rafmacalaba/datause-encoder-data huggingface dataset:
ture has found it hard to adjudicate between these two worldviews. One strand of the empirical literature has attempted to test particular theories of poverty traps. For example, Strauss and Thomas (1998) review studies which look for nonlinear relationships between health and productivity, and McKenzie and Woodruff (2003) test for non-convexities in returns to microenterprise investment. These studies generally have not found support for poverty traps caused by the particular mechanism being studied<sup>3</sup> , but leave open the question of whether poverty traps may still arise due to the non-studied processes.

A second strand of recent literature has therefore attempted to look directly at the dynamics of income, expenditure, or assets in order to test for nonconvexities and poverty traps. Lokshin and Ravallion (2004) use a six-year panel from Hungary and four-year panel from Russia to carry out nonlinear estimation of the relationship between current and lagged income. Almost one half of their sample has attrited by the end of the panel, and so they use a systems estimator which explicitly models attrition as a function of initial observed characteristics of the household. They do find the mapping from lagged income to current income to be nonlinear, but find no evidence of low-level threshold effects which would be associated with poverty traps. Jalan and Ravallion (2004) obtain similar findings using a six-year panel of income from four provinces in China.

Carter and Barrett (2005) criticize the use of short panels of income or expenditure to test for poverty traps by claiming that they are unable to distinguish between structural poverty and short-term transitory movements into and out of poverty.<sup>4</sup> This can be exacerbated by measurement error, which can lead a household to be mis-classified as poor in one period and correctly classified as non-poor in the next. They further note that many theories of poverty traps are based on an asset threshold, and propose study of the dynamics of asset poverty. Such an approach is followed by Lybbert et al. (2004), who use 17

> 3 However, see Dasgupta (1997) for a critique of empirical studies of the nutrition-based efficiency wage model.

> 4 They note (p.2) that “with long enough panels, this limitation might be moot, but in the short term...panels with more than two or three observations in a span of a few years remain quite uncommon”.

3
Table 5 presents the main results of the paper and provides estimation results of equations (3), (4), and (5), using GMM with lag 2 and 3.

_Table 5. Innovate Internally or Externally? Using GMM_

|Dep. Var.|(1)|(2)|(3)|
|---|---|---|---|
|ln revenue|All<br>|Internalvs. external<br>|Synergies<br>|
|ln_labor|0.355<sup>***</sup><br>(0.000)|0.357<sup>***</sup><br>(0.000)|0.362<sup>***</sup><br>(0.000)|
|ln_capital|0.005|0.001|0.017|
||(0.899)|(0.988)|(0.674)|
|ln_mateiral|0.640<sup>***</sup>|0.639<sup>***</sup>|0.642<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|
|ln_all|0.027<sup>**</sup><br>(0.003)|||
|ln_internal||0.013<br>(0.189)|0.046<br>(0.274)|
|ln_external||0.021*<br>(0.048)|0.030*<br>(0.045)|
|ln_int×ln_ext|||-0.006<br>(0.207)|
|export_stat|0.244<sup>***</sup>|0.242<sup>***</sup>|0.241<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|
|FDI_stat|0.275<sup>***</sup>|0.275<sup>***</sup>|0.280<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|
|# of obs.|15,735|15,735|15,735|
|# of groups|4,255|4,255|4,255|
|sum of coefficient|1.105|1.106|1.02|
|AR2 (p-value)|0.001|0.001|0.002|
|Hansen(p-value)|0.000|0.000|0.000|

_Note:_ Year and industry dummies were included but not reported. Standard errors are clustered and robust (White’s correction for heteroskedasticity).; p-values in parentheses. Significance level: * = 5 percent, ** = 1 percent, *** = 0.1 percent The instrument set for the differenced equation consists of the log of revenue, the log of innovation investment (for all, internal and external innovation), the log of material input, in 38
in PNG that precipitated a humanitarian disaster and a forced displacement crisis. The analysis also demonstrates that the change in reported prevalence of IPV was not as substantial and was less precisely estimated, and illustrates that one likely explanation is the underreporting of IPV incidents in modules that directly ask women about the incidents that they experience. And lastly, the analysis explores potential mechanisms that explain the decline in attitudes about IPV for the entire population of men and the decline in reported prevalence among a subset of the population of women.

