in Methods, Series F., No. 45. New York.

- USAID (U.S. Agency for International Development). 1976. "Population Analysis Paper." USAID Policy Paper, AIDTO Circ 288, Washington, D.C.

- USAID (U.S. Agency for International Development). 1982. "Women in Development." USAID Policy Paper, Washington, D.C.
services. This thesis has remained controveruial, with Baumol maintaining that this phenomenon is explained by the intrinsically labor-intensive nature of these activities in the face of a high income elasticity of demand and others (see for example Fuchs 1969) maintaining that difficulties in measuring output and prices of services may result in an unders.stimate of productivity growth in services.<sup>4</sup> Griliches 1992 addresses this controversy head on by exploring in detail how services output and prices are measured by official data collection units in developed countries. He concludes that for many sectors -- such as health care, financial services, and retail trade - - productivity measures require additional relevant data on the uses of consumer time and on household and firm activity not captured hy market-based statistics. For other sectors -- such as transport, communications, education, and -ther public services -- progress has been made in defining new approaches to productivity measurement. But no resolution of the original controversy is achieved. For example, although the Summers and HeRton chapter in this book (used below for international comparisons of services in final use) tends to support the Baumol hypcthesis, Griliches sums up his introduction with a more positive view of productivity growth in the serv.ce sector.<sup>5</sup> While efforts to measure productivity growth within services continue, important conceFtual arguments have been developed to support the existence of positive externalities from the service sector. Such arguments are found in Wallis and North 1986, Romer 1987 and 1991, and Giarini 1987 and 1989. Wallis 4 An example is the failure of national accounta to capture increases in productivity due to wide-scale introduction of computers.

s In their chapter, Summers and Heston base their analysis on final services; however, most of the growth of service activities in recent decades has been concentrated in intermediate services.
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

**Table 11: Unionized workforce**

|||Unionized firms<br>(percentage)*|Workforce<br>unionized at the<br>firm level<br>(percentage)|Unionized workers<br>(percentage)<sup>§</sup>|
|---|---|---|---|---|
||Botswana (2006)|18.1|11.7|5.0|
||Gambia (2006)|17.6|9.1|17.0|
||Ghana2007|50.4|24.4|42.0|
||Kenya (2003)|63.6|44.1|25.0|
|**e**|Namibia (2006)|50.5|36.2|29.0|
|**hon**|Swaziland (2006)|37.2|21.8|33.0|
|**lop**|Tanzania (2003)|51.6|40.1|38.0|
|**Ang**|Tanzania (2006)|46.7|34.8|34.0|
||Uganda (2003)|10.1|6.5|5.0|
||Uganda(2006)|8.3|2.6|4.0|
||Male|||22.0|
||Female|||27.0|
||**Total**|42.8|24.1|23.0|
||Benin (2004)|6.5|3.6|4.0|
||Burkina Faso (2006)|35.3|27.2|16.0|
||Burundi (2006)|1.3|1.3|3.0|
||Cameroon (2006)|31.1|17.9|34.0|
||Cape Verde (2006)|46.8|31.2|30.0|
|**ne**|DR Congo (2006)|23.9|15.1|30.0|
|**ho**|Guinea (2006)|8.8|3.8|15.0|
|**cop**|Madagascar (2005)|27.5|10.0|11.0|
|**ran**|Mali (2003)|43.7|29.6|29.0|
|**F**|Mauritania (2006)|42.8|27.8|24.0|
||Rwanda (2006)|12.0|8.8|10.0|
||Senegal(2003)|46.5|30.3|27.0|
||Male|||19.0|
||Female|||15.0|
||**Total**|24.5|14.8|18.0|

*: A firm is unionized if it has any number of unionized workers. Over half of the firms have no unionized workers at all.

§: In the workers' sample. Source: Enterprise Surveys 37
# **1. Introduction**

In this paper we empirically study the effects of the adoption and use of information and communication technologies (ICT) at the firm level on productivity, wages and employment turnover in the Argentine manufacturing sector.

The question of whether innovation affects productivity and labor outcomes has a long tradition that spans the literature of skilled biased technical change (Katz and Murphy, 1992), the more recent task‐based approach of Autor, Levy, and Murnane (2003) and Acemoglu and Autor (2011), and the job polarization arguments of Autor, Katz, and Kearney (2006), and Autor and Dorn (2013), among others. More closely related to digital technologies, Kruger (1993) finds a positive association between the use of computers and wages. More recent studies such as Autor, Levy, and Murnane (2003), Akerman et al (2015) and Michaels, Natraj, and Van Reenen (2014), emphasize that impacts on wages and employment are different by worker or occupation type.