However, there are many avenues that the analysis is unable to explore given data limitations. First, as discussed above, we are not fully able to identify how the roles and responsibilities of women change in the household, and thus we cannot fully identify all the ways in which the disaster might have changed the intrahousehold dynamics that might result in changes to IPV. More detailed survey instruments and combining quantitative household data with some more qualitative discussions could better identify how disasters might change household dynamics. Although more subjective in nature, more detailed questionnaires that inquire why women think their husbands might commit acts of intimate partner violence could help better explain these potential pathways and understand IPV more generally.

Second, these results are relatively short-term results and the data coverage following the disaster limits our ability to more fully investigate the full impacts of the disaster. Whether these changes persist more than seven months following the disaster is unable to be explored here; and we are unable to explore whether data collected even more closely in time to the disaster or whether data collected from regions even more strongly impacted than those analyzed here might have resulted in similar or even stronger findings. But the continuation of a survey in the midst of such disasters are relatively uncommon and these results add additional information relative to other settings where data might be collected long after the disaster ends.

And lastly, these results only investigate how disasters might change IPV. Although some of the results might have implications for what might improve the IPV situation in the country more generally, such as interventions that necessarily expand the roles and responsibilities for women inside the household, the results do not explain how such a dire IPV situation evolved and how to more fully reduce and eliminate IPV in the country.

22
Policy Research Working Paper 5738

# **Abstract**

Shortly following its independence in 1991, Tajikistan suffered a violent civil war. This study explores the effect of this conflict on education and labor market outcomes for men and women. The results are based on the data from the 2003 and 2007 Tajik Living Standards Measurement Surveys that were separated from the 1992–1998 Tajik civil war by five and nine years, respectively. The regression analysis that controls for the cohort and regional-level exposure points toward a persistent and lasting gap in the educational attainment by women who were of school age during the war and lived in the more conflict-affected regions as compared with women the same age who lived in the lesser affected regions and also to the older generation. These empirical results support the anecdotal and observational evidence about the decline in female educational attainment in Tajikistan. Interestingly, this group of young women is more likely to hold a job as compared with the rest of the analytical sample. Conditional on being employed, men and women in the more conflict-affected areas do not receive wages that are significantly different from wages received by men and women in the lesser affected areas.

This paper is a product of the Gender and Development Unit, Poverty Reduction and Economic Management Network, with generous funding from the Government of Norway. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at olga.shemyakina@econ. gatech.edu.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 1**

|**Short /**<br>**Medium Run**||**_∆ ( wT / wN ) _**|**_∆( LT / LN ) _**|**_∆p _**|
|---|---|---|---|---|
|**_∆AT > 0_**||> 0|> 0|> 0|
|**_Flexible Wage_**<br>**_∆AN > 0_**||< 0|< 0|< 0<br>(undersh.)|
|**_∆γ< 0_**||< 0|< 0|0 ><br>(oversh.)|
|**_Wage Rigidities_**<br>**_∆AT < 0_**|_A_<br>><br>_θ_|> 0|< 0|< 0|
||_A_<br><<br>_θ_|< 0|< 0|< 0, > 0|
|**Long Run**|||||
|**_∆AT > 0_**||> 0|> 0|> 0|
|**_Flexible Wage_**<br>**_∆AN > 0_**||< 0|< 0|< 0|
|**_∆γ< 0_**||< 0|< 0|0|
|**_Wage Rigidities_**<br>**_∆AT < 0_**|_A_<br>><br>_θ_|> 0|< 0|< 0|
||_A_<br><<br>_θ_|< 0|< 0|< 0|
|_γ_<br>_γ_<br>_φ_<br>−<br>=<br>_se_<br>_A_|||||

33
in physical and human capital than the current output mix without providing additional capital inputs are likely to distort resource use throughout the economy. The fact that financial capital remains relatively immobile internationally (Gordon and Bovenberg 1996) means that attempts to increase the accumulation of physical capital are likely to focus on stimulating domestic saving. Loayaza, Schmidt-Hebbel and Serven (2000) draw on a large number of studies to provide policy recommendations to this effect. Even if factor accumulation is less important for overall economic growth than has previously been thought (Easterly and Levine 2001), it seems likely to provide a strong stimulus to a shift in the composition of exports towards manufactures and services.

Given the weakness of capital markets in financing intangible assets like human capital governments tend to play a much larger role in guiding the accumulation of human capital than of physical capital. Accumulation of human capital is likely to have both level and growth effects on output, and to facilitate the transformation of the economy into one that produces relatively more human-capital-intensive goods. As Dessus (1999) notes, the impact of human capital accumulation on both output and on poverty reduction depends a great deal on the emphasis of the education system, and on its effectiveness. As Leamer _et al_ note, provision of education may need to be very proactive, attempting to take into account demands in the next cone of diversification associated with economic development, rather than in current activities. As Leamer _et al_ note, this may imply training workers for much more sophisticated activities than are undertaken in an initially very resource-dependent economy.

Attracting foreign direct investment may help to augment the available capital stock, although this source of capital is typically small relative to total investment. However, it is possible that foreign direct investment, or sub-contracting relationships (Deardorff and Djankov 2000) can help transfer the knowledge needed for rapid productivity growth. If attracting foreign investment leads to a focus on developing the institutions needed to improve the investment climate—for domestic as well as foreign investors—then it can play a particularly important role in development. Use of foreign investment implies a need for greater caution in the use of protection policies. Since 30
behind rice price movements. This study's purpose is to consider how relative price movements affect changes in child labor supply, because market integration leads to relative price shifts. Thus, even if market integration is not behind the observed price movements, the link between price movements and child labor supply can still inform about the effect of market integration on child labor supply.

However, this approach of linking child labor supply to price variation raises several concerns. Namely, any number of local economic factors may be associated with both rice price movements and child labor supply movements but have nothing to do with the causal effect of rice price changes on child labor supply. This section begins with a description of some of the more problematic confounding factors, describes how the empirical framework attempts to control for them, and ends with a discussion of some of the remaining problems that the empirical work cannot address. This discussion is largely based on Edmonds and Pavcnik (2003c). That study is devoted to the question of whether the relationship between price movements and child labor supply is driven by omitted factors correlated with price movements and child labor that have nothing to do with the causal effect of rice price movements on child labor. They find that the relationship between changes in rice prices and child labor supply is extremely robust. Even when they control for observable factors that can account for over 90 percent of the variation in rice prices, they find a response of child labor supply to price movements that is nearly identical to that observed without controlling for factors that might be associated with rice price movements.

# **4.1 Likely Sources of Spurious Correlation**

Sources of spurious correlation between price movements and child labor supply can be grouped into two general categories: time invariant and time varying attributes.

20
causes child domestics to be invisible, isolated and generally unprotected<sup>comparedto work that is</sup> carried out in a more public place. While not all child domestics<sup>sufferneglect,abuseor</sup> exploitation, nor is the work itself always dangerous or inhumane (Innocenti<sup>Digest,1999, Green,</sup> 1998), the invisibility of the work creates the need for attention and<sup>scrutiny.</sup> 50. Domestic service, which demands servility among its workers, is often<sup>associatedwith an</sup> inferior status, stigma and discrimination, which can induce psychological<sup>and emotional distress and</sup> erode self-esteem, particularly among those who start out at<sup>very earlyages(lJNICEF,1993,</sup> Innocenti Digest, 1999). The extent to which harmful conditions among<sup>childdomesticsexist is not</sup> known.

## **_Street Work_**

51. Children working in the streets, particularly those making a home in<sup>the streets,facethe</sup> most severe working conditions. These children are exposed to<sup>drugsand violence(withinthe</sup> home and on the street), as well as traffic accidents, extreme weather, poor<sup>sanitation,and</sup> psychological distress induced by ostracization and discrimination.