Regarding productivity, ICT adoption is associated with higher productivity outcomes at the country, industry and firm level. In fact, investment in ICT is credited with the increase in labor productivity in the US during the second half of the 1990s and the increasing productivity gap between the US and the EU during that same period. See Draca, Sadun, and Van Reenen (2007) for a literature review of growth accounting and econometric estimation results. The effects of ICT adoption on productivity are found to be largely heterogeneous and to depend on organizational capital and management practices (Bresnahan et al., 2002, Caroli and Van Reenen, 2001, Bloom, Sadun and Van Reenen, 2012).

Using a panel of Argentine manufacturing firms spanning the period 2010‐2012 we provide further evidence on the nature of the links between adoption of ICT, productivity, and labor outcomes. We study the effect of ICT adoption on productivity and wages; we explore complementarities with predetermined firm characteristics; and we estimate changes in labor force composition.

During the last decade, the Argentine government has created programs to promote technology adoption and innovation, such as the Argentine Technology Fund (FONTAR), the Trust Fund for the Promotion of the Software Industry (FONSOFT), and the Map of ICT 2
**the** student attended and that failure to control for the selectLon of students into **schools** on the basis of prLor academLc achLevement results in an overestimate of the variatLon in achievement attrlbutable to the school. Third, the **results** of the multLvariate analysas showed that student achievement _li_ higher when schools have a greater avaLlability of textbooks, a larger proport.zon of trained teachers and teachers who have taught at that school for a longer period of time. The availablity of textbooks and the length of teacher tenure at a particular school together accounted for 50 percent and 72 percent of the variation attributable to the school in Math and English, respectively, after accounting for the variation due to the academic intake of the students. This suggests that ra$sing the proportlon of trained teachers and, more importantly, improvLng the provision of textbooks and providing incentives for teachers to remaln in the same schools for a reasonable period of time are promising investment options to boost student achievement.

The analysis of school rankings shows that rankings based on raw results of achievement tests aggregated at whe school level are mieleading. some of the most effectlve schools w$1i never be detected if the prior ac&demic achLevement of students is not taken Lnto account. After controlling statistically for prior academic achievement and for basic resource inputs (textbooks and teacher tenure), some dietrLct council schools are more effectLve than what would be expected given their base level of resources and some high fee and missLon schools are less effective than what would be predicted given thelr resource base. Additionally, the interesting finding that there were more experienced teachers in the set of less effective schools as compared with the more effective schools suggests that newly trained recruLts have much to offer and that longer experience may erode teacher effectiven **es. ThiL** ie a common finding in developed countrLes. Typically the learning curve among teachers is steeper and levels of effort are higher durlng the beginning years of teaching. Research into these and other characteristice of the newly trained teachers in Zimbabwe should be conducted in order to assist policy makers in the development of strategies directed at maintainLng high levels of teacher effectiveness.

51
With these caveats in mind, the conclusions about poverty and labor market participation for the FSU countries are rather conventional (Tables 4 and 5). Households with employed heads have lower poverty rates than those with unemployed heads, while the addition of one or two household members who are unemployed sharply increases the poverty rate. In Estonia, a third unemployed member increases the poverty rate further, but this is not the case in Russia and in Kyrgyz Republic, perhaps for some of the reasons detailed above. However, all three countries demonstrated an increasing poverty gap with each additional unemployed household member.

Some occupations or-socio-economic groups are associated with a lower poverty rate than others. In all three countries, self-employed household heads live in households with-a lower poverty rate than average, as do heads who describe themselves as wageearners. However, those self-employed heads who are poor are poorer than average in Estonia. In all three countries, wage-earners live in households with a lower poverty gap. Households with pensioners and other transfer recipients as household heads have sharply higher poverty rates than average, but their average shortfall in expenditures is not markedly different from average.

Households with access to land-a private plot-had lower poverty rates than households without land, and the poverty gap was smaller, in Estonia and Kyrgyz Republic (Table 10). Unlike other studies of Russia (Klugman 1997, World Bank 1995b, Kolev 1996), this study found that Russian households with a plot were poorer than households without. As noted above, the change in methodology from imputing the value 52
20 DASGUPTA AND GROVER Table 2: Port access, output tariffs and economic activity