52. A 1996 study of street children in Brazil revealed that more than one<sup>half of the boys and</sup> one fifth of the girls experienced some form of street violence<sup>(violencebythe police,other adults,</sup> or street children) (Araujo, 1998). In 1997, however, violent encounters<sup>among boys decreased,and</sup> increased among girls. Also in 1996, 21 percent of the boys, as compared<sup>to12 percent of the girls,</sup> reported having used drugs. This pattern was reversed in 1997, when<sup>onethirdof the girls,as</sup> compared to 22 percent of the boys experimented with drugs. The<sup>dramaticincreasein exposureto</sup> violence and drugs among girls in 1997, however, could be the result of<sup>asamplingerror,rather than</sup> an actual trend. The profile of the girls in the 1997 sample is reported<sup>tobe dramaticallydifferent</sup> from that of 1998.

# **_Prostitution_**

53. One of the most harmful activities for children on the street<sup>is</sup> prostitution.<sup>Girlsaremost</sup> often the target of recruitment and coercion into the sex<sup>industry.TheBrazilianCenterfor</sup> Childhood and Adolescence of the Ministry of Social Services<sup>estimatedthat about a half million</sup> Brazilian girls are engaged in prostitution in large cities or<sup>in</sup> migrant<sup>settlementsin Amazonia</sup> 20
|Unmarried couples living together|8.9|
|---|---|
|Jewish people|11.1|
|People who speak a different language|7.0|
|Other (Specify:<br>)|n.a.|

# _Source: LiTSII_

The responses to this question reveal that exclusionary norms are widespread, but also that they differ widely across countries. The basic results are shown in Annex 1; figure 1 offers examples. By a large majority, drug users, pedophiles and heavy drinkers are all unwanted neighbors in almost all the countries covered in LiTS. This is followed by homosexuals and people living with HIV/AIDS, who are undesired neighbors by 55% and 43%, respectively, of those sampled. There is wide variation in respondents’ answers across countries; for example, 77% of Azeris and 4% of Swedes do not want people living with HIV/AIDS as neighbors. For homosexuals, the corresponding figures are 91% and 3.5%, again with Azerbaijan the highest and Sweden the lowest. Similarly, although 49% of respondents do not want gypsies (Roma) as neighbors; this percentage increases to 81% in the Czech Republic and falls to 22% in Bosnia and Herzegovina. Many other groups share the experience of often being unwanted neighbors: immigrants, people with a different religion or of a different race, Jewish, and cohabitating unmarried couples all face intermediate levels of being unwelcome. In Mongolia, Russia, and Turkey, for example, more than 30% do not want to live next to an immigrant.

**Figure 1: Unwanted neighbors (select groups and countries)**

<!-- Start of picture text --> 35.0<br>Which groups would you not like to have as neighbor?<br>Different race<br>30.0<br>Immigrants<br>Poor<br>25.0<br>Different language<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br><!-- End of picture text --> _Source: LiTS II_ 7
|**Ta**|**ble 1: Dire**<br>**I**|**ction of So**<br>**MPORTS**|**uth Asia’**|**s Trade( **<br>**E**|**Constant**<br>**XPORTS**|**US$ Milli**|**on, 1995)**<br>**SHARE OF**|**TOTAL T**|**RADE**|
|---|---|---|---|---|---|---|---|---|---|
||**1981**|**1990**|**1998**|**1981**|**1990**|**1998**|**1981**|**1990**|**1998**|
|South Asia|618|733|3,293|556|837|2,814|3.2|2.4|5.0|
|East Asia|2,084|4,764|15,000|688|2,198|5,548|7.5|10.8|16.7|
|Other Developing|11,579|11,868|21,529|5,624|8,100|12,523|46.7|31.1|27.8|
|Industrial Countries|11,091|20,201|29,702|4,593|15,549|32,281|42.6|55.6|50.5|

Source: IMF Direction of Trade Statistics. Notes: Oil imports are excluded from the South Asia’s data. All Middle East, the post-Soviet Republics and Eastern European countries are categorized under “Other Developing Countries (which excludes South Asia); East Asia also includes the Rep. of Korea, Taiwan (China), and Hong Kong (China). China is among the “Other Developing” category, as it is not traditionally identified under East Asia.

The above indicates that South Asia has gradually adjusted its direction of trade as it has evolved through both domestic reform and international competitive conditions. In the process, the South Asia region, as a trading partner, has also relatively increased in importance among regional members. But how significant is this emergence and can the South Asian countries be characterized as “natural trading partners”?

Table 2 provides a review of intra-regional trade between 1981 and 1998 at an aggregate level. The overall (official) intra-regional trade as a share of total trade has remained below 5 percent throughout this period. However, these aggregate figures are biased by India’s trade patterns, the dominant economy in the region, and therefore do not reflect the extent of intraregional trade of the smaller countries. There are, in fact, large differences in the relative importance of South Asian markets for individual member countries of the South Asian Association for Regional Cooperation (SAARC).<sup>8</sup> For example, Bhutan and Nepal trade disproportionately with the region, with shares of regional trade as high as 71 percent and 33 percent, respectively, in 1998 as seen from Table 2. Due to the landlocked nature of these two countries, trade takes place primarily with India. While Nepal has made some efforts to reduce its

> 8 See Annex Table A1.

7
# **2. Data**

We use the SARS-National Treasury panel (herein referred to as SARS-NT panel), which is an unbalanced panel data set of administrative tax data from 2010-2014.<sup>7</sup> The panel consists of four merged sources of administrative tax data: (i) company income tax (CIT) records from registered firms who submit tax forms; (ii) employee records from employee income tax certificates submitted by employers; (iii) value-added tax records from registered firms; and (iv) customs records from traders (Pieterse et al., 2018). The SARS-NT Panel constitutes a unique source for the study of firm-level behavior in South Africa, as it is at the level of individual firms and individuals, and provides a comprehensive, disaggregated picture of the economy in recent years.<sup>8</sup> The CIT records contain firm characteristics, such as the sector in which a firm operates, financial information from their income statements and balance sheets, and tax information. The employee records contain all employee related incomes, deductions, taxes, and payments made by the firm (such as skills development levy and unemployment insurance fund payments).

**Table 1: Key summary statistics**

||**2010**|**2011**|**2012**|**2013**|**2014**|
|---|---|---|---|---|---|
|**Total number of workers**|4 060 897|5 104 117|5 378 704|5 516 065|5 133 552|
|**Total number of firms**|122 516|130 387|129 860|126 484|108 150|
|**Mean number of workers per firm**|33.15|39.15|41.42|43.61|47.47|
|**Mean firm age**|12.02|12.70|13.38|14.06|14.92|
|**Mean sales per worker (R‘000)**|10.42|9.87|9.86|9.87|10.21|
|**Mean value added per worker (R’000)**|3.46|3.49|3.53|3.52|3.58|

_Source: SARS-NT Panel (own calculations)_ .

Table 1 above highlights the key summary statistics – variable definitions are provided in the data appendix. The SARS-NT panel covers formal workers employed by registered firms in the private sector.<sup>9</sup> From 2010 to 2014, the total number of workers ranges from 4 million to 5.5 million. The number of firms varies over the period, peaking at 130,387 firms in 2011. The average number of workers per firm increased from 33.15 in 2010 to 47.47 in 2014. Half the firms in our panel employ 8 or fewer employees, emphasizing the large number of small formal firms in terms of employment size present in the panel. The average firm age increased from

> 7 We use the same panel as Pieterse et al. (2018), Kreuser and Newman (2018) and Edwards et al. (2018). Differences across key variables are due to differences in how these variables are defined. Refer to the data appendix for further detail.

> 8 See Pieterse et al. (2018) for a detailed explanation of the key sources of tax data used to create the panel, questions that arise as a result of panel construction, potential biases in the resulting data and a comparison to other data sources.

> 9 Firms that do not report wage employees are excluded.

6