||∆Ou|tput|∆Empl|oyment|∆P|lant|
|---|---|---|---|---|---|---|
|Change in Tariff|Large|Small|Large|Small|Large|Small|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Secondary|-0.15|-0.04|-0.23|-0.08|-0.23|-0.16|
||(0.30)|(0.28)|(0.25)|(0.22)|(0.20)|(0.18)|
|Interior|-0.31|0.19|-0.24|-0.01|-0.35|-0.22|
||(0.55)|(0.47)|(0.40)|(0.41)|(0.34)|(0.30)|
|1994-00_×_Secondary|0.99<sup>_∗_</sup>|0.39|0.88<sup>_∗∗_</sup>|0.38|0.89<sup>_∗∗_</sup>|0.59<sup>_∗∗_</sup>|
||(0.51)|(0.40)|(0.41)|(0.32)|(0.40)|(0.29)|
|1994-00_×_Interior|0.55|-0.04|0.55|0.01|0.91|0.48|
||(0.81)|(0.64)|(0.64)|(0.52)|(0.57)|(0.42)|
|2000-09_×_Secondary|-0.46|-0.57|-0.25|-0.43|-0.29|-0.26|
||(0.39)|(0.38)|(0.32)|(0.33)|(0.30)|(0.25)|
|2000-09_×_Interior|-0.27|-1.04<sup>_∗_</sup>|-0.35|-0.49|-0.24|-0.02|
||(0.66)|(0.59)|(0.47)|(0.53)|(0.46)|(0.42)|
|Observations|864|897|864|897|864|897|
|_R_<sup>2</sup>|0.08|0.06|0.09|0.07|0.07|0.09|

The dependent variable is cumulative growth rate of output, employment and plants at the district level, weighted by district-level population in 1991. Industries are grouped into Large (larger than median rate of change in final product tariff between 1993 and 1997) and Small.<sup>_∗_</sup> ,<sup>_∗∗_</sup> and<sup>_∗∗∗_</sup> correspond to significance level at 10, 5 and 1 percent respectively. All regressions include state-period fixed effects. Standard errors are clustered at the district-level.

to access cheaper imported inputs through the primary region had minimal effect in moving industries towards the primary region connected with international gateways.

Although we exploit variation across regions within the same group of industries, it is possible that these industries are not spread uniformly across space. Thus, even though tariff cuts did not have a spatial dimension, the fact that industries may have locational attributes (e.g. for historical or geographical reasons industries may be agglomerated or co-located) means that tariff cuts may have worked through the spatial dimension as well. For instance, it is possible that industries that grew faster after trade liberalization were disproportionately located in the
experiencing conflict,<sup>11</sup> 2. Some countries that are experiencing conflict do not receive aid for exogenous reasons on account of a low draw for uit (which can be thought of as features of the current political relationship with the US, for example) and 3. The amount of aid received conditional on aid receipt can be random and independent of a country’s risk of conflict and can be determined by factors other than conflict, such as politics or ease of transport, here modeled as a high draw for uit.

To show how the time trends arising from the functions f(t) and g(t) can influence the NQ results, we choose functional forms for these components that correspond approximately to patterns observed in the data, and show through Monte Carlo simulations that NQ’s results are reproducible without the need to assume any causal effect of aid on conflict or any correlation beyond those above. Indeed, below we even introduce a negative causal effect of aid on conflict – the opposite of what NQ claim to find – and show that we can still replicate their findings.

In the first simulations, we assume that g(t) = f(t) = t-(1/36)*t<sup>2</sup> where t=1... 36. This form forces both prevalence of conflict and wheat production to follow an inverted “U” shape over a time horizon of 36 periods, the number of years in the dataset. The plots below simulate one draw of a fabricated dataset with 126 countries and 36 years for this model to show how the conflict and wheat production dynamics work. Figure A8 shows how the secular trend g(t) and the country-specific conflict vulnerability combine to determine conflict risk. Dark shaded dots are countries with high fragility, ai, while lighter shaded dots are less fragile countries, those with a low ai. Within any year, some countries are at greater risk of conflict than others because of their higher fragility, represented by the fact that dark dots always appear higher than light dots on the vertical axis. Any dots above the horizontal line are those whose conflict risk exceeds the threshold and are considered to be in active conflict. Across years, the overall risk of conflict is increasing in early periods and falling in later periods as shown by the shift of the dark dots up the vertical (conflict risk) axis in the first half of the period and shift down in the second half. The consequence of this DGP is that some (high fragility) countries nearly always experience conflict and

> 11 This is an oversimplification of the real aid process. In the NQ dataset, countries that are not experiencing conflict also receive aid, but the percentage of countries receiving aid is substantially and statistically significantly higher in countries that are experiencing conflict than in countries that are not (47.0% vs. 34.6%). We will soon relax this assumption to show that allowing components of aid to be unrelated to conflict does not change the estimation results and can explain other features of the results reported by NQ.

59